NCO User Guide A suite of netCDF operators Edition 5.0.2-alpha07, for NCO Version 5.0.2-alpha07 September 2021
by Charlie Zender Departments of Earth System Science and Computer Science University of California, Irvine Copyright c 1995–2021 Charlie Zender.
This is the first edition of the NCO User Guide, and is consistent with version 2 of texinfo.tex.
Published by Charlie Zender Department of Earth System Science 3200 Croul Hall University of California, Irvine Irvine, CA 92697-3100 USA
Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. The license is available online at http://www.gnu.org/copyleft/fdl.html
We gratefully acknowledge support for NCO development and maintenance provided by these institutions and programs: DOE ACME DE-SC0012998, LLNL-B625903, LLNL-B632442, NASA ACCESS NNX12AF48A and NNX14AH55A, and NSF SEI IIS-0431203, AGS-1541031, and OAC-2004993. This research was supported as part of the Energy Exascale Earth System Model (E3SM) project, formerly known as Accelerated Climate Modeling for Energy (ACME), funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research. This material is based upon work supported by the National Science Foundation.
The original author of this software, Charlie Zender, wants to improve it with the help of your suggestions, improvements, bug-reports, and patches. Charlie Zender
Table of Contents
Foreword ...... 1
Summary ...... 5
1 Introduction ...... 7 1.1 Availability...... 7 1.2 How to Use This Guide ...... 7 1.3 Operating systems compatible with NCO ...... 8 1.3.1 Compiling NCO for Microsoft Windows OS ...... 10 1.4 Symbolic Links ...... 10 1.5 Libraries ...... 11 1.6 netCDF2/3/4 and HDF4/5 Support ...... 12 1.7 Help Requests and Bug Reports ...... 15
2 Operator Strategies ...... 17 2.1 Philosophy ...... 17 2.2 Climate Model Paradigm ...... 17 2.3 Temporary Output Files ...... 17 2.4 Appending Variables ...... 19 2.5 Simple Arithmetic and Interpolation ...... 19 2.6 Statistics vs. Concatenation ...... 20 2.6.1 Concatenators ncrcat and ncecat ...... 20 2.6.2 Averagers nces, ncra, and ncwa ...... 21 2.6.3 Interpolator ncflint ...... 21 2.7 Large Numbers of Files ...... 21 2.8 Large Datasets ...... 23 2.9 Memory Requirements...... 24 2.9.1 Single and Multi-file Operators ...... 24 2.9.2 Memory for ncap2 ...... 26 2.10 Performance...... 26
3 Shared Features ...... 29 3.1 Internationalization...... 29 3.2 Metadata Optimization ...... 29 3.3 OpenMP Threading ...... 30 3.4 Command Line Options ...... 31 3.4.1 Truncating Long Options ...... 32 3.4.2 Multi-arguments ...... 32 3.5 Sanitization of Input ...... 33 3.6 Specifying Input Files ...... 34 3.7 Specifying Output Files...... 37 ii
3.8 Accessing Remote Files ...... 37 3.8.1 OPeNDAP ...... 39 3.9 Retaining Retrieved Files ...... 42 3.10 File Formats and Conversion ...... 42 3.10.1 File Formats ...... 43 3.10.2 Determining File Format ...... 44 3.10.3 File Conversion...... 45 3.10.4 Autoconversion ...... 46 3.11 Large File Support ...... 47 3.12 Subsetting Files ...... 48 3.13 Subsetting Coordinate Variables ...... 53 3.14 Group Path Editing ...... 53 3.14.1 Deletion, Truncation, and Flattening of Groups ...... 54 3.14.2 Moving Groups ...... 56 3.14.3 Dismembering Files...... 57 3.14.4 Checking CF-compliance...... 60 3.15 C and Fortran Index conventions ...... 63 3.16 Hyperslabs ...... 63 3.17 Stride ...... 66 3.18 Record Appending ...... 67 3.19 Subcycle ...... 68 3.20 Interleave ...... 69 3.21 Multislabs...... 71 3.22 Wrapped Coordinates ...... 74 3.23 Auxiliary Coordinates ...... 74 3.24 Grid Generation...... 77 3.25 Regridding ...... 86 Renormalization ...... 87 Regridder Options Table...... 90 3.26 Climatology and Bounds Support ...... 97 3.27 UDUnits Support ...... 98 3.28 Rebasing Time Coordinate ...... 101 3.29 Multiple Record Dimensions ...... 102 3.30 Missing values...... 103 3.31 Chunking ...... 104 3.32 Compression ...... 111 3.32.1 Linear Packing ...... 112 3.32.2 Precision-Preserving Compression...... 112 3.33 Deflation ...... 121 3.34 MD5 digests ...... 122 3.35 Buffer sizes...... 123 3.36 RAM disks ...... 124 3.37 Unbuffered I/O ...... 125 3.38 Packed data...... 126 Standard Packing Algorithm...... 126 Standard (Default) Unpacking Algorithm ...... 127 Non-Standard Packing and Unpacking Algorithms ...... 127 Handling of Packed Data by Other Operators ...... 128 iii
3.39 Operation Types ...... 129 3.40 Type Conversion ...... 134 3.40.1 Automatic type conversion ...... 134 3.40.2 Promoting Single-precision to Double ...... 136 3.40.3 Manual type conversion ...... 142 3.41 Batch Mode...... 143 3.42 Global Attribute Addition ...... 143 3.43 History Attribute ...... 144 3.44 File List Attributes...... 145 3.45 CF Conventions ...... 146 3.46 ARM Conventions ...... 150 3.47 Operator Version ...... 151
4 Reference Manual...... 153 4.1 ncap2 netCDF Arithmetic Processor...... 154 4.1.1 Syntax of ncap2 statements...... 155 4.1.2 Expressions ...... 156 4.1.3 Dimensions ...... 159 4.1.4 Left hand casting ...... 160 4.1.5 Arrays and hyperslabs ...... 162 4.1.6 Attributes ...... 165 4.1.7 Value List ...... 167 4.1.8 Number literals...... 168 4.1.9 if statement ...... 169 4.1.10 Print & String methods ...... 170 4.1.11 Missing values ncap2 ...... 173 4.1.12 Methods and functions ...... 175 4.1.13 RAM variables...... 178 4.1.14 Where statement ...... 179 4.1.15 Loops ...... 181 4.1.16 Include files ...... 182 4.1.17 sort methods ...... 182 4.1.18 UDUnits script ...... 187 4.1.19 Vpointer ...... 189 4.1.20 Irregular Grids ...... 191 4.1.21 Bilinear interpolation ...... 193 4.1.22 GSL special functions ...... 195 4.1.23 GSL interpolation ...... 203 4.1.24 GSL least-squares fitting ...... 204 4.1.25 GSL statistics ...... 206 4.1.26 GSL random number generation ...... 208 4.1.27 Examples ncap2 ...... 210 4.1.28 Intrinsic mathematical methods...... 213 4.1.29 Operator precedence and associativity ...... 215 4.1.30 ID Quoting...... 215 4.1.31 make bounds() function ...... 216 4.1.32 solar zenith angle function ...... 217 4.2 ncatted netCDF Attribute Editor ...... 218 iv
4.3 ncbo netCDF Binary Operator ...... 225 4.4 ncclimo netCDF Climatology Generator...... 230 Timeseries Reshaping mode, aka Splitting...... 241 MPAS-O/I/L considerations ...... 242 Annual climos ...... 242 Regridding Climos and Other Files ...... 243 Extended Climatologies...... 243 Coupled Runs ...... 246 Memory Considerations ...... 246 Single, Dedicated Nodes at LCFs...... 247 12 node MPI-mode Jobs ...... 249 What does ncclimo do? ...... 250 Assumptions, Approximations, and Algorithms (AAA) Employed: .. 250 4.5 ncecat netCDF Ensemble Concatenator ...... 253 4.6 nces netCDF Ensemble Statistics...... 256 4.7 ncflint netCDF File Interpolator ...... 260 4.8 ncks netCDF Kitchen Sink ...... 263 Options specific to ncks ...... 264 4.8.2 Filters for ncks ...... 283 4.9 ncpdq netCDF Permute Dimensions Quickly ...... 289 Packing and Unpacking Functions ...... 289 Dimension Permutation ...... 293 4.10 ncra netCDF Record Averager ...... 298 4.11 ncrcat netCDF Record Concatenator ...... 302 4.12 ncremap netCDF Remapper ...... 304 Fields not regridded by ncremap ...... 305 Options specific to ncremap...... 306 Limitations to ncremap ...... 337 4.13 ncrename netCDF Renamer ...... 341 4.14 ncwa netCDF Weighted Averager ...... 347 4.14.1 Mask condition ...... 348 4.14.2 Normalization and Integration ...... 349
5 Contributing ...... 353 5.1 Contributors ...... 353 5.2 Citation...... 355 5.3 Proposals for Institutional Funding ...... 356
6 Quick Start ...... 357 6.1 Daily data in one file ...... 357 6.2 Monthly data in one file ...... 357 6.3 One time point one file ...... 358 6.4 Multiple files with multiple time points ...... 358 v 7 CMIP5 Example ...... 359 7.1 Combine Files ...... 359 7.2 Global Distribution of Long-term Average ...... 365 7.3 Annual Average over Regions ...... 368 7.4 Monthly Cycle ...... 375 7.5 Regrid MODIS Data ...... 378 7.6 Add Coordinates to MODIS Data ...... 381 7.7 Permute MODIS Coordinates ...... 382
8 Parallel ...... 385
9 CCSM Example...... 387
10 References...... 395
General Index ...... 397
1
Foreword
NCO is the result of software needs that arose while I worked on projects funded by NCAR, NASA, and ARM. Thinking they might prove useful as tools or templates to others, it is my pleasure to provide them freely to the scientific community. Many users (most of whom I have never met) have encouraged the development of NCO. Thanks espcially to Jan Polcher, Keith Lindsay, Arlindo da Silva, John Sheldon, and William Weibel for stimulating suggestions and correspondence. Your encouragment motivated me to complete the NCO User Guide. So if you like NCO, send me a note! I should mention that NCO is not connected to or officially endorsed by Unidata, ACD, ASP, CGD, or Nike.
Charlie Zender May 1997 Boulder, Colorado
Major feature improvements entitle me to write another Foreword. In the last five years a lot of work has been done to refine NCO. NCO is now an open source project and appears to be much healthier for it. The list of illustrious institutions that do not endorse NCO continues to grow, and now includes UCI.
Charlie Zender October 2000 Irvine, California
The most remarkable advances in NCO capabilities in the last few years are due to con- tributions from the Open Source community. Especially noteworthy are the contributions of Henry Butowsky and Rorik Peterson.
Charlie Zender January 2003 Irvine, California
NCO was generously supported from 2004–2008 by US National Science Founda- tion (NSF) grant IIS-0431203 (http://www.nsf.gov/awardsearch/showAward.do? AwardNumber=0431203). This support allowed me to maintain and extend core NCO code, 2 NCO 5.0.2-alpha07 User Guide
and others to advance NCO in new directions: Gayathri Venkitachalam helped implement MPI; Harry Mangalam improved regression testing and benchmarking; Daniel Wang de- veloped the server-side capability, SWAMP; and Henry Butowsky, a long-time contributor, developed ncap2. This support also led NCO to debut in professional journals and meetings. The personal and professional contacts made during this evolution have been immensely rewarding.
Charlie Zender March 2008 Grenoble, France
The end of the NSF SEI grant in August, 2008 curtailed NCO development. Fortunately we could justify supporting Henry Butowsky on other research grants until May, 2010 while he developed the key ncap2 features used in our climate research. And recently the NASA ACCESS program commenced funding us to support netCDF4 group functionality. Thus NCO will grow and evade bit-rot for the foreseeable future.
I continue to receive with gratitude the thanks of NCO users at nearly every scientific meeting I attend. People introduce themselves, shake my hand and extol NCO, often effu- sively, while I grin in stupid embarassment. These exchanges lighten me like anti-gravity. Sometimes I daydream how many hours NCO has turned from grunt work to productive research for researchers world-wide, or from research into early happy-hours. It’s a cool feeling.
Charlie Zender April, 2012 Irvine, California
The NASA ACCESS 2011 program generously supported (Cooperative Agreement NNX12AF48A) NCO from 2012–2014. This allowed us to produce the first iteration of a Group-oriented Data Analysis and Distribution (GODAD) software ecosystem. Shifting more geoscience data analysis to GODAD is a long-term plan. Then the NASA ACCESS 2013 program agreed to support (Cooperative Agreement NNX14AH55A) NCO from 2014–2016. This support permits us to implement support for Swath-like Data (SLD). Most recently, the DOE has funded me to implement NCO re-gridding and parallelization in support of their ACME program. After many years of crafting NCO as an after-hours hobby, I finally have the cushion necessary to give it some real attention. And I’m looking forward to this next, and most intense yet, phase of NCO development. Foreword 3
Charlie Zender June, 2015 Irvine, California
The DOE Energy Exascale Earth System Model (E3SM) project (formerly ACME) has generously supported NCO development for the past four years. Supporting NCO for a mission-driven, high-performance climate model development effort has brought unprece- dented challenges and opportunities. After so many years of staid progress, the recent development speed has been both exhilirating and terrifying.
Charlie Zender May, 2019 Laguna Beach, California
5
Summary
This manual describes NCO, which stands for netCDF Operators. NCO is a suite of programs known as operators. Each operator is a standalone, command line program executed at the shell-level like, e.g., ls or mkdir. The operators take netCDF files (including HDF5 files constructed using the netCDF API) as input, perform an operation (e.g., averaging or hyperslabbing), and produce a netCDF file as output. The operators are primarily designed to aid manipulation and analysis of data. The examples in this documentation are typical applications of the operators for processing climate model output. This stems from their origin, though the operators are as general as netCDF itself.
7
1 Introduction
1.1 Availability The complete NCO source distribution is currently distributed as a compressed tarfile from http://sf.net/projects/nco and from http://dust.ess.uci.edu/nco/nco.tar. gz. The compressed tarfile must be uncompressed and untarred before building NCO. Un- compress the file withgunzip ‘ nco.tar.gz’. Extract the source files from the resulting tarfile withtar ‘ -xvf nco.tar’. GNU tar lets you perform both operations in one step with ‘tar -xvzf nco.tar.gz’. The documentation for NCO is called the NCO User Guide. The User Guide is available in PDF, Postscript, HTML, DVI,TEXinfo, and Info formats. These formats are included in the source distribution in the files nco.pdf, nco.ps, nco.html, nco.dvi, nco.texi, and nco.info*, respectively. All the documentation descends from a single source file, nco.texi1. Hence the documentation in every format is very similar. However, some of the complex mathematical expressions needed to describe ncwa can only be displayed in DVI, Postscript, and PDF formats. A complete list of papers and publications on/about NCO is available on the NCO home- page. Most of these are freely available. The primary refereed publications are ZeM06 and Zen08. These contain copyright restrictions which limit their redistribution, but they are freely available in preprint form from the NCO. If you want to quickly see what the latest improvements in NCO are (without down- loading the entire source distribution), visit the NCO homepage at http://nco.sf.net. The HTML version of the User Guide is also available online through the World Wide Web at URL http://nco.sf.net/nco.html. To build and use NCO, you must have netCDF installed. The netCDF homepage is http://www.unidata.ucar.edu/software/netcdf. New NCO releases are announced on the netCDF list and on the nco-announce mailing list http://lists.sf.net/mailman/listinfo/nco-announce.
1.2 How to Use This Guide Detailed instructions about how to download the newest version (http://nco.sf.net/# Source), and how to complie source code (http://nco.sf.net/#bld), as well as a FAQ (http://nco.sf.net/#FAQ) and descriptions of Known Problems (http://nco.sf.net/# bug) etc. are on our homepage (http://nco.sf.net/). There are twelve operators in the current version (5.0.2-alpha07). The function of each is explained in Chapter 4 [Reference Manual], page 153. Many of the tasks that NCO can accomplish are described during the explanation of common NCO Features (see Chapter 3 [Shared features], page 29). More specific use examples for each operator can be seen by visiting the operator-specific examples in the Chapter 4 [Reference Manual], page 153.
1 To produce these formats, nco.texi was simply run through the freely available programs texi2dvi, dvips, texi2html, and makeinfo. Due to a bug in TEX, the resulting Postscript file, nco.ps, contains the Table of Contents as the final pages. Thus if you print nco.ps, remember to insert the Table of Contents after the cover sheet before you staple the manual. 8 NCO 5.0.2-alpha07 User Guide
These can be found directly by prepending the operator name with the xmp_ tag, e.g., http://nco.sf.net/nco.html#xmp_ncks. Also, users can type the operator name on the shell command line to see all the available options, or type, e.g., ‘man ncks’ to see a help man-page.
NCO is a command-line language. You may either use an operator after the prompt (e.g., ‘$’ here), like, $ operator [options] input [output]
or write all commands lines into a shell script, as in the CMIP5 Example (see Chapter 7 [CMIP5 Example], page 359).
If you are new to NCO, the Quick Start (see Chapter 6 [Quick Start], page 357) shows simple examples about how to use NCO on different kinds of data files. More detailed “real- world” examples are in the Chapter 7 [CMIP5 Example], page 359. The [General Index], page 397, is presents multiple keyword entries for the same subject. If these resources do not help enough, please see Section 1.7 [Help Requests and Bug Reports], page 15.
1.3 Operating systems compatible with NCO In its time on Earth, NCO has been successfully ported and tested on so many 32- and 64-bit platforms that if we did not write them down here we would forget their names: IBM AIX 4.x, 5.x, FreeBSD 4.x, GNU/Linux 2.x, LinuxPPC, LinuxAlpha, LinuxARM, LinuxSparc64, LinuxAMD64, SGI IRIX 5.x and 6.x, MacOS X 10.x, DEC OSF, NEC Super-UX 10.x, Sun SunOS 4.1.x, Solaris 2.x, Cray UNICOS 8.x–10.x, and Microsoft Windows (95, 98, NT, 2000, XP, Vista, 7, 8, 10). If you port the code to a new operating system, please send me a note and any patches you required.
The major prerequisite for installing NCO on a particular platform is the successful, prior installation of the netCDF library (and, as of 2003, the UDUnits library). Unidata has shown a commitment to maintaining netCDF and UDUnits on all popular UNIX plat- forms, and is moving towards full support for the Microsoft Windows operating system (OS). Given this, the only difficulty in implementing NCO on a particular platform is standard- ization of various C-language API system calls. NCO code is tested for ANSI compliance by compiling with C99 compilers including those from GNU (‘gcc -std=c99 -pedantic -D_BSD_SOURCE -D_POSIX_SOURCE’ -Wall)2, Comeau Computing (‘como --c99’), Cray (‘cc’), HP/Compaq/DEC (‘cc’), IBM (‘xlc -c -qlanglvl=extc99’), Intel (‘icc -std=c99’), LLVM (‘clang’), NEC (‘cc’), PathScale (QLogic) (‘pathcc -std=c99’), PGI (‘pgcc -c9x’), SGI (‘cc -c99’), and Sun (‘cc’). NCO (all commands and the libnco library) and the C++ interface to netCDF (called libnco_c++) comply with the ISO C++ stan- dards as implemented by Comeau Computing (‘como’), Cray (‘CC’), GNU (‘g++ -Wall’), HP/Compaq/DEC (‘cxx’), IBM (‘xlC’), Intel (‘icc’), Microsoft (‘MVS’), NEC (‘c++’), Path- Scale (Qlogic) (‘pathCC’), PGI (‘pgCC’), SGI (‘CC -LANG:std’), and Sun (‘CC -LANG:std’). See nco/bld/Makefile and nco/src/nco_c++/Makefile.old for more details and exact settings.
2 The ‘_BSD_SOURCE’ token is required on some Linux platforms where gcc dislikes the network header files like netinet/in.h). Chapter 1: Introduction 9
Until recently (and not even yet), ANSI-compliant has meant compliance with the 1989 ISO C-standard, usually called C89 (with minor revisions made in 1994 and 1995). C89 lacks variable-size arrays, restricted pointers, some useful printf formats, and many mathemat- ical special functions. These are valuable features of C99, the 1999 ISO C-standard. NCO is C99-compliant where possible and C89-compliant where necessary. Certain branches in the code are required to satisfy the native SGI and SunOS C compilers, which are strictly ANSI C89 compliant, and cannot benefit from C99 features. However, C99 features are fully supported by modern AIX, GNU, Intel, NEC, Solaris, and UNICOS compilers. NCO requires a C99-compliant compiler as of NCO version 2.9.8, released in August, 2004.
The most time-intensive portion of NCO execution is spent in arithmetic operations, e.g., multiplication, averaging, subtraction. These operations were performed in Fortran by default until August, 1999. This was a design decision based on the relative speed of Fortran-based object code vs. C-based object code in late 1994. C compiler vectorization ca- pabilities have dramatically improved since 1994. We have accordingly replaced all Fortran subroutines with C functions. This greatly simplifies the task of building NCO on nominally unsupported platforms. As of August 1999, NCO built entirely in C by default. This al- lowed NCO to compile on any machine with an ANSI C compiler. In August 2004, the first C99 feature, the restrict type qualifier, entered NCO in version 2.9.8. C compilers can obtain better performance with C99 restricted pointers since they inform the compiler when it may make Fortran-like assumptions regarding pointer contents alteration. Subsequently, NCO requires a C99 compiler to build correctly3.
In January 2009, NCO version 3.9.6 was the first to link to the GNU Scientific Library (GSL). GSL must be version 1.4 or later. NCO, in particular ncap2, uses the GSL spe- cial function library to evaluate geoscience-relevant mathematics such as Bessel functions, Legendre polynomials, and incomplete gamma functions (see Section 4.1.22 [GSL special functions], page 195).
In June 2005, NCO version 3.0.1 began to take advantage of C99 mathematical spe- cial functions. These include the standarized gamma function (called tgamma() for “true gamma”). NCO automagically takes advantage of some GNU Compiler Collection (GCC) extensions to ANSI C.
As of July 2000 and NCO version 1.2, NCO no longer performs arithmetic operations in Fortran. We decided to sacrifice executable speed for code maintainability. Since no objective statistics were ever performed to quantify the difference in speed between the Fortran and C code, the performance penalty incurred by this decision is unknown. Sup- porting Fortran involves maintaining two sets of routines for every arithmetic operation. The USE_FORTRAN_ARITHMETIC flag is still retained in the Makefile. The file containing the Fortran code, nco_fortran.F, has been deprecated but a volunteer (Dr. Frankenstein?) could resurrect it. If you would like to volunteer to maintain nco_fortran.F please contact me.
3 NCO may still build with an ANSI or ISO C89 or C94/95-compliant compiler if the C pre-processor undefines the restrict type qualifier, e.g., by invoking the compiler with-Drestrict=’’ ‘ ’. 10 NCO 5.0.2-alpha07 User Guide
1.3.1 Compiling NCO for Microsoft Windows OS NCO has been successfully ported and tested on most Microsoft Windows operating systems including: XP SP2/Vista/7/10. Support is provided for compiling either native Windows executables, using the Microsoft Visual Studio Compiler (MVSC), or with Cygwin, the UNIX-emulating compatibility layer with the GNU toolchain. The switches necessary to accomplish both are included in the standard distribution of NCO. With Microsoft Visual Studio compiler, one must build NCO with C++ since MVSC does not support C99. Support for Qt, a convenient integrated development environment, was deprecated in 2017. As of NCO version 4.6.9 (September, 2017) please build native Windows executables with CMake: cd ~/nco/cmake cmake .. -DCMAKE_INSTALL_PREFIX=${HOME} make install The file nco/cmake/build.bat shows how deal with various path issues. As of NCO version 4.7.1 (December, 2017) the Conda package for NCO is available from the conda-forge channel on all three smithies: Linux, MacOS, and Windows. # Recommended install with Conda conda config --add channels conda-forge # Permananently add conda-forge conda install nco # Or, specify conda-forge explicitly as a one-off: conda install -c conda-forge nco Using the freely available Cygwin (formerly gnu-win32) development environment4, the compilation process is very similar to installing NCO on a UNIX system. Set the PVM_ARCH preprocessor token to WIN32. Note that defining WIN32 has the side effect of disabling Internet features of NCO (see below). NCO should now build like it does on UNIX. The least portable section of the code is the use of standard UNIX and Internet protocols (e.g., ftp, rcp, scp, sftp, getuid, gethostname, and header files
1.4 Symbolic Links NCO relies on a common set of underlying algorithms. To minimize duplication of source code, multiple operators sometimes share the same underlying source. This is accomplished
4 The Cygwin package is available from http://sourceware.redhat.com/cygwin Currently, Cygwin 20.x comes with the GNU C/C++ compilers (gcc, g++. These GNU compilers may be used to build the netCDF distribution itself. Chapter 1: Introduction 11
by symbolic links from a single underlying executable program to one or more invoked executable names. For example, nces and ncrcat are symbolically linked to the ncra executable. The ncra executable behaves slightly differently based on its invocation name (i.e., ‘argv[0]’), which can be nces, ncra, or ncrcat. Logically, these are three different operators that happen to share the same executable.
For historical reasons, and to be more user friendly, multiple synonyms (or pseudonyms) may refer to the same operator invoked with different switches. For example, ncdiff is the same as ncbo and ncpack is the same as ncpdq. We implement the symbolic links and synonyms by the executing the following UNIX commands in the directory where the NCO executables are installed.
ln -s -f ncbo ncdiff # ncbo --op_typ=’-’ ln -s -f ncra nces # ncra --pseudonym=’nces’ ln -s -f ncra ncrcat # ncra --pseudonym=’ncrcat’ ln -s -f ncbo ncadd # ncbo --op_typ=’+’ ln -s -f ncbo ncsubtract # ncbo --op_typ=’-’ ln -s -f ncbo ncmultiply # ncbo --op_typ=’*’ ln -s -f ncbo ncdivide # ncbo --op_typ=’/’ ln -s -f ncpdq ncpack # ncpdq ln -s -f ncpdq ncunpack # ncpdq --unpack # NB: Windows/Cygwin executable/link names have ’.exe’ suffix, e.g., ln -s -f ncbo.exe ncdiff.exe ...
The imputed command called by the link is given after the comment. As can be seen, some these links impute the passing of a command line argument to further modify the behavior of the underlying executable. For example, ncdivide is a pseudonym for ncbo --op_typ=’/’.
1.5 Libraries Like all executables, the NCO operators can be built using dynamic linking. This reduces the size of the executable and can result in significant performance enhancements on mul- tiuser systems. Unfortunately, if your library search path (usually the LD_LIBRARY_PATH environment variable) is not set correctly, or if the system libraries have been moved, re- named, or deleted since NCO was installed, it is possible NCO operators will fail with a message that they cannot find a dynamically loaded (aka shared object or ‘.so’) library. This will produce a distinctive error message, such as ‘ld.so.1: /usr/local/bin/nces: fatal: libsunmath.so.1: can’t open file: errno=2’. If you received an error message like this, ask your system administrator to diagnose whether the library is truly missing5, or whether you simply need to alter your library search path. As a final remedy, you may re-compile and install NCO with all operators statically linked.
5 The ldd command, if it is available on your system, will tell you where the executable is looking for each dynamically loaded library. Use, e.g., ldd ‘which nces‘. 12 NCO 5.0.2-alpha07 User Guide
1.6 netCDF2/3/4 and HDF4/5 Support netCDF version 2 was released in 1993. NCO (specifically ncks) began soon after this in 1994. netCDF 3.0 was released in 1996, and we were not exactly eager to convert all code to the newer, less tested netCDF implementation. One netCDF3 interface call (nc_ inq_libvers) was added to NCO in January, 1998, to aid in maintainance and debugging. In March, 2001, the final NCO conversion to netCDF3 was completed (coincidentally on the same day netCDF 3.5 was released). NCO versions 2.0 and higher are built with the -DNO_NETCDF_2 flag to ensure no netCDF2 interface calls are used. However, the ability to compile NCO with only netCDF2 calls is worth maintaining be- cause HDF version 4, aka HDF4 or simply HDF,6 (available from HDF (http://hdfgroup. org)) supports only the netCDF2 library calls (see http://hdfgroup.org/UG41r3_html/ SDS_SD.fm12.html#47784). There are two versions of HDF. Currently HDF version 4.x supports the full netCDF2 API and thus NCO version 1.2.x. If NCO version 1.2.x (or ear- lier) is built with only netCDF2 calls then all NCO operators should work with HDF4 files as well as netCDF files7. The preprocessor token NETCDF2_ONLY exists in NCO version 1.2.x to eliminate all netCDF3 calls. Only versions of NCO numbered 1.2.x and earlier have this capability. HDF version 5 became available in 1999, but did not support netCDF (or, for that matter, Fortran) as of December 1999. By early 2001, HDF5 did support Fortran90. Thanks to an NSF-funded “harmonization” partnership, HDF began to fully support the netCDF3 read interface (which is employed by NCO 2.x and later). In 2004, Unidata and THG began a project to implement the HDF5 features necessary to support the netCDF API. NCO version 3.0.3 added support for reading/writing netCDF4-formatted HDF5 files in October, 2005. See Section 3.10 [File Formats and Conversion], page 42, for more details. HDF support for netCDF was completed with HDF5 version version 1.8 in 2007. The netCDF front-end that uses this HDF5 back-end was completed and released soon after as netCDF version 4. Download it from the netCDF4 (http://my.unidata.ucar.edu/ content/software/netcdf/netcdf-4) website. NCO version 3.9.0, released in May, 2007, added support for all netCDF4 atomic data types except NC_STRING. Support for NC_STRING, including ragged arrays of strings, was finally added in version 3.9.9, released in June, 2009. Support for additional netCDF4 features has been incremental. We add one netCDF4 feature at a time. You must build NCO with netCDF4 to obtain this support. NCO supports many netCDF4 features including atomic data types, Lempel-Ziv com- pression (deflation), chunking, and groups. The new atomic data types are NC_UBYTE, NC_USHORT, NC_UINT, NC_INT64, and NC_UINT64. Eight-byte integer support is an espe- cially useful improvement from netCDF3. All NCO operators support these types, e.g.,
6 The Hierarchical Data Format, or HDF, is another self-describing data format similar to, but more elaborate than, netCDF. HDF comes in two flavors, HDF4 and HDF5. Often people use the shorthand HDF to refer to the older format HDF4. People almost always use HDF5 to refer to HDF5. 7 One must link the NCO code to the HDF4 MFHDF library instead of the usual netCDF library. Appar- ently ‘MF’ stands for Multi-file not for Mike Folk. In any case, until about 2007 the MFHDF library only supported netCDF2 calls. Most people will never again install NCO 1.2.x and so will never use NCO to write HDF4 files. It is simply too much trouble. Chapter 1: Introduction 13
ncks copies and prints them, ncra averages them, and ncap2 processes algebraic scripts with them. ncks prints compression information, if any, to screen. NCO version 3.9.1 (June, 2007) added support for netCDF4 Lempel-Ziv deflation. Lempel-Ziv deflation is a lossless compression technique. See Section 3.33 [Deflation], page 121, for more details. NCO version 3.9.9 (June, 2009) added support for netCDF4 chunking in ncks and ncecat. NCO version 4.0.4 (September, 2010) completed support for netCDF4 chunking in the remaining operators. See Section 3.31 [Chunking], page 104, for more details. NCO version 4.2.2 (October, 2012) added support for netCDF4 groups in ncks and ncecat. Group support for these operators was complete (e.g., regular expressions to select groups and Group Path Editing) as of NCO version 4.2.6 (March, 2013). See Section 3.14 [Group Path Editing], page 53, for more details. Group support for all other operators was finished in the NCO version 4.3.x series completed in December, 2013. Support for netCDF4 in the first arithmetic operator, ncbo, was introduced in NCO version 4.3.0 (March, 2013). NCO version 4.3.1 (May, 2013) completed this support and introduced the first example of automatic group broadcasting. See Section 4.3 [ncbo netCDF Binary Operator], page 225, for more details. netCDF4-enabled NCO handles netCDF3 files without change. In addition, it automag- ically handles netCDF4 (HDF5) files: If you feed NCO netCDF3 files, it produces netCDF3 output. If you feed NCO netCDF4 files, it produces netCDF4 output. Use the handy-dandy ‘-4’ switch to request netCDF4 output from netCDF3 input, i.e., to convert netCDF3 to netCDF4. See Section 3.10 [File Formats and Conversion], page 42, for more details. When linked to a netCDF library that was built with HDF4 support8, NCO automatically supports reading HDF4 files and writing them as netCDF3/netCDF4/HDF5 files. NCO can only write through the netCDF API, which can only write netCDF3/netCDF4/HDF5 files. So NCO can read HDF4 files, perform manipulations and calculations, and then itmust write the results in netCDF format. NCO support for HDF4 has been quite functional since December, 2013. For best results install NCO versions 4.4.0 or later on top of netCDF versions 4.3.1 or later. Getting to this point has been an iterative effort where Unidata improved netCDF library capabilities in response to our requests. NCO versions 4.3.6 and earlier do not explicitly support HDF4, yet should work with HDF4 if compiled with a version of netCDF (4.3.2 or later?) that does not unexpectedly die when probing HDF4 files with standard netCDF calls. NCO versions 4.3.7–4.3.9 (October–December, 2013) use a special flag to circumvent netCDF HDF4 issues. The user must tell these versions of NCO that an input file is HDF4 format by using the ‘--hdf4’ switch. When compiled with netCDF version 4.3.1 (20140116) or later, NCO versions 4.4.0 (Jan- uary, 2014) and later more gracefully handle HDF4 files. In particular, the--hdf4 ‘ ’ switch is obsolete. Current versions of NCO use netCDF to determine automatically whether the underlying file is HDF4, and then take appropriate precautions to avoid netCDF4 API
8 The procedure for doing this is documented at http://www.unidata.ucar.edu/software/netcdf/docs/ build_hdf4.html. 14 NCO 5.0.2-alpha07 User Guide
calls that fail when applied to HDF4 files (e.g., nc_inq_var_chunking(), nc_inq_var_ deflate()). When compiled with netCDF version 4.3.2 (20140423) or earlier, NCO will report that chunking and deflation properties of HDF4 files as HDF4_UNKNOWN, because de- termining those properties was impossible. When compiled with netCDF version 4.3.3-rc2 (20140925) or later, NCO versions 4.4.6 (October, 2014) and later fully support chunking and deflation features of HDF4 files. Unfortunately, netCDF version 4.7.4 (20200327) intro- duced a regression that breaks this functionality for all NCO versions until we first noticed the regression a year later implemented a workaround to restore this functionality as of 4.9.9-alpha02 (20210327). The ‘--hdf4’ switch is supported (for backwards compatibility) yet redundant (i.e., does no harm) with current versions of NCO and netCDF. Converting HDF4 files to netCDF: Since NCO reads HDF4 files natively, it is now easy to convert HDF4 files to netCDF files directly, e.g., ncks fl.hdf fl.nc # Convert HDF4->netCDF4 (NCO 4.4.0+, netCDF 4.3.1+) ncks --hdf4 fl.hdf fl.nc # Convert HDF4->netCDF4 (NCO 4.3.7-4.3.9) The most efficient and accurate way to convert HDF4 data to netCDF format is to convert to netCDF4 using NCO as above. Many HDF4 producers (NASA!) love to use netCDF4 types, e.g., unsigned bytes, so this procedure is the most typical. Conversion of HDF4 to netCDF4 as above suffices when the data will only be processed by NCO and other netCDF4-aware tools. However, many tools are not fully netCDF4-aware, and so conversion to netCDF3 may be desirable. Obtaining any netCDF file from an HDF4 is easy: ncks -3 fl.hdf fl.nc # HDF4->netCDF3 (NCO 4.4.0+, netCDF 4.3.1+) ncks -4 fl.hdf fl.nc # HDF4->netCDF4 (NCO 4.4.0+, netCDF 4.3.1+) ncks -6 fl.hdf fl.nc # HDF4->netCDF3 64-bit (NCO 4.4.0+, ...) ncks -7 -L 1 fl.hdf fl.nc # HDF4->netCDF4 classic (NCO 4.4.0+, ...) ncks --hdf4 -3 fl.hdf fl.nc # HDF4->netCDF3 (netCDF 4.3.0-) ncks --hdf4 -4 fl.hdf fl.nc # HDF4->netCDF4 (netCDF 4.3.0-) ncks --hdf4 -6 fl.hdf fl.nc # HDF4->netCDF3 64-bit (netCDF 4.3.0-) ncks --hdf4 -7 fl.hdf fl.nc # HDF4->netCDF4 classic (netCDF 4.3.0-) As of NCO version 4.4.0 (January, 2014), these commands work even when the HDF4 file contains netCDF4 atomic types (e.g., unsigned bytes, 64-bit integers) because NCO can autoconvert everything to atomic types supported by netCDF39. As of NCO version 4.4.4 (May, 2014) both ncl_convert2nc and NCO have built-in, automatic workarounds to handle element names that contain characters that are legal in HDF though are illegal in netCDF. For example, slashes and leading special characters
9 Prior to NCO version 4.4.0 (January, 2014), we recommended the ncl_convert2nc tool to convert HDF to netCDF3 when both these are true: 1. You must have netCDF3 and 2. the HDF file contains netCDF4 atomic types. More recent versions of NCO handle this problem fine, and include other advantages so we no longer recommend ncl_convert2nc because ncks is faster and more space-efficient. Both automati- cally convert netCDF4 types to netCDF3 types, yet ncl_convert2nc cannot produce full netCDF4 files. In contrast, ncks will happily convert HDF straight to netCDF4 files with netCDF4 types. Hence ncks can and does preserve the variable types. Unsigned bytes stay unsigned bytes. 64-bit integers stay 64-bit integers. Strings stay strings. Hence, ncks conversions often result in smaller files than ncl_convert2nc conversions. Another tool useful for converting netCDF3 to netCDF4 files, and whose functionality is, we think, also matched or exceeded by ncks, is the Python script nc3tonc4 by Jeff Whitaker. Chapter 1: Introduction 15
are are legal in HDF and illegal in netCDF element (i.e., group, variable, dimension, and attribute) names. NCO converts these forbidden characters to underscores, and retains the original names of variables in automatically produced attributes named hdf_name10. Finally, in February 2014, we learned that the HDF group has a project called H4CF (de- scribed here (http://hdfeos.org/software/h4cflib.php)) whose goal is to make HDF4 files accessible to CF tools and conventions. Their project includes a tool named h4tonccf that converts HDF4 files to netCDF3 or netCDF4 files. We are not yet sure what advantages or features h4tonccf has that are not in NCO, though we suspect both methods have their own advantages. Corrections welcome. As of 2012, netCDF4 is relatively stable software. Problems with netCDF4 and HDF libraries have mainly been fixed. Binary NCO distributions shipped as RPMs and as debs have used the netCDF4 library since 2010 and 2011, respectively. One must often build NCO from source to obtain netCDF4 support. Typically, one specifies the root of the netCDF4 installation directory. Do this withthe NETCDF4_ROOT variable. Then use your preferred NCO build mechanism, e.g., export NETCDF4_ROOT=/usr/local/netcdf4 # Set netCDF4 location cd ~/nco;./configure --enable-netcdf4 # Configure mechanism -or- cd ~/nco/bld;./make NETCDF4=Y allinone # Old Makefile mechanism We carefully track the netCDF4 releases, and keep the netCDF4 atomic type support and other features working. Our long term goal is to utilize more of the extensive new netCDF4 feature set. The next major netCDF4 feature we are likely to utilize is parallel I/O. We will enable this in the MPI netCDF operators.
1.7 Help Requests and Bug Reports We generally receive three categories of mail from users: help requests, bug reports, and feature requests. Notes saying the equivalent of “Hey, NCO continues to work great and it saves me more time everyday than it took to write this note” are a distant fourth. There is a different protocol for each type of request. The preferred etiquette forall communications is via NCO Project Forums. Do not contact project members via personal e-mail unless your request comes with money or you have damaging information about our personal lives. Please use the Forums—they preserve a record of the questions and answers so that others can learn from our exchange. Also, since NCO is both volunteer-driven and government-funded, this record helps us provide program officers with information they need to evaluate our project. Before posting to the NCO forums described below, you might first register (https:// sf.net/account/register.php) your name and email address with SourceForge.net or else all of your postings will be attributed to nobody. Once registered you may choose to monitor
10 Two real-world examples: NCO translates the NASA CERES dimension (FOV) Footprints to _FOV_ Footprints, and Cloud & Aerosol, Cloud Only, Clear Sky w/Aerosol, and Clear Sky (yes, the dimen- sion name includes whitespace and special characters) to Cloud & Aerosol, Cloud Only, Clear Sky w_ Aerosol, and Clear Sky ncl_convert2nc makes the element name netCDF-safe in a slightly different manner, and also stores the original name in the hdf_name attribute. 16 NCO 5.0.2-alpha07 User Guide
any forum and to receive (or not) email when there are any postings including responses to your questions. We usually reply to the forum message, not to the original poster. If you want us to include a new feature in NCO, please consider implementing the feature yourself and sending us the patch. If that is beyond your ken, then send a note to the NCO Discussion forum (http://sf.net/p/nco/discussion/9829). Read the manual before reporting a bug or posting a help request. Sending questions whose answers are not in the manual is the best way to motivate us to write more docu- mentation. We would also like to accentuate the contrapositive of this statement. If you think you have found a real bug the most helpful thing you can do is simplify the problem to a manageable size and then report it. The first thing to do is to make sure you are running the latest publicly released version of NCO. Once you have read the manual, if you are still unable to get NCO to perform a docu- mented function, submit a help request. Follow the same procedure as described below for reporting bugs (after all, it might be a bug). That is, describe what you are trying to do, and include the complete commands (run with ‘-D 5’), error messages, and version of NCO (with ‘-r’). Some commands behave differently depending on the exact order and rankof dimensions in the pertinent variables. In such cases we need you to provide that metadata, e.g., the text results of ‘ncks -m’ on your input and/or output files. Post your help request to the NCO Help forum (http://sf.net/p/nco/discussion/9830). If you think you used the right command when NCO misbehaves, then you might have found a bug. Incorrect numerical answers are the highest priority. We usually fix those within one or two days. Core dumps and sementation violations receive lower priority. They are always fixed, eventually. How do you simplify a problem that reveal a bug? Cut out extraneous variables, di- mensions, and metadata from the offending files and re-run the command until it nolonger breaks. Then back up one step and report the problem. Usually the file(s) will be very small, i.e., one variable with one or two small dimensions ought to suffice. Run the operator with ‘-r’ and then run the command with ‘-D 5’ to increase the verbosity of the debug- ging output. It is very important that your report contain the exact error messages and compile-time environment. Include a copy of your sample input file, or place one on a pub- licly accessible location, of the file(s). If you are sure it is a bug, post the full report tothe NCO Project buglist (http://sf.net/p/nco/bugs). Otherwise post all the information to NCO Help forum (http://sf.net/p/nco/discussion/9830). Build failures count as bugs. Our limited machine access means we cannot fix all build failures. The information we need to diagnose, and often fix, build failures are the three files output by GNU build tools, nco.config.log.${GNU_TRP}.foo, nco.configure.${GNU_ TRP}.foo, and nco.make.${GNU_TRP}.foo. The file configure.eg shows how to produce these files. Here ${GNU_TRP} is the “GNU architecture triplet”, the chip-vendor-OS string returned by config.guess. Please send us your improvements to the examples supplied in configure.eg. The regressions archive at http://dust.ess.uci.edu/nco/rgr contains the build output from our standard test systems. You may find you can solve the build problem yourself by examining the differences between these files and your own. 17
2 Operator Strategies
2.1 Philosophy The main design goal is command line operators which perform useful, scriptable operations on netCDF files. Many scientists work with models and observations which produce too much data to analyze in tabular format. Thus, it is often natural to reduce and massage this raw or primary level data into summary, or second level data, e.g., temporal or spatial averages. These second level data may become the inputs to graphical and statistical pack- ages, and are often more suitable for archival and dissemination to the scientific community. NCO performs a suite of operations useful in manipulating data from the primary to the second level state. Higher level interpretive languages (e.g., IDL, Yorick, Matlab, NCL, Perl, Python), and lower level compiled languages (e.g., C, Fortran) can always perform any task performed by NCO, but often with more overhead. NCO, on the other hand, is limited to a much smaller set of arithmetic and metadata operations than these full blown languages. Another goal has been to implement enough command line switches so that frequently used sequences of these operators can be executed from a shell script or batch file. Finally, NCO was written to consume the absolute minimum amount of system memory required to perform a given job. The arithmetic operators are extremely efficient; their exact memory usage is detailed in Section 2.9 [Memory Requirements], page 24.
2.2 Climate Model Paradigm NCO was developed at NCAR to aid analysis and manipulation of datasets produced by General Circulation Models (GCMs). GCM datasets share many features with other gridded scientific datasets and so provide a useful paradigm for the explication ofthe NCO operator set. Examples in this manual use a GCM paradigm because latitude, longitude, time, temperature and other fields related to our natural environment are as easy to visualize for the layman as the expert.
2.3 Temporary Output Files NCO operators are designed to be reasonably fault tolerant, so that a system failure or user- abort of the operation (e.g., with C-c) does not cause loss of data. The user-specified output- file is only created upon successful completion of the operation1. This is accomplished by performing all operations in a temporary copy of output-file. The name of the temporary output file is constructed by appending .pid
1 The ncrename and ncatted operators are exceptions to this rule. See Section 4.13 [ncrename netCDF Renamer], page 341. 18 NCO 5.0.2-alpha07 User Guide
Also, note this feature increases the execution time of the operator by approximately the time it takes to copy the output-file2. Finally, note this fault-tolerant feature allows the output-file to be the same as the input-file without any danger of “overlap”. Over time many “power users” have requested a way to turn-off the fault-tolerance safety feature that automatically creates a temporary file. Often these users build and execute production data analysis scripts that are repeated frequently on large datasets. Obviating an extra file write can then conserve significant disk space and time. For this purpose NCO has, since version 4.2.1 in August, 2012, made configurable the controls over temporary file creation. The ‘--wrt_tmp_fl’ and equivalent ‘--write_tmp_fl’ switches ensure NCO writes output to an intermediate temporary file. This is and has always been the default behavior so there is currently no need to specify these switches. However, the default may change some day, especially since writing to RAM disks (see Section 3.36 [RAM disks], page 124) may some day become the default. The ‘--no_tmp_fl’ switch causes NCO to write directly to the final output file instead of to an intermediate temporary file. “Power users” may wish to invoke this switch to increase performance (i.e., reduce wallclock time) when manipulating large files. When eschewing temporary files, users may forsake the ability to have the same name for both output-file and input-file since, as described above, the temporary file prevented overlap issues. However, if the user creates the output filein RAM (see Section 3.36 [RAM disks], page 124) then it is still possible to have the same name for both output-file and input-file. ncks in.nc out.nc # Default: create out.pid.tmp.nc then move to out.nc ncks --wrt_tmp_fl in.nc out.nc # Same as default ncks --no_tmp_fl in.nc out.nc # Create out.nc directly on disk ncks --no_tmp_fl in.nc in.nc # ERROR-prone! Overwrite in.nc with itself ncks --create_ram --no_tmp_fl in.nc in.nc # Create in RAM, write to disk ncks --open_ram --no_tmp_fl in.nc in.nc # Read into RAM, write to disk There is no reason to expect the fourth example to work. The behavior of overwriting a file while reading from the same file is undefined, much as is thecat shellcommand‘ foo > foo’. Although it may “work” in some cases, it is unreliable. One way around this is to use ‘--create_ram’ so that the output file is not written to disk until the input fileis closed, See Section 3.36 [RAM disks], page 124. However, as of 20130328, the behavior of the ‘--create_ram’ and ‘--open_ram’ examples has not been thoroughly tested. The NCO authors have seen compelling use cases for utilizing the RAM switches, though not (yet) for combining them with ‘--no_tmp_fl’. NCO implements both options because they are largely independent of eachother. It is up to “power users” to discover which best fit their needs. We welcome accounts of your experiences posted to the forums. Other safeguards exist to protect the user from inadvertently overwriting data. If the output-file specified for a command is a pre-existing file, then the operator willprompt the user whether to overwrite (erase) the existing output-file, attempt to append to it, or abort the operation. However, in processing large amounts of data, too many interactive questions slows productivity. Therefore NCO also implements two ways to override its own safety features, the ‘-O’ and ‘-A’ switches. Specifying ‘-O’ tells the operator to overwrite any existing output-file without prompting the user interactively. Specifying ‘-A’ tells the
2 The OS-specific system move command is used. This is mv for UNIX, and move for Windows. Chapter 2: Operator Strategies 19
operator to attempt to append to any existing output-file without prompting the user inter- actively. These switches are useful in batch environments because they suppress interactive keyboard input.
2.4 Appending Variables Adding variables from one file to another is often desirable. This is referred toas appending, although some prefer the terminology merging 3 or pasting. Appending is often confused with what NCO calls concatenation. In NCO, concatenation refers to splicing a variable along the record dimension. The length along the record dimension of the output is the sum of the lengths of the input files. Appending, on the other hand, refers to copying a variable from one file to another file which may or may not already contain the variable4. NCO can append or concatenate just one variable, or all the variables in a file at the same time.
In this sense, ncks can append variables from one file to another file. This capability is invoked by naming two files on the command line, input-file and output-file. When output- file already exists, the user is prompted whether to overwrite, append/replace, or exit from the command. Selecting overwrite tells the operator to erase the existing output-file and replace it with the results of the operation. Selecting exit causes the operator to exit—the output-file will not be touched in this case. Selecting append/replace causes the operator to attempt to place the results of the operation in the existing output-file, See Section 4.8 [ncks netCDF Kitchen Sink], page 263. The simplest way to create the union of two files is
ncks -A fl_1.nc fl_2.nc This puts the contents of fl_1.nc into fl_2.nc. The ‘-A’ is optional. On output, fl_2.nc is the union of the input files, regardless of whether they share dimensions and variables, or are completely disjoint. The append fails if the input files have differently named record dimensions (since netCDF supports only one), or have dimensions of the same name but different sizes.
2.5 Simple Arithmetic and Interpolation Users comfortable with NCO semantics may find it easier to perform some simple mathe- matical operations in NCO rather than higher level languages. ncbo (see Section 4.3 [ncbo netCDF Binary Operator], page 225) does file addition, subtraction, multiplication, divi- sion, and broadcasting. It even does group broadcasting. ncflint (see Section 4.7 [ncflint netCDF File Interpolator], page 260) does file addition, subtraction, multiplication and in- terpolation. Sequences of these commands can accomplish simple yet powerful operations from the command line.
3 The terminology merging is reserved for an (unwritten) operator which replaces hyperslabs of a variable in one file with hyperslabs of the same variable from another file 4 Yes, the terminology is confusing. By all means mail me if you think of a better nomenclature. Should NCO use paste instead of append? 20 NCO 5.0.2-alpha07 User Guide
2.6 Statistics vs. Concatenation The most frequently used operators of NCO are probably the statisticians (i.e., tools that do statistics) and concatenators. Because there are so many types of statistics like averaging (e.g., across files, within a file, over the record dimension, over other dimensions, withor without weights and masks) and of concatenating (across files, along the record dimension, along other dimensions), there are currently no fewer than five operators which tackle these two purposes: ncra, nces, ncwa, ncrcat, and ncecat. These operators do share many capabilities5, though each has its unique specialty. Two of these operators, ncrcat and ncecat, concatenate hyperslabs across files. The other two operators, ncra and nces, compute statistics across (and/or within) files6. First, let’s describe the concatenators, then the statistics tools. 2.6.1 Concatenators ncrcat and ncecat Joining together independent files along a common record dimension is called concatena- tion. ncrcat is designed for concatenating record variables, while ncecat is designed for concatenating fixed length variables. Consider five files, 85.nc, 86.nc,... 89.nc each con- taining a year’s worth of data. Say you wish to create from them a single file, 8589.nc containing all the data, i.e., spanning all five years. If the annual files make use ofthe same record variable, then ncrcat will do the job nicely with, e.g., ncrcat 8?.nc 8589.nc. The number of records in the input files is arbitrary and can vary from file to file. See Section 4.11 [ncrcat netCDF Record Concatenator], page 302, for a complete description of ncrcat. However, suppose the annual files have no record variable, and thus their data areall fixed length. For example, the files may not be conceptually sequential, but rather members of the same group, or ensemble. Members of an ensemble may have no reason to contain a record dimension. ncecat will create a new record dimension (named record by default) with which to glue together the individual files into the single ensemble file. If ncecat is used on files which contain an existing record dimension, that record dimension is converted to a fixed-length dimension of the same name and a new record dimension (named record) is created. Consider five realizations, 85a.nc, 85b.nc,... 85e.nc of 1985 predictions from the same climate model. Then ncecat 85?.nc 85_ens.nc glues together the individual realizations into the single file, 85_ens.nc. If an input variable was dimensioned [lat,lon], it will have dimensions [record,lat,lon] in the output file. A restriction of ncecat is that the hyperslabs of the processed variables must be the same from file to file. Normally this means all the input files are the same size, and contain data on different realizations ofthe same variables. See Section 4.5 [ncecat netCDF Ensemble Concatenator], page 253, for a complete description of ncecat. ncpdq makes it possible to concatenate files along any dimension, not just the record dimension. First, use ncpdq to convert the dimension to be concatenated (i.e., extended
5 Currently nces and ncrcat are symbolically linked to the ncra executable, which behaves slightly differ- ently based on its invocation name (i.e., ‘argv[0]’). These three operators share the same source code, and merely have different inner loops. 6 The third averaging operator, ncwa, is the most sophisticated averager in NCO. However, ncwa is in a different class than ncra and nces because it operates on a single file per invocation (as opposed to multiple files). On that single file, however, ncwa provides a richer set of averaging options—including weighting, masking, and broadcasting. Chapter 2: Operator Strategies 21
with data from other files) into the record dimension. Second, use ncrcat to concatenate these files. Finally, if desirable, use ncpdq to revert to the original dimensionality. As a concrete example, say that files x_01.nc, x_02.nc,... x_10.nc contain time-evolving datasets from spatially adjacent regions. The time and spatial coordinates are time and x, respectively. Initially the record dimension is time. Our goal is to create a single file that contains joins all the spatially adjacent regions into one single time-evolving dataset. for idx in 01 02 03 04 05 06 07 08 09 10; do # Bourne Shell ncpdq -a x,time x_${idx}.nc foo_${idx}.nc # Make x record dimension done ncrcat foo_??.nc out.nc # Concatenate along x ncpdq -a time,x out.nc out.nc # Revert to time as record dimension Note that ncrcat will not concatenate fixed-length variables, whereas ncecat concate- nates both fixed-length and record variables along a new record variable. To conserve system memory, use ncrcat where possible. 2.6.2 Averagers nces, ncra, and ncwa The differences between the averagers ncra and nces are analogous to the differences be- tween the concatenators. ncra is designed for averaging record variables from at least one file, while nces is designed for averaging fixed length variables from multiple files. ncra per- forms a simple arithmetic average over the record dimension of all the input files, with each record having an equal weight in the average. nces performs a simple arithmetic average of all the input files, with each file having an equal weight in the average. Notethat ncra cannot average fixed-length variables, but nces can average both fixed-length and record variables. To conserve system memory, use ncra rather than nces where possible (e.g., if each input-file is one record long). The file output from nces will have the same dimensions (meaning dimension names as well as sizes) as the input hyperslabs (see Section 4.6 [nces netCDF Ensemble Statistics], page 256, for a complete description of nces). The file out- put from ncra will have the same dimensions as the input hyperslabs except for the record dimension, which will have a size of 1 (see Section 4.10 [ncra netCDF Record Averager], page 298, for a complete description of ncra). 2.6.3 Interpolator ncflint ncflint can interpolate data between or two files. Since no other operators have this ability, the description of interpolation is given fully on the ncflint reference page (see Section 4.7 [ncflint netCDF File Interpolator], page). 260 Note that this capability also allows ncflint to linearly rescale any data in a netCDF file, e.g., to convert between differing units.
2.7 Large Numbers of Files Occasionally one desires to digest (i.e., concatenate or average) hundreds or thousands of input files. Unfortunately, data archives (e.g., NASA EOSDIS) may not name netCDF files in a format understood by the ‘-n loop’ switch (see Section 3.6 [Specifying Input Files], page 34) that automagically generates arbitrary numbers of input filenames. The-n ‘ loop’ switch has the virtue of being concise, and of minimizing the command line. This helps keeps output file small since the command line is stored as metadata inthe history attribute (see Section 3.43 [History Attribute], page 144). However, the ‘-n loop’ switch is useless when 22 NCO 5.0.2-alpha07 User Guide
there is no simple, arithmetic pattern to the input filenames (e.g., h00001.nc, h00002.nc, ... h90210.nc). Moreover, filename globbing does not work when the input files aretoo numerous or their names are too lengthy (when strung together as a single argument) to be passed by the calling shell to the NCO operator7. When this occurs, the ANSI C-standard argc-argv method of passing arguments from the calling shell to a C-program (i.e., an NCO operator) breaks down. There are (at least) three alternative methods of specifying the input filenames to NCO in environment-limited situations. The recommended method for sending very large numbers (hundreds or more, typically) of input filenames to the multi-file operators is to pass the filenames withthe UNIX standard input feature, aka stdin: # Pipe large numbers of filenames to stdin /bin/ls | grep ${CASEID}_’...... ’.nc | ncecat -o foo.nc This method avoids all constraints on command line size imposed by the operating system. A drawback to this method is that the history attribute (see Section 3.43 [History Attribute], page 144) does not record the name of any input files since the names were not passed as positional arguments on the command line. This makes it difficult later to determine the data provenance. To remedy this situation, multi-file operators store the number of input files in the nco_input_file_number global attribute and the input file list itself in the nco_input_file_list global attribute (see Section 3.44 [File List Attributes], page 145). Although this does not preserve the exact command used to generate the file, it does retains all the information required to reconstruct the command and determine the data provenance. A second option is to use the UNIX xargs command. This simple example selects as input to xargs all the filenames in the current directory that match a given pattern. For illustration, consider a user trying to average millions of files which each have a six character filename. If the shell buffer cannot hold the results of the corresponding globbing operator, ??????.nc, then the filename globbing technique will fail. Instead we express the filename pattern as an extended regular expression, ...... \.nc (see Section 3.12 [Subsetting Files], page 48). We use grep to filter the directory listing for this pattern and to pipe the results to xargs which, in turn, passes the matching filenames to an NCO multi-file operator, e.g., ncecat. # Use xargs to transfer filenames on the command line /bin/ls | grep ${CASEID}_’...... ’.nc | xargs -x ncecat -o foo.nc The single quotes protect the only sensitive parts of the extended regular expression (the grep argument), and allow shell interpolation (the ${CASEID} variable substitution) to proceed unhindered on the rest of the command. xargs uses the UNIX pipe feature to append the suitably filtered input file list to the end ofthe ncecat command options. The -o foo.nc switch ensures that the input files supplied by xargs are not confused with the output file name. xargs does, unfortunately, have its own limit (usually about 20,000 characters) on the size of command lines it can pass. Give xargs the ‘-x’ switch to ensure it
7 The exact length which exceeds the operating system internal limit for command line lengths varies across OSs and shells. GNU bash may not have any arbitrary fixed limits to the size of command line arguments. Many OSs cannot handle command line arguments (including results of file globbing) exceeding 4096 characters. Chapter 2: Operator Strategies 23
dies if it reaches this internal limit. When this occurs, use either the stdin method above, or the symbolic link presented next.
Even when its internal limits have not been reached, the xargs technique may not be sophisticated enough to handle all situations. A full scripting language like Perl or Python can handle any level of complexity of filtering input filenames, and any number of filenames. The technique of last resort is to write a script that creates symbolic links between the irregular input filenames and a set of regular, arithmetic filenames thatthe‘-n loop’ switch understands. For example, the following Perl script creates a monotonically enumerated symbolic link to up to one million .nc files in a directory. If there are 999,999 netCDF files present, the links are named 000001.nc to 999999.nc:
# Create enumerated symbolic links /bin/ls | grep \.nc | perl -e \ ’$idx=1;while(
The ‘-n loop’ option tells the NCO operator to automatically generate the filnames of the symbolic links. This circumvents any OS and shell limits on command-line size. The symbolic links are easily removed once NCO is finished. One drawback to this method is that the history attribute (see Section 3.43 [History Attribute], page 144) retains the filename list of the symbolic links, rather than the data files themselves. This makes it difficult to determine the data provenance at a later date.
2.8 Large Datasets Large datasets are those files that are comparable in size to the amount of random access memory (RAM) in your computer. Many users of NCO work with files larger than 100 MB. Files this large not only push the current edge of storage technology, they present special problems for programs which attempt to access the entire file at once, such as nces and ncecat. If you work with a 300 MB files on a machine with only 32 MB of memory then you will need large amounts of swap space (virtual memory on disk) and NCO will work slowly, or even fail. There is no easy solution for this. The best strategy is to work on a machine with sufficient amounts of memory and swap space. Since about 2004, many usershave begun to produce or analyze files exceeding 2 GB in size. These users should familiarize themselves with NCO’s Large File Support (LFS) capabilities (see Section 3.11 [Large File Support], page 47). The next section will increase your familiarity with NCO’s memory requirements. With this knowledge you may re-design your data reduction approach to divide the problem into pieces solvable in memory-limited situations.
If your local machine has problems working with large files, try running NCO from a more powerful machine, such as a network server. If you get a memory-related core dump (e.g., ‘Error exit (core dumped)’) on a GNU/Linux system, or the operation ends before the entire output file is written, try increasing the process-available memory with ulimit:
ulimit -f unlimited 24 NCO 5.0.2-alpha07 User Guide
This may solve constraints on clusters where sufficient hardware resources exist yet where system administrators felt it wise to prevent any individual user from consuming too much of resource. Certain machine architectures, e.g., Cray UNICOS, have special commands which allow one to increase the amount of interactive memory. On Cray systems, try to increase the available memory with the ilimit command. The speed of the NCO operators also depends on file size. When processing large files the operators may appear to hang, or do nothing, for large periods of time. In order to see what the operator is actually doing, it is useful to activate a more verbose output mode. This is accomplished by supplying a number greater than 0 to the ‘-D debug-level’ (or ‘--debug-level’, or ‘--dbg_lvl’) switch. When the debug-level is non-zero, the operators report their current status to the terminal through the stderr facility. Using ‘-D’ does not slow the operators down. Choose a debug-level between 1 and 3 for most situations, e.g., nces -D 2 85.nc 86.nc 8586.nc. A full description of how to estimate the actual amount of memory the multi-file NCO operators consume is given in Section 2.9 [Memory Requirements], page 24.
2.9 Memory Requirements Many people use NCO on gargantuan files which dwarf the memory available (free RAM plus swap space) even on today’s powerful machines. These users want NCO to consume the least memory possible so that their scripts do not have to tediously cut files into smaller pieces that fit into memory. We commend these greedy users for pushing NCO to its limits! This section describes the memory NCO requires during operation. The required memory depends on the underlying algorithms, datatypes, and compression, if any. The description below is the memory usage per thread. Users with shared memory machines may use the threaded NCO operators (see Section 3.3 [OpenMP Threading], page 30). The peak and sustained memory usage will scale accordingly, i.e., by the number of threads. In all cases the memory use refers to the uncompressed size of the data. The netCDF4 library automatically decompresses variables during reads. The filesize can easily belie the true size of the uncompressed data. In other words, the usage below can be taken at face value for netCDF3 datasets only. Chunking will also affect memory usage on netCDF4 operations. Memory consumption patterns of all operators are similar, with the exception of ncap2. 2.9.1 Single and Multi-file Operators The multi-file operators currently comprise the record operators, ncra and ncrcat, and the ensemble operators, nces and ncecat. The record operators require much less memory than the ensemble operators. This is because the record operators operate on one single record (i.e., time-slice) at a time, whereas the ensemble operators retrieve the entire variable into memory. Let MS be the peak sustained memory demand of an operator, FT be the memory required to store the entire contents of all the variables to be processed in an input file, FR be the memory required to store the entire contents of a single record of each of the variables to be processed in an input file, VR be the memory required to store a single record of the largest record variable to be processed in an input file, VT be the memory required to store the largest variable to be processed in an input file, VI be the memory required to store the largest variable which is not processed, but is copied from the initial file to the output file. All operators require MI = VI during the initial copying Chapter 2: Operator Strategies 25
of variables from the first input file to the output file. This isthe initial (and transient) memory demand. The sustained memory demand is that memory required by the operators during the processing (i.e., averaging, concatenation) phase which lasts until all the input files have been processed. The operators have the following memory requirements: ncrcat requires MS <= VR. ncecat requires MS <= VT . ncra requires MS = 2FR+VR. nces requires MS = 2FT +VT . ncbo requires MS <= 3VT (both input variables and the output variable). ncflint requires MS <= 3VT (both input variables and the output variable). ncpdq requires MS <= 2VT (one input variable and the output variable). ncwa requires MS <= 8VT (see below). Note that only variables that are processed, e.g., averaged, concatenated, or differenced, contribute to MS. Variables that do not appear in the output file (see Section 3.12 [Subsetting Files], page 48) are never read and contribute nothing to the memory requirements.
Further note that some operators perform internal type-promotion on some variables prior to arithmetic (see Section 3.40 [Type Conversion], page 134). For example, ncra, nces, and ncwa all promote integer types to double-precision floating-point prior to arith- metic, then perform the arithmetic, then demote back to the original integer type after arithmetic. This preserves the on-disk storage type while obtaining the precision advan- tages of double-precision floating-point arithmetic. Since version 4.3.6 (released in Septem- ber, 2013), NCO also by default converts single-precision floating-point to double-precision prior to arithmetic, which incurs the same RAM penalty. Hence, the sustained memory required for integer variables and single-precision floats are two or four-times their on-disk, uncompressed, unpacked sizes if they meet the rules for automatic internal promotion. Put another way, disabling auto-promotion of single-precision variables (with ‘--flt’) consid- erably reduces the RAM footprint of arithmetic operators.
The ‘--open_ram’ switch (and switches that invoke it like ‘--ram_all’ and ‘--diskless_all’) incurs a RAM penalty. These switches cause each input file to be copied to RAM upon opening. Hence any operator invoking these switches utilizes an additional FT of RAM (i.e., MS+ = FT ). See Section 3.36 [RAM disks], page 124, for further details.
ncwa consumes between two and eight times the memory of an NC_DOUBLE variable in order to process it. Peak consumption occurs when storing simultaneously in memory one input variable, one tally array, one input weight, one conformed/working weight, one weight tally, one input mask, one conformed/working mask, and one output variable. NCO’s tally arrays are of type C-type long, whose size is eight-bytes on all modern computers, the same as NC_DOUBLE8. When invoked, the weighting and masking features contribute up to three-eighths and two-eighths of these requirements apiece. If weights and masks are not specified (i.e., no-w ‘ ’ or ‘-a’ options) then ncwa requirements drop to MS <= 3VT (one input variable, one tally array, and the output variable). The output variable is the same size as the input variable when averaging only over a degenerate dimension. However, normally the output variable is much smaller than the input, and is often a simple scalar, in which case the memory requirements drop by 1VT since the output array requires essentially no memory.
8 By contrast NC_INT and its deprecated synonym NC_LONG are only four-bytes. Perhaps this is one reason why the NC_LONG token is deprecated. 26 NCO 5.0.2-alpha07 User Guide
All of this is subject to the type promotion rules mentioned above. For example, ncwa averaging a variable of type NC_FLOAT requires MS <= 16VT (rather than MS <= 8VT ) since all arrays are (at least temporarily) composed of eight-byte elements, twice the size of the values on disk. Without mask or weights, the requirements for NC_FLOAT are MS <= 6VT (rather than MS <= 3VT as for NC_DOUBLE) due to temporary internal promotion of both the input variable and the output variable to type NC_DOUBLE. The ‘--flt’ option that suppresses promotion reduces this to MS <= 4VT (the tally elements do not change size), and to MS <= 3VT when the output array is a scalar. The above memory requirements must be multiplied by the number of threads thr nbr (see Section 3.3 [OpenMP Threading], page 30). If this causes problems then reduce (with ‘-t thr_nbr’) the number of threads. 2.9.2 Memory for ncap2 ncap2 has unique memory requirements due its ability to process arbitrarily long scripts of any complexity. All scripts acceptable to ncap2 are ultimately processed as a sequence of binary or unary operations. ncap2 requires MS <= 2VT under most conditions. An exception to this is when left hand casting (see Section 4.1.4 [Left hand casting], page 160) is used to stretch the size of derived variables beyond the size of any input variables. Let VC be the memory required to store the largest variable defined by left hand casting. In this case, MS <= 2VC. ncap2 scripts are complete dynamic and may be of arbitrary length. A script that contains many thousands of operations, may uncover a slow memory leak even though each single operation consumes little additional memory. Memory leaks are usually identifiable by their memory usage signature. Leaks cause peak memory usage to increase monotonically with time regardless of script complexity. Slow leaks are very difficult to find. Sometimes a malloc() (or new[]) failure is the only noticeable clue to their existence. If you have good reasons to believe that a memory allocation failure is ultimately due to an NCO memory leak (rather than inadequate RAM on your system), then we would be very interested in receiving a detailed bug report.
2.10 Performance An overview of NCO capabilities as of about 2006 is in Zender, C. S. (2008), “Analysis of Self-describing Gridded Geoscience Data with netCDF Operators (NCO)”, Environ. Mod- ell. Softw., doi:10.1016/j.envsoft.2008.03.004. This paper is also available at http://dust. ess.uci.edu/ppr/ppr_Zen08.pdf. NCO performance and scaling for arithmetic operations is described in Zender, C. S., and H. J. Mangalam (2007), “Scaling Properties of Common Statistical Opera- tors for Gridded Datasets”, Int. J. High Perform. Comput. Appl., 21(4), 485-498, doi:10.1177/1094342007083802. This paper is also available at http://dust.ess.uci.edu/ ppr/ppr_ZeM07.pdf. It is helpful to be aware of the aspects of NCO design that can limit its performance: 1. No data buffering is performed during nc_get_var and nc_put_var operations. Hy- perslabs too large to hold in core memory will suffer substantial performance penalties because of this. Chapter 2: Operator Strategies 27
2. Since coordinate variables are assumed to be monotonic, the search for bracketing the user-specified limits should employ a quicker algorithm, like bisection, than thetwo- sided incremental search currently implemented. 3. C format, FORTRAN format, signedness, scale format and add offset attributes are ignored by ncks when printing variables to screen. 4. In the late 1990s it was discovered that some random access operations on large files on certain architectures (e.g., UNICOS) were much slower with NCO than with similar operations performed using languages that bypass the netCDF interface (e.g., Yorick). This may have been a penalty of unnecessary byte-swapping in the netCDF interface. It is unclear whether such problems exist in present day (2007) netCDF/NCO environ- ments, where unnecessary byte-swapping has been reduced or eliminated.
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3 Shared Features
Many features have been implemented in more than one operator and are described here for brevity. The description of each feature is preceded by a box listing the operators for which the feature is implemented. Command line switches for a given feature are consistent across all operators wherever possible. If no “key switches” are listed for a feature, then that particular feature is automatic and cannot be controlled by the user.
3.1 Internationalization
Availability: All operators ¨
NCO support for internationalization of textual input and output (e.g., Warning mes- © sages) is nascent. We introduced the first foreign language string catalogues (French and Spanish) in 2004, yet did not activate these in distributions because the catalogues were nearly empty. We seek volunteers to populate our templates with translations for their favorite languages.
3.2 Metadata Optimization
Availability: All operators ¨ Short options: None Long options: ‘--hdr_pad’, ‘--header_pad’
NCO supports padding headers to improve the speed of future metadata operations. Use © the ‘--hdr_pad’ and ‘--header_pad’ switches to request that hdr pad bytes be inserted into the metadata section of the output file. There is little downside to padding a header with kilobyte of space, since subsequent manipulation of the file will annotate the history attribute with all commands, let alone any explicit metadata additions with ncatted. ncks --hdr_pad=1000 in.nc out.nc # Pad header with 1 kB space ncks --hdr_pad=10000 in.nc out.nc # Pad header with 10 kB space Future metadata expansions will not incur the netCDF3 performance penalty of copying the entire output file unless the expansion exceeds the amount of header padding. This can be beneficial when it is known that some metadata will be added at a future date. The operators that benefit most from judicious use of header padding are ncatted and ncrename, since they only alter metadata. This optimization exploits the netCDF library nc__enddef() function. This function behaves differently with different storage formats. It will improve speed of future metadata expansion with CLASSIC and 64bit netCDF files, though not necessarily with NETCDF4 files, i.e., those created by the netCDF interface tothe HDF5 library (see Section 3.10 [File Formats and Conversion], page 42). netCDF3 formats use a simple sequential ordering that 30 NCO 5.0.2-alpha07 User Guide
requires copying the file if the size of new metadata exceeds the available padding. netCDF4 files use internal file pointers that allow flexibility at inserting and removing data without necessitating copying the whole file.
3.3 OpenMP Threading
Availability: ncclimo, ncks, ncremap ¨ Short options: ‘-t’ Long options: ‘--thr_nbr’, ‘--threads’, ‘--omp_num_threads’
NCO supports shared memory parallelism (SMP) when compiled with an OpenMP- © enabled compiler. Threads requests and allocations occur in two stages. First, users may request a specific number of threads thr nbr with the ‘-t’ switch (or its long option equiva- lents, ‘--thr_nbr’, ‘--threads’, and ‘--omp_num_threads’). If not user-specified, OpenMP obtains thr nbr from the OMP_NUM_THREADS environment variable, if present, or from the OS, if not.
Caveat: Unfortunately, threading does not improve NCO throughput (i.e., wallclock ¨ time) because nearly all NCO operations are I/O-bound. This means that NCO spends negligible time doing anything compared to reading and writing. The only exception is regridding with ncremap which uses ncks under-the-hood. As of 2017, threading works only for regridding, thus this section is relevant only to ncclimo, ncks, and ncremap. We have seen some and can imagine other use cases where ncwa, ncpdq, and ncap2 (with long scripts) will complete faster due to threading. The main benefits of threading so far have been to isolate the serial from parallel portions of code. This parallelism is now exploited by OpenMP but then runs into the I/O bottleneck during output. The bottleneck will be ameliorated for large files by the use of MPI-enabled calls in the netCDF4 library when the underlying filesystem is parallel (e.g., PVFS or JFS). Implementation of the parallel output calls in NCO is not a goal of our current funding and would require new volunteers or funding.
NCO may modify thr nbr according to its own internal settings before it requests any © threads from the system. Certain operators contain hard-code limits to the number of threads they request. We base these limits on our experience and common sense, and to reduce potentially wasteful system usage by inexperienced users. For example, ncrcat is extremely I/O-intensive so we restrict thr nbr <= 2 for ncrcat. This is based on the notion that the best performance that can be expected from an operator which does no arithmetic is to have one thread reading and one thread writing simultaneously. In the future (perhaps with netCDF4), we hope to demonstrate significant threading improvements with operators like ncrcat by performing multiple simultaneous writes. Compute-intensive operators (ncremap) benefit most from threading. The greatest in- creases in throughput due to threading occur on large datasets where each thread performs millions, at least, of floating-point operations. Otherwise, the system overhead of setting up threads probably outweighs the speed enhancements due to SMP parallelism. However, we Chapter 3: Shared Features 31
have not yet demonstrated that the SMP parallelism scales beyond four threads for these operators. Hence we restrict thr nbr <= 4 for all operators. We encourage users to play with these limits (edit file nco_omp.c) and send us their feedback. Once the initial thr nbr has been modified for any operator-specific limits, NCO requests the system to allocate a team of thr nbr threads for the body of the code. The operating system then decides how many threads to allocate based on this request. Users may keep track of this information by running the operator with dbg lvl > 0. By default, threaded operators attach one global attribute, nco_openmp_thread_number, to any file they create or modify. This attribute contains the number of threads theop- erator used to process the input files. This information helps to verify that the answers with threaded and non-threaded operators are equal to within machine precision. This information is also useful for benchmarking.
3.4 Command Line Options
Availability: All operators ¨
NCO achieves flexibility by using command line options. These options are implemented © in all traditional UNIX commands as single letter switches, e.g., ‘ls -l’. For many years NCO used only single letter option names. In late 2002, we implemented GNU/POSIX extended or long option names for all options. This was done in a backward compatible way such that the full functionality of NCO is still available through the familiar single letter options. Many features of NCO introduced since 2002 now require the use of long options, simply because we have nearly run out of single letter options. More importantly, mnemonics for single letter options are often non-intuitive so that long options provide a more natural way of expressing intent. Extended options, also called long options, are implemented using the system-supplied getopt.h header file, if possible. This provides the getopt_long function to NCO1. The syntax of short options (single letter options) is -key value (dash-key-space-value). Here, key is the single letter option name, e.g., ‘-D 2’. The syntax of long options (multi-letter options) is --long_name value (dash-dash-key- space-value), e.g., ‘--dbg_lvl 2’ or --long_name=value (dash-dash-key-equal-value), e.g., ‘--dbg_lvl=2’. Thus the following are all valid for the ‘-D’ (short version) or ‘--dbg_lvl’ (long version) command line option. ncks -D 3 in.nc # Short option, preferred form ncks -D3 in.nc # Short option, alternate form ncks --dbg_lvl=3 in.nc # Long option, preferred form ncks --dbg_lvl 3 in.nc # Long option, alternate form
1 If a getopt_long function cannot be found on the system, NCO will use the getopt_long from the my_getopt package by Benjamin Sittler [email protected]. This is BSD-licensed software available from http://www.geocities.com/ResearchTriangle/Node/9405/#my_getopt. 32 NCO 5.0.2-alpha07 User Guide
The third example is preferred for two reasons. First, ‘--dbg_lvl’ is more specific and less ambiguous than ‘-D’. The long option format makes scripts more self documenting and less error-prone. Often long options are named after the source code variable whose value they carry. Second, the equals sign = joins the key (i.e., long name) to the value in an uninterruptible text block. Experience shows that users are less likely to mis-parse commands when restricted to this form. 3.4.1 Truncating Long Options GNU implements a superset of the POSIX standard. Their superset accepts any unambigu- ous truncation of a valid option: ncks -D 3 in.nc # Short option ncks --dbg_lvl=3 in.nc # Long option, full form ncks --dbg=3 in.nc # Long option, OK unambiguous truncation ncks --db=3 in.nc # Long option, OK unambiguous truncation ncks --d=3 in.nc # Long option, ERROR ambiguous truncation The first four examples are equivalent and will work as expected. The final examplewill exit with an error since ncks cannot disambiguate whether ‘--d’ is intended as a truncation of ‘--dbg_lvl’, of ‘--dimension’, or of some other long option. NCO provides many long options for common switches. For example, the debugging level may be set in all operators with any of the switches ‘-D’, ‘--debug-level’, or ‘--dbg_lvl’. This flexibility allows users to choose their favorite mnemonic. For some, it willbe‘--debug’ (an unambiguous truncation of ‘--debug-level’, and other will prefer ‘--dbg’. Interactive users usually prefer the minimal amount of typing, i.e., ‘-D’. We recommend that re-usable scripts employ long options to facilitate self-documentation and maintainability. This manual generally uses the short option syntax in examples. This is for historical reasons and to conserve space in printed output. Users are expected to pick the unambiguous truncation of each option name that most suits their taste. 3.4.2 Multi-arguments As of NCO version 4.6.2 (November, 2016), NCO accepts multiple key-value pair options for a single feature to be joined together into a single extended argument called a multi-argument, sometimes abbreviated MTA. Only four NCO features accept multiple key-value pairs that can be aggregated into multi-arguments. These features are: Global Attribute Addition options indicated via ‘--gaa’ (see Section 3.42 [Global Attribute Addition], page 143); Image Manipulation indicated via ‘--trr’2, Precision-Preserving Compression options are indicated via ‘--ppc’ (see Section 3.32.2 [Precision-Preserving Compression], page 112); and Regridding options are indicated via ‘--rgr’ (see Section 3.25 [Regridding], page 86). Arguments to these four indicator options take the form of key-value pairs, e.g., ‘--rgr key=val’. These four features have so many options that making each key its own command line option would pollute the namespace of NCO’s global options. Yet supplying multiple options to each indicator option one-at-a-time can result in command lines overpopulated with indicator switches (e.g., ‘--rgr’):
2 NCO supports decoding ENVI images in support of the DOE Terraref project. These options are indicated via the ncks ‘--trr’ switch, and are otherwise undocumented. Please contact us if more support and documentation of handling of ENVI BIL, BSQ, and BIP images would be helpful Chapter 3: Shared Features 33
ncks --rgr grd_ttl=’Title’ --rgr grid=grd.nc --rgr latlon=129,256 \ --rgr lat_typ=fv --rgr lon_typ=grn_ctr ... Multi-arguments combine all the indicator options into one option that receives a single argument that comprises all the original arguments glued together by a delimiter, which is, by default, ‘#’. Thus the multi-argument version of the above example is ncks --rgr grd_ttl=’Title’#grid=grd.nc#latlon=129,256#lat_typ=fv#lon_typ=grn_ctr Note the aggregation of all key=val pairs into a single argument. NCO simply splits this argument at each delimiter, and processes the sub-arguments as if they had been passed with their own indicator option. Multi-arguments produce the same results, and may be mixed with, traditional indicator options supplied one-by-one. As mentioned previously, the multi-argument delimiter string is, by default, the hash-sign ‘#’. When any key=val pair contains the default delimiter, the user must specify a custom delimiter string so that options are parsed correctly. The options to change the multi- argument delimiter string are ‘--mta_dlm=delim_string’ or ‘--dlm_mta=delim_string’, where delim string can be any single or multi-character string that (1) is not contained in any key or val string; and (2) will not confuse the shell. For example, to use multi- arguments to pass a string that includes the hash symbol (the default delimiter is ‘#’), one must also change the delimiter so something besides hash, e.g., a colon ‘:’: ncks --dlm=":" --gaa foo=bar:foo2=bar2:foo3,foo4="hash # is in value" ncks --dlm=":" --gaa foo=bar:foo2=bar2:foo3,foo4="Thu Sep 15 13\:03\:18 PDT 2016" ncks --dlm="csz" --gaa foo=barcszfoo2=bar2cszfoo3,foo4="Long text" In the second example, the colons that are escaped with the backslash become literal characters. Many characters have special shell meanings and so must be escaped by a single or double backslash or enclosed in single quotes to prevent interpolation. These special characters include ‘:’, ‘$’, ‘%’, ‘*’, ‘@’, and ‘&’. If val is a long text string that could contain the default delimiter, then delimit with a unique multi-character string such as ‘csz’ in the third example. As of NCO version 4.6.7 (May, 2017), multi-argument flags no longer need be specified as key-value pairs. By definition a flag sets a boolean value to either True or False.Pre- viously MTA flags had to employ key-value pair syntax, e.g., ‘--rgr infer=Y’ or ‘--rgr no_cll_msr=anything’ in order to parse correctly. Now the MTA parser accepts flags in the more intuitive syntax where they are listed by name, i.e., the flag name alone indicates the flag to set, e.g.,--rgr ‘ infer’ or ‘--rgr no_cll_msr’ are valid. A conse- quence of this is that flags in multi-argument strings appear as straightforward flag names, e.g., ‘--rgr infer#no_cll_msr#latlon=129,256’. It is also valid to prefix flags in multi- arument strings with single or double-dashes to make the flags more visible, e.g., ‘--rgr latlon=129,256#--infer#-no_cll_msr’.
3.5 Sanitization of Input
Availability: All operators ¨
© 34 NCO 5.0.2-alpha07 User Guide
NCO is often installed in system directories (although not with Conda), and on some production machines it may have escalated privileges. Since NCO manipulates files by using system() calls (e.g., to move and copy them with mv and cp) it makes sense to audit it for vulnerabilities and protect it from malicious users trying to exploit security gaps. Securing NCO against malicious attacks is multi-faceted, and involves careful memory management and auditing of user-input. In versions 4.7.3–4.7.6 (March-September, 2018), NCO implements a whitelist of characters allowed in user-specified filenames. This whitelist proved unpopular mainly because it proscribed certain character combinations that could appear in automatically generated files, and was therefore turned-off in 4.7.7 and following versions. The whitelist is described here for posterity and for possible improvement and re-introduction: The purpose of the whitelist was to prevent malicious users from injecting filename strings that could be used for attacks. The whitelist allowed only these characters: abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 1234567890_-.@ :%/ The backslash character \ was also whitelisted for Windows only. This whitelist allows filenames to be URLs, include username prefixes, and standard non-alphabetic characters. The implied blacklist included these characters ;|<>[](),&*?’" This blacklist rules-out strings that may contain dangerous commands and injection attacks. If you would like any of these characters whitelisted, please contact us and include a compelling real-world use-case. The DAP protocol supports accessing files with so-called “constraint expressions”. NCO allows access to a wider set of whitelisted characters for files whose names indicate the DAP protocol. This is defined as any filename beginning with the string‘http://’, ‘https://’, or ‘dap4://’. The whitelist for these files is expanded to include these characters: #=:[];|{}/<> The whitelist method is straightforward, and does not interfere with NCO’s globbing feature. The whitelist applies only to filenames because they are handled by shell commands passed to the system() function. However, the whitelist method is applicable to other user- input such as variable lists, hyperslab arguments, etc. Hence, the whitelist could be applied to other user-input in the future. 3.6 Specifying Input Files
Availability (-n): nces, ncecat, ncra, ncrcat ¨ Availability (-p): All operators Short options: ‘-n’, ‘-p’ Long options: ‘--nintap’, ‘--pth’, ‘--path’
It is important that users be able to specify multiple input files without typing every © filename in full, often a tedious task even by graduate student standards. There arefour Chapter 3: Shared Features 35
different ways of specifying input files to NCO: explicitly typing each, using UNIX shell wild- cards, and using the NCO ‘-n’ and ‘-p’ switches (or their long option equivalents, ‘--nintap’ or ‘--pth’ and ‘--path’, respectively). Techniques to augment these methods to specify ar- bitrary numbers (e.g., thousands) and patterns of filenames are discussed separately (see Section 2.7 [Large Numbers of Files], page 21). To illustrate these methods, consider the simple problem of using ncra to average five input files, 85.nc, 86.nc,... 89.nc, and store the results in 8589.nc. Here are the four methods in order. They produce identical answers. ncra 85.nc 86.nc 87.nc 88.nc 89.nc 8589.nc ncra 8[56789].nc 8589.nc ncra 8?.nc 8589.nc ncra -p input-path 85.nc 86.nc 87.nc 88.nc 89.nc 8589.nc ncra -n 5,2,1 85.nc 8589.nc The first method (explicitly specifying all filenames) works by brute force. Thesec- ond method relies on the operating system shell to glob (expand) the regular expression 8[56789].nc. The shell then passes the valid filenames (those which match the regular expansion) to ncra. In this case ncra never knows that a regular expression was used, because the shell intercepts and expands and matches the regular expression before ncra is actually invoked. The third method is uses globbing with a different regular expression that is less safe (it will also match unwanted files such as 81.nc and 8Z.nc if present). The fourth method uses the ‘-p input-path’ argument to specify the directory where all the input files reside. NCO prepends input-path (e.g., /data/username/model) to all input-files (though not to output-file). Thus, using ‘-p’, the path to any number of input files need only be specified once. Note input-path need not end with ‘/’; the ‘/’ is automatically generated if necessary. The last method passes (with ‘-n’) syntax concisely describing the entire set of filenames3. This option is only available with the multi-file operators: ncra, ncrcat, nces, and ncecat. By definition, multi-file operators are able to process an arbitrary numberof input-files. This option is very useful for abbreviating lists of filenames representable as alphanu- meric prefix+numeric suffix+.+filetype where alphanumeric prefix is a string of arbitrary length and composition, numeric suffix is a fixed width field of digits, and filetype is a standard filetype indicator. For example, in the file ccm3_h0001.nc, we have alphanu- meric prefix = ccm3_h, numeric suffix = 0001, and filetype = nc. NCO decodes lists of such filenames encoded using the-n ‘ ’ syntax. The simpler (three- argument) ‘-n’ usage takes the form -n file_number,digit_number,numeric_increment where file number is the number of files, digit number is the fixed number of numeric dig- its comprising the numeric suffix, and numeric increment is the constant, integer-valued difference between the numeric suffix of any two consecutive files. The value of alphanu- meric prefix is taken from the input file, which serves as a template for decoding thefile- names. In the example above, the encoding -n 5,2,1 along with the input file name 85.nc
3 The ‘-n’ option is a backward-compatible superset of the NINTAP option from the NCAR CCM Processor. The CCM Processor was custom-written Fortran code maintained for many years by Lawrence Buja at NCAR, and phased-out in the late 1990s. NCO copied some ideas, like NINTAP-functionality, from CCM Processor capabilities. 36 NCO 5.0.2-alpha07 User Guide
tells NCO to construct five (5) filenames identical to the template 85.nc except that the final two (2) digits are a numeric suffix to be incremented by one (1) for each successivefile. Currently filetype may be either be empty, nc, h5, cdf, hdf, hd5, or he5. If present, these filetype suffixes (and the preceding .) are ignored by NCO as it uses the ‘-n’ arguments to locate, evaluate, and compute the numeric suffix component of filenames. Recently the ‘-n’ option has been extended to allow convenient specification of file- names with “circular” characteristics. This means it is now possible for NCO to auto- matically generate filenames which increment regularly until a specified maximum value, and then wrap back to begin again at a specified minimum value. The corresponding ‘-n’ usage becomes more complex, taking one or two additional arguments for a total of four or five, respectively: -n file_number,digit_number,numeric_increment[,numeric_ max[,numeric_min]] where numeric max, if present, is the maximum integer-value of nu- meric suffix and numeric min, if present, is the minimum integer-value of numeric suffix. Consider, for example, the problem of specifying non-consecutive input files where the filename suffixes end with the month index. In climate modeling it is common tocreate summertime and wintertime averages which contain the averages of the months June–July– August, and December–January–February, respectively: ncra -n 3,2,1 85_06.nc 85_0608.nc ncra -n 3,2,1,12 85_12.nc 85_1202.nc ncra -n 3,2,1,12,1 85_12.nc 85_1202.nc The first example shows that three arguments tothe‘-n’ option suffice to specify con- secutive months (06, 07, 08) which do not “wrap” back to a minimum value. The second example shows how to use the optional fourth and fifth elements of the-n ‘ ’ option to specify a wrap value. The fourth argument to ‘-n’, when present, specifies the maximum integer value of numeric suffix. In the example the maximum value is 12, and will be formatted as 12 in the filename string. The fifth argument-n to‘ ’, when present, specifies the minimum integer value of numeric suffix. The default minimum filename suffix is 1, which is format- ted as 01 in this case. Thus the second and third examples have the same effect, that is, they automatically generate, in order, the filenames 85_12.nc, 85_01.nc, and 85_02.nc as input to NCO.
As of NCO version 4.5.2 (September, 2015), NCO supports an optional sixth argument to ‘-n’, the month-indicator. The month-indicator affirms to NCO that the right-most digits being manipulated in the generated filenames correspond to month numbers (with January formatted as 01 and December as 12). Moreover, it assumes digits to the left of the month are the year. The full (six-argument) ‘-n’ usage takes the form -n file_number,digit_ number,month_increment,max_month,min_month,‘yyyymm’. The ‘yyyymm’ string is a clunky way (can you think of a clearer way?) to tell NCO to enumerate files in year- month mode. When present, ‘yyyymm’ string causes NCO to automatically generate series of filenames whose right-most two digits increment from min month by month increment up to max month and then the leftmost digits (i.e., the year) increment by one, and the whole process is repeated until the file number filenames are generated. ncrcat -n 3,6,1,12,1 198512.nc 198512_198502.nc ncrcat -n 3,6,1,12,1,yyyymm 198512.nc 198512_198602.nc ncrcat -n 3,6,1,12,12,yyyymm 198512.nc 198512_198712.nc Chapter 3: Shared Features 37
The first command above concatenates three files198512.nc ( , 198501.nc, 198502.nc) into the output file. The second command above concatenates three files(198512.nc, 198601.nc, 198602.nc). The ‘yyyymm’-indicator causes the left-most digits to increment each time the right-most two digits reach their maximum and then wrap. The first command does not have the indicator so it is always 1985. The third command concatenates three files (198512.nc, 198612.nc, 198712.nc).
3.7 Specifying Output Files
Availability: All operators ¨ Short options: ‘-o’ Long options: ‘--fl_out’, ‘--output’
NCO commands produce no more than one output file, fl out. Traditionally, users spec- © ify fl out as the final argument to the operator, following all input file names. Thisisthe positional argument method of specifying input and ouput file names. The positional ar- gument method works well in most applications. NCO also supports specifying fl out using the command line switch argument method, ‘-o fl_out’. Specifying fl out with a switch, rather than as a positional argument, allows fl out to precede input files in the argument list. This is particularly useful with multi-file operators for three reasons. Multi-file operators may be invoked with hundreds (or more) filenames. Visual or automatic location of fl out in such a list is difficult when the only syntactic distinction between input and output files is their position. Second, specification ofalong list of input files may be difficult (see Section 2.7 [Large Numbers of Files], page 21). Making the input file list the final argument to an operator facilitates using xargs for this purpose. Some alternatives to xargs are heinous and undesirable. Finally, many users are more comfortable specifying output files with ‘-o fl_out’ near the beginning of an argument list. Compilers and linkers are usually invoked this way. Users should specify fl out using either (not both) method. If fl out is specified twice (once with the switch and once as the last positional argument), then the positional argu- ment takes precedence.
3.8 Accessing Remote Files
Availability: All operators ¨ Short options: ‘-p’, ‘-l’ Long options: ‘--pth’, ‘--path’, ‘--lcl’, ‘--local’
All NCO operators can retrieve files from remote sites as well as from the local file system. © A remote site can be an anonymous FTP server, a machine on which the user has rcp, scp, or sftp privileges, NCAR’s Mass Storage System (MSS), or an OPeNDAP server. Examples of each are given below, following a brief description of the particular access protocol. 38 NCO 5.0.2-alpha07 User Guide
To access a file via an anonymous FTP server, simply supply the remote file’s URL. Anonymous FTP usually requires no further credentials, e.g., no .netrc file is necessary. FTP is an intrinsically insecure protocol because it transfers passwords in plain text format. Users should access sites using anonymous FTP, or better yet, secure FTP (SFTP, see below) when possible. Some FTP servers require a login/password combination for a valid user account. NCO allows transactions that require additional credentials so long as the required information is stored in the .netrc file. Usually this information is the remote machine name, login, and password, in plain text, separated by those very keywords, e.g., machine dust.ess.uci.edu login zender password bushlied Eschew using valuable passwords for FTP transactions, since .netrc passwords are po- tentially exposed to eavesdropping software4. SFTP, i.e., secure FTP, uses SSH-based security protocols that solve the security issues associated with plain FTP. NCO supports SFTP protocol access to files specified with a homebrew syntax of the form sftp://machine.domain.tld:/path/to/filename Note the second colon following the top-level-domain, tld. This syntax is a hybrid between an FTP URL and standard remote file syntax. To access a file using rcp or scp, specify the Internet address of the remote file. Of course in this case you must have rcp or scp privileges which allow transparent (no password entry required) access to the remote machine. This means that ~/.rhosts or ~/ssh/authorized_ keys must be set accordingly on both local and remote machines. To access a file on a High Performance Storage System (HPSS) (such as that at NCAR, ECMWF, LANL, DKRZ, LLNL) specify the full HPSS pathname of the remote file and use the ‘--hpss’ flag. Then NCO will attempt to detect whether the local machine has direct (synchronous) HPSS access. If so, NCO attempts to use the Hierarchical Storage Interface (HSI) command hsi get5. The following examples show how one might analyze files stored on remote systems. ncks -l . ftp://dust.ess.uci.edu/pub/zender/nco/in.nc ncks -l . sftp://dust.ess.uci.edu:/home/ftp/pub/zender/nco/in.nc ncks -l . dust.ess.uci.edu:/home/zender/nco/data/in.nc ncks -l . /ZENDER/nco/in.nc # NCAR (broken old MSS path) ncks -l . --hpss /home/zender/nco/in.nc # NCAR HPSS ncks -l . http://thredds-test.ucar.edu/thredds/dodsC/testdods/in.nc The first example works verbatim if your system is connected to the Internet andisnot behind a firewall. The second example works if you have sftp access to the machine
4 NCO does not implement command line options to specify FTP logins and passwords because copying those data into the history global attribute in the output file (done by default) poses an unacceptable security risk. 5 The hsi command must be in the user’s path in one of the following directories: /usr/local/bin, /opt/hpss/bin, or /ncar/opt/hpss/hsi. Tell us if the HPSS installation at your site places the hsi command in a different location, and we will add that location to the list of acceptable paths tosearch for hsi. Chapter 3: Shared Features 39
dust.ess.uci.edu. The third example works if you have rcp or scp access to the machine dust.ess.uci.edu. The fourth and fifth examples work on NCAR computers with local access to the HPSS hsi get command6. The sixth command works if your local version of NCO is OPeNDAP-enabled (this is fully described in Section 3.8.1 [OPeNDAP], page 39), or if the remote file is accessible via wget. The above commands can be rewritten using the ‘-p input-path’ option as follows: ncks -p ftp://dust.ess.uci.edu/pub/zender/nco -l . in.nc ncks -p sftp://dust.ess.uci.edu:/home/ftp/pub/zender/nco -l . in.nc ncks -p dust.ess.uci.edu:/home/zender/nco -l . in.nc ncks -p /ZENDER/nco -l . in.nc ncks -p /home/zender/nco -l . --hpss in.nc # HPSS ncks -p http://thredds-test.ucar.edu/thredds/dodsC/testdods \ -l . in.nc Using ‘-p’ is recommended because it clearly separates the input-path from the filename itself, sometimes called the stub. When input-path is not explicitly specified using ‘-p’, NCO internally generates an input-path from the first input filename. The automatically generated input-path is constructed by stripping the input filename of everything following the final/ ‘ ’ character (i.e., removing the stub). The ‘-l output-path’ option tells NCO where to store the remotely retrieved file. It has no effect on locally-retrieved files, oron the output file. Often the path to a remotely retrieved file is quite different thanthepath on the local machine where you would like to store the file. If-l ‘ ’ is not specified then NCO internally generates an output-path by simply setting output-path equal to input- path stripped of any machine names. If ‘-l’ is not specified and the remote file resides on a detected HPSS system, then the leading character of input-path,‘/’, is also stripped from output-path. Specifying output-path as ‘-l ./’ tells NCO to store the remotely retrieved file and the output file in the current directory.-l Notethat‘ .’ is equivalent to ‘-l ./’ though the latter is syntactically more clear.
3.8.1 OPeNDAP The Distributed Oceanographic Data System (DODS) provides useful replacements for com- mon data interface libraries like netCDF. The DODS versions of these libraries implement network transparent access to data via a client-server data access protocol that uses the HTTP protocol for communication. Although DODS-technology originated with oceanogra- phy data, it applyies to virtually all scientific data. In recognition of this, the data access protocol underlying DODS (which is what NCO cares about) has been renamed the Open- source Project for a Network Data Access Protocol, OPeNDAP. We use the terms DODS and OPeNDAP interchangeably, and often write OPeNDAP/DODS for now. In the future we will deprecate DODS in favor of DAP or OPeNDAP, as appropriate7.
6 NCO supported the old NCAR Mass Storage System (MSS) until version 4.0.7 in April, 2011. NCO supported MSS-retrievals via a variety of mechanisms including the msread, msrcp, and nrnet commands invoked either automatically or with sentinels like ncks -p mss:/ZENDER/nco -l . in.nc. Once the MSS was decommissioned in March, 2011, support for these retrieval mechanisms was replaced by support for HPSS. 7 DODS is being deprecated because it is ambiguous, referring both to a protocol and to a collection of (oceanography) data. It is superceded by two terms. DAP is the discipline-neutral Data Access Protocol at the heart of DODS. The National Virtual Ocean Data System (NVODS) refers to the collection of 40 NCO 5.0.2-alpha07 User Guide
NCO may be DAP-enabled by linking NCO to the OPeNDAP libraries. This is described in the OPeNDAP documentation and automagically implemented in NCO build mechanisms8. The ./configure mechanism automatically enables NCO as OPeNDAP clients if it can find the required OPeNDAP libraries. Since about 2010 the netCDF library can be configured (with --enable-dap) to build DAP directly into the netCDF library, which NCO automat- ically links to, so DAP need not be installed as a third-party library. It has been so many years since NCO has needed to support linking to DAP installed outside of the netCDF library that is is unclear whether this configuration9. still works. The $DODS_ROOT envi- ronment variable may be used to override the default OPeNDAP library location at NCO compile-time. Building NCO with bld/Makefile and the command make DODS=Y adds the (non-intuitive) commands to link to the OPeNDAP libraries installed in the $DODS_ROOT directory. The file doc/opendap.sh contains a generic script intended to help users install OPeNDAP before building NCO. The documentation at the OPeNDAP Homepage (http:// www.opendap.org) is voluminous. Check there and on the DODS mail lists (http://www. unidata.ucar.edu/software/dods/home/mailLists/). to learn more about the extensive capabilities of OPeNDAP10. Once NCO is DAP-enabled the operators are OPeNDAP clients. All OPeNDAP clients have network transparent access to any files controlled by a OPeNDAP server. Simply specify the input file path(s) in URL notation and all NCO operations may be performed on remote files made accessible bya OPeNDAP server. This command tests the basic functionality of OPeNDAP-enabled NCO clients: % ncks -O -o ~/foo.nc -C -H -v one -l /tmp \ -p http://thredds-test.ucar.edu/thredds/dodsC/testdods in.nc % ncks -H -v one ~/foo.nc one = 1 The one = 1 outputs confirm (first) that ncks correctly retrieved data via the OPeNDAP protocol and (second) that ncks created a valid local copy of the subsetted remote file. With minor changes to the above command, netCDF4 can be used as both the input and output file format: % ncks -4 -O -o ~/foo.nc -C -H -v one -l /tmp \ -p http://thredds-test.ucar.edu/thredds/dodsC/testdods in_4.nc % ncks -H -v one ~/foo.nc
oceanography data and oceanographic extensions to DAP. In other words, NVODS is implemented with OPeNDAP. OPeNDAP is also the open source project which maintains, develops, and promulgates the DAP standard. OPeNDAP and DAP really are interchangeable. Got it yet? 8 Automagic support for DODS version 3.2.x was deprecated in December, 2003 after NCO version 2.8.4. NCO support for OPeNDAP versions 3.4.x commenced in December, 2003, with NCO version 2.8.5. NCO support for OPeNDAP versions 3.5.x commenced in June, 2005, with NCO version 3.0.1. NCO support for OPeNDAP versions 3.6.x commenced in June, 2006, with NCO version 3.1.3. NCO support for OPeNDAP versions 3.7.x commenced in January, 2007, with NCO version 3.1.9. 9 The minimal set of libraries required to build NCO as OPeNDAP clients, where OPeNDAP is supplied as a separate library apart from libnetcdf.a, are, in link order, libnc-dap.a, libdap.a, and libxml2 and libcurl.a. 10 We are most familiar with the OPeNDAP ability to enable network-transparent data access. OPeNDAP has many other features, including sophisticated hyperslabbing and server-side processing via constraint expressions. If you know more about this, please consider writing a section on “OPeNDAP Capabilities of Interest to NCO Users” for incorporation in the NCO User Guide. Chapter 3: Shared Features 41
one = 1 And, of course, OPeNDAP-enabled NCO clients continue to support orthogonal features such as UDUnits (see Section 3.27 [UDUnits Support], page 98): % ncks -u -C -H -v wvl -d wvl,’0.4 micron’,’0.7 micron’ \ -p http://thredds-test.ucar.edu/thredds/dodsC/testdods in_4.nc % wvl[0]=5e-07 meter The next command is a more advanced example which demonstrates the real power of OPeNDAP-enabled NCO clients. The ncwa client requests an equatorial hyperslab from remotely stored NCEP reanalyses data of the year 1969. The NOAA OPeNDAP server (hope- fully!) serves these data. The local ncwa client then computes and stores (locally) the regional mean surface pressure (in Pa). ncwa -O -C -a lat,lon,time -d lon,-10.,10. -d lat,-10.,10. \ http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.dailyavgs/surface/pres.sfc.1969.nc ~/foo.nc All with one command! The data in this particular input file also happen to be packed (see Section 4.1.12 [Methods and functions], page 175), although this complication is transparent to the user since NCO automatically unpacks data before attempting arithmetic. NCO obtains remote files from the OPeNDAP server (e.g., www.cdc.noaa.gov) rather than the local machine. Input files are first copied to the local machine, then processed. The OPeNDAP server performs data access, hyperslabbing, and transfer to the local machine. This allows the I/O to appear to NCO as if the input files were local. The local machine performs all arithmetic operations. Only the hyperslabbed output data are transferred over the network (to the local machine) for the number-crunching to begin. The advantages of this are obvious if you are examining small parts of large files stored at remote locations. Natually there are many versions of OPeNDAP servers supplying data and bugs in the server can appear to be bugs in NCO. However, with very few exceptions11 an NCO command that works on a local file must work across an OPeNDAP connection or else there is a bug in the server. This is because NCO does nothing special to handle files served by OPeNDAP, the whole process is (supposed to be) completely transparent to the client NCO software. Therefore it is often useful to try NCO commands on various OPeNDAP servers in order to isolate whether a problem may be due to a bug in the OPeNDAP server on a particular machine. For this purpose, one might try variations of the following commands that access files on public OPeNDAP servers: # Strided access to HDF5 file ncks -v Time -d Time,0,10,2 http://eosdap.hdfgroup.uiuc.edu:8080/opendap/data/NASAFILES/hdf5/BUV-Nimbus04_L3zm_v01-00-2012m0203t144121.h5 # Strided access to netCDF3 file ncks -O -D 1 -d time,1 -d lev,0 -d lat,0,100,10 -d lon,0,100,10 -v u_velocity http://nomads.ncep.noaa.gov:9090/dods/rtofs/rtofs_global20140303/rtofs_glo_2ds_forecast_daily_prog ~/foo.nc These servers were operational at the time of writing, March 2014. Unfortunately, admin- istrators often move or rename path directories. Recommendations for additional public OPeNDAP servers on which to test NCO are welcome.
11 For example, DAP servers do not like variables with periods (“.”) in their names even though this is perfectly legal with netCDF. Such names may cause the DAP service to fail because DAP interprets the period as structure delimiter in an HTTP query string. 42 NCO 5.0.2-alpha07 User Guide
3.9 Retaining Retrieved Files
Availability: All operators ¨ Short options: ‘-R’ Long options: ‘--rtn’, ‘--retain’
In order to conserve local file system space, files retrieved from remote locations are © automatically deleted from the local file system once they have been processed. Many NCO operators were constructed to work with numerous large (e.g., 200 MB) files. Retrieval of multiple files from remote locations is done serially. Each file is retrieved, processed, then deleted before the cycle repeats. In cases where it is useful to keep the remotely-retrieved files on the local file system after processing, the automatic removal feature may bedisabled by specifying ‘-R’ on the command line. Invoking -R disables the default printing behavior of ncks. This allows ncks to retrieve remote files without automatically trying to print them. See Section 4.8 [ncks netCDF Kitchen Sink], page 263, for more details.
Note that the remote retrieval features of NCO can always be used to retrieve any file, including non-netCDF files, via SSH, anonymous FTP, or msrcp. Often this method is quicker than using a browser, or running an FTP session from a shell window yourself. For example, say you want to obtain a JPEG file from a weather server. ncks -R -p ftp://weather.edu/pub/pix/jpeg -l . storm.jpg
In this example, ncks automatically performs an anonymous FTP login to the remote machine and retrieves the specified file. When ncks attempts to read the local copy of storm.jpg as a netCDF file, it fails and exits, leaving storm.jpg in the current directory.
If your NCO is DAP-enabled (see Section 3.8.1 [OPeNDAP], page 39), then you may use NCO to retrieve any files (including netCDF, HDF, etc.) served by an OPeNDAP server to your local machine. For example, ncks -R -l . -p \ http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.dailyavgs/surface \ pres.sfc.1969.nc
It may occasionally be useful to use NCO to transfer files when your other preferred methods are not available locally.
3.10 File Formats and Conversion
Availability: ncap2, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa ¨ Short options: ‘-3’, ‘-4’, ‘-5’, ‘-6’, ‘-7’ Long options: ‘--3’, ‘--4’, ‘--5’, ‘--6’, ‘--64bit_offset’, ‘--7’, ‘--fl_fmt’, ‘--netcdf4’
© Chapter 3: Shared Features 43
All NCO operators support (read and write) all three (or four, depending on how one counts) file formats supported by netCDF4. The default output file format for alloperators is the input file format. The operators listed under “Availability” above allow the userto specify the output file format independent of the input file format. These operators allow the user to convert between the various file formats. (The operators ncatted and ncrename do not support these switches so they always write the output netCDF file in the same format as the input netCDF file.) 3.10.1 File Formats netCDF supports five types of files: CLASSIC, 64BIT_OFFSET, 64BIT_DATA, NETCDF4, and NETCDF4_CLASSIC. The CLASSIC (aka CDF1) format is the traditional 32-bit offset written by netCDF2 and netCDF3. As of 2005, nearly all netCDF datasets were in CLASSIC format. The 64BIT_OFFSET (originally called plain old 64BIT) (aka CDF2) format was added in Fall, 2004. As of 2010, many netCDF datasets were in 64BIT_OFFSET format. As of 2013, an increasing number of netCDF datasets were in NETCDF4_CLASSIC format. The 64BIT_DATA (aka CDF5 or PNETCDF) format was added to netCDF in January, 2016. The NETCDF4 format uses HDF5 as the file storage layer. The files are (usually) created, accessed, and manipulated using the traditional netCDF3 API (with numerous extensions). The NETCDF4_CLASSIC format refers to netCDF4 files created with the NC_CLASSIC_MODEL mask. Such files use HDF5 as the back-end storage format (unlike netCDF3), though they incorporate only netCDF3 features. Hence NETCDF4_CLASSIC files are entirely readable by applications that use only the netCDF3 API (though the applications must be linked with the netCDF4 library). NCO must be built with netCDF4 to write files in the new NETCDF4 and NETCDF4_CLASSIC formats, and to read files in these formats. Datasets in the default CLASSIC or the newer 64BIT_OFFSET formats have maximum backwards-compatibility with older applications. NCO has deep support for NETCDF4 formats. If backwards compatibility is important, and your datasets are too large for netCDF3, use NETCDF4_CLASSIC instead of CLASSIC format files. NCO support for the NETCDF4 format is complete and many high- performance disk/RAM efficient workflows utilize this format. As mentioned above, all operators write use the input file format for output files unless told otherwise. Toggling the short option ‘-6’ or the long option ‘--6’ or ‘--64bit_offset’ (or their key-value equivalent ‘--fl_fmt=64bit_offset’) produces the netCDF3 64-bit offset format named 64BIT_OFFSET. NCO must be built with netCDF 3.6 or higher to produce a 64BIT_OFFSET file. As of NCO version 4.6.9 (September, 2017), toggling the short option ‘-5’ or the long options ‘--5’, ‘--64bit_data’, ‘--cdf5’, or ‘--pnetcdf’ (or their key-value equivalent ‘--fl_fmt=64bit_data’) produces the netCDF3 64-bit data format named 64BIT_DATA. This format is widely used by MPI-enabled modeling codes because of its long association with PnetCDF. NCO must be built with netCDF 4.4 or higher to produce a 64BIT_DATA file. Using the ‘-4’ switch (or its long option equivalents ‘--4’ or ‘--netcdf4’), or setting its key-value equivalent ‘--fl_fmt=netcdf4’ produces a NETCDF4 file (i.e., with all supported HDF5 features). Using the ‘-7’ switch (or its long option equivalent ‘--7’12, or setting its key-value equivalent ‘--fl_fmt=netcdf4_classic’ produces a NETCDF4_CLASSIC file (i.e.,
12 The reason (and mnemonic) for ‘-7’ is that NETCDF4_CLASSIC files include great features of both netCDF3 (compatibility) and netCDF4 (compression, chunking) and, well, 3 + 4 = 7. 44 NCO 5.0.2-alpha07 User Guide
with all supported HDF5 features like compression and chunking but without groups or new atomic types). Operators given the ‘-3’ (or ‘--3’) switch without arguments will (attempt to) produce netCDF3 CLASSIC output, even from netCDF4 input files. Note that NETCDF4 and NETCDF4_CLASSIC are the same binary format. The latter simply causes a writing application to fail if it attempts to write a NETCDF4 file that cannot be completely read by the netCDF3 library. Conversely, NETCDF4_CLASSIC indicates to a reading application that all of the file contents are readable with the netCDF3 library. NCO has supported reading/writing basic NETCDF4 and NETCDF4_CLASSIC files since October, 2005.
3.10.2 Determining File Format Input files often end with the generic .nc suffix that leaves (perhaps by intention) the internal file format ambiguous. There are at least three ways to discover the internal format of a netCDF-supported file. These methods determine whether it is a classic (32-bit offset) or newer 64-bit offset netCDF3 format, or is a netCDF4 format. Each method returns the information using slightly different terminology that becomes easier to understand with practice. First, examine the first line of global metadata output by‘ncks -M’: % ncks -M foo_3.nc Summary of foo_3.nc: filetype = NC_FORMAT_CLASSIC, 0 groups ... % ncks -M foo_6.nc Summary of foo_6.nc: filetype = NC_FORMAT_64BIT_OFFSET, 0 groups ... % ncks -M foo_5.nc Summary of foo_5.nc: filetype = NC_FORMAT_CDF5, 0 groups ... % ncks -M foo_7.nc Summary of foo_7.nc: filetype = NC_FORMAT_NETCDF4_CLASSIC, 0 groups ... % ncks -M foo_4.nc Summary of foo_4.nc: filetype = NC_FORMAT_NETCDF4, 0 groups ... This method requires a netCDF4-enabled NCO version 3.9.0+ (i.e., from 2007 or later). As of NCO version 4.4.0 (January, 2014), ncks will also print the extended or underlying format of the input file. The extended filetype will be one of the six underlying formats that are accessible through the netCDF API. These formats are NC_FORMATX_NC3 (classic and 64- bit versions of netCDF3 formats), NC_FORMATX_NC_HDF5 (classic and extended versions of netCDF4, and “pure” HDF5 format), NC_FORMATX_NC_HDF4 (HDF4 format), NC_FORMATX_ PNETCDF (PnetCDF format), NC_FORMATX_DAP2 (accessed via DAP2 protocol), and NC_ FORMATX_DAP4 (accessed via DAP4 protocol). For example, % ncks -D 2 -M hdf.hdf Summary of hdf.hdf: filetype = NC_FORMAT_NETCDF4 (representation of \ extended/underlying filetype NC_FORMAT_HDF4), 0 groups ... % ncks -D 2 -M http://thredds-test.ucar.edu/thredds/dodsC/testdods/in.nc Summary of http://thredds-test.ucar.edu/thredds/dodsC/testdods/in.nc: \ filetype = NC_FORMAT_CLASSIC (representation of extended/underlying \ filetype NC_FORMATX_DAP2), 0 groups % ncks -D 2 -M foo_4.nc Chapter 3: Shared Features 45
Summary of foo_4.nc: filetype = NC_FORMAT_NETCDF4 (representation of \ extended/underlying filetype NC_FORMAT_HDF5), 0 groups The extended filetype determines some of the capabilities that netCDF has to alterthe file. Second, query the file with ‘ncdump -k’: % ncdump -k foo_3.nc classic % ncdump -k foo_6.nc 64-bit offset % ncdump -k foo_5.nc cdf5 % ncdump -k foo_7.nc netCDF-4 classic model % ncdump -k foo_4.nc netCDF-4 This method requires a netCDF4-enabled netCDF 3.6.2+ (i.e., from 2007 or later). The third option uses the POSIX-standard od (octal dump) command: % od -An -c -N4 foo_3.nc C D F 001 % od -An -c -N4 foo_6.nc C D F 002 % od -An -c -N4 foo_5.nc C D F 005 % od -An -c -N4 foo_7.nc 211 H D F % od -An -c -N4 foo_4.nc 211 H D F This option works without NCO and ncdump. Values of ‘C D F 001’ and ‘C D F 002’ indicate 32-bit (classic) and 64-bit netCDF3 formats, respectively, while values of ‘211 H D F’ indicate either of the newer netCDF4 file formats. 3.10.3 File Conversion Let us demonstrate converting a file from any netCDF-supported input format into any netCDF output format (subject to limits of the output format). Here the input file in.nc may be in any of these formats: netCDF3 (classic, 64bit offset, 64bit data), netCDF4 (classic and extended), HDF4, HDF5, HDF-EOS (version 2 or 5), and DAP. The switch determines the output format written in the comment:13 ncks --fl_fmt=classic in.nc foo_3.nc # netCDF3 classic ncks --fl_fmt=64bit_offset in.nc foo_6.nc # netCDF3 64bit-offset ncks --fl_fmt=64bit_data in.nc foo_5.nc # netCDF3 64bit-data
13 The switches ‘-5’, ‘--5’, and ‘pnetcdf’ are reserved for PnetCDF files, i.e., NC_FORMAT_CDF5. Such files are similar to netCDF3 classic files, yet also support 64-bit offsets and the additional netCDF4 atomic types. 46 NCO 5.0.2-alpha07 User Guide
ncks --fl_fmt=cdf5 in.nc foo_5.nc # netCDF3 64bit-data ncks --fl_fmt=netcdf4_classic in.nc foo_7.nc # netCDF4 classic ncks --fl_fmt=netcdf4 in.nc foo_4.nc # netCDF4 ncks -3 in.nc foo_3.nc # netCDF3 classic ncks --3 in.nc foo_3.nc # netCDF3 classic ncks -6 in.nc foo_6.nc # netCDF3 64bit-offset ncks --64 in.nc foo_6.nc # netCDF3 64bit-offset ncks -5 in.nc foo_5.nc # netCDF3 64bit-data ncks --5 in.nc foo_5.nc # netCDF3 64bit-data ncks -4 in.nc foo_4.nc # netCDF4 ncks --4 in.nc foo_4.nc # netCDF4 ncks -7 in.nc foo_7.nc # netCDF4 classic ncks --7 in.nc foo_7.nc # netCDF4 classic Of course since most operators support these switches, the “conversions” can be done at the output stage of arithmetic or metadata processing rather than requiring a separate step. Producing (netCDF3) CLASSIC or 64BIT_OFFSET or 64BIT_DATA files from NETCDF4_ CLASSIC files always works.
3.10.4 Autoconversion Because of the dearth of support for netCDF4 amongst tools and user communities (includ- ing the CF conventions), it is often useful to convert netCDF4 to netCDF3 for certain appli- cations. Until NCO version 4.4.0 (January, 2014), producing netCDF3 files from netCDF4 files only worked if the input files contained no netCDF4-specific features (e.g., atomictypes, multiple record dimensions, or groups). As of NCO version 4.4.0, ncks supports autoconver- sion of many netCDF4 features to their closest netCDF3-compatible representations. Since converting netCDF4 to netCDF3 results in loss of features, “automatic down-conversion” may be a more precise description of what we term autoconversion.
NCO employs three algorithms to downconvert netCDF4 to netCDF3:
1. Autoconversion of atomic types: Autoconversion automatically promotes NC_UBYTE to NC_SHORT, and NC_USHORT to NC_INT. It automatically demotes the three types NC_ UINT, NC_UINT64, and NC_INT64 to NC_INT. And it converts NC_STRING to NC_CHAR. All numeric conversions work for attributes and variables of any rank. Two numeric types (NC_UBYTE and NC_USHORT) are promoted to types with greater range (and greater storage). This extra range is often not used so promotion perhaps conveys the wrong impression. However, promotion never truncates values or loses data (this perhaps justifies the extra storage). Three numericNC_UINT types( , NC_UINT64 and NC_INT64) are demoted. Since the input range is larger than the output range, demotion can result in numeric truncation and thus loss of data. In such cases, it would possible to convert the data to floating-point values instead. If this feature interests you, please bethe squeaky wheel and let us know. String conversions (to NC_CHAR) work for all attributes, but not for variables. This is because attributes are at most one-dimensional and may be of any size whereas variables require gridded dimensions that usually do not fit the ragged sizes of text strings. Hence scalar NC_STRING attributes are correctly converted to and stored as NC_CHAR attributes Chapter 3: Shared Features 47
in the netCDF3 output file, but NC_STRING variables are not correctly converted. If this limitation annoys or enrages you, please let us know by being the squeaky wheel. 2. Convert multiple record dimensions to fixed-size dimensions. Many netCDF4 and HDF5 datasets have multiple unlimited dimensions. Since a netCDF3 file may have at most one unlimited dimension, all but possibly one unlimited dimension from the input file must be converted to fixed-length dimensions prior to storing netCDF4 inputas netCDF3 output. By invoking --fix_rec_dmn all the user ensures the output file will adhere to netCDF3 conventions and the user need not know the names of the specific record dimensions to fix. See Section 4.8 [ncks netCDF Kitchen Sink], page 263, for a description of the ‘--fix_rec_dmn’ option. 3. Flattening (removal) of groups. Many netCDF4 and HDF5 datasets have group hierar- chies. Since a netCDF3 file may not have any groups, groups in the input file mustbe removed. This is also called “flattening” the hierarchical file. See Section 3.14 [Group Path Editing], page 53, for a description of the GPE option ‘-G :’ to flatten files. Putting the three algorithms together, one sees that the recipe to convert netCDF4 to netCDF4 becomes increasingly complex as the netCDF4 features in the input file become more elaborate: # Convert file with netCDF4 atomic types ncks -3 in.nc4 out.nc3 # Convert file with multiple record dimensions + netCDF4 atomic types ncks -3 --fix_rec_dmn=all in.nc4 out.nc3 # Convert file with groups, multiple record dimensions + netCDF4 atomic types ncks -3 -G : --fix_rec_dmn=all in.nc4 out.nc3 Future versions of NCO may automatically invoke the record dimension fixation and group flattening when converting to netCDF3 (rather than requiring it be specified manu- ally). If this feature would interest you, please let us know.
3.11 Large File Support
Availability: All operators ¨ Short options: none Long options: none
NCO has Large File Support (LFS), meaning that NCO can write files larger than 2 GB on © some 32-bit operating systems with netCDF libraries earlier than version 3.6. If desired, LFS support must be configured when both netCDF and NCO are installed. netCDF versions 3.6 and higher support 64-bit file addresses as part of the netCDF standard. We recommend that users ignore LFS support which is difficult to configure and is implemented in NCO only to support netCDF versions prior to 3.6. This obviates the need for configuring explicit LFS support in applications (such as NCO) that now support 64-bit files directly through the netCDF interface. See Section 3.10 [File Formats and Conversion], page 42, for instructions on accessing the different file formats, including 64-bit files, supported by themodern netCDF interface. 48 NCO 5.0.2-alpha07 User Guide
If you are still interested in explicit LFS support for netCDF versions prior to 3.6, know that LFS support depends on a complex, interlocking set of operating system14 and netCDF support issues. The netCDF LFS FAQ (http://my.unidata.ucar.edu/content/ software/netcdf/faq-lfs.html) describes the various file size limitations imposed by different versions of the netCDF standard. NCO and netCDF automatically attempt to configure LFS at build time.
3.12 Subsetting Files
Options -g grp ¨ Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa Short options: ‘-g’ Long options: ‘--grp’ and ‘--group’ Options -v var and -x Availability: (ncap2), ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa Short options: ‘-v’, ‘-x’ Long options: ‘--variable’, ‘--exclude’ or ‘--xcl’ Options --unn Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa Short options: Long options: ‘--unn’ and ‘--union’ Options --grp_xtr_var_xcl Availability: ncks Short options: Long options: ‘--gxvx’ and ‘--grp_xtr_var_xcl’
Subsetting variables refers to explicitly specifying variables and groups to be included © or excluded from operator actions. Subsetting is controlled by the ‘-v var[,...]’ and ‘-x’ options for directly specifying variables. Specifying groups, whether in addition to or instead of variables, is quite similar and is controlled by the ‘-g grp[,...]’ and ‘-x’ options. A list of variables or groups to extract is specified following the-v ‘ ’ and ‘-g’ options, e.g., ‘-v time,lat,lon’ or ‘-g grp1,grp2’. Both options may be specified simultaneously and NCO will extract the intersection of the lists, i.e., only variables of the specified names found in groups of the specified names. The--unn ‘ ’ option causes NCO to extract the union, rather than the intersection, of the specified groups and variables. Not using the‘-v’ or ‘-g’ option is equivalent to specifying all variables or groupp, respectively. The ‘-x’ option causes the list of variables specified with-v ‘ ’ to be excluded rather than extracted. Thus ‘-x’ saves typing when you only want to extract fewer than half of the variables in a file. ncks -x -v v1,v2 in.nc out.nc # Extract all variables except v1, v2 ncks -C -x -v lat,lon in.nc out.nc # Extract all except lat, lon
14 Linux and AIX do support LFS. Chapter 3: Shared Features 49
The first example above shows the typical use-x of‘ ’ to subset all variables except a few into the output. Note that v1 and v2 will be retained in the output if they are coordinate-like variables (see Section 3.13 [Subsetting Coordinate Variables], page 53) associated with any extracted variable. If one wishes to exclude coordinate-like variables despite their being referenced by extracted variables, one must use the ‘-C’ (or synonym ‘--xcl_ass_var’) option as shown in the second example.
Variables or groups explicitly specified for extraction with-v ‘ var[,...]’ or ‘-g grp[,...]’ must be present in the input file or an error will result. Variables explic- itly specified for exclusion with ‘-x -v var[,...]’ need not be present in the input file. To accord with the sophistication of the underlying hierarchy, group subsetting is controlled by a few powerful yet subtle syntactical distinctions. When learning this syntax it is helpful to keep in mind the similarity between group hierarchies and directory structures.
As of NCO 4.4.4 (June, 2014), ncks (alone) supports an option to include specified groups yet exclude specified variables. The ‘--grp_xtr_var_xcl’ switch (with long option equivalent ‘--gxvx’) extracts all contents of groups given as arguments to ‘-g grp[,...]’, except for variables given as arguments to ‘-v var[,...]’. Use this when one or a few variables in hierarchical files are not to be extracted, and all other variables are. Thisis useful when coercing netCDF4 files into netCDF3 files such as with converting, flattening, or dismembering files (see Section 3.14.1 [Flattening Groups], page 54).
ncks --grp_xtr_var_xcl -g g1 -v v1 # Extract all of group g1 except v1
Two properties of subsetting, recursion and anchoring, are best illustrated by reminding the user of their UNIX equivalents. The UNIX command mv src dst moves src and all its subdirectories (and all their subdirectories etc.) to dst. In other words mv is, by default, recursive. In contrast, the UNIX command cp src dst moves src, and only src, to dst, If src is a directory, not a file, then that command fails. One must explicitly request tocopy directories recursively, i.e., with cp -r src dst. In NCO recursive extraction (and copying) of groups is the default (like with mv, not with cp). Recursion is turned off by appending a trailing slash to the path.
These UNIX commands also illustrate a property we call anchoring. The command mv src dst moves (recursively) the source directory src to the destination directory dst. If src begins with the slash character then the specified path is relative to the root directory, otherwise the path is relative to the current working directory. In other words, an initial slash character anchors the subsequent path to the root directory. In NCO an initial slash anchors the path at the root group. Paths that begin and end with slash characters (e.g., //, /g1/, and /g1/g2/) are both anchored and non-recursive.
Consider the following commands, all of which may be assumed to end with ‘in.nc out.nc’:
ncks -g g1 # Extract, recursively, all groups with a g1 component ncks -g g1/ # Extract, non-recursively, all groups terminating in g1 ncks -g /g1 # Extract, recursively, root group g1 ncks -g /g1/ # Extract, non-recursively root group g1 ncks -g // # Extract, non-recursively the root group 50 NCO 5.0.2-alpha07 User Guide
The first command is probably the most useful and common. It would extract these groups, if present, and all their direct ancestors and children: /g1, /g2/g1, and /g3/g1/g2. In other words, the simplest form of ‘-g grp’ grabs all groups that (and their direct ancestors and children, recursively) that have grp as a complete component of their path. A simple string match is insufficient, grp must be a complete component (i.e., group name) in the path. The option ‘-g g1’ would not extract these groups because g1 is not a complete component of the path: /g12, /fg1, and /g1g1. The second command above shows how a terminating slash character / cancels the recursive copying of groups. An argument to ‘-g’ which terminates with a slash character extracts the group and its direct ancestors, but none of its children. The third command above shows how an initial slash character / anchors the argument to the root group. The third command would not extract the group /g2/g1 because the g1 group is not at the root level, but it would extract, any group /g1 at the root level and all its children, recursively. The fourth command is the non-recursive version of the third command. The fifth command is a special case of the fourth command. As mentioned above, both ‘-v’ and ‘-g’ options may be specified simultaneously and NCO will, by default, extract the intersection of the lists, i.e., the specified variables found in the specified groups15. The ‘--unn’ option causes NCO to extract the union, rather than the intersection, of the specified groups and variables. Consider the following commands (which may be assumed to end with ‘in.nc out.nc’): # Intersection-mode subsetting (default) ncks -g g1 -v v1 # Yes: /g1/v1, /g2/g1/v1. No: /v1, /g2/v1 ncks -g /g1 -v v1 # Yes: /g1/v1, /g1/g2/v1. No: /v1, /g2/v1, /g2/g1/v1 ncks -g g1/ -v v1 # Yes: /g1/v1, /g2/g1/v1. No: /v1, /g2/v1, /g1/g2/v1 ncks -v g1/v1 # Yes: /g1/v1, /g2/g1/v1. No: /v1, /g2/v1, /g1/g2/v1 ncks -g /g1/ -v v1 # Yes: /g1/v1. No: /g2/g1/v1, /v1, /g2/v1 ... ncks -v /g1/v1 # Yes: /g1/v1. No: /g2/g1/v1, /v1, /g2/v1 ...
# Union-mode subsetting (invoke with --unn or --union) ncks -g g1 -v v1 --unn # All variables in g1 or progeny, or named v1 ncks -g /g1 -v v1 --unn # All variables in /g1 or progeny, or named v1 ncks -g g1/ -v v1 --unn # All variables in g1 or named v1 ncks -g /g1/ -v v1 --unn # All variables in /g1 or named v1 The first command-g (‘ g1 -v v1’) extracts the variable v1 from any group named g1 or descendent g1. The second command extracts v1 from any root group named g1 and any descendent groups as well. The third and fourth commands are equivalent ways of extracting v1 only from the root group named g1 (not its descendents). The fifth and sixth commands are equivalent ways of extracting the variable v1 only from the root group named g1. Subsetting in union-mode (with ‘--unn’) causes all variables to be extracted which meet either one or both of the specifications of the variable and group specifications. Union-mode subsetting is simply the logical “OR” of intersection-mode subsetting. As discussed below, the group and variable specifications may be comma separated lists of regular expressions for added control over subsetting.
15 Intersection-mode can also be explicitly invoked with the ‘--nsx’ or ‘--intersection’ switches. These switches are supplied for clarity and consistency and do absolutely nothing since intersection-mode is the default. Chapter 3: Shared Features 51
Remember, if averaging or concatenating large files stresses your systems memory or disk resources, then the easiest solution is often to subset (with ‘-g’ and/or ‘-v’) to retain only the most important variables (see Section 2.9 [Memory Requirements], page 24). ncks in.nc out.nc # Extract all groups and variables ncks -v scl # Extract variable scl from all groups ncks -g g1 # Extract group g1 and descendents ncks -x -g g1 # Extract all groups except g1 and descendents ncks -g g2,g3 -v scl # Extract scl from groups g2 and g3 Overwriting and appending work as expected: # Replace scl in group g2 in out.nc with scl from group g2 from in.nc ncks -A -g g2 -v scl in.nc out.nc Due to its special capabilities, ncap2 interprets the ‘-v’ switch differently (see Section 4.1 [ncap2 netCDF Arithmetic Processor], page 154). For ncap2, the ‘-v’ switch takes no arguments and indicates that only user-defined variables should be output. ncap2 neither accepts nor understands the -x and -g switches.
Regular expressions the syntax that NCO use pattern-match object names in netCDF file against user requests. The user can select all variables beginning with the string ‘DST’ from an input file by supplying the regular expression^DST ‘ ’ to the ‘-v’ switch, i.e., ‘-v ’^DST’’. The meta-characters used to express pattern matching operations are ‘^$+?.*[]{}|’. If the regular expression pattern matches any part of a variable name then that variable is selected. This capability is also called wildcarding, and is very useful for sub-setting large data files. Extended regular expressions are defined by the POSIX grep -E (aka egrep) command. As of NCO 2.8.1 (August, 2003), variable name arguments to the ‘-v’ switch may contain extended regular expressions. As of NCO 3.9.6 (January, 2009), variable names arguments to ncatted may contain extended regular expressions. As of NCO 4.2.4 (November, 2012), group name arguments to the ‘-g’ switch may contain extended regular expressions. Because of its wide availability, NCO uses the POSIX regular expression library regex. Regular expressions of arbitary complexity may be used. Since netCDF variable names are relatively simple constructs, only a few varieties of variable wildcards are likely to be useful. For convenience, we define the most useful pattern matching operators here:
‘^’ Matches the beginning of a string ‘$’ Matches the end of a string ‘.’ Matches any single character
The most useful repetition and combination operators are
‘?’ The preceding regular expression is optional and matched at most once ‘*’ The preceding regular expression will be matched zero or more times ‘+’ The preceding regular expression will be matched one or more times 52 NCO 5.0.2-alpha07 User Guide
‘|’ The preceding regular expression will be joined to the following regular ex- pression. The resulting regular expression matches any string matching either subexpression.
To illustrate the use of these operators in extracting variables and groups, consider file in_grp.nc with groups g0–g9, and subgroups s0–s9, in each of those groups, and file in.nc with variables Q, Q01–Q99, Q100, QAA–QZZ, Q_H2O, X_H2O, Q_CO2, X_CO2. ncks -v ’.+’ in.nc # All variables (default) ncks -v ’Q.?’ in.nc # Variables that contain Q ncks -v ’^Q.?’ in.nc # Variables that start with Q ncks -v ’^Q+.?.’ in.nc # Q, Q0--Q9, Q01--Q99, QAA--QZZ, etc. ncks -v ’^Q..’ in.nc # Q01--Q99, QAA--QZZ, etc. ncks -v ’^Q[0-9][0-9]’ in.nc # Q01--Q99, Q100 ncks -v ’^Q[[:digit:]]{2}’ in.nc # Q01--Q99 ncks -v ’H2O$’ in.nc # Q_H2O, X_H2O ncks -v ’H2O$|CO2$’ in.nc # Q_H2O, X_H2O, Q_CO2, X_CO2 ncks -v ’^Q[0-9][0-9]$’ in.nc # Q01--Q99 ncks -v ’^Q[0-6][0-9]|7[0-3]’ in.nc # Q01--Q73, Q100 ncks -v ’(Q[0-6][0-9]|7[0-3])$’ in.nc # Q01--Q73 ncks -v ’^[a-z]_[a-z]{3}$’ in.nc # Q_H2O, X_H2O, Q_CO2, X_CO2 ncks -g ’g.’ in_grp.nc # 10 Groups g0-g9 ncks -g ’s.’ in_grp.nc # 100 sub-groups g0/s0, g0/s1, ... g9/s9 ncks -g ’g.’ -v ’v.’ in_grp.nc # All variables ’v.’ in groups ’g.’ Beware—two of the most frequently used repetition pattern matching operators, ‘*’ and ‘?’, are also valid pattern matching operators for filename expansion (globbing) at the shell- level. Confusingly, their meanings in extended regular expressions and in shell-level filename expansion are significantly different. In an extended regular expression, ‘*’ matches zero or more occurences of the preceding regular expression. Thus ‘Q*’ selects all variables, and ‘Q+.*’ selects all variables containing ‘Q’ (the ‘+’ ensures the preceding item matches at least once). To match zero or one occurence of the preceding regular expression, use ‘?’. Documentation for the UNIX egrep command details the extended regular expressions which NCO supports. One must be careful to protect any special characters in the regular expression specifica- tion from being interpreted (globbed) by the shell. This is accomplish by enclosing special characters within single or double quotes
ncra -v Q?? in.nc out.nc # Error: Shell attempts to glob wildcards ncra -v ’^Q+..’ in.nc out.nc # Correct: NCO interprets wildcards ncra -v ’^Q+..’ in*.nc out.nc # Correct: NCO interprets, Shell globs The final example shows that commands may use a combination of variable wildcarding and shell filename expansion (globbing). For globbing, ‘*’ and ‘?’ have nothing to do with the preceding regular expression! In shell-level filename expansion, ‘*’ matches any string, including the null string and ‘?’ matches any single character. Documentation for bash and csh describe the rules of filename expansion (globbing). Chapter 3: Shared Features 53
3.13 Subsetting Coordinate Variables
Availability: ncap2, ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa ¨ Short options: ‘-C’, ‘-c’ Long options: ‘--no_coords’, ‘--no_crd’, ‘--xcl_ass_var’, ‘--crd’, ‘--coords’, ‘--xtr_ass_var’
© By default, coordinates variables associated with any variable appearing in the input- file will be placed in the output-file, even if they are not explicitly specified, e.g., with the ‘-v’ switch. Thus variables with a latitude coordinate lat always carry the values of lat with them into the output-file. This automatic inclusion feature can be disabled with ‘-C’, which causes NCO to exclude (or, more precisely, not to automatically include) coordinates and associated variables from the extraction list. However, using ‘-C’ does not preclude the user from including some coordinates in the output files simply by explicitly selecting the coordinates and associated variables with the -v option. The ‘-c’ option, on the other hand, is a shorthand way of automatically specifying that all coordinate and associated variables in input-files should appear in output-file. The user can thereby select all coordinate variables without even knowing their names.
The meaning of “coordinates” in these two options has expanded since about 2009 from simple one dimensional coordinates (per the NUG) definition) to any and all associated variables. This includes multi-dimensional coordinates as well as a menagerie of associated variables defined by the CF metadata conventions: As of NCO version 4.4.5 (July, 2014) both ‘-c’ and ‘-C’ honor the CF ancillary_variables convention described in Section 3.45 [CF Conventions], page 146. As of NCO version 4.0.8 (April, 2011) both ‘-c’ and ‘-C’ honor the CF bounds convention described in Section 3.45 [CF Conventions], page 146. As of NCO version 4.6.4 (January, 2017) both ‘-c’ and ‘-C’ honor the CF cell_measures convention described in Section 3.45 [CF Conventions], page 146. As of NCO version 4.4.9 (May, 2015) both ‘-c’ and ‘-C’ honor the CF climatology convention described in Section 3.45 [CF Conventions], page 146. As of NCO version 3.9.6 (January, 2009) both ‘-c’ and ‘-C’ honor the CF coordinates convention described in Section 3.45 [CF Conventions], page 146. As of NCO version 4.6.4 (January, 2017) both ‘-c’ and ‘-C’ honor the CF formula_terms conven- tion described in Section 3.45 [CF Conventions], page 146. As of NCO version 4.6.0 (May, 2016) both ‘-c’ and ‘-C’ honor the CF grid_mapping convention described in Section 3.45 [CF Conventions], page 146.
The expanded categories of variables controlled by ‘-c’ and ‘-C’ justified adding a more descriptive switch. As of NCO version 4.8.0 (May, 2019) the switch ‘--xcl_ass_var’, which stands for “exclude associated variables”, is synonymous with ‘-C’ and ‘--xtr_ass_var’, which stands for “extract associated variables”, is synonymous with ‘-c’.
3.14 Group Path Editing 54 NCO 5.0.2-alpha07 User Guide
Options -G gpe_dsc ¨ Availability: ncbo, ncecat, nces, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa Short options: ‘-G’ Long options: ‘--gpe’
Group Path Editing, or GPE, allows the user to restructure (i.e., add, remove, and © rename groups) in the output file relative to the input file based on the instructions they provide. As of NCO 4.2.3 (November, 2012), all operators that accept netCDF4 files with groups accept the ‘-G’ switch, or its long-option equivalent ‘--gpe’. To master GPE one must understand the meaning of the required gpe dsc structure/argument that specifies the transformation of input-to-output group paths. Each gpe dsc contains up to three elements (two are optional) in the following order: gpe dsc = grp pth:lvl nbr or grp pth@lvl nbr grp pth Group Path. This (optional) component specifies the output group path that should be appended after any editing (i.e., deletion or truncation) of the input path is performed. lvl nbr The number of levels to delete (from the head) or truncate (from the tail) of the input path.
If both components of the argument are present, then a single character, either the colon or at-sign (: or @), must separate them. If only grp pth is specifed, the separator character may be omitted, e.g., ‘-G g1’. If only lvl nbr is specifed, the separator character is still required to indicate it is a lvl nbr arugment and not a grp pth, e.g., ‘-G :-1’ or ‘-G @1’. If the at-sign separator character @ is used instead of the colon separator character :, then the following lvl nbr arugment must be positive and it will be assumed to refer to Truncation-Mode. Hence, ‘-G :-1’ is the same as ‘-G @1’. This is simply a way of making the lvl nbr argument positive-definite. 3.14.1 Deletion, Truncation, and Flattening of Groups GPE has three editing modes: Delete, Truncate, and Flatten. Select one of GPE’s three editing modes by supplying a lvl nbr that is positive, negative, or zero for Delete-, Truncate- and Flatten-mode, respectively. In Delete-mode, lvl nbr is a positive integer which specifies the maximum number of group path components (i.e., groups) that GPE will try to delete from the head of grp pth. For example lvl nbr = 3 changes the input path /g1/g2/g3/g4/g5 to the output path /g4/g5. Input paths with lvl nbr or fewer components (groups) are completely erased and the output path commences from the root level. In other words, GPE is tolerant of specifying too many group components to delete. It deletes as many as possible, without complaint, and then begins to flatten the file (which fails if namespace conflicts arise). In Truncate-mode, lvl nbr is a negative integer which specifies the maximum number of group path components (i.e., groups) that GPE will try to truncate from the tail of grp pth. For example lvl nbr = −3 changes the input path /g1/g2/g3/g4/g5 to the output path Chapter 3: Shared Features 55
/g1/g2. Input paths with lvl nbr or fewer components (groups) are completely erased and the output path commences from the root level. In Flatten-mode, indicated by the separator character alone or with lvl nbr = 0, GPE removes the entire group path from the input file and constructs the output path beginning at the root level. For example -G :0 and -G : are identical and change the input path /g1/g2/g3/g4/g5 to the output path / whereas -G g1:0 and -G g1: are identical and result in the output path /g1 for all variables. Subsequent to the alteration of the input path by the specified editing mode, if any, GPE prepends (in Delete Mode) or Appends (in Truncate-mode) any specifed grp pth to the out- put path. For example -G g2 changes the input paths / and /g1 to /g2 and /g1/g2, respec- tively. Likewise, -G g2/g3 changes the input paths / and /g1 to /g2/g3 and /g1/g2/g3, respectively. When grp pth and lvl nbr are both specified, the editing actions are taken in sequence so that, e.g., -G g1/g2:2 changes the input paths / and /h1/h2/h3/h4 to /g1/g2 and /g1/g2/h3/h4, respectively. Likewise, -G g1/g2:-2 changes the input paths / and /h1/h2/h3/h4 to /g1/g2 and /h1/h2/g1/g2, respectively. Combining GPE with subsetting (see Section 3.12 [Subsetting Files], page 48) yields powerful control over the extracted (or excluded) variables and groups and their placement in the output file as shown by the following commands. All commands below maybe assumed to end with ‘in.nc out.nc’. # Prepending paths without editing: ncks # /g?/v? -> /g?/v? ncks -v v1 # /g?/v1 -> /g?/v1 ncks -g g1 # /g1/v? -> /g1/v? ncks -G o1 # /g?/v? -> /o1/g?/v? ncks -G o1 -g g1 # /g1/v? -> /o1/g1/v? ncks -g g1 -v v1 # /g1/v1 -> /g1/v1 ncks -G o1 -v v1 # /g?/v1 -> /o1/g?/v1 ncks -G o1 -g g1 -v v1 # /g1/v1 -> /o1/g1/v1 ncks -G g1 -g / -v v1 # /v1 -> /g1/v1 ncks -G g1/g2 -v v1 # /g?/v1 -> /g1/g2/g?/v1 # Delete-mode: Delete from and Prepend to path head # Syntax: -G [ppn]:lvl_nbr = # of levels to delete ncks -G :1 -g g1 -v v1 # /g1/v1 -> /v1 ncks -G :1 -g g1/g1 -v v1 # /g1/g1/v1 -> /g1/v1 ncks -G :2 -g g1/g1 -v v1 # /g1/g1/v1 -> /v1 ncks -G :2 -g g1 -v v1 # /g1/v1 -> /v1 ncks -G g2:1 -g g1 -v v1 # /g1/v1 -> /g2/v1 ncks -G g2:2 -g g1/g1 -v v1 # /g1/g1/v1 -> /g2/v1 ncks -G g2:1 -g / -v v1 # /v1 -> /g2/v1 ncks -G g2:1 -v v1 # /v1 -> /g2/v1 ncks -G g2:1 -g g1/g1 -v v1 # /g1/g1/v1 -> /g2/g1/v1 # Flatten-mode: Remove all input path components # Syntax: -G [apn]: colon without numerical argument ncks -G : -v v1 # /g?/v1 -> /v1 ncks -G : -g g1 -v v1 # /g1/v1 -> /v1 56 NCO 5.0.2-alpha07 User Guide
ncks -G : -g g1/g1 -v v1 # /g1/g1/v1 -> /v1 ncks -G g2: -v v1 # /g?/v1 -> /g2/v1 ncks -G g2: # /g?/v? -> /g2/v? ncks -G g2: -g g1/g1 -v v1 # /g1/g1/v1 -> /g2/v1 # Truncate-mode: Truncate from and Append to path tail # Syntax: -G [apn]:-lvl_nbr = # of levels to truncate # NB: -G [apn]:-lvl_nbr is equivalent to -G [apn]@lvl_nbr ncks -G :-1 -g g1 -v v1 # /g1/v1 -> /v1 ncks -G :-1 -g g1/g2 -v v1 # /g1/g2/v1 -> /g1/v1 ncks -G :-2 -g g1/g2 -v v1 # /g1/g2/v1 -> /v1 ncks -G :-2 -g g1 -v v1 # /g1/v1 -> /v1 ncks -G g2:-1 -v v1 # /g?/v1 -> /g2/v1 ncks -G g2:-1 -g g1 -v v1 # /g1/v1 -> /g2/v1 ncks -G g1:-1 -g g1/g2 -v v1 # /g1/g2/v1 -> /g1/g1/v1
3.14.2 Moving Groups Until fall 2013 (netCDF version 4.3.1-pre1), netCDF contained no library function for re- naming groups, and therefore ncrename cannot rename groups. However, NCO built on earlier versions of netCDF than 4.3.1 can use a GPE-based workaround mechanism to “re- name” groups. The GPE mechanism actually moves (i.e., copies to a new location) groups, a more arduous procedure than simply renaming them. GPE applies to all selected groups, so, in the general case, one must move only the desired group to a new file, and then merge that new file with the original to obtain a file where the desired group has been “renamed” and all else is unchanged. Here is how to “rename” group /g4 to group /f4 with GPE instead of ncrename ncks -O -G f4:1 -g g4 ~/nco/data/in_grp.nc ~/tmp.nc # Move /g4 to /f4 ncks -O -x -g g4 ~/nco/data/in_grp.nc ~/out.nc # Excise /g4 ncks -A ~/tmp.nc ~/out.nc # Add /f4 to new file If the original group g4 is not excised from out.nc (step two above), then the final output file would contain both g4 and a copy named f4. Thus GPE can be used to both “rename” and copy groups. The recommended way to rename groups when when netCDF version 4.3.1 is availale is to use ncrename (see Section 4.13 [ncrename netCDF Renamer], page 341). One may wish to flatten hierarchical group files for many reasons. These include 1.To obtain flat netCDF3 files for use with tools that do not work with netCDF4 files,2.To split-apart hierarchies to re-assemble into different hierarchies, and 3. To provide a subset of a hierarchical file with the simplest possible storage structure. ncks -O -G : -g cesm -3 ~/nco/data/cmip5.nc ~/cesm.nc # Extract /cesm to / The -3 switch16 specifies the output dataset should be in netCDF3 format, the -G : option flattens all extracted groups, and the -g cesm option extracts only the cesm group and leaves all other groups (e.g., ecmwf, giss).
16 Note that the -3 switch should appear after the -G and -g switches. This is due to an artifact of the GPE implementation which we wish to remove in the future. Chapter 3: Shared Features 57
3.14.3 Dismembering Files Let us show how to completely disaggregate (or, more memorably) dismember a hierarchical dataset. For now we take this to mean: store each group as a standalone flat dataset in netCDF3 format. This can be accomplished by looping the previous example over all groups. This script ncdismember dismembers the input file fl in specified in the first argument and places the resulting files in the directory drc out specified by the second argument:
cat > ~/ncdismember << ’EOF’ #!/bin/sh
# Purpose: Dismember netCDF4/HDF5 hierarchical files. CF-check them. # Place each input file group in separate netCDF3 output file # Described in NCO User Guide at http://nco.sf.net/nco.html#dismember # Requirements: NCO 4.3.x+, UNIX shell utilities awk, grep, sed # Optional: Decker CFchecker https://bitbucket.org/mde_/cfchecker
# Usage: # ncdismember
# Command-line argument defaults fl_in="${HOME}/nco/data/mdl_1.nc" # [sng] Input file to dismember/check drc_out="${DATA}/nco/tmp" # [sng] Output directory cf_chk=’0’ # [flg] Perform CF-compliance check? Which checker? cf_vrs=’1.5’ # [sng] Compliance-check this CF version (e.g., ’1.5’) opt=’’ # [flg] Additional ncks options (e.g., ’--fix_rec_dmn=all’) # Use single quotes to pass multiple arguments to opt=${5} # Otherwise arguments would be seen as ${5}, ${6}, ${7} ...
# Command-line argument option parsing if [ -n "${1}" ]; then fl_in=${1}; fi 58 NCO 5.0.2-alpha07 User Guide
if [ -n "${2}" ]; then drc_out=${2}; fi if [ -n "${3}" ]; then cf_chk=${3}; fi if [ -n "${4}" ]; then cf_vrs=${4}; fi if [ -n "${5}" ]; then opt=${5}; fi
# Prepare output directory echo "NCO dismembering file ${fl_in}" fl_stb=$(basename ${fl_in}) drc_out=${drc_out}/${fl_stb} mkdir -p ${drc_out} cd ${drc_out} chk_dck=’n’ chk_nrc=’n’ if [ ${cf_chk} = ’nerc’ ]; then chk_nrc=’y’ fi # chk_nrc if [ ${cf_chk} != ’0’ ] && [ ${cf_chk} != ’nerc’ ]; then chk_dck=’y’ hash cfchecker 2>/dev/null || { echo >&2 "Local cfchecker command not found, will smallify and upload to NERC checker instead"; chk_nrc=’y’; chk_dck=’n’; } fi # !cf_chk # Obtain group list grp_lst=‘ncks -m ${fl_in} | grep ’// group’ | awk ’{$1=$2=$3="";sub(/^ */,"",$0);print}’‘ IFS=$’\n’ # Change Internal-Field-Separator from
echo ’Using remote CFchecker http://puma.nerc.ac.uk/cgi-bin/cf-checker.pl’ cf_lcn=’http://puma.nerc.ac.uk/cgi-bin/cf-checker.pl’ for fl in ${drc_out}/*.nc ; do fl_sml=${fl} cf_out=${fl%.nc}.html dmns=‘ncdump -h ${fl_in} | sed -n -e ’/dimensions/,/variables/p’ | grep = | sed -e ’s/=.*//’‘ hyp_sml=’’ for dmn in ${dmns}; do dmn_lc=‘echo ${dmn} | tr "[:upper:]" "[:lower:]"‘ if [ ${dmn_lc} = ’lat’ ] || [ ${dmn_lc} = ’latitude’ ] || [ ${dmn_lc} = ’lon’ ] || [ ${dmn_lc} = ’longitude’ ] || [ ${dmn_lc} = ’time’ ]; then hyp_sml=‘echo ${hyp_sml}" -d ${dmn},0"‘ fi # !dmn_lc done # Create small version of input file by sampling only first element of lat, lon, time ncks -O ${hyp_sml} ${fl} ${fl_sml} # Send small file to NERC checker curl --form cfversion=1.6 --form upload=@${fl_sml} --form press="Check%20file" ${cf_lcn} -o ${cf_out} # Strip most HTML to improve readability cat ${cf_out} | sed -e "s/<[^>]*>//g" -e "/DOCTYPE/,/\]\]/d" -e "s/CF-Convention//g" -e "s/Output of//g" -e "s/Compliance Checker//g" -e "s/Check another//g" -e "s/CF-Checker follows//g" -e "s/Received//g" -e "s/for NetCDF//g" -e "s/NetCDF format//g" -e "s/against CF version 1//g" -e "s/\.\.\.//g" echo "Full NERC compliance-check log for ${fl} in ${cf_out}" done fi # !nerc EOF chmod 755 ~/ncdismember # Make command executable /bin/mv -f ~/ncdismember ~/sh # Store in location on $PATH, e.g., /usr/local/bin
zender@roulee:~$ ncdismember ~/nco/data/mdl_1.nc ${DATA}/nco/tmp NCO dismembering file /home/zender/nco/data/mdl_1.nc /data/zender/nco/tmp/mdl_1.nc/cesm.cesm_01.nc /data/zender/nco/tmp/mdl_1.nc/cesm.cesm_02.nc /data/zender/nco/tmp/mdl_1.nc/cesm.nc /data/zender/nco/tmp/mdl_1.nc/ecmwf.ecmwf_01.nc /data/zender/nco/tmp/mdl_1.nc/ecmwf.ecmwf_02.nc /data/zender/nco/tmp/mdl_1.nc/ecmwf.nc /data/zender/nco/tmp/mdl_1.nc/root.nc
A (potentially more portable) binary executable could be written to dismember all groups with a single invocation, yet dismembering without loss of information is possible now with this simple script on all platforms with UNIXy utilities. Note that all dimensions inherited by groups in the input file are correctly placed by ncdismember into the flat files. Moreover, each output file preserves the group metadata of all ancestor groups, including the global metadata from the input file. As written, the script could fail on groups that contain advanced netCDF4 features because the user requests (with the ‘-3’ switch) that output be netCDF3 classic format. However, ncks detects many format incompatibilities in advance and works around them. For example, ncks autoconverts netCDF4-only atomic-types (such as NC_STRING and NC_UBYTE) to corresponding netCDF3 atomic types (NC_CHAR and NC_ SHORT) when the output format is netCDF3. 60 NCO 5.0.2-alpha07 User Guide
3.14.4 Checking CF-compliance One application of dismembering is to check the CF-compliance of each group in a file. When invoked with the optional third argumnt ‘cf’, ncdismember passes each file it generates to freely available compliance checkers, such as cfchecker17. zender@roulee:~$ ncdismember ~/nco/data/mdl_1.nc /data/zender/nco/tmp cf NCO dismembering file /home/zender/nco/data/mdl_1.nc CFchecker reports CF-compliance of each group in flat netCDF3 format WARNING: Using the default (non-CF) Udunits database cesm.cesm_01.nc: INFO: INIT: running CFchecker version 1.5.15 INFO: INIT: checking compliance with convention CF-1.5 INFO: INIT: using standard name table version: 25, last modified: 2013-07-05T05:40:30Z INFO: INIT: using area type table version: 2, date: 10 July 2013 INFO: 2.4: no axis information found in dimension variables, not checking dimension order WARNING: 3: variable "tas1" contains neither long_name nor standard_name attribute WARNING: 3: variable "tas2" contains neither long_name nor standard_name attribute INFO: 3.1: variable "tas1" does not contain units attribute INFO: 3.1: variable "tas2" does not contain units attribute ------cesm.cesm_02.nc: ... By default the CF version checked is determined automatically by cfchecker. The user can override this default by supplying a supported CF version, e.g., ‘1.3’, as an optional fourth argument to ncdismember. Current valid CF options are ‘1.0’, ‘1.1’, ‘1.2’, ‘1.3’, ‘1.4’, and ‘1.5’. Our development and testing of ncdismember is funded by our involvement in NASA’s Dataset Interoperability Working Group (DIWG (https://wiki.earthdata.nasa.gov/ display/ESDSWG/Dataset+Interoperability+Working+Group)), though our interest ex- tends beyond NASA datasets. Taken together, NCO’s features (autoconversion to netCDF3 atomic types, fixing multiple record dimensions, autosensing HDF4 input, scoping rules for CF conventions) make ncdismember reliable and friendly for both dismembering hierarchi- cal files and for CF-compliance checks. Most HDF4 and HDF5 datasets can be checked for CF-compliance with a one-line command. Example compliance checks of common NASA datasets are at http://dust.ess.uci.edu/diwg. Our long-term goal is to enrich the hier- archical data model with the expressivity and syntactic power of CF conventions. NASA asked the DIWG to prepare a one-page summary of the procedure necessary to check HDF files for CF-compliance: cat > ~/ncdismember.txt << ’EOF’ Preparing an RPM-based OS to Test HDF & netCDF Files for CF-Compliance
By Charlie Zender, UCI & NASA Dataset Interoperability Working Group (DIWG)
17 CFchecker is developed by Michael Decker and Martin Schultz at Forschungszentrum J¨ulich and dis- tributed at https://bitbucket.org/mde_/cfchecker. Chapter 3: Shared Features 61
Installation Summary: 1. HDF4 [with internal netCDF support _disabled_] 2. HDF5 3. netCDF [with external HDF4 support _enabled_] 4. NCO 5. numpy 6. netcdf4-python 7. python-lxml 8. CFunits-python 9. CFChecker 10. ncdismember
All 10 packages can use default installs _except_ HDF4 and netCDF. Following instructions for Fedora Core 20 (FC20), an RPM-based Linux OS Feedback and changes for other Linux-based OS’s welcome to zender at uci.edu ${H4DIR}, ${H5DIR}, ${NETCDFDIR}, ${NCODIR}, may all be different For simplicity CZ sets them all to /usr/local
# 1. HDF4. Build in non-default manner. Turn-off its own netCDF support. # Per http://www.unidata.ucar.edu/software/netcdf/docs/build_hdf4.html # HDF4 support not necessary though it makes ncdismember more comprehensive wget -c http://www.hdfgroup.org/ftp/HDF/HDF_Current/src/hdf-4.2.9.tar.gz tar xvzf hdf-4.2.9.tar.gz cd hdf-4.2.9 ./configure --enable-shared --disable-netcdf --disable-fortran --prefix=${H4DIR} make && make check && make install
# 2. HDF5. Build normally. RPM may work too. Please let me know if so. # HDF5 is a necessary pre-requisite for netCDF4 wget -c ftp://ftp.unidata.ucar.edu/pub/netcdf/netcdf-4/hdf5-1.8.11.tar.gz tar xvzf hdf5-1.8.11.tar.gz cd hdf5-1.8.11 ./configure --enable-shared --prefix=${H5DIR} make && make check && make install
# 3. netCDF version 4.3.1 or later. Build in non-default manner with HDF4. # Per http://www.unidata.ucar.edu/software/netcdf/docs/build_hdf4.html # Earlier versions of netCDF may fail checking some HDF4 files wget -c ftp://ftp.unidata.ucar.edu/pub/netcdf/netcdf-4.3.2.tar.gz tar xvzf netcdf-4.3.2.tar.gz cd netcdf-4.3.2 CPPFLAGS="-I${H5DIR}/include -I${H4DIR}/include" \ LDFLAGS="-L${H5DIR}/lib -L${H4DIR}/lib" \ ./configure --enable-hdf4 --enable-hdf4-file-tests make && make check && make install
# 4. NCO version 4.4.0 or later. Some RPMs available. Or install by hand. 62 NCO 5.0.2-alpha07 User Guide
# Later versions of NCO have much better support for ncdismember wget http://nco.sourceforge.net/src/nco-4.4.4.tar.gz . tar xvzf nco-4.4.4.tar.gz cd nco-4.4.4 ./configure --prefix=${NCODIR} make && make install
# 5. numpy sudo yum install numpy -y
# 6. netcdf4-python sudo yum install netcdf4-python -y
# 7. python-lxml sudo yum install python-lxml -y
# 8. CFunits-python. No RPM available. Must install by hand. # http://code.google.com/p/cfunits-python/ wget http://cfunits-python.googlecode.com/files/cfunits-0.9.6.tar.gz . tar xvzf cfunits-0.9.6.tar.gz cd cfunits-0.9.6 sudo python setup.py install
# 9. CFChecker. No RPM available. Must install by hand. # https://bitbucket.org/mde_/cfchecker wget https://bitbucket.org/mde_/cfchecker/downloads/CFchecker-1.5.15.tar.bz2 . tar xvjf CFchecker-1.5.15.tar.bz2 cd CFchecker sudo python setup.py install
# 10. ncdismember. Copy script from http://nco.sf.net/nco.html#ncdismember # Store dismembered files somewhere, e.g., ${DATA}/nco/tmp/hdf mkdir -p ${DATA}/nco/tmp/hdf # Many datasets work with a simpler command... ncdismember ~/nco/data/in.nc ${DATA}/nco/tmp/hdf cf 1.5 ncdismember ~/nco/data/mdl_1.nc ${DATA}/nco/tmp/hdf cf 1.5 ncdismember ${DATA}/hdf/AMSR_E_L2_Rain_V10_200905312326_A.hdf \ ${DATA}/nco/tmp/hdf cf 1.5 ncdismember ${DATA}/hdf/BUV-Nimbus04_L3zm_v01-00-2012m0203t144121.h5 \ ${DATA}/nco/tmp/hdf cf 1.5 ncdismember ${DATA}/hdf/HIRDLS-Aura_L3ZAD_v06-00-00-c02_2005d022-2008d077.he5 ${DATA}/nco/tmp/hdf cf 1.5 # Some datasets, typically .h5, require the --fix_rec_dmn=all argument ncdismember_${DATA}/hdf/GATMO_npp_d20100906_t1935191_e1935505_b00012_c20110707155932065809_noaa_ops.h5 ${DATA}/nco/tmp/hdf cf 1.5 --fix_rec_dmn=all ncdismember ${DATA}/hdf/mabel_l2_20130927t201800_008_1.h5 \ ${DATA}/nco/tmp/hdf cf 1.5 --fix_rec_dmn=all EOF Chapter 3: Shared Features 63
A PDF version of these instructions is available here (http://dust.ess.uci.edu/diwg/ ncdismember.pdf).
3.15 C and Fortran Index conventions
Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa ¨ Short options: ‘-F’ Long options: ‘--fortran’
The ‘-F’ switch changes NCO to read and write with the Fortran index convention. By © default, NCO uses C-style (0-based) indices for all I/O. In C, indices count from 0 (rather than 1), and dimensions are ordered from slowest (inner-most) to fastest (outer-most) vary- ing. In Fortran, indices count from 1 (rather than 0), and dimensions are ordered from fastest (inner-most) to slowest (outer-most) varying. Hence C and Fortran data storage conventions represent mathematical transposes of eachother. Note that record variables contain the record dimension as the most slowly varying dimension. See Section 4.9 [ncpdq netCDF Permute Dimensions Quickly], page 289, for techniques to re-order (including trans- pose) dimensions and to reverse data storage order. Consider a file 85.nc containing 12 months of data in the record dimension time. The following hyperslab operations produce identical results, a June-July-August average of the data: ncra -d time,5,7 85.nc 85_JJA.nc ncra -F -d time,6,8 85.nc 85_JJA.nc Printing variable three dmn var in file in.nc first with the C indexing convention, then with Fortran indexing convention results in the following output formats: % ncks --trd -v three_dmn_var in.nc lat[0]=-90 lev[0]=1000 lon[0]=-180 three_dmn_var[0]=0 ... % ncks --trd -F -v three_dmn_var in.nc lon(1)=0 lev(1)=100 lat(1)=-90 three_dmn_var(1)=0 ...
3.16 Hyperslabs
Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa ¨ Short options: ‘-d dim,[min][,[max][,[stride]]]’ Long options: ‘--dimension dim,[min][,[max][,[stride]]]’, ‘--dmn dim,[min][,[max][,[stride]]]’
A hyperslab is a subset of a variable’s data. The coordinates of a hyperslab are specified © with the -d dim,[min][,[max][,[stride]]] short option (or with the same arguments to 64 NCO 5.0.2-alpha07 User Guide
the ‘--dimension’ or ‘--dmn’ long options). At least one hyperslab argument (min, max, or stride) must be present. The bounds of the hyperslab to be extracted are specified by the associated min and max values. A half-open range is specified by omitting either the min or max parameter. The separating comma must be present to indicate the omission of one of these arguments. The unspecified limit is interpreted as the maximum or minimum value in the unspecified direction. A cross-section at a specific coordinate is extracted by specifying only the min limit and omitting a trailing comma. Dimensions not mentioned are passed with no reduction in range. The dimensionality of variables is not reduced (in the case of a cross-section, the size of the constant dimension will be one). # First and second longitudes ncks -F -d lon,1,2 in.nc out.nc # Second and third longitudes ncks -d lon,1,2 in.nc out.nc As of version 4.2.1 (August, 2012), NCO allows one to extract the last N elements of a hyperslab. Negative integers as min or max elements of a hyperslab specification indicate offsets from the end (Python also uses this convention). Consistent with this convention, the value ‘-1’ (negative one) indicates the last element of a dimension, and negative zero is algebraically equivalent to zero and so indicates the first element of a dimension. Previously, for example, ‘-d time,-2,-1’ caused a domain error. Now it means select the penultimate and last timesteps, independent of the size of the time dimension. Select only the first and last timesteps, respectively, with ‘-d time,0’ and ‘-d time,-1’. Negative integers work for min and max indices, though not for stride. # Second through penultimate longitudes ncks -d lon,1,-2 in.nc out.nc # Second through last longitude ncks -d lon,1,-1 in.nc out.nc # Second-to-last to last longitude ncks -d lon,-3,-1 in.nc out.nc # Second-to-last to last longitude ncks -d lon,-3, in.nc out.nc The ‘-F’ argument, if any, applies the Fortran index convention only to indices specified as positive integers: # First through penultimate longitudes ncks -F -d lon,1,-2 in.nc out.nc (-F affects only start index) # First through last longitude ncks -F -d lon,1,-1 in.nc out.nc # Second-to-last to penultimate longitude (-F has no effect) ncks -F -d lon,-3,-1 in.nc out.nc # Second-to-last to last longitude (-F has no effect) ncks -F -d lon,-3, in.nc out.nc Coordinate values should be specified using real notation with a decimal point required in the value, whereas dimension indices are specified using integer notation without a decimal point. This convention serves only to differentiate coordinate values from dimension indices. It is independent of the type of any netCDF coordinate variables. In other words, even if Chapter 3: Shared Features 65
coordinates are defined as integers, specify them with decimal points to have the command interpret them as values, rather than indices. For a given dimension, the specified limits must both be coordinate values (with decimal points) or dimension indices (no decimal points).
If values of a coordinate-variable are used to specify a range or cross-section, then the coordinate variable must be monotonic (values either increasing or decreasing). In this case, command-line values need not exactly match coordinate values for the specified dimension. Ranges are determined by seeking the first coordinate value to occur in the closed range [min,max] and including all subsequent values until one falls outside the range. The coor- dinate value for a cross-section is the coordinate-variable value closest to the specified value and must lie within the range or coordinate-variable values. The stride argument, if any, must be a dimension index, not a coordinate value. See Section 3.17 [Stride], page 66, for more information on the stride option.
# All longitude values between 1 and 2 degrees ncks -d lon,1.0,2.0 in.nc out.nc # All longitude values between 1 and 2 degrees ncks -F -d lon,1.0,2.0 in.nc out.nc # Every other longitude value between 0 and 90 degrees ncks -F -d lon,0.0,90.0,2 in.nc out.nc
As shown, we recommend using a full floating-point suffix of .0 instead of simply . in order to make obvious the selection of hyperslab elements based on coordinate value rather than index.
User-specified coordinate limits are promoted to double-precision values while searching for the indices which bracket the range. Thus, hyperslabs on coordinates of type NC_CHAR are computed numerically rather than lexically, so the results are unpredictable.
The relative magnitude of min and max indicate to the operator whether to expect a wrapped coordinate (see Section 3.22 [Wrapped Coordinates], page 74), such as longitude. If min > max, the NCO expects the coordinate to be wrapped, and a warning message will be printed. When this occurs, NCO selects all values outside the domain [max < min], i.e., all the values exclusive of the values which would have been selected if min and max were swapped. If this seems confusing, test your command on just the coordinate variables with ncks, and then examine the output to ensure NCO selected the hyperslab you expected (coordinate wrapping is currently only supported by ncks).
Because of the way wrapped coordinates are interpreted, it is very important to make sure you always specify hyperslabs in the monotonically increasing sense, i.e., min < max (even if the underlying coordinate variable is monotonically decreasing). The only exception to this is when you are indeed specifying a wrapped coordinate. The distinction is crucial to understand because the points selected by, e.g., -d longitude,50.,340., are exactly the complement of the points selected by -d longitude,340.,50..
Not specifying any hyperslab option is equivalent to specifying full ranges of all dimen- sions. This option may be specified more than once in a single command (each hyperslabbed dimension requires its own -d option). 66 NCO 5.0.2-alpha07 User Guide
3.17 Stride
Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa ¨ Short options: ‘-d dim,[min][,[max][,[stride]]]’ Long options: ‘--dimension dim,[min][,[max][,[stride]]]’, ‘--dmn dim,[min][,[max][,[stride]]]’
All data operators support specifying a stride for any and all dimensions at the same © time. The stride is the spacing between consecutive points in a hyperslab. A stride of 1 picks all the elements of the hyperslab, and a stride of 2 skips every other element, etc.. ncks multislabs support strides, and are more powerful than the regular hyperslabs supported by the other operators (see Section 3.21 [Multislabs], page 71). Using the stride option for the record dimension with ncra and ncrcat makes it possible, for instance, to average or concatenate regular intervals across multi-file input data sets. The stride is specified as the optional fourth argument-d tothe‘ ’ hyperslab specification: -d dim,[min][,[max][,[stride]]]. Specify stride as an integer (i.e., no decimal point) following the third comma in the ‘-d’ argument. There is no default value for stride. Thus using ‘-d time,,,2’ is valid but ‘-d time,,,2.0’ and ‘-d time,,,’ are not. When stride is specified but min is not, there is an ambiguity as to whether the extracted hyperslab should begin with (using C-style, 0-based indexes) element 0 or element ‘stride-1’. NCO must resolve this ambiguity and it chooses element 0 as the first element of the hyperslab when min is not specified. Thus ‘-d time,,,stride’ is syntactically equivalent to ‘-d time,0,,stride’. This means, for example, that specifying the operation ‘-d time,,,2’ on the array ‘1,2,3,4,5’ selects the hyperslab ‘1,3,5’. To obtain the hyperslab ‘2,4’ instead, simply explicitly specify the starting index as 1, i.e., ‘-d time,1,,2’. For example, consider a file 8501_8912.nc which contains 60 consecutive months of data. Say you wish to obtain just the March data from this file. Using 0-based subscripts (see Section 3.15 [C and Fortran Index Conventions], page 63) these data are stored in records 2, 14, . . . 50 so the desired stride is 12. Without the stride option, the procedure is very awkward. One could use ncks five times and then use ncrcat to concatenate the resulting files together: for idx in 02 14 26 38 50; do # Bourne Shell ncks -d time,${idx} 8501_8912.nc foo.${idx} done foreach idx (02 14 26 38 50) # C Shell ncks -d time,${idx} 8501_8912.nc foo.${idx} end ncrcat foo.?? 8589_03.nc rm foo.?? With the stride option, ncks performs this hyperslab extraction in one operation: ncks -d time,2,,12 8501_8912.nc 8589_03.nc See Section 4.8 [ncks netCDF Kitchen Sink], page 263, for more information on ncks. Chapter 3: Shared Features 67
Applying the stride option to the record dimension in ncra and ncrcat makes it possible, for instance, to average or concatenate regular intervals across multi-file input data sets. ncra -F -d time,3,,12 85.nc 86.nc 87.nc 88.nc 89.nc 8589_03.nc ncrcat -F -d time,3,,12 85.nc 86.nc 87.nc 88.nc 89.nc 8503_8903.nc
3.18 Record Appending
Availability: ncra, ncrcat ¨ Short options: None Long options: ‘--rec_apn’, ‘--record_append’
As of version 4.2.6 (March, 2013), NCO allows both Multi-File, Multi-Record operators © (ncra and ncrcat) to append their output directly to the end of an existing file. This feature may be used to augment a target file, rather than construct it from scratch. This helps, for example, when a timeseries is concatenated from input data that becomes available in stages rather than all at once. In such cases this switch significantly speeds writing. Consider the use case where one wishes to preserve the contents of fl_1.nc, and add to them new records contained in fl_2.nc. Previously the output had to be placed in a third file, fl_3.nc (which could also safely be named fl_2.nc), via ncrcat -O fl_1.nc fl_2.nc fl_3.nc Under the hood this operation copies all information in fl_1.nc and fl_2.nc not once but twice. The first copy is performed through the netCDF interface, as all datafrom fl_1.nc and fl_2.nc are extracted and placed in the output file. The second copy occurs (usually much) more quickly as the (by default) temporary output file is copied (sometimes a quick re-link suffices) to the final output file (see Section 2.3 [Temporary Output Files], page 17). All this copying is expensive for large files. The ‘--record_append’ switch appends all records in fl_2.nc to the end (after the last record) of fl_1.nc: ncrcat --rec_apn fl_2.nc fl_1.nc The ordering of the filename arguments may seem non-intuitive. If the record variable represents time in these files, then the values in fl_1.nc precede those in fl_2.nc, so why do the files appear in the reverse order on the command line? fl_1.nc is the last file named because it is the pre-existing output file to which we will append all the other input files listed (in this case only fl_2.nc). The contents of fl_1.nc are completely preserved, and only values in fl_2.nc (and any other input files) are copied. This switch avoids the necessity of copying all of fl_1.nc through the netCDF interface to a new output file. The ‘--rec_apn’ switch automatically puts NCO into append mode (see Section 2.4 [Appending Variables], page 19), so specifying ‘-A’ is redundant, and simultaneously specifying overwrite mode with ‘-O’ causes an error. By default, NCO works in an intermediate temporary file. Power users may combine ‘--rec_apn’ with the ‘--no_tmp_fl’ switch (see Section 2.3 [Temporary Output Files], page 17): 68 NCO 5.0.2-alpha07 User Guide
ncrcat --rec_apn --no_tmp_fl fl_2.nc fl_1.nc This avoids creating an intermediate file, and copies only the minimal amount ofdata (i.e., all of fl_2.nc). Hence, it is fast. We recommend users try to understand the safety trade-offs involved. One side-effect of--rec_apn ‘ ’ to be aware of is how attributes are handled. When ap- pending files, NCO typically overwrites attributes for existing variables in the destination file with the corresponding attributes from the same variable in the source file. The exception to this rule is when ‘--rec_apn’ is invoked. As of version 4.7.9 (January, 2019), NCO leaves unchanged the attributes for existing variables in the destination file. This is primarily to ensure that calendar attributes (e.g., units, calendar) of the record coordinate, if any, are maintained, so that the data appended to them can be re-based to the existing units. Otherwise rebasing would fail or require rewriting the entire file which is counter to the purpose of ‘--rec_apn’.
3.19 Subcycle
Availability: ncra, ncrcat ¨ Short options: ‘-d dim,[min][,[max][,[stride][,[subcycle]]]]’ Long options: ‘--mro’‘--dimension dim,[min][,[max][,[stride][,[subcycle]]]]’ ‘--dmn dim,[min][,[max][,[stride][,[subcycle]]]]’
As of version 4.2.1 (August, 2012), NCO allows both Multi-File, Multi-Record operators, © ncra and ncrcat, to extract and operate on multiple groups of records. These groups may be connected to physical sub-cycles of a periodic nature, e.g., months of a year, or hours of a day. Or they may be thought of as groups of a specifed duration. We call this the subcycle feature, sometimes abbreviated SSC18. The subcycle feature allows processing of groups of records separated by regular intervals of records. It is perhaps best illustrated by an extended example that describes how to solve the same problem both with and without the SSC feature. Creating seasonal cycles is a common task in climate data processing. Suppose a 150- year climate simulation produces 150 output files, each comprising 12 records, each record a monthly mean: 1850.nc, 1851.nc, ... 1999.nc. Our goal is to create a single file that contains the climatological summertime (June, July, and August, aka JJA) mean. Tradi- tionally, we would first compute the climatological monthly mean for each month of summer. Each of these is a 150-year mean, i.e., # Step 1: Create climatological monthly files clm06.nc..clm08.nc for mth in {6..8}; do mm=‘printf "%02d" $mth‘ ncra -O -F -d time,${mm},,12 -n 150,4,1 1850.nc clm${mm}.nc done # Step 2: Average climatological monthly files into summertime mean
18 When originally released in 2012 this was called the duration feature, and was abbreviated DRN. Chapter 3: Shared Features 69
ncra -O clm06 clm07.nc clm08.nc clm_JJA.nc So far, nothing is unusual and this task can be performed by any NCO version. The SSC feature makes obsolete the need for the shell loop used in Step 1 above. The new SSC option aggregates more than one input record at a time before performing arithmetic operations, and, with an additional switch, allows archival of those results in multiple-record output (MRO) files. This reduces the task of producing the climatological summertime mean to one step: # Step 1: Compute climatological summertime mean ncra -O -F -d time,6,,12,3 -n 150,4,1 1850.nc clm_JJA.nc The SSC option instructs ncra (or ncrcat) to process files in groups of three records. To better understand the meaning of each argument to the ‘-d’ hyperslab option, read it this way: “for the time dimension start with the sixth record, continue without end, repeat the process every twelfth record, and define a sub-cycle as three consecutive records”. A separate option, ‘--mro’, instructs ncra to output its results from each sub-group, and to produce a Multi-Record Output (MRO) file rather than a Single-Record Output (SRO) file. Unless Multi-Record-Output is indicated (either--mro with‘ ’ or implicitly, as with interleave-mode), ncra collects together all sub-groups, operates on their ensemble, and produces a single output record. Adding ‘--mro’ to the above example causes ncra to archive all (150) annual summertime means to one file: # Step 1: Archive all 150 summertime means in one file ncra --mro -O -F -d time,6,,12,3 -n 150,4,1 1850.nc 1850_2009_JJA.nc # ...or all (150) annual means... ncra --mro -O -d time,,,12,12 -n 150,4,1 1850.nc 1850_2009.nc These operations generate and require no intermediate files. This contrasts to previous NCO methods, which require generating, averaging, then catenating 150 files. The--mro ‘ ’ option only works on ncra and has no effect on (or rather is redundant for) ncrcat, since ncrcat always outputs all selected records.
3.20 Interleave
Availability: ncra, ncrcat ¨ Short options: ‘-d dim,[min][,[max][,[stride][,[subcycle][,[interleave]]]]]’ Long options: ‘--mro’‘--dimension dim,[min][,[max][,[stride][,[subcycle][,[interleave]]]]]’ ‘--dmn dim,[min][,[max][,[stride][,[subcycle][,[interleave]]]]]’
As of version 4.9.4 (September, 2020), NCO allows both Multi-File, Multi-Record oper- © ators, ncra and ncrcat, to extract, interleave, and operate on multiple groups of records. Interleaving (or de-interleaving, depending on one’s perspective) means altering the order of records in a group to be processed. Specifically, the interleaving feature (sometimes ab- breviated ILV) causes the operator to treat as sequential records those that are separated by multiples of the specified interleave parameter within a group or sub-cycle of records. 70 NCO 5.0.2-alpha07 User Guide
The interleave feature sequences records with respect to their position relative to the beginning of each sub-cycle. Records a multiple of interleave from sub-cycle beginning are first extractedncrcat ( ) or reduced (ncra), then records offset from these by one, two, et cetera up to interleave − 1. In this manner interleaving extracts an inner (intra-sub-cycle) loop that preserves high-frequency signals relative to the longer stride between sub-cycles. Thus interleaving allows deconvolution of periodic phenomena within a time-series. Processing simple arithmetic sequences is a helpful way to understand what interleaving does. Here are some examples to reify the abstract. Let in1.nc contain the record-array [1..10], in2.nc contain [11..20], and in12.nc contain [1..20]. ncra -d time,,,,10,5 in1.nc ~/foo.nc # 3.5, 4.5, 5.5, 6.5, 7.5 ncrcat -d time,0,4,,6,2 in1.nc ~/foo.nc # 1, 3, 5, 2, 4, 6 (+WARNING) ncrcat -d time,2,,10,4,2 in12.nc ~/foo.nc # 3, 5, 4, 6, 13, 15, 14, 16 ncra -d time,2,,10,4,2 in12.nc ~/foo.nc # 4, 5, 14, 15 ncra -d time,,,,10,2 in1.nc in2.nc ~/foo.nc # 5, 6, 15, 16 ncra -d time,,,,10,2 in12.nc ~/foo.nc # 5, 6, 15, 16 Interleaving is perhaps best illustrated by an extended example that describes how to solve the same problem both with and without the ILV feature. Consider as an example an interannual timeseries archived at a high-enough temporal frequency to resolve the diurnal cycle with tpd timesteps-per-day. Many climate models and re-analyses are archived at hourly, tri-hourly, or six-hourly resolution yielding tpd = 24, 8, or 6, respectively. Our goal is to extract a monthly mean diurnal cycle from this timeseries. Suppose a 150-year climate simulation produces 150 output files, each comprising 365 days of hourly data, or 8760 records, each record an hourly mean: 1850.nc, 1851.nc, ... 1999.nc. Our goal is to create a single file that contains the climatological monthly mean diurnal cycle for, say, March, which contains 31 days or 744 hourly records that commence on the 60th day of the 356-day year, with record index 1416. Traditionally, we might first compute the climatological monthly mean for hour of the day, then combine those into a full diurnal cycle: # Step 1: Create climatological hourly files hr00.nc..hr23.nc for hr in {0..23}; do hh=‘printf "%02d" $hr‘ let srt=${hr}+1416 # Alternatively, use UDUnits by setting srt=1850-03-01T00:00:01 ncra -O -d time,${srt},,8760 -n 150,4,1 1850.nc hr${hh}.nc done # Step 2: Concatenate climatological hourly files into diurnal cycle ncrcata -O hr??.nc clm_drn.nc
So far, nothing is unusual and this task can be performed by any NCO version. The ILV feature obsoletes the need for the shell loop used in Step 1 above.
The new ILV option aggregates more than one input record at a time before performing arithmetic operations, and, with an additional switch, allows archival of those results in multiple-record output (MRO) files. This reduces the task of producing the climatological summertime mean to one step: Chapter 3: Shared Features 71
# Step 1: Archive all 150 March-mean diurnal cycles in one file ncra -O -d time,1850-03-01T00:00:01,,8760,744,24 -n 150,4,1 1850.nc clm_drn.nc The ILV option instructs ncra (or ncrcat) to process files in groups of 31 days (744 hourly records) interleaved with a 24-record cycle. The end result will have 150 sets of 24-timesteps representing the diurnal cycle of March in every year. A given timestep is the mean of the same hour of the day for every day in March of that year.
3.21 Multislabs
Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat ¨ Short options: ‘-d dim,[min][,[max][,[stride]]]’ Long options: ‘--dimension dim,[min][,[max][,[stride]]]’, ‘--dmn dim,[min][,[max][,[stride]]]’ ‘--msa_usr_rdr’, ‘--msa_user_order’
A multislab is a union of one or more hyperslabs. One defines multislabs by chaining © together hyperslab commands, i.e., -d options (see Section 3.16 [Hyperslabs], page 63). Support for specifying a multi-hyperslab or multislab for any variable was first added to ncks in late 2002. The other operators received these capabilities in April 2008. Multi-slabbing is often referred to by the acronym MSA, which stands for “Multi-Slabbing Algorithm”. As explained below, the user may additionally request that the multislabs be returned in the user-specified order, rather than the on-disk storage order. Although MSA user-ordering has been available in all operators since 2008, most users were unaware of it since the documentation (below, and in the man pages) was not written until July 2013. Multislabs overcome many restraints that limit simple hyperslabs. A single -d op- tion can only specify a contiguous and/or a regularly spaced multi-dimensional data ar- ray. Multislabs are constructed from multiple -d options and may therefore have non- regularly spaced arrays. For example, suppose it is desired to operate on all longitudes from 10.0 to 20.0 and from 80.0 to 90.0 degrees. The combined range of longitudes is not selectable in a single hyperslab specfication of the form ‘-d dimension,min,max’ or ‘-d dimension,min,max,stride’ because its elements are irregularly spaced in coordinate space (and presumably in index space too). The multislab specification for obtaining these values is simply the union of the hyperslabs specifications that comprise the multislab, i.e., ncks -d lon,10.,20. -d lon,80.,90. in.nc out.nc ncks -d lon,10.,15. -d lon,15.,20. -d lon,80.,90. in.nc out.nc Any number of hyperslabs specifications may be chained together to specify the multislab. MSA creates an output dimension equal in size to the sum of the sizes of the multislabs. This can be used to extend and or pad coordinate grids. Users may specify redundant ranges of indices in a multislab, e.g., ncks -d lon,0,4 -d lon,2,9,2 in.nc out.nc This command retrieves the first five longitudes, and then every other longitude value up to the tenth. Elements 0, 2, and 4 are specified by both hyperslab arguments (hence 72 NCO 5.0.2-alpha07 User Guide
this is redundant) but will count only once if an arithmetic operation is being performed. This example uses index-based (not coordinate-based) multislabs because the stride option only supports index-based hyper-slabbing. See Section 3.17 [Stride], page 66, for more information on the stride option. Multislabs are more efficient than the alternative of sequentially performing hyperslab operations and concatenating the results. This is because NCO employs a novel multislab algorithm to minimize the number of I/O operations when retrieving irregularly spaced data from disk. The NCO multislab algorithm retrieves each element from disk once and only once. Thus users may take some shortcuts in specifying multislabs and the algorithm will obtain the intended values. Specifying redundant ranges is not encouraged, but may be useful on occasion and will not result in unintended consequences. Suppose the Q variable contains three dimensional arrays of distinct chemical con- stituents in no particular order. We are interested in the NOy species in a certain geographic range. Say that NO, NO2, and N2O5 are elements 0, 1, and 5 of the species dimension of Q. The multislab specification might look something like ncks -d species,0,1 -d species,5 -d lon,0,4 -d lon,2,9,2 in.nc out.nc Multislabs are powerful because they may be specified for every dimension at the same time. Thus multislabs obsolete the need to execute multiple ncks commands to gather the desired range of data. The MSA user-order switch ‘--msa_usr_rdr’ (or ‘--msa_user_order’, both of which shorten to ‘--msa’) requests that the multislabs be output in the user-specified order from the command-line, rather than in the input-file on-disk storage order. This allows the user to perform complex data re-ordering in one operation that would otherwise require cumbersome steps of hyperslabbing, concatenating, and permuting. Consider the example of converting datasets stored with the longitude coordinate Lon ranging from [−180,180) to datasets that follow the [0,360) convention. % ncks -H -v Lon in.nc Lon[0]=-180 Lon[1]=-90 Lon[2]=0 Lon[3]=90 What is needed is a simple way to rotate longitudes. Although simple in theory, this task requires both mathematics to change the numerical value of the longitude coordinate, data hyperslabbing to split the input on-disk arrays at Greenwich, and data re-ordering within to stitch the western hemisphere onto the eastern hemisphere at the date-line. The ‘--msa’ user-order switch overrides the default that data are output in the same order in which they are stored on-disk in the input file, and instead stores them in the same order asthe multi-slabs are given to the command line. This default is intuitive and is not important in most uses. However, the MSA user-order switch allows users to meet their output order needs by specifying multi-slabs in a certain order. Compare the results of default ordering to user-ordering for longitude: % ncks -O -H -v Lon -d Lon,0.,180. -d Lon,-180.,-1.0 in.nc Lon[0]=-180 Chapter 3: Shared Features 73
Lon[1]=-90 Lon[2]=0 Lon[3]=90 % ncks -O -H --msa -v Lon -d Lon,0.,180. -d Lon,-180.,-1.0 in.nc Lon[0]=0 Lon[1]=90 Lon[2]=-180 Lon[3]=-90
The two multi-slabs are the same but they can be presented to screen, or to an output file, in either order. The second example shows how to place the western hemisphere after the eastern hemisphere, although they are stored in the opposite order in the input file.
With this background, one sees that the following commands suffice to rotate the input file by 180 degrees longitude:
% ncks -O -v LatLon --msa -d Lon,0.,180. -d Lon,-180.,-1.0 in.nc out.nc % ncap2 -O -s ’where(Lon < 0) Lon=Lon+360’ out.nc out.nc % ncks --trd -C -H -v LatLon ~/nco/data/in.nc Lat[0]=-45 Lon[0]=-180 LatLon[0]=0 Lat[0]=-45 Lon[1]=-90 LatLon[1]=1 Lat[0]=-45 Lon[2]=0 LatLon[2]=2 Lat[0]=-45 Lon[3]=90 LatLon[3]=3 Lat[1]=45 Lon[0]=-180 LatLon[4]=4 Lat[1]=45 Lon[1]=-90 LatLon[5]=5 Lat[1]=45 Lon[2]=0 LatLon[6]=6 Lat[1]=45 Lon[3]=90 LatLon[7]=7 % ncks --trd -C -H -v LatLon ~/out.nc Lat[0]=-45 Lon[0]=0 LatLon[0]=2 Lat[0]=-45 Lon[1]=90 LatLon[1]=3 Lat[0]=-45 Lon[2]=180 LatLon[2]=0 Lat[0]=-45 Lon[3]=270 LatLon[3]=1 Lat[1]=45 Lon[0]=0 LatLon[4]=6 Lat[1]=45 Lon[1]=90 LatLon[5]=7 Lat[1]=45 Lon[2]=180 LatLon[6]=4 Lat[1]=45 Lon[3]=270 LatLon[7]=5
The analogous commands to rotate all fields in a global dataset by 180 degrees in theother direction, i.e., from [0,360) to [−180,180), are:
ncks -O --msa -d lon,181.,360. -d lon,0.,180.0 in.nc out.nc ncap2 -O -s ’where(lon > 180) lon=lon-360’ out.nc out.nc
There are other workable, valid methods to rotate data, yet none are simpler nor more efficient than utilizing MSA user-ordering. Some final comments on applying this algorithm: Be careful to specify hemispheres that do not overlap, e.g., by inadvertently specifying coordinate ranges that both include Greenwich or the date-line. Some users will find using index-based rather than coordinate-based hyperslabs makes this clearer. 74 NCO 5.0.2-alpha07 User Guide
3.22 Wrapped Coordinates
Availability: ncks ¨ Short options: ‘-d dim,[min][,[max][,[stride]]]’ Long options: ‘--dimension dim,[min][,[max][,[stride]]]’, ‘--dmn dim,[min][,[max][,[stride]]]’
© A wrapped coordinate is a coordinate whose values increase or decrease monotonically (nothing unusual so far), but which represents a dimension that ends where it begins (i.e., wraps around on itself). Longitude (i.e., degrees on a circle) is a familiar example of a wrapped coordinate. Longitude increases to the East of Greenwich, England, where it is defined to be zero. Halfway around the globe, the longitude is 180 degrees East (orWest). Continuing eastward, longitude increases to 360 degrees East at Greenwich. The longitude values of most geophysical data are either in the range [0,360), or [−180,180). In either case, the Westernmost and Easternmost longitudes are numerically separated by 360 degrees, but represent contiguous regions on the globe. For example, the Saharan desert stretches from roughly 340 to 50 degrees East. Extracting the hyperslab of data representing the Sahara from a global dataset presents special problems when the global dataset is stored consecutively in longitude from 0 to 360 degrees. This is because the data for the Sahara will not be contiguous in the input-file but is expected by the user to be contiguous in the output-file. In this case, ncks must invoke special software routines to assemble the desired output hyperslab from multiple reads of the input-file.
Assume the domain of the monotonically increasing longitude coordinate lon is 0 < lon < 360. ncks will extract a hyperslab which crosses the Greenwich meridian simply by specifying the westernmost longitude as min and the easternmost longitude as max. The following commands extract a hyperslab containing the Saharan desert:
ncks -d lon,340.,50. in.nc out.nc ncks -d lon,340.,50. -d lat,10.,35. in.nc out.nc
The first example selects data in the same longitude range as the Sahara. Thesecond example further constrains the data to having the same latitude as the Sahara. The coor- dinate lon in the output-file, out.nc, will no longer be monotonic! The values of lon will be, e.g., ‘340, 350, 0, 10, 20, 30, 40, 50’. This can have serious implications should you run out.nc through another operation which expects the lon coordinate to be monotoni- cally increasing. Fortunately, the chances of this happening are slim, since lon has already been hyperslabbed, there should be no reason to hyperslab lon again. Should you need to hyperslab lon again, be sure to give dimensional indices as the hyperslab arguments, rather than coordinate values (see Section 3.16 [Hyperslabs], page 63).
3.23 Auxiliary Coordinates Chapter 3: Shared Features 75
Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat ¨ Short options: ‘-X lon_min,lon_max,lat_min,lat_max’ Long options: ‘--auxiliary lon_min,lon_max,lat_min,lat_max’
Utilize auxiliary coordinates specified in values of the coordinate variable’s standard_ © name attributes, if any, when interpreting hyperslab and multi-slab options. Also ‘--auxiliary’. This switch supports hyperslabbing cell-based grids (aka unstructured grids) over coordinate ranges. When these grids are stored as 1D-arrays of cell data, this feature is helpful at hyperslabbing and/or performing arithmetic on selected geo- graphic regions. This feature cannot be used to select regions of 2D grids (instead use the ncap2 where statement for such grids Section 4.1.14 [Where statement], page 179). This feature works on datasets that associate coordinate variables to grid-mappings us- ing the CF-convention (see Section 3.45 [CF Conventions], page 146) coordinates and standard_name attributes described here (http://cfconventions.org/cf-conventions/ cf-conventions.html#coordinate-system). Currently, NCO understands auxiliary coor- dinate variables pointed to by the standard_name attributes for latitude and longitude. Cells that contain a value within the user-specified West-East-South-North (aka WESN) bounding box [lon min,lon max,lat min,lat max] are included in the output hyperslab.
The sides of the WESN) bounding box must be specified in degrees (not radians). The specified coordinates must be within the valid data range. This includes boxes thatwrap the origin of the longitude coordinate. For example, if the longitude coordinate is stored in [0,360], then a bounding box that straddles the Greenwich meridian in Africa would be specified as, e.g., ,[350 10, −20, 20], not as [350, 370, −20, 20]. A cell-based or unstructured grid collapses the horizontal spatial information (latitude and longitude) and stores it along a one-dimensional coordinate that has a one-to-one map- ping to both latitude and longitude coordinates. Rectangular (in longitude and latitude) horizontal hyperslabs cannot be selected using the typical procedure (see Section 3.16 [Hy- perslabs], page 63) of separately specifying ‘-d’ arguments for longitude and latitude. In- stead, when the ‘-X’ is used, NCO learns the names of the latitude and longitude coordinates by searching the standard_name attribute of all variables until it finds the two variables whose standard_name’s are “latitude” and “longitude”, respectively. This standard_name attribute for latitude and longitude coordinates follows the CF-convention (see Section 3.45 [CF Conventions], page 146). Putting it all together, consider a variable gds 3dvar output from simulations on a cell- based geodesic grid. Although the variable contains three dimensions of data (time, latitude, and longitude), it is stored in the netCDF file with only two dimensions, time and gds_crd. % ncks -m -C -v gds_3dvar ~/nco/data/in.nc gds_3dvar: type NC_FLOAT, 2 dimensions, 4 attributes, chunked? no, \ compressed? no, packed? no, ID = 41 gds_3dvar RAM size is 10*8*sizeof(NC_FLOAT) = 80*4 = 320 bytes gds_3dvar dimension 0: time, size = 10 NC_DOUBLE, dim. ID = 20 \ (CRD)(REC) gds_3dvar dimension 1: gds_crd, size = 8 NC_FLOAT, dim. ID = 17 (CRD) gds_3dvar attribute 0: long_name, size = 17 NC_CHAR, value = \ 76 NCO 5.0.2-alpha07 User Guide
Geodesic variable gds_3dvar attribute 1: units, size = 5 NC_CHAR, value = meter gds_3dvar attribute 2: coordinates, size = 15 NC_CHAR, value = \ lat_gds lon_gds gds_3dvar attribute 3: purpose, size = 64 NC_CHAR, value = \ Test auxiliary coordinates like those that define geodesic grids
The coordinates attribute lists the names of the latitude and longitude coordinates, lat_gds and lon_gds, respectively. The coordinates attribute is recommended though optional. With it, the user can immediately identify which variables contain the latitude and longitude coordinates. Without a coordinates attribute it would be unclear at first glance whether a variable resides on a cell-based grid. In this example, time is a normal record dimension and gds_crd is the cell-based dimension.
The cell-based grid file must contain two variables whose standard_name attributes are “latitude”, and “longitude”:
% ncks -m -C -v lat_gds,lon_gds ~/nco/data/in.nc lat_gds: type NC_DOUBLE, 1 dimensions, 4 attributes, \ chunked? no, compressed? no, packed? no, ID = 37 lat_gds RAM size is 8*sizeof(NC_DOUBLE) = 8*8 = 64 bytes lat_gds dimension 0: gds_crd, size = 8 NC_FLOAT, dim. ID = 17 (CRD) lat_gds attribute 0: long_name, size = 8 NC_CHAR, value = Latitude lat_gds attribute 1: standard_name, size = 8 NC_CHAR, value = latitude lat_gds attribute 2: units, size = 6 NC_CHAR, value = degree lat_gds attribute 3: purpose, size = 62 NC_CHAR, value = \ 1-D latitude coordinate referred to by geodesic grid variables
lon_gds: type NC_DOUBLE, 1 dimensions, 4 attributes, \ chunked? no, compressed? no, packed? no, ID = 38 lon_gds RAM size is 8*sizeof(NC_DOUBLE) = 8*8 = 64 bytes lon_gds dimension 0: gds_crd, size = 8 NC_FLOAT, dim. ID = 17 (CRD) lon_gds attribute 0: long_name, size = 9 NC_CHAR, value = Longitude lon_gds attribute 1: standard_name, size = 9 NC_CHAR, value = longitude lon_gds attribute 2: units, size = 6 NC_CHAR, value = degree lon_gds attribute 3: purpose, size = 63 NC_CHAR, value = \ 1-D longitude coordinate referred to by geodesic grid variables
In this example lat_gds and lon_gds represent the latitude or longitude, respectively, of cell-based variables. These coordinates (must) have the same single dimension (gds_crd, in this case) as the cell-based variables. And the coordinates must be one-dimensional— multidimensional coordinates will not work.
This infrastructure allows NCO to identify, interpret, and process (i.e., hyperslab) the variables on cell-based grids as easily as it works with regular grids. To time-average all the values between zero and 180 degrees longitude and between plus and minus 30 degress latitude, we use
ncra -O -X 0.,180.,-30.,30. -v gds_3dvar in.nc out.nc Chapter 3: Shared Features 77
NCO accepts multiple ‘-X’ arguments for cell-based grid multi-slabs, just as it accepts multiple ‘-d’ arguments for multi-slabs of regular coordinates. ncra -O -X 0.,180.,-30.,30. -X 270.,315.,45.,90. in.nc out.nc The arguments to ‘-X’ are always interpreted as floating-point numbers, i.e., as coordi- nate values rather than dimension indices so that these two commands produce identical results ncra -X 0.,180.,-30.,30. in.nc out.nc ncra -X 0,180,-30,30 in.nc out.nc By contrast, arguments to ‘-d’ require decimal places to be recognized as coordinates not indices (see Section 3.16 [Hyperslabs], page 63). We recommend always using decimal points with ‘-X’ arguments to avoid confusion.
3.24 Grid Generation
Availability: ncks ¨ Short options: None Long options: ‘--rgr key=val’ (multiple invocations allowed)
As of NCO version 4.5.2 (August, 2015), ncks generates accurate and complete SCRIP- © format gridfiles for select grid types, including uniform, capped and Gaussian rectangular, latitude/longitude grids, global or regional. The grids are stored in an external grid-file. All options pertinent to the grid geometry and metadata are passed to NCO via key-value pairs prefixed by the--rgr ‘ ’ option, or its synonym, ‘--regridding’. The option ‘--rgr’ (and its long option equivalents such as ‘--regridding’) indicates the argument syntax will be key=val. As such, ‘--rgr’ and its synonyms are indicator options that accept arguments supplied one-by-one like ‘--rgr key1=val1 --rgr key2=val2’, or aggregated together in multi-argument format like ‘--rgr key1=val1#key2=val2’ (see Section 3.4.2 [Multi-arguments], page 32). The text strings that describe the grid and name the file are important aids to convey the grid geometry to other users. These arguments, and their corresponding keys, are the grid title (grd ttl), and grid filenamegrid ( ), respectively. The numbers of latitudes (lat nbr) and longitudes (lon nbr) are independent, and together determine the grid storage size. These four options should be considered mandatory, although NCO provides defaults for any arguments omitted. The remaining arguments depend on the whether the grid is global or regional. For global grids, one should specify only two more arguments, the latitude (lat typ) and longitude (lon typ) grid-types. These types are chosen as described below from a small selection of options that together define the most common rectangular global grids. For regional grids, one must specify the bounding box, i.e., the edges of the rectangular grid on the North (lat nrt), South (lat sth), East (lat est), and West (lat nrt) sides. Specifying a bounding box for global grids is redundant and will cause an error to ensure the user intends a global grid. NCO assumes that regional grids are uniform, though it will attempt to produce 78 NCO 5.0.2-alpha07 User Guide
regional grids of other types if the user specifies other latitudelat ( typ) and longitude (lon typ) grid-types, e.g., Gaussian or Cap. Edges of a regional bounding box may be specified individually, or in the single-argument forms. The full description of grid-generation arguments, and their corresponding keys, is:
Grid Title: grd ttl It is surprisingly difficult to discern the geometric configuration of a gridfrom the coordinates of a SCRIP-format gridfile. A human-readable grid description should be placed in grd ttl. Examples include “CAM-FV scalar grid 129x256” and “T42 Gaussian grid”. Grid File: scrip grid The grid-generation API was bolted-on to NCO and contains some tempo- rary kludges. For example, the output grid filename is distinct from the out- put filename of the host ncks command. Specify the output gridfile name scrip grid with keywords grid or scrip, e.g., ‘--rgr grid=scrip_grid’ or ‘--rgr scrip=t42_SCRIP.20150901.nc’. It is conventional to include a dates- tamp in the gridfile name. This helps users identify up-to-date and out-of-date grids. Any valid netCDF file may be named as the source (e.g., in.nc). It will not be altered. The destination file (e.g., foo.nc) will be overwritten. Its contents are immaterial. Grid Types: lat typ, lon typ The keys that hold the longitude and latitude gridtypes (which are, by the way, independent of eachother) are lon typ and lat typ. The lat typ options for global grids are ‘uni’ for Uniform, ‘cap’ (or ‘fv’) for Cap19, and ‘gss’ for Gaussian. These values are all case-independent, so ‘Gss’ and ‘gss’ both work. As of version 4.7.7 (September, 2018), NCO generates perfectly symmetric interface latitudes for Gaussian grids. Previously the interface latitude generation mech- anism could accumulate small rounding errors (~1.0e-14). Now symmetry prop- erties are used to ensure perfect symmetry. All other Gaussian grids we have seen compute interfaces as the arithmetic mean of the adjacent Gaussian lati- tudes, which is patently wrong. To our knowledge NCO is the only map soft- ware that generates accurate interface latitudes for a Gaussian grid. We use a Newton-Raphson iteration technique to identify the interface latitudes that enclose the area indicated by the Gaussian weight. As its name suggests, the latitudes in a Uniform-latitude grid are uniformly spaced20. The Uniform-latitude grid may have any number of latitudes. NCO
19 The term FV confusing because it is correct to call any Finite Volume grid (including arbitrary polygons) an FV grid. However, an FV grid has also been used for many years to described the particular type of rectangular grid with caps at the poles used to discretize global model grids for use with the Lin- Rood dynamical core. To reduce confusion, we use “Cap grid” to refer to the latter and reserv FV as a straightforward acronym for Finite Volume. 20 A Uniform grid in latitude could be called “equi-angular” in latitude, but NCO reserves the term Equi- angular or “eqa” for grids that have the same uniform spacing in both latitude and longitude, e.g., 1◦x1◦ or 2◦x2◦. NCO reserves the term Regular to refer to grids that are monotonic and rectangular grids. Confusingly, the angular spacing in a Regular grid need not be uniform, it could be irregular, such as in a Chapter 3: Shared Features 79
can only generate longitude grids (below) that are uniformly spaced, so the Uniform-latitude grids we describe are also uniform in the 2D sense. Uni- form grids are intuitive, easy to visualize, and simple to program. Hence their popularity in data exchange, visualization, and archives. Moreover, regional grids (unless they include the poles), are free of polar singularities, and thus are well-suited to storage on Uniform grids. Theoretically, a Uniform-latitude grid could have non-uniform longitudes, but NCO currently does not implement non-uniform longitude grids. Their mathematical properties (convergence and excessive resolution at the poles, which can appear as singularities) make Uniform grids fraught for use in global models. One purpose Uniform grids serve in modeling is as “offset” or “staggered” grids, meaning grids whose centers are the interfaces of another grid. The Finite-Volume (FV) method is often used to represent and solve the equations of motion in climate-related fields. Many FV solutions (includ- ing the popular Lin-Rood method as used in the CESM CAM-FV atmospheric model) evaluate scalar (i.e., non-vector) fields (e.g., temperature, water vapor) at gridcell centers of what is therefore called the scalar grid. FV methods (like Lin-Rood) that employ an Arakawa C-grid or D-grid formulation define veloc- ities on the edges of the scalar grid. This CAM-FV velocity grid is therefore “staggered” or “offset” from the CAM-FV scalar grid by one-half gridcell. The CAM-FV scalar latitude grid has gridpoints (the “caps”) centered on each pole to avoid singularities. The offset of a Cap-grid is a Uniform-grid, so theUni- form grid is often called an FV-”offset” or “staggered” grid. Hence an NCO Uniform grid is equivalent to an NCL “Fixed Offset” grid. For example, a 128x256 Uniform grid is the offset or staggered version of a 129x256 Capgrid (aka FV-grid). Referring the saucer-like cap-points at the poles, NCO uses the term “Cap grid” to describe the latitude portion of the FV-scalar grid as used by the CAM-FV Lin-Rood dynamics formulation. NCO accepts the shorthand FV, and the more descriptive “Yarmulke”, as synonyms for Cap. A Cap-latitude grid differs from a Uniform-latitude grid in many ways: Most importantly, Cap grids are 2D-representations of numerical grids with cap-midpoints instead of zonal-teeth convergence at the poles. The rectangular 2D-representation of each cap contains gridcells shaped like sharp teeth that converge at the poles similar to the Uniform grid, but the Cap gridcells are meant to be aggregated into a single cell centered at the pole in a dynamical transport algorithm. In other words, the polar teeth are a convenient way to encode a non-rectangular grid in memory into a rectangular array on disk. Hence Cap grids have the unusual property that the poles are labeled as being both the centers and the outer interfaces of all polar gridcells. Second, Cap grids are uniform in angle except at the poles, where the latitudes span half the meridional range of the rest of the gridcells. Even though in the host dynamical model the Cap grid polar points are melded into caps uniform (in angle) with the rest of the grid, the disk representation on disk is not uniform. Nevertheless,
Gaussian grid. The term Regular is not too useful in grid-generation, because so many other parameters (spacing, centering) are necessary to disambiguate it. 80 NCO 5.0.2-alpha07 User Guide
some call the Cap grid a uniform-angle grid because the information contained at the poles is aggregated in memory to span twice the range of a single polar gridcell (which has half the normal width). NCL uses the term “Fixed grid” for a Cap grid. The “Fixed” terminology seems broken. Finally, Gaussian grids are the Cartesian representation of global spectral trans- form models. Gaussian grids typically have an even number of latitudes and so do not have points at the poles. All three latitude grid-type supported by NCO (Uniform, Cap, and Gaussian) are Regular grids in that they are monotonic. The lon typ options for global grids are ‘grn_ctr’ and ‘180_ctr’ for the first gridcell centered at Greenwich or 180 degrees, respecitvely. And ‘grn_wst’ and ‘180_wst’ for Greenwich or 180 degress lying on the western edge of the first gridcell. Many global models use the ‘grn_ctr’ longitude grid as their “scalar grid” (where, e.g., temperature, humidity, and other scalars are defined). The “staggered” or “offset” grid (where often the dynamics variables are defined) then must have the ‘grn_wst’ longitude convention. That way the centers of the scalar grid are the vertices of the offset grid, and visa versa. Grid Resolution: lat nbr, lon nbr The number of gridcells in the horizontal spatial dimensions are lat nbr and lon nbr, respectively. There are no restrictions on lon nbr for any gridtype. Latitude grids do place some restrictions on lat nbr (see above). As of NCO version 4.5.3, released in October, 2015, the ‘--rgr latlon=lat_nbr,lon_nbr’ switch may be used to simultaneously specify both latitude and longitude, e.g., ‘--rgr latlon=180,360’. Latitude Direction: lat drc The lat drc option is specifies whether latitudes monotonically increase orde- crease in rectangular grids. The two possible values are ‘s2n’ for grids that begin with the most southerly latitude and end with the most northerly, and ‘n2s’ for grids that begin with the most northerly latitude and end with the most southerly. By default NCO creates grids whose latitudes run south-to- north. Hence this option is only necessary to create a grid whose latitudes run north-to-south. Grid Edges: lon wst, lon est, lat sth, lat nrt The outer edges of a regional rectangular grid are specified by the North (lat nrt), South (lat sth), East (lat est), and West (lat nrt) sides. Latitudes and longigudes must be specified in degrees (not radians). Latitude edges must be between -90 and 90. Longitude edges may be positive or negative and sep- arated by no more than 360 degrees. The edges may be specified individually with four arguments, consecutively separated by the multi-argument delimiter (‘#’ by default), or together in a short list to the pre-ordered options ‘wesn’ or ‘snwe’. These three specifications are equivalent: ncks ... --rgr lat_sth=30.0 --rgr lat_nrt=70.0 --rgr lon_wst=-120.0 --rgr lon_est=-90.0 ... ncks ... --rgr lat_sth=30.0#lat_nrt=70.0#lon_wst=-120.0#lon_est=-90.0 ... ncks ... --rgr snwe=30.0,70.0,-120.0,-90.0 ... The first example above supplies the bounding box with four key=val pairs. The second example above supplies the bounding box with a single option in multi-argument format Chapter 3: Shared Features 81
(see Section 3.4.2 [Multi-arguments], page 32). The third example uses a convenience switch introduced to reduce typing. Generating common grids: # Through version 4.7.5 (August, 2018), ncks performed grid-generation # 180x360 (1x1 degree) Equi-Angular grid, first longitude centered at Greenwich ncks --rgr ttl=’Equi-Angular grid 180x360’#latlon=180,360#lat_typ=uni#lon_typ=grn_ctr \ --rgr scrip=${DATA}/grids/180x360_SCRIP.20150901.nc \ ~zender/nco/data/in.nc ~/foo.nc
# As of version 4.7.6 (August, 2018), ncremap supports more concise commands ncremap -G ttl=’Equi-Angular grid 180x360’#latlon=180,360#lat_typ=uni#lon_typ=grn_ctr \ -g ${DATA}/grids/180x360_SCRIP.20180901.nc
# 180x360 (1x1 degree) Equi-Angular grid, first longitude west edge at Greenwich ncremap -G ttl=’Equi-Angular grid 180x360’#latlon=180,360#lat_typ=uni#lon_typ=grn_wst \ -g ${DATA}/grids/180x360wst_SCRIP.20180301.nc
# 129x256 CAM-FV grid, first longitude centered at Greenwich ncremap -G ttl=’CAM-FV scalar grid 129x256’#latlon=129,256#lat_typ=fv#lon_typ=grn_ctr \ -g ${DATA}/grids/129x256_SCRIP.20150901.nc
# 192x288 CAM-FV grid, first longitude centered at Greenwich ncremap -G ttl=’CAM-FV scalar grid 192x288’#latlon=192,288#lat_typ=fv#lon_typ=grn_ctr \ -g ${DATA}/grids/192x288_SCRIP.20160301.nc
# 361x576 NASA MERRA2 FV grid, first longitude centered at DateLine ncremap -G ttl=’NASA MERRA2 Cap grid 361x576’#latlon=361,576#lat_typ=cap#lon_typ=180_ctr \ -g ${DATA}/grids/merra2_361x576.20201001.nc
# 1441x2880 CAM-FV grid, first longitude centered at Greenwich ncremap -G ttl=’CAM-FV scalar grid 1441x2880’#latlon=1441,2880#lat_typ=fv#lon_typ=grn_ctr \ -g ${DATA}/grids/1441x2880_SCRIP.20170901.nc
# 1440x2880 MOSART grid, first longitude west edge at DateLine ncremap -7 -L 1 \ -G ttl=’MOSART 1440x2880’#latlon=1440,2880#lat_typ=uni#lon_typ=180_wst \ -g ${DATA}/grids/r0125_1440x2880.20210401.nc
# 91x180 CAM-FV grid, first longitude centered at Greenwich (2 degree grid) ncremap -G ttl=’CAM-FV scalar grid 91x180’#latlon=91,180#lat_typ=fv#lon_typ=grn_ctr \ -g ${DATA}/grids/91x180_SCRIP.20170401.nc
# 25x48 CAM-FV grid, first longitude centered at Greenwich (7.5 degree grid) ncremap -G ttl=’CAM-FV scalar grid 25x48’#latlon=25,48#lat_typ=fv#lon_typ=grn_ctr \ -g ${DATA}/grids/25x48_SCRIP.20170401.nc 82 NCO 5.0.2-alpha07 User Guide
# 128x256 Equi-Angular grid, Greenwich west edge of first longitude # CAM-FV offset grid for 129x256 CAM-FV scalar grid above ncremap -G ttl=’Equi-Angular grid 128x256’#latlon=128,256#lat_typ=uni#lon_typ=grn_wst \ -g ${DATA}/grids/128x256_SCRIP.20150901.nc
# T42 Gaussian grid, first longitude centered at Greenwich ncremap -G ttl=’T42 Gaussian grid’#latlon=64,128#lat_typ=gss#lon_typ=grn_ctr \ -g ${DATA}/grids/t42_SCRIP.20180901.nc
# T62 Gaussian grid, first longitude centered at Greenwich, NCEP2 T62 Gaussian grid ncremap -G ttl=’NCEP2 T62 Gaussian grid’#latlon=94,192#lat_typ=gss#lon_typ=grn_ctr#lat_drc=n2s \ -g ${DATA}/grids/ncep2_t62_SCRIP.20191001.nc
# F256 Full Gaussian grid, first longitude centered at Greenwich ncremap -7 -L 1 \ -G ttl=’ECMWF IFS F256 Full Gaussian grid 512x1024’#latlon=512,1024#lat_typ=gss#lon_typ=grn_ctr#lat_drc=n2s \ -g ${DATA}/grids/f256_scrip.20201001.nc
# 513x1024 FV grid, first longitude centered at Greenwich ncremap -7 -L 1 \ -G ttl=’FV scalar grid 513x1024’#latlon=513,1024#lat_typ=fv#lon_typ=grn_ctr \ -g ${DATA}/grids/513x1024_SCRIP.20201001.nc
# 1025x2048 FV grid, first longitude centered at Greenwich ncremap -7 -L 1 \ -G ttl=’FV scalar grid 1025x2048’#latlon=1025,2048#lat_typ=fv#lon_typ=grn_ctr \ -g ${DATA}/grids/1025x2048_SCRIP.20201001.nc
# F640 Full Gaussian grid, first longitude centered at Greenwich ncremap -7 -L 1 \ -G ttl=’ECMWF IFS F640 Full Gaussian grid 1280x2560’#latlon=1280,2560#lat_typ=gss#lon_typ=grn_ctr#lat_drc=n2s \ -g ${DATA}/grids/f640_scrip.20190601.nc
# NASA Climate Modeling Grid (CMG) 3600x7200 (0.05x0.05 degree) Equi-Angular grid # Date-line west edge of first longitude, east edge of last longitude # Write to compressed netCDF4-classic file to reduce filesize ~140x from 2.2 GB to 16 MB ncremap -7 -L 1 \ -G ttl=’Equi-Angular grid 3600x7200 (NASA CMG)’#latlon=3600,7200#lat_typ=uni#lon_typ=180_wst \ -g ${DATA}/grids/3600x7200_SCRIP.20160301.nc
# DOE E3SM/ACME High Resolution Topography (1 x 1 km grid) for Elevation Classes # Write to compressed netCDF4-classic file to reduce filesize from ~85 GB to 607 MB ncremap -7 -L 1 \ -G ttl=’Global latxlon = 18000x36000 ~1 x 1 km’#latlon=18000,36000#lat_typ=uni#lon_typ=grn_ctr \ -g ${DATA}/grids/grd_18000x36000_SCRIP.nc
# 1x1 degree Equi-Angular Regional grid over Greenland, centered longitudes Chapter 3: Shared Features 83
ncremap -G ttl=’Equi-Angular Greenland 1x1 degree grid’#latlon=30,90#snwe=55.0,85.0,-90.0,0.0#lat_typ=uni#lon_typ=grn_ctr \ -g ${HOME}/greenland_1x1.nc
# 721x1440 ECMWF ERA5 resolution ncremap -7 --dfl_lvl=1 -G ttl=’Cap/FV ECMWF ERA5 grid 0.25x0.25 degree, dimensions 721x1440, cell centers on Poles/Equator and Prime Meridian/Date Line’#latlon=721,1440#lat_drc=n2s#lat_typ=cap#lon_typ=grn_ctr \ -g ${DATA}/grids/era5.nc
# 105x401 Greenland ERA5 ncremap -G ttl=’Equi-Angular Greenland 0.25x0.25 degree ERA5 north-to-south grid’#latlon=105,401#snwe=58.875,85.125,-87.125,13.125#lat_typ=uni#lat_drc=n2s#lon_typ=grn_ctr \ -g ${DATA}/grids/greenland_0.25x0.25_era5.nc
# Greenland r025 with SNWE = 59,84,-73,-11 (in round numbers) with RACMO ice mask ncremap -G ttl=’Equi-Angular Greenland 0.25x0.25 degree r025 south-to-north grid’#latlon=100,250#snwe=58.875,83.875,-73.25,-10.75#lat_typ=uni#lat_drc=s2n#lon_typ=grn_ctr \ -g ${DATA}/grids/greenland_r025_100x250.nc
# NASA Climate Modeling Grid (CMG) 3600x7200 (0.05x0.05 degree, 3’x3’) Equi-Angular grid # With land mask derived mainly from GLOBE 30" topography and anywhere Gardner 30" land ice data is valid # Date-line west edge of first longitude, east edge of last longitude # Write to compressed netCDF4-classic file to reduce filesize ~140x from 2.2 GB to 16 MB ncremap -7 -L 1 \ -G ttl=’Equi-Angular grid 3-minute=0.05 degree resolution = 3600x7200, NASA CMG boundaries, with land mask derived mainly from GLOBE 30" topography and anywhere Gardner 30" land ice data is valid’#latlon=3600,7200#lat_typ=uni#lon_typ=180_wst \ -g ${DATA}/grids/r005_3600x7200_globe_gardner_landmask.20210501.nc Often researchers face the problem not of generating a known, idealized grid but of understanding an unknown, possibly irregular or curvilinear grid underlying a dataset pro- duced elsewhere. NCO will infer the grid of a datafile by examining its coordinates (and boundaries, if available), reformat that information as necessary to diagnose gridcell areas, and output the results in SCRIP format. As of NCO version 4.5.3, released in October, 2015, the ‘--rgr infer’ flag activates the machinery to infer the grid rather than construct the grid from other user-specified switches. To infer the grid properties, NCO interrogates input-file for horizontal coordinate information, such as the presence of dimension names rooted in latitude/longitude-naming traditions and conventions. Once NCO identifies the likely horizontal dimensions it looks for horizontal coordinates and bounds. If bounds are not found, NCO assumes the underlying grid comprises quadrilateral cells whose edges are midway between cell centers, for both rectilinear and curvilinear grids. # Infer AIRS swath grid from input, write it to grd_scrip.nc ncks --rgr infer --rgr scrip=${DATA}/sld/rgr/grd_scrip.nc \ ${DATA}/sld/raw/AIRS.2014.10.01.202.L2.TSurfStd.Regrid010.1DLatLon.nc ~/foo.nc When inferring grids, the grid filegrd_scrip.nc ( ) is written in SCRIP format, the input fileAIRS...nc ( ) is read, and the output file (foo.nc) is overwritten (its contents are immaterial). As of NCO version 4.6.6, released in April, 2017, inferred 2D rectangular grids may also be written in UGRID-format (defined here (http://ugrid-conventions.github.io/ ugrid-conventions)). Request a UGRID mesh with the option ‘--rgr ugrid=fl_ugrid’. Currently both UGRID and SCRIP grids must be requested in order to produce the UGRID output, e.g., 84 NCO 5.0.2-alpha07 User Guide
ncks --rgr infer --rgr ugrid=${HOME}/grd_ugrid.nc \ --rgr scrip=${HOME}/grd_scrip.nc ~/skl_180x360.nc ~/foo.nc The SCRIP gridfile and UGRID meshfile metadata produced for the equiangular 1-by-1 degree global grid are: zender@aerosol:~$ ncks -m ~/grd_scrip.nc netcdf grd_scrip { dimensions: grid_corners = 4 ; grid_rank = 2 ; grid_size = 64800 ;
variables: double grid_area(grid_size) ; grid_area:units = "steradian" ;
double grid_center_lat(grid_size) ; grid_center_lat:units = "degrees" ;
double grid_center_lon(grid_size) ; grid_center_lon:units = "degrees" ;
double grid_corner_lat(grid_size,grid_corners) ; grid_corner_lat:units = "degrees" ;
double grid_corner_lon(grid_size,grid_corners) ; grid_corner_lon:units = "degrees" ;
int grid_dims(grid_rank) ;
int grid_imask(grid_size) ; } // group /
zender@aerosol:~$ ncks -m ~/grd_ugrid.nc netcdf grd_ugrid { dimensions: maxNodesPerFace = 4 ; nEdges = 129240 ; nFaces = 64800 ; nNodes = 64442 ; two = 2 ;
variables: int mesh ; mesh:cf_role = "mesh_topology" ; mesh:standard_name = "mesh_topology" ; mesh:long_name = "Topology data" ; Chapter 3: Shared Features 85
mesh:topology_dimension = 2 ; mesh:node_coordinates = "mesh_node_x mesh_node_y" ; mesh:face_node_connectivity = "mesh_face_nodes" ; mesh:face_coordinates = "mesh_face_x mesh_face_y" ; mesh:face_dimension = "nFaces" ; mesh:edge_node_connectivity = "mesh_edge_nodes" ; mesh:edge_coordinates = "mesh_edge_x mesh_edge_y" ; mesh:edge_dimension = "nEdges" ;
int mesh_edge_nodes(nEdges,two) ; mesh_edge_nodes:cf_role = "edge_node_connectivity" ; mesh_edge_nodes:long_name = "Maps every edge to the two nodes that it connects" ; mesh_edge_nodes:start_index = 0 ;
double mesh_edge_x(nEdges) ; mesh_edge_x:standard_name = "longitude" ; mesh_edge_x:long_name = "Characteristic longitude of 2D mesh face" ; mesh_edge_x:units = "degrees_east" ;
double mesh_edge_y(nEdges) ; mesh_edge_y:standard_name = "latitude" ; mesh_edge_y:long_name = "Characteristic latitude of 2D mesh face" ; mesh_edge_y:units = "degrees_north" ;
int mesh_face_nodes(nFaces,maxNodesPerFace) ; mesh_face_nodes:cf_role = "face_node_connectivity" ; mesh_face_nodes:long_name = "Maps every face to its corner nodes" ; mesh_face_nodes:start_index = 0 ; mesh_face_nodes:_FillValue = -2147483648 ;
double mesh_face_x(nFaces) ; mesh_face_x:standard_name = "longitude" ; mesh_face_x:long_name = "Characteristic longitude of 2D mesh edge" ; mesh_face_x:units = "degrees_east" ;
double mesh_face_y(nFaces) ; mesh_face_y:standard_name = "latitude" ; mesh_face_y:long_name = "Characteristic latitude of 2D mesh edge" ; mesh_face_y:units = "degrees_north" ;
double mesh_node_x(nNodes) ; mesh_node_x:standard_name = "longitude" ; mesh_node_x:long_name = "Longitude of mesh nodes" ; mesh_node_x:units = "degrees_east" ;
double mesh_node_y(nNodes) ; mesh_node_y:standard_name = "latitude" ; 86 NCO 5.0.2-alpha07 User Guide
mesh_node_y:long_name = "Latitude of mesh nodes" ; mesh_node_y:units = "degrees_north" ; } // group / Another task that arises in regridding is characterizing new grids. In such cases it can be helpful to have a “skeleton” version of a dataset on the grid, so that grid center and interfaces locations can be assessed, continental outlines can be examined, or the skeleton can be manually populated with data rather than relying on a model. SCRIP files can be difficult to visualize and manipulate, so NCO will provide, if requested, a so-called skeleton file on the user-specified grid. Asof NCO version 4.5.3, released in October, 2015, the ‘--rgr skl=fl_skl’ switch outputs the skeleton file to fl skl. The skeleton file may then be examined in a dataset viewer, populated with data, and generally serve as a template for what to expect from datasets of the same geometry. # Generate T42 Gaussian grid file t42_SCRIP.nc and skeleton file t42_skl.nc ncks --rgr skl=${DATA}/grids/t42_skl.nc --rgr scrip=${DATA}/grids/t42_SCRIP.nc \ --rgr latlon=64,128#lat_typ=gss#lon_typ=Grn_ctr \ ~zender/nco/data/in.nc ~/foo.nc When generating skeleton files, both the grid filet42_SCRIP.nc ( ) and the skeleton file (t42_skl.nc) are written, the input file (in.nc) is ignored, and the output file (foo.nc) is overwritten (its contents are immaterial).
3.25 Regridding
Availability: ncclimo, ncks, ncremap ¨ Short options: None Long options: ‘--map map-file’ or ‘--rgr_map map-file’ ‘--rgr key=val’ (multiple invocations allowed) ‘--rnr=rnr_thr’ or ‘--rgr_rnr=rnr_thr’ or ‘--renormalize=rnr_thr’ or ‘--renormalization_threshold=rnr_thr’
NCO includes extensive regridding features in ncclimo (as of version 4.6.0 in May, 2016), © ncremap (as of version 4.5.4 in November, 2015) and ncks (since version 4.5.0 in June, 2015). Regridding can involve many choices, options, inputs, and outputs. The appropriate operator for this workflow is the ncremap script which automatically handles many details of regridding and passes the required commands to ncks and external programs. Occasionally users need access to lower-level remapping functionality present in ncks and not exposed to direct manipulation through ncremap or ncclimo. This section describes the lower-level functionality and switches as implemented in ncks. Knowing what these features are will help ncremap and ncclimo users understand the full potential of these operators. ncks supports horizontal regridding of datasets where the grids and weights are all stored in an external map-file. Use the ‘--map’ or ‘--rgr_map’ options to spec- ify the map-file, and NCO will regrid the input-file to a new (or possibly the same, aka, an identity mapping) horizontal grid in the output-file, using the input and output grids and mapping weights specified in the ESMF- or SCRIP-format map-file. Currently Chapter 3: Shared Features 87
NCO understands the mapfile formats pioneered by SCRIP (http://oceans11.lanl.gov/ svn/SCRIP/trunk/SCRIP) and later extended by ESMF (http://www.earthsystemcog. org/projects/regridweightgen), and adopted (along with Exodus) by TempestRemap (https://github.com/ClimateGlobalChange/tempestremap.git). Those references doc- ument quirks in their respectively weight-generation algorithms as to map formats, grid specification, and weight generation. NCO itself produces map-files in the for- mat recommended by CMIP6 and described here (https://docs.google.com/document/ d/1BfVVsKAk9MAsOYstwFSWI2ZBt5mrO_Nmcu7rLGDuL08). This format differs from ESMF map-file format chiefly in that its metadata are slightly more evolved, self-descriptive, and standardized. Originally NCO supported only weight-application, which is what most people mean by “regridding”. As of version 4.9.0, released in December, 2019, NCO also supports weight- generation. Thus NCO can now apply weights generated by ESMF, NCO, SCRIP, and TempestRemap. NCO reads-in pre-stored weights from the map-file and applies them to (almost) every variable, thereby creating a regridded output-file. Specify regridding with a standard ncks command and options along with the additional specification of a map-file: # Regrid entire file, same output format as input: ncks --map=map.nc in.nc out.nc # Entire file, netCDF4 output: ncks -4 --map=map.nc in.nc out.nc # Deflated netCDF4 output ncks -4 -L 1 --map=map.nc in.nc out.nc # Selected variables ncks -v FS.?,T --map=map.nc in.nc out.nc # Threading ncks -t 8 --map=map.nc in.nc out.nc # Deflated netCDF4 output, threading, selected variables: ncks -4 -L 1 -t 8 -v FS.?,T --map=map.nc in.nc out.nc OpenMP threading works well with regridding large datasets. Threading improves throughput of regridding 1–10 GB files by factors of 2–5. Options specific to regridding are described below. NCO supports 1D⇒1D, 1D⇒2D, 2D⇒1D, and 2D⇒2D regridding for any unstructured 1D-grid and any rectangular 2D-grid. This has been tested by converting among and between Gaussian, equiangular, FV, unstructured cubed-sphere grids, and regionally refined grids. Support for irregular 2D- and regional grids (e.g., swath-like data) is planned. Renormalization Conservative regridding is, for first-order accurate algorithms, a straightforward procedure of identifying gridcell overlap and apportioning values correctly from source to destination. The presence of missing values forces a decision on how to handle destination gridcells where some but not all source cells are valid. NCO allows the user to choose between two distinct weight-application algorithms: “conservative” and “renormalized”. The “conservative” al- gorithm uses all valid data from the input grid on the output grid once and only once. Destination cells receive the weighted valid values of the source cells. This is conservative because the global integrals of the source and destination fields are equal. Another name 88 NCO 5.0.2-alpha07 User Guide
for the “conservative” weight-application method is therefore “integral-preserving”. The “renormalized” algorithm divides the destination value by the sum of the valid weights. This produces values equal to the mean of the valid input values, but extended to the entire destination gridcell. Thus renormalization is equivalent to extrapolating valid data to miss- ing regions. Another name for the “renormalized” weight-application method is therefore “mean-preserving”. Input and output integrals are unequal and renormalized regridding is not conservative. Both algorithms produce identical answers when no missing data maps to the destination gridcell. The renormalized algorithm is useful because it solves some problems, like producing physically unrealistic temperature values, at the expense of incurring others, like non- conservation. Many land and ocean modelers eschew unrealistic gridpoint values, and con- servative weight-application often produces “weird” values along coastlines or missing data gaps where state variables are regridded to/from small fractions of a gridcell. Renormaliza- tion ensures the output values are physically consistent, although the integral of their value times area is not preserved.
By default, NCO implements the “conservative” algorithm because it has useful proper- ties, is simpler to understand, and requires no additional parameters. To employ the “renor- malized” algorithm instead, use the ‘--rnr’, ‘--rgr_rnr’, ‘--rnr_thr’, or ‘--renormalize’ options to supply rnr thr, the threshold weight for valid destination values. Valid values must cover at least the fraction rnr thr of the destination gridcell to meet the threshold for a non-missing destination value. When rnr thr is exceeded, the mean valid value is renor- malized by the valid area and placed in the destination gridcell. If the valid area covers less than rnr thr, then the destination gridcell is assigned the missing value. Valid values of rnr thr range from zero to one. Keep in mind though, that this threshold is potentially a divisor, and values of zero or very near to zero can lead to floating-point underflow and divide-by-zero errors. For convenience NCO permits users to specify a rnr thr = 0.0 thresh- old weight. This indicates that any valid data should be represented and renormalized on the output grid. Also, renormalization can be explicitly prevented or turned-off by setting rnr thr to either of the values ‘off’ or ‘none’: ncks --map=map.nc in.nc out.nc # Conservative (integral-preserving) ncks --rnr=off --map=map.nc in.nc out.nc # Conservative (integral-preserving) ncks --rnr=0.1 --map=map.nc in.nc out.nc # Renormalized (mean-preserving with threshold) ncks --rnr=0.0 --map=map.nc in.nc out.nc # Renormalized (mean-preserving) The first and second examples use the default conservative algorithm. The third example specifies that valid values must cover at least 10% of the destination gridcell tomeetthe threshold for a non-missing destination value. With valid destination areas of, say 25% or 50%, the renormalized algorithm would produce destination values greater than the conservative algorithm by factors of four or two, respectively. In practice, it may make sense to use the default “conservative” algorithm when per- forming conservative regridding, and the “renormalized” algorithm when performing other regridding such as bilinear interpolation or nearest-neighbor. Another consideration is whether the fields being regridded are fluxes or state variables. For example, temperature (unlike heat) and concentrations (amount per unit volume) are not physically conserved quantities under areal-regridding so it often makes sense to interpolate them in a non- Chapter 3: Shared Features 89
conservative fashion, to preserve their fine-scale structure. Few researchers can digest the unphysical values of temperature that the “conservative” option will produce in regions rife with missing values. A counter-example is fluxes, which should be physically conserved under areal-regridding. One should consider both the type of field and its conservation properties when choosing a regridding strategy. The regridded value of a variable x at a destination location d can be generally repre- sented as Ps=N µ σ x x = s=1 s s,d s d Ps=N s=1 µsσs,d where xd is the d’th element of the regridded variable, xs is the s’th element of the raw (native grid) variable, µs = 1 if xs is valid and µs = 0 if xs is the missing value, and σs,d is the overlap weight of s’th source gridcell with the d’th destination gridcell, and N is the total number of source gridcells that overlap (partially or fully) with the destination gridcell. The number of overlap gridcells N is a property of the source and destination grids and the regridding algorithm. The weight-generation software determines N by “intersecting” the grids, taking into account higher-order (e.g., local gradient) contributions if the algo- rithm so-demands, and then generates the overlap weights σs,d accordingly. Both source and destination grids may indicate valid gridcells with a mask flag that is binary-valued, zero or one, such that ms = 1 (i.e., unmasked) for source gridcells allowed to contribute to the des- tination grid, and ms = 0 (i.e., masked) for gridcells that are forbidden from contributing to the destination grid. There are subtle distinctions between the mask flag ms, and the missing value flag µs. The mask flag ms does not appear in the formula above because the weight-generator produces no weights for masked source gridcells. Doing otherwise would waste storage space in the map-file, because such weights are, by definition, zero. Further- more the masks ms and md are time-invariant properties of the grids, whereas missing value fields µs (and thus µd) are potentially time-varying characteristics of the fields. Although µs should in theory be treated the same as ms when computing mapping weights σs,d, in practice this is not done. Different fields may have different patterns of missing values, and managing per-field map-files would be difficult, so traditionally all fields are remappedwith the same map-file. That said, it can make sense to treat flux fields and state-variable fields with distinct algorithms, so that a different map-file might be employed for each classof fields. Ps=N The weight-generation software normalizes σs,d such that s=1 σs,d = 1 when unmasked (ms = 1) source gridcells completely overlap the destination gridcell. In this case we also Ps=N have s=1 ms = N. Furthermore, if all contributing gridpoints are valid values (i.e., not Ps=N missing values) then µs = 1 so that s=1 µs = N. For complete overlap with no masked P values and no missing values, then µs = ms = σs,d = 1 and the generic averaging Ps=N expression above reduces to a simple weighted mean xd = s=1 σs,dxs.
Ps=N µ s σ x x = s=1 s s s,d s d Ps=N s=1 µsssσs,d 90 NCO 5.0.2-alpha07 User Guide
Regridder Options Table NCO automatically annotates the output with relevant metadata such as coordinate bounds, axes, and vertices (`ala CF). These annotations include
Horizontal Dimension Names: lat dmn, lon dmn The name of the horizontal spatial dimensions assumed to represent latitude and longitude in 2D rectangular input files are lat dmn nm and lon dmn nm, which default to lat and lon, respectively. Variables that contain a lat dmn nm-dimension and a lon dmn nm-dimension on a 2D-rectangular in- put grid will be regridded, and variables regridded to a 2D-rectangular out- put grid will all contain the lat dmn nm- and lon dmn nm-dimensions. To treat different dimensions as latitude and longitude, use the options ‘--rgr lat_dmn_nm=lat_dmn_nm’ and ‘--rgr lon_dmn_nm=lon_dmn_nm’. These op- tions applied only to inferring and generating grids until NCO version 4.7.9 (February, 2019). Since then, these options also determine the dimension names in regridded output files.
Horizontal Coordinate Names: lat, lon The name of the horizontal spatial coordinates that represent latitude and lon- gitude in input files are lat nm and lon nm, and default to lat and lon, re- spectively. Variables that contain a lat dmn nm-dimension and a lon dmn nm- dimension on a 2D input grid will be regridded, and output regridded variables will all contain the lat nm- and lon nm-variables. Unless the lat dmn nm- and lon dmn nm-dimensions are explicitly configured otherwise, they will share the same name as the lat nm- and lon nm-variables. Thus variables regridded to a 2D-rectangular output grid usually have lat nm- and lon nm as coordi- nate variables. Variables regridded to a 1D-unstructured output grid will have lat nm and lon nm as auxiliary coordinate variables. Variables regridded to a 2D-curvilinear output grid will have lat nm and lon nm as multi-dimensional auxiliary coordinate variables. To treat different variables as latitude and lon- gitude, use the options ‘--rgr lat_nm=lat_nm’ and ‘--rgr lon_nm=lon_nm’. Before NCO version 4.7.9 (February, 2019), lat nm and lon nm specified both the variable names and, where applicable (i.e., on 2D-grids), the dimensions of the horizontal coordinates in output files. Now the horizontal variable and dimension names in output files may be separately specified.
Unstructured Dimension Name: col The name of the horizontal spatial dimension assumed to delineate an unstruc- tured grid is col nm, which defaults to ncol (number of columns), the name CAM employs. Other common names for the columns in an unstructured grid include lndgrid (used by CLM), and nCells (used by MPAS-O). Variables that contain the col nm-dimension on an unstructured input grid will be regridded, and regridded variables written to an unstructured output grid will all contain the col nm-dimension. To treat a different dimension as unstructured, use the option ‘--rgr col_nm=col_nm’. Note: Often there is no coordinate variable for the col nm-dimension, i.e., there is no variable named col nm, although such a coordinate could contain useful information about the unstructured grid. Chapter 3: Shared Features 91
Structured Grid Standard Names and Units Longitude and latitude coordinates (both regular and auxiliary, i.e., for unstruc- tured grids) receive CF standard_name values of latitude and longitude, CF axes attributes with values X and Y, and units attributes with values degrees_ east and degrees_north, respectively. Unstructured Grid Auxiliary Coordinates Unstructured grid auxiliary coordinates for longitude and latitude receive CF coordinates attributes with values lon and lat, respectively. Structured Grid Bounds Variables: bnd, lat bnd, lon bnd Structured grids with 1D-coordinates use the dimension bnd nm (which de- faults to nbnd) with the spatial bounds variables in lat bnd nm and lon bnd nm which default to lon_bnds and lat_bnds, respectively. By default spatial bounds for such structured grids parallel the oft-used temporal bounds dimen- sion (nbnd=2) and variable (time_bnds). Bounds are attached to the hor- izontal spatial dimensions via their bounds attributes. Change the spatial bounds dimension with the option ‘--rgr bnd_nm=bnd_nm’. Rename the spatial bounds variables with the options ‘--rgr lat_bnd_nm=lat_bnd_nm’ and ‘--rgr lon_bnd_nm=lon_bnd_nm’. Unstructured Grid Bounds Variables: bnd, lat bnd, lon bnd Unstructured grids with 1D-coordinates use the dimension bnd nm (which de- faults to nv, number of vertices) for the spatial bounds variables lat bnd nm and lon bnd nm which default to lat_vertices and lon_vertices, respectively. It may be impossible to re-use the temporal bounds dimension (often nbnd) for un- structure grids, because the gridcells are not rectangles, and thus require speci- fication of all vertices for each gridpoint, rather than only two parallel interfaces per dimension. These bounds are attached to the horizontal spatial dimensions via their bounds attributes. Change the spatial bounds dimension with the op- tion ‘--rgr bnd_nm=bnd_nm’. Rename the spatial bounds variables with the op- tions ‘--rgr lat_bnd_nm=lat_bnd_nm’ and ‘--rgr lon_bnd_nm=lon_bnd_nm’. The temporal bounds dimension in unstructured grid output remains as in the input-file, usually nbnd. Vertical Dimension Names: lev dmn, ilev dmn The name of the dimension(s) associated with the vertical coordinate(s) in multi-level input files are lev dmn nm and ilev dmn nm, which default to lev and ilev, respectively. Variables that contain a lev dmn nm-dimension or an ilev dmn nm-dimension will be vertically interpolated to the speci- fied (with ‘vrt_fl=vrt_fl’) vertical output grid, and will all contain the lev dmn nm- and, for hybrid-sigma/pressure interface variables, ilev dmn nm- dimensions. To treat different dimensions as the midlayer and interface level dimensions, use the options ‘--rgr lev_dmn_nm=lev_dmn_nm’ and ‘--rgr ilev_dmn_nm=ilev_dmn_nm’ options. Pure-pressure grids should use the ‘--rgr lev_dmn_nm=lev_dmn_nm’ option (to reduce option proliferation, there is no plev dmn nm option). These options were introduced in NCO version 4.9.0 (December, 2019). These options also determine the vertical dimension names in vertically interpolated output files. 92 NCO 5.0.2-alpha07 User Guide
Vertical Coordinate Names: lev, ilev, plev The name of the vertical coordinate variables that represent midpoint lev- els and interface levels in hybrid-sigma/pressuure input files are lev nm and ilev nm, and default to lev and ilev, respectively. While the vertical coor- dinate in pure-pressure vertical grid files (i.e., the template-file to which data will be interpolated) must be named plev, the vertical coordinate in pure- pressure data files (i.e., the files to be interpolated) may be changed with the ‘--rgr plev_nm=plev_nm’ option. The name of the vertical coordinate variable that represents pressure levels in pure-pressure grid input data files is plev nm, and it defaults to plev. To reduce proliferation of command- line options and internal code complexity, the variable and dimension op- tions for pure-pressure vertical coordinate output names re-use the “lev” op- tions, i.e., ‘--rgr lev_nm_out=lev_nm_out’ option. Variables that contain a lev dmn nm-dimension or a ilev dmn nm-dimension on hybrid-sigma/pressure input grid, or a plev dmn nm-dimension on a pure pressure grid, will be re- gridded, and output in vertically interpolated files on a hybrid-sigma/pressure grid will all contain the lev nm- and ilev nm-variables, and output on a pure- pressure grid will contain the lev nm coordinate. Unless the lev dmn nm and ilev dmn nm dimensions are explicitly configured otherwise, they will share the same name as the lev nm/plev nm and ilev nm-variables, respectively. Thus variables regridded to a hybrid-sigma/pressure output grid usually have lev nm- and ilev nm as coordinate variables. Variables regridded to a pure-pressure out- put grid will only have a single vertical coordinate variable, lev nm, which will be an associated coordinate variable if lev dmn nm differs from lev nm. To treat different variables as level and interface-level coordinates, use the options ‘--rgr lev_nm=lev_nm’ and ‘--rgr ilev_nm=ilev_nm’. Before NCO version 4.9.0 (December, 2019), lev nm and ilev nm specified both the variable names and, where applicable (i.e., on 2D-grids), the dimensions of the vertical coordi- nates in output files. Now the vertical variable and dimension names in output files may be separately specified. Gridcell Area: area The variable area nm (which defaults to area) is, by default, (re-)created in the output file to hold the gridcell area in steradians. To store the area in a different variable, use the option ‘--rgr area=area_nm’. The area nm variable receives a standard_name attribute of cell_area, a units attribute of steradian (the SI unit of solid angle), and a cell_methods attribute with value lat, lon: sum, which indicates that area nm is extensive, meaning that its value depends on the gridcell boundaries. Since area nm is a property of the grid, it is read directly from the map-file rather than regridded itself. To omit the area variable from the output file, set the no area out flag. The --no_cll_msr switch to ncremap and ncclimo does this automatically. Gridcell Fraction: frc The variable frc nm (which defaults to frac_b) is automatically copied to the output file to hold the valid fraction of each gridcell when certain conditions are met. First, the regridding method must be conservative. Second, at least one value of frc nm must be non-unity. These conditions ensure that whenever frac- Chapter 3: Shared Features 93
tional gridcells affect the regridding, they are also placed in the output file.To store the fraction in a different variable, use the option--rgr ‘ frc_nm=frc_nm’. The frc nm variable receives a cell_methods attribute with value lat, lon: sum, which indicates that frc nm is extensive, meaning that its value depends on the gridcell boundaries. Since frc nm is a property of the grid, it is read directly from the map-file rather than regridded itself. Gridcell Mask: mask The variable msk nm (which defaults to mask) can, if present, be copied from the map-file to hold the gridcell mask on the destination grid in output-file. To store the mask in a different variable, use the option ‘--rgr msk_nm=msk_nm’. Since msk nm is a property of the grid, it is read directly from the map-file rather than regridded itself. To include the mask variable in the output file, set the msk out flag. To omit the mask variable from the output file, setthe no msk out flag. In grid inferral and map-generation modes, this option tells the regridder to generate an integer mask map from the variable msk nm. The mask will be one (i.e., points at that location will contribute to regridding weights) where msk nm has valid values. The mask will be zero (i.e., points at that location will not contribute to regridding weights) where msk nm has a missing value. This feature is useful when creating weights between masked grids, e.g., ocean-only points or land-only points. Latitude weights: lat wgt Rectangular 2D-grids use the variable lat wgt nm, which defaults to gw (orig- inally for “Gaussian weight”), to store the 1D-weight appropriate for area- weighting the latitude grid. To store the latitude weight in a different variable, use the option ‘--rgr lat_wgt=lat_wgt_nm’. The lat wgt nm variable will not appear in 1D-grid output. Weighting statistics by latitude (i.e., by lat wgt nm will produce the same answers (up-to round-off error) as weighting by area (i.e., by area nm) in grids that have both variables. The former requires less memory because lat wgt nm is 1D), whereas the latter is more general because area nm works on any grid. Provenance Attributes The map-file and input-file names are stored in the output-file global attributes mapping_file and source_file, respectively. Staggered Grid Coordinates and Weights Owing to its heritage as an early CCM analysis tool, NCO tries to create output interoperable with other CESM analysis tools. Like many models, CAM com- putes and archives thermodynamic state variables on gridcell centers, and com- putes dynamics variables (zonal and meridional winds U and V, respectively) on gridcell edges (interfaces). The dual-grid, sometimes called the “staggered grid”, formed by connecting edge centers is thus the natural location for storing output dynamics variables. Most dynamical cores of CAM archives horizontal winds at gridcell centers under the names U, and V. For CAM-FV, these are interpolated from the computed interface winds archived as US, and VS (which are on the staggered grid coordinate system). Some analysis packages, such as the AMWG diagnostics, require access to these dual-grid coordinates with the 94 NCO 5.0.2-alpha07 User Guide
names slat and slon (for “staggered” latitude and longitude). Until NCO ver- sion 4.9.8 (released March, 2021), the NCO regridder output these coordinates, along with the latitude weights (called w_stag), by default when the input was on a cap (aka FV) grid so that the result could be processed by AMWG diag- nostics. Setting the no stagger flag turns-off archiving the staggered grid (i.e., slat, slon, and w_stag). Do this with the --no_stg_grd flag in ncremap. ncclimo always sets this --no_stagger flag. As of NCO version 4.9.8 (released March, 2021), the default ncremap and ncclimo behavior is to omit the stag- gered grid. The new flag --stg_grd turns-on outputting the staggered grid, and thus recovers the previous default behavior.
One may supply muliple ‘--rgr key=value’ options to simultaneously customize multi- ple grid-field names. The following examples may all be assumed to end with thestandard options ‘--map=map.nc in.nc out.nc’. ncks --rgr lat_nm=latitude --rgr lon_nm=longitude ncks --rgr col_nm=column --rgr lat_wgt=lat_wgt ncks --rgr bnd_nm=bounds --rgr lat_bnd_nm=lat_bounds --rgr lon_bnd_nm=lon_bounds ncks --rgr bnd_nm=vertices --rgr lat_bnd_nm=lat_vrt --rgr lon_bnd_nm=lon_vrt The first command causes the regridder to associate the latitude and longitude dimen- sions with the dimension names latitude and longitude (instead of the defaults, lat and lon). The second command causes the regridder to associate the independent columns in an unstructured grid with the dimension name column (instead of the default, ncol) and the variable containing latitude weights to be named lat_wgt (instead of the default, gw). The third command associates the latitude and longitude bounds with the dimension bounds (instead of the default, nbnd) and the variables lat_bounds and lon_bounds (instead of the defaults, lat_bnds and lon_bnds, respectively). The fourth command associates the lati- tude and longitude bounds with the dimension vertices (instead of the default, nv) and the variables lat_vrt and lon_vrt (instead of the defaults, lat_vertices and lon_vertices, respectively). When used with an identity remapping files, regridding can signficantly enhance the metadata and therefore the dataset usability. Consider these selected metadata (those unchanged are not shown for brevity) associated with the variable FSNT from typical un- structured grid (CAM-SE cubed-sphere) output before and after an identity regridding: # Raw model output before regridding netcdf ne30_FSNT { dimensions: nbnd = 2 ; ncol = 48602 ; time = UNLIMITED ; // (1 currently)
variables: float FSNT(time,ncol) ; FSNT:long_name = "Net solar flux at top of model" ;
double time(time) ; Chapter 3: Shared Features 95
time:long_name = "time" ; time:bounds = "time_bnds" ;
double time_bnds(time,nbnd) ; time_bnds:long_name = "time interval endpoints" ; } // group /
# Same model output after identity regridding netcdf dogfood { dimensions: nbnd = 2 ; ncol = 48602 ; nv = 5 ; time = 1 ;
variables: float FSNT(time,ncol) ; FSNT:long_name = "Net solar flux at top of model" ; FSNT:coordinates = "lat lon" ;
double lat(ncol) ; lat:long_name = "latitude" ; lat:standard_name = "latitude" ; lat:units = "degrees_north" ; lat:axis = "Y" ; lat:bounds = "lat_vertices" ; lat:coordinates = "lat lon" ;
double lat_vertices(ncol,nv) ; lat_vertices:long_name = "gridcell latitude vertices" ;
double lon(ncol) ; lon:long_name = "longitude" ; lon:standard_name = "longitude" ; lon:units = "degrees_east" ; lon:axis = "X" ; lon:bounds = "lon_vertices" ; lon:coordinates = "lat lon" ;
double lon_vertices(ncol,nv) ; lon_vertices:long_name = "gridcell longitude vertices" ;
double time(time) ; time:long_name = "time" ; time:bounds = "time_bnds" ;
double time_bnds(time,nbnd) ; 96 NCO 5.0.2-alpha07 User Guide
time_bnds:long_name = "time interval endpoints" ; } // group /
The raw model output lacks the CF coordinates and bounds attributes that the re- gridder adds. The metadata turns lat and lon into auxiliary coordinate variables (see Section 3.23 [Auxiliary Coordinates], page 74) which can then be hyperslabbed (with ‘-X’) using latitude/longitude coordinates bounding the region of interest:
% ncks -u -H -X 314.6,315.3,-35.6,-35.1 -v FSNT dogfood.nc time[0]=31 ncol[0] FSNT[0]=344.575 W/m2
ncol[0] lat[0]=-35.2643896828 degrees_north
ncol[0] nv[0] lat_vertices[0]=-35.5977213708 ncol[0] nv[1] lat_vertices[1]=-35.5977213708 ncol[0] nv[2] lat_vertices[2]=-35.0972113817 ncol[0] nv[3] lat_vertices[3]=-35.0972113817 ncol[0] nv[4] lat_vertices[4]=-35.0972113817
ncol[0] lon[0]=315 degrees_east
ncol[0] nv[0] lon_vertices[0]=315 ncol[0] nv[1] lon_vertices[1]=315 ncol[0] nv[2] lon_vertices[2]=315.352825437 ncol[0] nv[3] lon_vertices[3]=314.647174563 ncol[0] nv[4] lon_vertices[4]=314.647174563
time[0]=31 days since 1979-01-01 00:00:00
time[0]=31 nbnd[0] time_bnds[0]=0 time[0]=31 nbnd[1] time_bnds[1]=31
Thus auxiliary coordinate variables help to structure unstructured grids. The expanded metadata annotations from an identity regridding may obviate the need to place unstruc- tured data on a rectangular grid. For example, statistics for regions that can be expressed as unions of rectangular regions can now be performed on the native (unstructured) grid.
Here are some quick examples of regridding from common models. All examples require ‘in.nc out.nc’ at the end.
# Identity re-map E3SM/ACME CAM-SE Cubed-Sphere output (to improve metadata) ncks --map=${DATA}/maps/map_ne30np4_to_ne30np4_aave.20150603.nc # Convert E3SM/ACME CAM-SE Cubed Sphere output to rectangular lat/lon ncks --map=${DATA}/maps/map_ne30np4_to_fv129x256_aave.150418.nc # Convert CAM3 T42 output to Cubed-Sphere grid ncks --map=${DATA}/maps/map_ne30np4_to_t42_aave.20150601.nc Chapter 3: Shared Features 97
3.26 Climatology and Bounds Support Availability: nces, ncra, ncrcat Short options: None Long options: ‘--cb=yr_srt,yr_end,mth_srt,mth_end,tpd’ ‘--clm_bnd=yr_srt,yr_end,mth_srt,mth_end,tpd’ ‘--clm_nfo=yr_srt,yr_end,mth_srt,mth_end,tpd’ ‘--climatology_information=yr_srt,yr_end,mth_srt,mth_end,tpd’
(NB: This section describes support for generating CF-compliant bounds variables and attributes, i.e., metadata. For instructions on constructing climatologies themselves, see the ncclimo documentation). As of NCO version 4.9.4 (September, 2020) ncra introduces the ‘--clm_bnd’ option, a powerful method to fully implement the CF bounds, climatology, and cell_methods attributes defined by Section 3.45 [CF Conventions], page 146. The new method updates the previous ‘--cb’ and ‘--c2b’ methods introduced in version 4.6.0 which only worked for monthly mean data. The newer --cb method also works for cli- matological diurnally resolved input, and for datasets that contain more than more than one record. This option takes as argument a comma-separated list of five relevant input parameters: ‘--cb=yr_srt,yr_end,mth_srt,mth_end,tpd’, where yr srt is the climatol- ogy start-year, yr end is the climatology end-year, mth srt is the climatology start-month (in [1..12] format), mth end is the climatology end-month (in [1..12] format), and tpd is the number of timestpes per day (with the special exception that tpd = 0 indicates monthly data, not diurnally-resolved data). For example, a seasonal summer climatology created from monthly mean input data spanning June, 2000 to August, 2020 should call ncra with ‘--clm_bnd=2000,2020,6,8,0’, whereas a diurnally resolved climatology of the same period with 6-hourly input data resolution would use ‘--clm_bnd=2000,2020,6,8,4’. The ncclimo command internally uses --clm_bnd extensively. # Average monthly means into a climatological month ncra --cb=2014,2016,1,1,0 2014_01.nc 2015_01.nc 2016_01.nc clm_JAN.nc # Average seasonally contiguous climatological monthly means into NH winter ncra --cb=2013,2016,12,2,0 -w 31,31,28 DEC.nc JAN.nc FEB.nc DJF.nc # Average seasonally discontiguous climatological means into NH winter ncra --cb=2014,2016,1,12,0 -w 31,28,31 JAN.nc FEB.nc DEC.nc JFD.nc # Reduce four climatological seasons to make an annual climatology ncra --cb=2014,2016,1,12,0 -w 92,92,91,90 MAM.nc JJA.nc SON.nc DJF.nc ANN.nc # Reduce twelve monthly climatologies to make into an annual climatology ncra --cb=2014,2016,1,12,0 -w 31,28,31,30,31,30,31,31,30,31,30,31 clm_??.nc ANN.nc In the fourth and fifth examples, NCO uses the number of input files (3 and 4, respec- tively) to discriminate between seasonal and annual climatologies since the other arguments to ‘--cb’ are identical. When using this option, NCO expects each output file to contain max(1,tpd) records. nces and ncra both accept the ‘--cb’ option. While ncra almost always reduces the input dataset over the record dimension, nces never does. This makes it easy to use nces to combine and create climatologies of diurnally resolved input files. # Average diurnally resolved monthly means into a climatology 98 NCO 5.0.2-alpha07 User Guide
nces --cb=2014,2016,1,1,8 2014_01.nc 2015_01.nc 2016_01.nc clm_JAN.nc # Average seasonally contiguous diurnally resolved means into a season nces --cb=2013,2016,12,2,8 -w 31,31,28 DEC.nc JAN.nc FEB.nc DJF.nc # Average seasonally discontiguous diurnally resolved means into a season nces --cb=2014,2016,1,12,8 -w 31,28,31 JAN.nc FEB.nc DEC.nc JFD.nc # Reduce four diurnally resolved seasons to make an annual climatology nces --cb=2014,2016,1,12,8 -w 92,92,91,90 MAM.nc JJA.nc SON.nc DJF.nc ANN.nc # Reduce twelve diurnally resolved months to make into an annual climatology nces --cb=2014,2016,1,12,8 -w 31,28,31,30,31,30,31,31,30,31,30,31 clm_??.nc ANN.nc Every input in the above set of examples must have eight records, and that number will appear in the output as well.
3.27 UDUnits Support
Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa ¨ Short options: ‘-d dim,[min][,[max][,[stride]]]’ Long options: ‘--dimension dim,[min][,[max][,[stride]]]’, ‘--dmn dim,[min][,[max][,[stride]]]’
There is more than one way to hyperskin a cat. The UDUnits (http://www.unidata. © ucar.edu/software/udunits) package provides a library which, if present, NCO uses to translate user-specified physical dimensions into the physical dimensions of data storedin netCDF files. Unidata provides UDUnits under the same terms as netCDF, so sites should install both. Compiling NCO with UDUnits support is currently optional but may become required in a future version of NCO. Two examples suffice to demonstrate the power and convenience of UDUnits support. First, consider extraction of a variable containing non-record coordinates with physical dimensions stored in MKS units. In the following example, the user extracts all wavelengths in the visible portion of the spectrum in terms of the units very frequently used in visible spectroscopy, microns: % ncks --trd -C -H -v wvl -d wvl,"0.4 micron","0.7 micron" in.nc wvl[0]=5e-07 meter The hyperslab returns the correct values because the wvl variable is stored on disk with a length dimension that UDUnits recognizes in the units attribute. The automagical algorithm that implements this functionality is worth describing since understanding it helps one avoid some potential pitfalls. First, the user includes the physical units of the hyperslab dimensions she supplies, separated by a simple space from the numerical values of the hyperslab limits. She encloses each coordinate specifications in quotes so that the shell does not break the value-space-unit string into separate arguments before passing them to NCO. Double quotes ("foo") or single quotes (’foo’) are equally valid for this purpose. Second, NCO recognizes that units translation is requested because each hyperslab argument contains text characters and non-initial spaces. Third, NCO determines whether the wvl is dimensioned with a coordinate variable that has a units attribute. In this case, wvl itself is Chapter 3: Shared Features 99
a coordinate variable. The value of its units attribute is meter. Thus wvl passes this test so UDUnits conversion is attempted. If the coordinate associated with the variable does not contain a units attribute, then NCO aborts. Fourth, NCO passes the specified and desired dimension strings (microns are specified by the user, meters are required by NCO) to the UDUnits library. Fifth, the UDUnits library that these dimension are commensurate and it returns the appropriate linear scaling factors to convert from microns to meters to NCO. If the units are incommensurate (i.e., not expressible in the same fundamental MKS units), or are not listed in the UDUnits database, then NCO aborts since it cannot determine the user’s intent. Finally, NCO uses the scaling information to convert the user-specified hyperslab limits into the same physical dimensions as those of the corresponding cooridinate variable on disk. At this point, NCO can perform a coordinate hyperslab using the same algorithm as if the user had specified the hyperslab without requesting units conversion. The translation and dimensional interpretation of time coordinates shows a more power- ful, and probably more common, UDUnits application. In this example, the user prints all data between 4 PM and 7 PM on December 8, 1999, from a variable whose time dimension is hours since the year 1900: % ncks -u -H -C -v time_udunits -d time_udunits,"1999-12-08 \ 16:00:0.0","1999-12-08 19:00:0.0" in.nc time_udunits[1]=876018 hours since 1900-01-01 00:00:0.0 Here, the user invokes the stride (see Section 3.17 [Stride], page 66) capability to obtain every other timeslice. This is possible because the UDUnits feature is additive, not exclusive—it works in conjunction with all other hyperslabbing (see Section 3.16 [Hyperslabs], page 63) options and in all operators which support hyperslabbing. The following example shows how one might average data in a time period spread across multiple input files ncra -d time,"1939-09-09 12:00:0.0","1945-05-08 00:00:0.0" \ in1.nc in2.nc in3.nc out.nc Note that there is no excess whitespace before or after the individual elements of the ‘-d’ argument. This is important since, as far as the shell knows, ‘-d’ takes only one command- line argument. Parsing this argument into its component dim,[min][,[max][,[stride]]] elements (see Section 3.16 [Hyperslabs], page 63) is the job of NCO. When unquoted whitespace is present between these elements, the shell passes NCO arugment fragments which will not parse as intended.
NCO implemented support for the UDUnits2 library with version 3.9.2 (August, 2007). The UDUnits2 (http://www.unidata.ucar.edu/software/udunits/udunits-2/ udunits2.html) package supports non-ASCII characters and logarithmic units. We are interested in user-feedback on these features.
One aspect that deserves mention is that UDUnits, and thus NCO, supports run-time definition of the location of the relevant UDUnits databases. UDUnits2 (specifically, the function ut_read_xml()) uses the environment variable UDUNITS2_XML_PATH, if any, to find its all-important XML database, named udunits2.xml by default. If UDUNITS2_XML_PATH is undefined, then UDUnits2 looks in the fall-back default initial location that was hardcoded when the UDUnits2 library was built. This location varies depending upon your operating system and UDUnits2 ncompilation settings. If UDUnits2 is correctly linked yet cannot 100 NCO 5.0.2-alpha07 User Guide
find the XML database in either of these locations, then NCO will report that the UDUnits2 library has failed to initialize. To fix this, export the full location (path+name) of the UDUnits2 XML database file udunits2.xml to the shell: export UDUNITS2_XML_PATH=’/opt/local/share/udunits/udunits2.xml’ One can then invoke (without recompilation) NCO again, and UDUnit2 should work. This run-time flexibility can enable the full functionality of pre-built binaries on machines with libraries in different locations. The UDUnits (http://www.unidata.ucar.edu/software/udunits) package documen- tation describes the supported formats of time dimensions. Among the metadata conven- tions that adhere to these formats are the Climate and Forecast (CF) Conventions (http:// cf-pcmdi.llnl.gov) and the Cooperative Ocean/Atmosphere Research Data Service (COARDS) Conventions (http://ferret.wrc.noaa.gov/noaa_coop/coop_cdf_profile. html). The following ‘-d arguments’ extract the same data using commonly encountered time dimension formats: -d time,’1918-11-11 00:00:0.0’,’1939-09-09 00:00:0.0’ -d time,’1918-11-11 00:00:0.0’,’1939-09-09 00:00:0.0’ -d time,’1918-11-11T00:00:0.0Z’,’1939-09-09T00:00:0.0Z’ -d time,’1918-11-11’,’1939-09-09’ -d time,’1918-11-11’,’1939-9-9’ All of these formats include at least one dash - in a non-leading character position (a dash in a leading character position is a negative sign). NCO assumes that a space, colon, or non-leading dash in a limit string indicates that a UDUnits units conversion is requested. Some date formats like YYYYMMDD that are valid in UDUnits are ambiguous to NCO because it cannot distinguish a purely numerical date (i.e., no dashes or text characters in it) from a coordinate or index value: -d time,1918-11-11 # Interpreted as the date November 11, 1918 -d time,19181111 # Interpreted as time-dimension index 19181111 -d time,19181111. # Interpreted as time-coordinate value 19181111.0 Hence, use the YYYY-MM-DD format rather than YYYYMMDD for dates. As of version 4.0.0 (January, 2010), NCO supports some calendar attributes specified by the CF conventions.
Supported types: "365 day"/"noleap", "360 day", "gregorian", "standard" Unsupported types: "366 day"/"all leap","proleptic gregorian","julian","none"
Unsupported types default to mixed Gregorian/Julian as defined by UDUnits. An Example: Consider the following netCDF variable variables: double lon_cal(lon_cal) ; lon_cal:long_name = "lon_cal" ; Chapter 3: Shared Features 101
lon_cal:units = "days since 1964-2-28 0:0:0" ; lon_cal:calendar = "365_day" ; data: lon_cal = 1,2,3,4,5,6,7,8,9,10; ‘ncks -v lon_cal -d lon_cal,’1964-3-1 0:00:0.0’,’1964-3-4 00:00:0.0’’ results in lon_cal=1,2,3,4. netCDF variables should always be stored with MKS (i.e., God’s) units, so that appli- cation programs may assume MKS dimensions apply to all input variables. The UDUnits feature is intended to alleviate NCO users’ pain when handling MKS units. It connects users who think in human-friendly units (e.g., miles, millibars, days) to extract data which are always stored in God’s units, MKS (e.g., meters, Pascals, seconds). The feature is not intended to encourage writers to store data in esoteric units (e.g., furlongs, pounds per square inch, fortnights).
3.28 Rebasing Time Coordinate
Availability: ncra, ncrcat Short options: None ¨
Time rebasing is invoked when numerous files share a common record coordinate, and © the record coordinate basetime (not the time increment, e.g., days or hours) changes among input files. The rebasing is performed automatically if and only if UDUnits is installed. Rebasing occurs when the record coordinate is a time-based variable, and times are recorded in units of a time-since-basetime, and the basetime changes from file to file. Since the output file can have only one unit (i.e., one basetime) for the record coordinate, NCO, in such cases, chooses the units of the first input file to be the units of the output file. It isnecessaryto “rebase” all the input record variables to this output time unit in order for the output file to have the correct values. For example suppose the time coordinate is in hours and each day in January is stored in its own daily file. Each daily file records the temperature variable tpt(time) with an (unadjusted) time coordinate value between 0–23 hours, and uses the units attribute to advance the base time: file01.nc time:units="hours since 1990-1-1" file02.nc time:units="hours since 1990-1-2" ... file31.nc time:units="hours since 1990-1-31" // Mean noontime temperature in January ncra -v tpt -d time,"1990-1-1 12:00:00","1990-1-31 23:59:59",24 \ file??.nc noon.nc
// Concatenate day2 noon through day3 noon records ncrcat -v tpt -d time,"1990-1-2 12:00:00","1990-1-3 11:59:59" \ file01.nc file02.nc file03.nc noon.nc 102 NCO 5.0.2-alpha07 User Guide
// Results: time is "re-based" to the time units in "file01.nc" time=36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, \ 51, 52, 53, 54, 55, 56, 57, 58, 59 ;
// If we repeat the above command but with only two input files... ncrcat -v tpt -d time,"1990-1-2 12:00:00","1990-1-3 11:59:59" \ file02.nc file03 noon.nc
// ...then output time coordinate is based on time units in "file02.nc" time = 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, \ 26, 27, 28, 29, 30, 31, 32, 33, 34, 35 ;
As of NCO version 4.2.1 (August, 2012), NCO automatically rebases not only the record coordinate (time, here) but also any cell boundaries associated with the record coordinate (e.g., time_bnds) (see Section 3.45 [CF Conventions], page 146).
As of NCO version 4.4.9 (May, 2015), NCO also rebases any climatology boundaries associated with the record coordinate (e.g., climatology_bounds) (see Section 3.45 [CF Conventions], page 146).
As of NCO version 4.6.3 (December, 2016), NCO also rebases the time coordinate when the units differ between files. For example the first file mayhave units="days since 2014-03-01" and the second file units="hours since 2014-03-10 00:00".
3.29 Multiple Record Dimensions
Availability: ncecat, ncpdq Short options: None ¨ Long options: ‘--mrd’
© The netCDF3 file format allows only one record dimension, and that dimension must be the first dimension (i.e., the least rapidly varying dimension) of any variable inwhichit appears. This imposes certain rules on how operators must perform operations that alter the ordering of dimensions or the number of record variables. The netCDF4 file format has no such restrictions. Files and variables may have any number of record dimensions in any order. This additional flexibility of netCDF4 can only be realized by selectively abandoning the constraints that would make operations behave completely consistently between netCDF3 and netCDF4 files.
NCO chooses, by default, to impose netCDF3-based constraints on netCDF4 files. This reduces the number of unanticipated consequences and keeps the operators functioning in a familiar way. Put another way, NCO limits production of additional record dimensions so processing netCDF4 files leads to the same results as processing netCDF3 files. Userscan override this default with the ‘--mrd’ (or ‘--multiple_record_dimension’) switch, which enables netCDF4 variables to accumulate additional record dimensions. Chapter 3: Shared Features 103
How can additional record dimensions be produced? Most commonly ncecat (in record- aggregate mode) defines a new leading record dimension. In netCDF4 files this becomes an additional record dimension unless the original record dimension is changed to a fixed dimension (as must be done in netCDF3 files). Also when ncpdq reorders dimensions it can preserve the “record” property of record variables. ncpdq tries to define as a record dimension whichever dimension ends up first in a record variable, and, in netCDF4 files, this becomes an additional record dimension unless the original record dimension is changed to a fixed dimension (as must be done in netCDF3 files). It it easierif ncpdq and ncecat do not increase the number of record dimensions in a variable so that is the default. Use ‘--mrd’ to override this.
3.30 Missing values
Availability: ncap2, ncbo, ncclimo, nces, ncflint, ncpdq, ncra, ncremap, ncwa ¨ Short options: None
The phrase missing data refers to data points that are missing, invalid, or for any reason © not intended to be arithmetically processed in the same fashion as valid data. All NCO arithmetic operators attempt to handle missing data in an intelligent fashion. There are four steps in the NCO treatment of missing data: 1. Identifying variables that may contain missing data. NCO follows the convention that missing data should be stored with the FillValue specified in the variable’s _FillValue attributes. The only way NCO recognizes that a variable may contain missing data is if the variable has a _FillValue attribute. In this case, any elements of the variable which are numerically equal to the FillValue are treated as missing data. NCO adopted the behavior that the default attribute name, if any, assumed to specify the value of data to ignore is _FillValue with version 3.9.2 (August, 2007). Prior to that, the missing_value attribute, if any, was assumed to specify the value of data to ignore. Supporting both of these attributes simultaneously is not practical. Hence the behavior NCO once applied to missing value it now applies to any FillValue. NCO now treats any missing value as normal data21. It has been and remains most advisable to create both _FillValue and missing_ value attributes with identical values in datasets. Many legacy datasets contain only missing_value attributes. NCO can help migrating datasets between these conven- tions. One may use ncrename (see Section 4.13 [ncrename netCDF Renamer], page 341) to rename all missing_value attributes to _FillValue: ncrename -a .missing_value,_FillValue inout.nc Alternatively, one may use ncatted (see Section 4.2 [ncatted netCDF Attribute Editor], page 218) to add a _FillValue attribute to all variables
21 The old functionality, i.e., where the ignored values are indicated by missing_value not _FillValue, may still be selected at NCO build time by compiling NCO with the token definition CPPFLAGS=’- UNCO_USE_FILL_VALUE’. 104 NCO 5.0.2-alpha07 User Guide
ncatted -O -a _FillValue,,o,f,1.0e36 inout.nc 2. Converting the FillValue to the type of the variable, if neccessary. Consider a variable var of type var type with a _FillValue attribute of type att type containing the value FillValue. As a guideline, the type of the _FillValue attribute should be the same as the type of the variable it is attached to. If var type equals att type then NCO straightforwardly compares each value of var to FillValue to de- termine which elements of var are to be treated as missing data. If not, then NCO converts FillValue from att type to var type by using the implicit conversion rules of C, or, if att type is NC_CHAR22, by typecasting the results of the C function strtod(_ FillValue). You may use the NCO operator ncatted to change the _FillValue at- tribute and all data whose data is FillValue to a new value (see Section 4.2 [ncatted netCDF Attribute Editor], page 218). 3. Identifying missing data during arithmetic operations. When an NCO arithmetic operator processes a variable var with a _FillValue at- tribute, it compares each value of var to FillValue before performing an operation. Note the FillValue comparison imposes a performance penalty on the operator. Arith- metic processing of variables which contain the _FillValue attribute always incurs this penalty, even when none of the data are missing. Conversely, arithmetic processing of variables which do not contain the _FillValue attribute never incurs this penalty. In other words, do not attach a _FillValue attribute to a variable which does not contain missing data. This exhortation can usually be obeyed for model generated data, but it may be harder to know in advance whether all observational data will be valid or not. 4. Treatment of any data identified as missing in arithmetic operators. NCO averagers (ncra, nces, ncwa) do not count any element with the value FillValue towards the average. ncbo and ncflint define a FillValue result when either of the input values is a FillValue. Sometimes the FillValue may change from file to file ina multi-file operator, e.g., ncra. NCO is written to account for this (it always compares a variable to the FillValue assigned to that variable in the current file). Suffice itto say that, in all known cases, NCO does “the right thing”. It is impossible to determine and store the correct result of a binary operation in a single variable. One such corner case occurs when both operands have differing FillValue attributes, i.e., attributes with different numerical values. Since the output (result) of the operation can only have one FillValue, some information may be lost. In this case, NCO always defines the output variable to have the same FillValue as the first input variable. Prior to performing the arithmetic operation, all values of the second operand equal to the second FillValue are replaced with the first FillValue. Then the arithmetic operation proceeds as normal, comparing each element of each operand to a single FillValue. Comparing each element to two distinct FillValue’s would be much slower and would be no likelier to yield a more satisfactory answer. In practice, judicious choice of FillValue values prevents any important information from being lost.
3.31 Chunking
22 For example, the DOE ARM program often uses att type = NC_CHAR and FillValue = ‘-99999.’. Chapter 3: Shared Features 105
Availability: ncap2, ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa ¨ Short options: none Long options: ‘--cnk_byt sz_byt’, ‘--chunk_byte sz_byt’ ‘--cnk_csh sz_byt’, ‘--chunk_cache sz_byt’ ‘--cnk_dmn dmn_nm,sz_lmn’, ‘--chunk_dimension dmn_nm,sz_lmn’ ,‘--cnk_map cnk_map’, ‘--chunk_map cnk_map’, ‘--cnk_min sz_byt’, ‘--chunk_min sz_byt’, ‘--cnk_plc cnk_plc’, ‘--chunk_policy cnk_plc’, ‘--cnk_scl sz_lmn’, ‘--chunk_scalar sz_lmn’
All netCDF4-enabled NCO operators that define variables support a plethora © of chunksize options. Chunking can significantly accelerate or degrade read/write access to large datasets. Dataset chunking issues are described by THG and Unidata here (http://www.hdfgroup.org/HDF5/doc/H5.user/Chunking. html), here (http://www.unidata.ucar.edu/blogs/developer/en/entry/ chunking_data_why_it_matters), and here (http://www.unidata.ucar.edu/blogs/ developer/en/entry/chunking_data_choosing_shapes). NCO authors are working on generalized algorithms and applications of chunking strategies (stay tuned for more in 2018). As of NCO version 4.6.5 (March, 2017), NCO supports run-time alteration of the chunk cache size. By default, the cache size is set (by the --with-chunk-cache-size option to configure) at netCDF compile time. The --cnk_csh sz option sets the cache size to sz bytes for all variables. When the debugging level is set (with -D dbg_lvl) to three or higher, NCO prints the current value of the cache settings for informational purposes. Also ‘--chunk_cache’. Increasing cache size from the default can dramatically accelerate time to aggregate and rechunk multiple large input datasets, e.g., ncrcat -4 -L 1 --cnk_csh=1000000000 --cnk_plc=g3d --cnk_dmn=time,365 \ --cnk_dmn=lat,1800 --cnk_dmn=lon,3600 in*.nc4 out.nc In this example all 3D variables the input datasets (which may or may not be chunked already) are re-chunked to a size of 365 along the time dimension. Because the default chunk cache size of about 4 MB is too small to manipulate the large chunks, we reset the cache to 1 GB. The operation completes much faster, and subsequent reads along the time dimension will be much more rapid. The NCO chunking implementation is designed to be flexible. Users control four aspects of the chunking implementation. These are the chunking policy, chunking map, chunksize, and minimum chunksize. The chunking policy determines which variables to chunk, and the chunking map determines how (with what exact sizes) to chunk those variables. These are high-level mechanisms that apply to an entire file and all variables and dimensions. The chunksize option allows per-dimension specification of sizes that will override the selected (or default) chunking map. The distinction between elements and bytes is subtle yet crucial to understand. Elements refers to values of an array, whereas bytes refers to the memory size required to hold the elements. These measures differ by a factor of four or eight for NC_FLOAT or NC_DOUBLE, respectively. The option ‘--cnk_scl’ takes an argument sz lmn measured in elements. 106 NCO 5.0.2-alpha07 User Guide
The options ‘--cnk_byt’, ‘--cnk_csh’, and ‘--cnk_min’ take arguments sz byt measured in bytes. Use the ‘--cnk_min=sz_byt’ option to set the minimum size in bytes (not elements) of variables to chunk. This threshold is intended to restrict use of chunking to variables for which it is efficient. By default this minimum variable size for chunking is twicethe system blocksize (when available) and is 8192 bytes otherwise. Users may set this to any value with the ‘--cnk_min=sz_byt’ switch. To guarantee that chunking is performed on all arrays, regardless of size, set the minimum size to one byte (not to zero bytes). The chunking implementation is similar to a hybrid of the ncpdq packing policies (see Section 4.9 [ncpdq netCDF Permute Dimensions Quickly], page 289) and hyperslab specifi- cations (see Section 3.16 [Hyperslabs], page 63). Each aspect is intended to have a sensible default, so that many users only need to set one switch to obtain sensible chunking. Power users can tune chunking with the three switches in tandem to obtain optimal performance.
By default, NCO preserves the chunking characteristics of the input file in the output file23. In other words, preserving chunking requires no switches or user intervention. Users specify the desired chunking policy with the ‘-P’ switch (or its long option equiv- alents, ‘--cnk_plc’ and ‘--chunk_policy’) and its cnk plc argument. As of August, 2014, six chunking policies are implemented:
Chunk All Variables Definition: Chunk all variables possible. For obvious reasons, scalar variables cannot be chunked. Alternate invocation: ncchunk cnk plc key values: ‘all’, ‘cnk_all’, ‘plc_all’ Mnemonic: All
Chunk Variables with at least Two Dimensions [default] Definition: Chunk all variables possible with at least two dimensions Alternate invocation: none cnk plc key values: ‘g2d’, ‘cnk_g2d’, ‘plc_g2d’ Mnemonic: Greater than or equal to 2 Dimensions
Chunk Variables with at least Three Dimensions Definition: Chunk all variables possible with at least three dimensions Alternate invocation: none cnk plc key values: ‘g3d’, ‘cnk_g3d’, ‘plc_g3d’ Mnemonic: Greater than or equal to 3 Dimensions
23 This behavior became the default in November 2014 with NCO version 4.4.7. Prior versions would always use netCDF default chunking in the output file when no NCO chunking switches were activated, regardless of the chunking in the input file. Chapter 3: Shared Features 107
Chunk One-Dimensional Record Variables Definition: Chunk all 1-D record variables Alternate invocation: none Any specified (with ‘--cnk_dmn’) record dimension chunksizes will be applied only to 1-D record variables (and to no other variables). Other dimensions may be chunked with their own ‘--cnk_dmn’ options that will apply to all variables. cnk plc key values: ‘r1d’, ‘cnk_r1d’, ‘plc_r1d’ Mnemonic: Record 1 -D variables
Chunk Variables Containing Explicitly Chunked Dimensions Definition: Chunk all variables possible that contain at least one dimension whose chunksize was explicitly set with the ‘--cnk_dmn’ option. Alternate invocation: none cnk plc key values: ‘xpl’, ‘cnk_xpl’, ‘plc_xpl’ Mnemonic: EXPLicitly specified dimensions
Chunk Variables that are already Chunked Definition: Chunk only variables that are already chunked in the input file. When used in conjunction with ‘cnk_map=xst’ this option preserves and copies the chunking parameters from the input to the output file. Alternate invocation: none cnk plc key values: ‘xst’, ‘cnk_xst’, ‘plc_xst’ Mnemonic: EX iST ing chunked variables
Chunk Variables with NCO recommendations Definition: Chunk all variables according to NCO best practices. This is a virtual option that ensures the chunking policy is (in the subjective opinion of the authors) the best policy for typical usage. As of NCO version 4.4.8 (February, 2015), this virtual policy implements ‘map_rew’ for 3-D variables and ‘map_lfp’ for all other variables. Alternate invocation: none cnk plc key values: ‘nco’, ‘cnk_nco’, ‘plc_nco’ Mnemonic: N etC DFOperator
Unchunking Definition: Unchunk all variables possible. The HDF5 storge layer requires that record variables (i.e., variables that contain at least one record dimension) must be chunked. Also variables that are compressed or use checksums must be chunked. Such variables cannot be unchunked. Alternate invocation: ncunchunk cnk plc key values: ‘uck’, ‘cnk_uck’, ‘plc_uck’, ‘none’, ‘unchunk’ Mnemonic: U nC hunK 108 NCO 5.0.2-alpha07 User Guide
Equivalent key values are fully interchangeable. Multiple equivalent options are provided to satisfy disparate needs and tastes of NCO users working with scripts and from the command line. The chunking algorithms must know the chunksizes of each dimension of each variable to be chunked. The correspondence between the input variable shape and the chunksizes is called the chunking map. The user specifies the desired chunking map with the‘-M’ switch (or its long option equivalents, ‘--cnk_map’ and ‘--chunk_map’) and its cnk map argument. Nine chunking maps are currently implemented:
Chunksize Equals Dimension Size Definition: Chunksize defaults to dimension size. Explicitly specify chunksizes for particular dimensions with ‘--cnk_dmn’ option. In most cases this chunk- size will be applied in all variables that contain the specified dimension. Some chunking policies noted above allow (fxm), and others (fxm) prevent this chunk- size from applying to all variables. cnk map key values: ‘dmn’, ‘cnk_dmn’, ‘map_dmn’ Mnemonic: DiM eN sion
Chunksize Equals Dimension Size except Record Dimension Definition: Chunksize equals dimension size except record dimension hassize one. Explicitly specify chunksizes for particular dimensions with ‘--cnk_dmn’ option. cnk map key values: ‘rd1’, ‘cnk_rd1’, ‘map_rd1’ Mnemonic: Record Dimension size 1
Chunksize Equals Scalar Size Specified Definition: Chunksize for all dimensions is set with the‘--cnk_scl=sz_lmn’ option. For this map sz lmn itself becomes the chunksize of each dimension. This is in contrast to the cnk prd map, where the rth root of sz lmn) becomes the chunksize of each dimension. cnk map key values: ‘scl’, ‘cnk_scl’, ‘map_scl’ Mnemonic: SC aLar cnk map key values: ‘xpl’, ‘cnk_xpl’, ‘map_xpl’ Mnemonic: EXPLicitly specified dimensions
Chunksize Product Matches Scalar Size Specified Definition: The product of the chunksizes for each variable matches (approx- imately equals) the size specified with the ‘--cnk_scl=sz_lmn’ option. A di- mension of size one is said to be degenerate. For a variable of rank R (i.e., with R non-degenerate dimensions), the chunksize in each non-degenerate dimension is (approximately) the Rth root of sz lmn. This is in contrast to the cnk scl map, where sz lmn itself becomes the chunksize of each dimension. cnk map key values: ‘prd’, ‘cnk_prd’, ‘map_prd’ Mnemonic: PRoDuct Chapter 3: Shared Features 109
Chunksize Lefter Product Matches Scalar Size Specified Definition: The product of the chunksizes for each variable (approximately) equals the size specified with the ‘--cnk_byt=sz_byt’ (not ‘--cnk_dfl’) op- tion. This is accomplished by using dimension sizes as chunksizes for the right- most (most rapidly varying) dimensions, and then “flexing” the chunksize of the leftmost (least rapidly varying) dimensions such that the product of all chunk- sizes matches the specified size. All L-dimensions to the left of and including the first record dimension define the left-hand side. To be precise, ifthetotal size (in bytes) of the variable is var sz, and if the specified (with--cnk_byt ‘ ’) product of the R “righter” dimensions (those that vary more rapidly than the first record dimension) is sz byt, then chunksize (in bytes) of each of the L lefter dimensions is (approximately) the Lth root of var sz/sz byt. This map was first proposed by Chris Barker. cnk map key values: ‘lfp’, ‘cnk_lfp’, ‘map_lfp’ Mnemonic: LeF ter Product
Chunksize Equals Existing Chunksize Definition: Chunksizes are copied from the input to the output file for every variable that is chunked in the input file. Variables not chunked in the input file will be chunked with default mappings. cnk map key values: ‘xst’, ‘cnk_xst’, ‘map_xst’ Mnemonic: EX iST
Chunksize Balances 1D and (N-1)-D Access to N-D Variable [default for netCDF4 input] Definition: Chunksizes are chosen so that 1-D and(N-1)-D hyperslabs of 3-D variables (e.g., point-timeseries orn latitude/longitude surfaces of 3-D fields) both require approximately the number of chunks. Hence their access time should be balanced. Russ Rew explains the motivation and derivation for this strategy here (http://www.unidata.ucar.edu/blogs/developer/en/entry/ chunking_data_choosing_shapes). cnk map key values: ‘rew’, ‘cnk_rew’, ‘map_rew’ Mnemonic: Russ REW
Chunksizes use netCDF4 defaults Definition: Chunksizes are determined by the underlying netCDF library. All variables selected by the current chunking policy have their chunksizes deter- mined by netCDF library defaults. The default algorithm netCDF uses to determine chunksizes has changed through the years, and thus depends on the netCDF library version. This map can be used to reset (portions of) previously chunked files to default chunking values. cnk map key values: ‘nc4’, ‘cnk_nc4’, ‘map_nc4’ Mnemonic: N etC DF4 110 NCO 5.0.2-alpha07 User Guide
Chunksizes use NCO recommendations [default for netCDF3 input] Definition: Chunksizes are determined by the currently recommended NCO map. This is a virtual option that ensures the chunking map is (in the subjective opinion of the authors) the best map for typical usage. As of NCO version 4.4.9 (May, 2015), this virtual map calls ‘map_lfp’. cnk map key values: ‘nco’, ‘cnk_nco’, ‘map_nco’ Mnemonic: N etC DFOperator
It is possible to combine the above chunking map algorithms with user-specified per- dimension (though not per-variable) chunksizes that override specific chunksizes determined by the maps above. The user specifies the per-dimension chunksizes with the (equivalent) long options ‘--cnk_dmn’ or ‘--chunk_dimension’). The option takes two comma-separated arguments, dmn nm,sz lmn, which are the dimension name and its chunksize (in elements, not bytes), respectively. The ‘--cnk_dmn’ option may be used as many times as necessary. The default behavior of chunking depends on several factors. As mentioned above, when no chunking options are explicitly specified by the user, then NCO preserves the chunking characteristics of the input file in the output file. This is equivalent to specifying both cnk plc and cnk map as “existing”, i.e., ‘--cnk_plc=xst --cnk_map=xst’. If output netCDF4 files are chunked with the default behavior of the netCDF4 library. When any chunking parameter except ‘cnk_plc’ or ‘cnk_map’ is specified (such as ‘cnk_dmn’ or ‘cnk_scl’), then the “existing” policy and map are retained and the out- put chunksizes are modified where necessary in accord with the user-specified parameter. When ‘cnk_map’ is specified and ‘cnk_plc’ is not, then NCO picks (what it thinks is) the optimal chunking policy. This has always been policy ‘map_g2d’. When ‘cnk_plc’ is spec- ified and ‘cnk_map’ is not, then NCO picks (what it thinks is) the optimal chunking map. This has always been map ‘map_rd1’. To start afresh and return to netCDF4 chunking defaults, select ‘cnk_map=nc4’. # Simple chunking and unchunking ncks -O -4 --cnk_plc=all in.nc out.nc # Chunk in.nc ncks -O -4 --cnk_plc=unchunk in.nc out.nc # Unchunk in.nc
# Chunk data then unchunk it, printing informative metadata ncks -O -4 -D 4 --cnk_plc=all ~/nco/data/in.nc ~/foo.nc ncks -O -4 -D 4 --cnk_plc=uck ~/foo.nc ~/foo.nc
# Set total chunksize to 8192 B ncks -O -4 -D 4 --cnk_plc=all --cnk_byt=8192 ~/nco/data/in.nc ~/foo.nc
# More complex chunking procedures, with informative metadata ncks -O -4 -D 4 --cnk_scl=8 ~/nco/data/in.nc ~/foo.nc ncks -O -4 -D 4 --cnk_scl=8 dstmch90_clm.nc ~/foo.nc ncks -O -4 -D 4 --cnk_dmn lat,64 --cnk_dmn lon,128 dstmch90_clm.nc \ ~/foo.nc ncks -O -4 -D 4 --cnk_plc=uck ~/foo.nc ~/foo.nc Chapter 3: Shared Features 111
ncks -O -4 -D 4 --cnk_plc=g2d --cnk_map=rd1 --cnk_dmn lat,32 \ --cnk_dmn lon,128 dstmch90_clm_0112.nc ~/foo.nc
# Chunking works with all operators... ncap2 -O -4 -D 4 --cnk_scl=8 -S ~/nco/data/ncap2_tst.nco \ ~/nco/data/in.nc ~/foo.nc ncbo -O -4 -D 4 --cnk_scl=8 -p ~/nco/data in.nc in.nc ~/foo.nc ncecat -O -4 -D 4 -n 12,2,1 --cnk_dmn lat,32 \ -p /data/zender/dstmch90 dstmch90_clm01.nc ~/foo.nc ncflint -O -4 -D 4 --cnk_scl=8 ~/nco/data/in.nc ~/foo.nc ncpdq -O -4 -D 4 -P all_new --cnk_scl=8 -L 5 ~/nco/data/in.nc ~/foo.nc ncrcat -O -4 -D 4 -n 12,2,1 --cnk_dmn lat,32 \ -p /data/zender/dstmch90 dstmch90_clm01.nc ~/foo.nc ncwa -O -4 -D 4 -a time --cnk_plc=g2d --cnk_map=rd1 --cnk_dmn lat,32 \ --cnk_dmn lon,128 dstmch90_clm_0112.nc ~/foo.nc Chunking policy ‘r1d’ changes the chunksize of 1-D record variables (and no other vari- ables) to that specified (with ‘--cnk_dmn’) chunksize. Any specified record dimension chunk- sizes will be applied to 1-D record variables only. Other dimensions may be chunked with their own ‘--cnk_dmn’ options that will apply to all variables. For example, ncks --cnk_plc=r1d --cnk_dmn=time,1000. in.nc out.nc This sets time chunks to 1000 only in 1-D record variables. Without the ‘r1d’ policy, time chunks would change in all variables. It is appropriate to conclude by informing users about an aspect of chunking that may not be expected. Three types of variables are always chunked: Record variables, Deflated (compressed) variables, and Checksummed variables. Hence all variables that contain a record dimension are also chunked (since data must be chunked in all dimensions, not just one). Unless otherwise specified by the user, the other (fixed, non-record) dimensions of record variables are assigned default chunk sizes. The HDF5 layer does all this automati- cally to optimize the on-disk variable/file storage geometry of record variables. Do notbe surprised to learn that files created without any explicit instructions to activate chunking nevertheless contain chunked variables.
3.32 Compression
Availability: ncbo, ncecat, nces, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa ¨ Short options: None Long options: ‘--ppc var1[,var2[,...]]=prc’, ‘--precision_preserving_compression var1[,var2[,...]]=prc’, ‘--quantize var1[,var2[,...]]=prc’
NCO implements or accesses four different compression algorithms, the standard lossless © DEFLATE algorithm and three lossy compression algorithms. All four algorithms reduce the on-disk size of a dataset while sacrificing no (lossless) or a tolerable amount (lossy) of 112 NCO 5.0.2-alpha07 User Guide
precision. First, NCO can access the lossless DEFLATE algorithm, a combination of Lempel- Ziv encoding and Huffman coding, algorithm on any netCDF4 dataset (see Section 3.33 [Deflation], page 121). Because it is lossless, this algorithm re-inflates deflated data to their full original precision. This algorithm is accessed via the HDF5 library layer (which itself calls the zlib library also used by gzip), and is unavailable with netCDF3. 3.32.1 Linear Packing The three lossy compression algorithms are Linear Packing (see Section 3.38 [Packed data], page 126), and two precision-preserving algorithms. Linear packing quantizes data of a higher precision type into a lower precision type (often NC_SHORT) that thus stores a fewer (though constant) number of bytes per value. Linearly packed data unpacks into a (much) smaller dynamic range than the floating-point data can represent. The type-conversion and reduced dynamic range of the data allows packing to eliminate bits typically used to store an exponent, thus improving its packing efficiency. Packed data also can also be deflated for additional space savings. A limitation of linear packing is that unpacking data stored as integers into the linear range defined by scale_factor and add_offset rapidly loses precision outside of a narrow range of floating-point values. Variables packed as NC_SHORT, for example, can represent only about 64000 discrete values in the range −32768 ∗ scalef actor + addoffset to 32767 ∗ scalef actor + addoffset. The precision of packed data equals the value of scale_factor, and scale_factor is usually chosen to span the range of valid data, not to represent the intrinsic precision of the variable. In other words, the precision of packed data cannot be specified in advance because it depends on the range of values to quantize. 3.32.2 Precision-Preserving Compression NCO implemented the final two lossy compression algorithms in version 4.4.8 (February, 2015). These are both Precision-Preserving Compression (PPC) algorithms and since stan- dard terminology for precision is remarkably imprecise, so is our nomenclature. The op- erational definition of “significant digit” in our precision preserving algorithms isthatthe exact value, before rounding or quantization, is within one-half the value of the decimal place occupied by the Least Significant Digit (LSD) of the rounded value. For example, the value pi = 3.14 correctly represents the exact mathematical constant pi to three significant digits because the LSD of the rounded value (i.e., 4) is in the one-hundredths digit place, and the difference between the exact value and the rounded value is less than one-half of one one-hundredth, i.e., (3.14159265358979323844 − 3.14 = 0.00159 < 0.005). One PPC algorithm preserves the specified total Number of Signifcant Digits (NSD) of the value. For example there is only one significant digit in the weight of most “eight-hundred pound gorillas” that you will encounter, i.e., so nsd = 1. This is the most straightforward measure of precision, and thus NSD is the default PPC algorithm. The other PPC algorithm preserves the number of Decimal Significant Digits (DSD), i.e., the number of significant digits following (positive, by convention) or preceding (negative) the decimal point. For example, ‘0.008’ and ‘800’ have, respectively, three and negative two digits digits following the decimal point, corresponding to dsd = 3 and dsd = −2. The only justifiable NSD for a given value depends on intrinsic accuracy and error char- acteristics of the model or measurements, and not on the units with which the value is Chapter 3: Shared Features 113
stored. The appropriate DSD for a given value depends on these intrinsic characteristics and, in addition, the units of storage. This is the fundamental difference between the NSD and DSD approaches. The eight-hundred pound gorilla always has nsd = 1 regardless of whether the value is stored in pounds or in some other unit. DSD corresponding to this weight is dsd = −2 if the value is stored in pounds, dsd = 4 if stored in megapounds.
Users may wish to express the precision to be preserved as either NSD or DSD. Invoke PPC with the long option ‘--ppc var=prc’, or give the same arguments to the synonyms ‘--precision_preserving_compression’, or to ‘--quantize’. Here var is the variable to quantize, and prc is its precision. The option ‘--ppc’ (and its long option equivalents such as ‘--quantize’) indicates the argument syntax will be key=val. As such, ‘--ppc’ and its synonyms are indicator options that accept arguments supplied one-by-one like ‘--ppc key1=val1 --ppc key2=val2’, or aggregated together in multi-argument format like ‘--ppc key1=val1#key2=val2’ (see Section 3.4.2 [Multi-arguments], page 32). The default algorithm assumes prc specifies NSD precision, e.g., ‘T=2’ means nsd = 2. Prepend prc with a decimal point to specify DSD precision, e.g., ‘T=.2’ means dsd = 2. NSD precision must be specified as a positive integer. DSD precision may be a positive or negative integer; and is specified as the negative base 10 logarithm of the desired precision, in accordwith common usage. For example, specifying ‘T=.3’ or ‘T=.-2’ tells the DSD algorithm to store only enough bits to preserve the value of T rounded to the nearest thousandth or hundred, respectively.
Setting var to default has the special meaning of applying the associated NSD or DSD algorithm to all floating point variables except coordinate variables. Variables not affected by default include integer and non-numeric atomic types, coordinates, and variables men- tioned in the bounds, climatology, or coordinates attribute of any variable. NCO applies PPC to coordinate variables only if those variables are explicitly specified (i.e., not with the ‘default=prc’ mechanism. NCO applies PPC to integer-type variables only if those variables are explicitly specified (i.e., not with the‘default=prc’, and only if the DSD algorithm is invoked with a negative prc. To prevent PPC from applying to certain non-coordinate vari- ables (e.g., gridcell_area or gaussian_weight), explicitly specify a precision exceeding 7 (for NC_FLOAT) or 15 (for NC_DOUBLE) for those variables. Since these are the maximum representable precisions in decimal digits, NCO turns-off PPC (i.e., does nothing) when more precision is requested.
The time-penalty for compressing and uncompressing data varies according to the al- gorithm. The Number of Significant DigitNSD ( ) algorithm quantizes by bitmasking, and employs no floating-point math. The Decimal Significant Digit(DSD) algorithm quantizes by rounding, which does require floating-point math. Hence NSD is likely faster than DSD, though the difference has not been measured. NSD creates a bitmask to alter the significand of IEEE 754 floating-point data. The bitmask is one for all bits to be retained andzeroor one for all bits to be ignored. The algorithm assumes that the number of binary digits (i.e., bits) necessary to represent a single base-10 digit is ln(10)/ln(2) = 3.32. The exact numbers of bits Nbit retained for single and double precision values are ceil(3.32 ∗ nsd) + 1 and ceil(3.32 ∗ nsd) + 2, respectively. Once these reach 23 and 53, respectively, bitmasking is completely ineffective. This occurs at nsd = 6.3 and 15.4, respectively. 114 NCO 5.0.2-alpha07 User Guide
The DSD algorithm, by contrast, uses rounding to remove undesired precision. The rounding24 zeroes the greatest number of significand bits consistent with the desired preci- sion. To demonstrate the change in IEEE representation caused by PPC rounding algorithms, consider again the case of pi, represented as an NC_FLOAT. The IEEE 754 single precision representations of the exact value (3.141592...), the value with only three significant digits treated as exact (3.140000...), and the value as stored (3.140625) after PPC-rounding with either the NSD (prc = 3) or DSD (prc = 2) algorithm are, respectively, S Exponent Fraction (Significand) Decimal Notes 0 100000001 0010010000111111011011 # 3.14159265 Exact 0 100000001 0010001111010111000011 # 3.14000000 0 100000001 0010010000000000000000 # 3.14062500 NSD = 3 0 100000001 0010010000000000000000 # 3.14062500 DSD = 2 The string of trailing zero-bits in the rounded values facilitates byte-stream compression. Note that the NSD and DSD algorithms do not always produce results that are bit-for-bit identical, although they do in this particular case. Reducing the preserved precision of NSD-rounding produces increasingly long strings of identical-bits amenable to compression: S Exponent Fraction (Significand) Decimal Notes 0 100000001 0010010000111111011011 # 3.14159265 Exact 0 100000001 0010010000111111011011 # 3.14159265 NSD = 8 0 100000001 0010010000111111011010 # 3.14159262 NSD = 7 0 100000001 0010010000111111011000 # 3.14159203 NSD = 6 0 100000001 0010010000111111000000 # 3.14158630 NSD = 5 0 100000001 0010010000111100000000 # 3.14154053 NSD = 4 0 100000001 0010010000000000000000 # 3.14062500 NSD = 3 0 100000001 0010010000000000000000 # 3.14062500 NSD = 2 0 100000001 0010000000000000000000 # 3.12500000 NSD = 1 The consumption of about 3 bits per digit of base-10 precision is evident, as is the coincidence of a quantized value that greatly exceeds the mandated precision for NSD = 2. Although the NSD algorithm generally masks some bits for all nsd <= 7 (for NC_FLOAT), compression algorithms like DEFLATE may need byte-size-or-greater (i.e., at least eight- bit) bit patterns before their algorithms can take advantage of of encoding such patterns for compression. Do not expect significantly enhanced compression from nsd > 5 (for NC_ FLOAT) or nsd > 14 (for NC_DOUBLE). Clearly values stored as NC_DOUBLE (i.e., eight-bytes) are susceptible to much greater compression than NC_FLOAT for a given precision because their significands explicitly contain 53 bits rather than 23 bits. Maintaining non-biased statistical properties during lossy compression requires special attention. The DSD algorithm uses rint(), which rounds toward the nearest even integer.
24 Rounding is performed by the internal math library rint() family of functions that were standardized in C99. The exact alorithm employed is val := rint(scale ∗ val)/scale where scale is the nearest power of 2 that exceeds 10 ∗ ∗prc, and the inverse of scale is used when prc < 0. For ppc = 3 or ppc = −2, for example, we have scale = 1024 and scale = 1/128. Chapter 3: Shared Features 115
Thus DSD has no systematic bias. However, the NSD algorithm uses a bitmask technique susceptible to statistical bias. Zeroing all non-significant bits is guaranteed to produce numbers quantized to the specified tolerance, i.e., half of the decimal value of the position occupied by the LSD. However, always zeroing the non-significant bits results in quantized numbers that never exceed the exact number. This would produce a negative bias in statistical quantities (e.g., the average) subsequently derived from the quantized numbers. To avoid this bias, our NSD implementation rounds non-significant bits down (to zero) orup (to one) in an alternating fashion when processing array data. In general, the first element is rounded down, the second up, and so on. This results in a mean bias quite close to zero. The only exception is that the floating-point value of zero is never quantized upwards. For simplicity, NSD always rounds scalars downwards. Although NSD or DSD are different algorithms under the hood, they both replace the (unwanted) least siginificant bits of the IEEE significand with a string of consecutive zeroes. Byte-stream compression techniques, such as the gzip DEFLATE algorithm compression available in HDF5, always compress zero-strings more efficiently than random digits. The net result is netCDF files that utilize compression can be significantly reduced insize. This feature only works when the data are compressed, either internally (by netCDF) or externally (by another user-supplied mechanism). It is most straightfoward to compress data internally using the built-in compression and decompression supported by netCDF4. For convenience, NCO automatically activates file-wide Lempel-Ziv deflation (see Section 3.33 [Deflation], page 121) level one (i.e., ‘-L 1’) when PPC is invoked on any variable in a netCDF4 output file. This makes PPC easier to use effectively, since the user need not explicitly specify deflation. Any explicitly specified deflation (including no deflation, ‘-L 0’) will override the PPC deflation default. If the output file is a netCDF3 format, NCO will emit a message suggesting internal netCDF4 or external netCDF3 compression. netCDF3 files compressed by an external utility such as gzip accrue approximately the same benefits (shrinkage) as netCDF4, although with netCDF3 the user or provider must uncompress (e.g., gunzip) the file before accessing the data. There is no benefit to rounding numbers and storing them in netCDF3 files unless such custom compression/decompression is employed. Without that, one may as well maintain the undesired precision. The user accesses PPC through a single switch, ‘--ppc’, repeated as many times as necessary. To apply the NSD algorithm to variable u use, e.g., ncks -7 --ppc u=2 in.nc out.nc The output file will preserve only two significant digits of u. The options ‘-4’ or ‘-7’ ensure a netCDF4-format output (regardless of the input file format) to support internal compression. It is recommended though not required to write netCDF4 files after PPC. For clarity the ‘-4/-7’ switches are omitted in subsequent examples. NCO attaches attributes that indicate the algorithm used and degree of precision retained for each variable affected by PPC. The NSD and DSD algorithms store the attributes number_of_significant_digits and least_significant_digit25, respectively.
25 A suggestion by Rich Signell and the nc3tonc4 tool by Jeff Whitaker inspired NCO to implement PPC. Note that NCO implements a different DSD algorithm than nc3tonc4, and produces slightly different (not bit-for-bit) though self-consistent and equivalent results. nc3tonc4 records the precision of its DSD algo- rithm in the attribute least_significant_digit and NCO does the same for consistency. The Unidata blog here (http://www.unidata.ucar.edu/blogs/developer/en/entry/compression_by_bit_shaving) 116 NCO 5.0.2-alpha07 User Guide
It is safe to attempt PPC on input that has already been rounded. Variables can be made rounder, not sharper, i.e., variables cannot be “un-rounded”. Thus PPC attempted on an input variable with an existing PPC attribute proceeds only if the new rounding level exceeds the old, otherwise no new rounding occurs (i.e., a “no-op”), and the original PPC attribute is retained rather than replaced with the newer value of prc. To request, say, five significant digitsnsd ( = 5) for all fields, except, say, wind speeds which are only known to integer values (dsd = 0) in the supplied units, requires ‘--ppc’ twice: ncks -4 --ppc default=5 --ppc u,v=.0 in.nc out.nc To preserve five digits in all variables except coordinate variables and u and v, use the ‘default’ option and separately specify the exceptions: ncks --ppc default=5 --ppc u,v=20 in.nc out.nc The ‘--ppc’ option may be specified any number of times to support varying precision types and levels, and each option may aggregate all the variables with the same precision ncks --ppc p,w,z=5 --ppc q,RH=4 --ppc T,u,v=3 in.nc out.nc ncks --ppc p,w,z=5#q,RH=4#T,u,v=3 in.nc out.nc # Multi-argument format Any var argument may be a regular expression. This simplifies generating lists of related variables: ncks --ppc Q.?=5 --ppc FS.?,FL.?=4 --ppc RH=.3 in.nc out.nc ncks --ppc Q.?=5#FS.?,FL.?=4#RH=.3 in.nc out.nc # Multi-argument format
Although PPC-rounding instantly reduces data precision, on-disk storage reduction only occurs once the data are compressed.
How can one be sure the lossy data are sufficiently precise? PPC preserves all significant digits of every value. The DSD algorithm uses floating-point math to round each value optimally so that it has the maximum number of zeroed bits that preserve the specified precision. The NSD algorithm uses a theoretical approach (3.2 bits per base-10 digit), tuned and tested to ensure the worst case quantization error is less than half the value of the minimum increment in the least significant digit. We define several metrics to quantify the quantization error. The mean error¯ and + mean absolute error ¯ incurred in quantizing a variable from its true values xi to quantized values qi are, respectively,
Pi=N µ m w (x − q ) Pi=N µ m w |x − q | ¯ = i=1 i i i i i and ¯+ = i=1 i i i i i Pi=N Pi=N i=1 µimiwi i=1 µimiwi
also shows how to compress IEEE floating-point data by zeroing insignificant bits. The author, John Caron, writes that the technique has been called “bit-shaving”. We call the algorithm of always rounding- up “bit-setting”. And we named the algorithm produced by alternately rounding up and down (with a few other bells and whistles) “bit-grooming”. Imagine orthogonally raking an already-groomed Japanese rock garden. The criss-crossing tracks increase the pattern’s entropy, and this entropy produces self- compensating instead of accumulating errors during statistical operations. Chapter 3: Shared Features 117
where µi is 1 unless xi equals the missing value, mi is 1 unless xi is masked, and wi is the weight. The maximum and minimum errors max and min are both signed
max = max(xi − qi) and min = min(xi − qi)
+ + while the maximum and minimum absolute errors mabs and mibs are positive-definite.
+ mabs = max|xi − qi| = max(|max|, |min|)
+ mibs = min|xi − qi| = min(|max|, |min|) + Typically mibs = 0 for quantization, since many exact values need no quantization. Bit- shifting zeros into the least significant bitsLSB ( s) always underestimates true values so that
max = 0. Conversely, bit-shifting ones into the LSBs always overestimates true values so that min = 0. Our NSD algorithm is balanced because it alternates bit-shifting zeroes and + + ones. Balanced algorithms should yield max ≈ −min, mabs ≈ mibs, and ¯ ≈ 0. + + The three most important error metrics for quantization are mabs, ¯ , and ¯. The upper + + bound (worst case) quantization performance is mabs. ¯ measures the absolute mean accuracy of quantization, and does not allow positive and negative offsets to compensate + + eachother and conceal poor performance. The difference bewtween mabs and ¯ indicates how much of an outlier the worst case is. The mean accuracy ¯ indicates whether statistical properties of quantized numbers will accurately reflect the true values. All three metrics are expressed in terms of the fraction of the ten’s place occupied by the LSD. If the LSD is the hundreds digit or the thousandths digit, then the metrics are frac- tions of 100, or of 1/100, respectively. PPC algorithms should produce maximum absolute errors no greater than 0.5 in these units. If the LSD is the hundreds digit, then quantized versions of true values will be within fifty of the true value. It is much easier to satisfy this tolerance for a true value of 100 (only 50% accuracy required) than for 999 (5% accuracy required). Thus the minimum accuracy guaranteed for nsd = 1 ranges from 5–50%. For this reason, the best and worst cast performance usually occurs for true values whose LSD value is close to one and nine, respectively. Of course most users prefer prc > 1 because accuracies increase exponentially with prc. Continuing the previous example to prc = 2, quantized versions of true values from 1000–9999 will also be within 50 of the true value, i.e., have accuracies from 0.5–5%. In other words, only two significant digits are necessary to guarantee better than 5% accuracy in quantization. We recommend that dataset pro- ducers and users consider quantizing datasets with nsd = 3. This guarantees accuracy of 0.05–0.5% for individual values. Statistics computed from ensembles of quantized values will, assuming the mean error ¯ is small, have much better accuracy than 0.5%. This accuracy is the most that can be justified for many applications. To demonstrate these principles we conduct error analyses on an artificial, reproducible dataset, and on an actual dataset of observational analysis values.26 The table summarizes quantization accuracy based on the three metrics.
NSD Number of Significant Digits.
26 The artificial dataset employed is one million evenly spaced values from 1.0–2.0. The analysis dataare N = 13934592 values of the temperature field from the NASA MERRA analysis of 20130601. 118 NCO 5.0.2-alpha07 User Guide
Emabs Maximum absolute error.
Emebs Mean absolute error.
Emean Mean error.
Artificial Data: N=1000000 values in [1.0,2.0) in steps of 1.0e-6 Single-Precision Double-Precision Single-Precision NSD Emabs Emebs Emean Emabs Emebs Emean DSD Emabs Emebs Emean 1 0.31 0.11 4.1e-4 0.31 0.11 4.0e-4 1 0.30 0.11 -8.1e-4 2 0.39 0.14 6.8e-5 0.39 0.14 5.5e-5 2 0.39 0.14 -1.3e-4 3 0.49 0.17 1.0e-6 0.49 0.17 -5.5e-7 3 0.49 0.17 -2.0e-5 4 0.30 0.11 3.2e-7 0.30 0.11 -6.1e-6 4 0.30 0.11 5.1e-8 5 0.37 0.13 3.1e-7 0.38 0.13 -5.6e-6 5 0.38 0.13 2.6e-6 6 0.36 0.12 -4.4e-7 0.48 0.17 -4.1e-7 6 0.48 0.17 7.2e-6 7 0.00 0.00 0.0 0.30 0.10 1.5e-7 7 0.00 0.00 0.0
Observational Analysis: N=13934592 values MERRA Temperature 20130601 Single-Precision NSD Emabs Emebs Emean 1 0.31 0.11 2.4e-3 2 0.39 0.14 3.8e-4 3 0.49 0.17 -9.6e-5 4 0.30 0.11 2.3e-3 5 0.37 0.13 2.2e-3 6 0.36 0.13 1.7e-2 7 0.00 0.00 0.0
All results show that PPC quantization performs as expected. Absolute maximum errors Emabs < 0.5 for all prc. For 1 <= prc <= 6, quantization results in comparable maximum absolute and mean absolute errors Emabs and Emebs, respectively. Mean errors Emean are orders of magnitude smaller because quantization produces over- and under-estimated values in balance. When prc = 7, quantization of single-precision values is ineffective, because all available bits are used to represent the maximum precision of seven digits. The maximum and mean absolute errors Emabs and Emebs are nearly identical across algorithms, precisions, and dataset types. This is consistent with both the artificial data and empirical data being random, and thus exercising equally strengths and weaknesses of the algorithms over the course of millions of input values. We generated artificial arrays with many different starting values and interval spacing and all gave qualitatively similar results. The results presented are the worst obtained.
The artificial data has much smaller mean error Emean than the observational analysis. The reason why is unclear. It may be because the temperature field is concentrated in particular ranges of values (and associated quantization errors) prevalent on Earth, e.g., 200 < T < 320. It is worth noting that the mean error Emean < 0.01 for 1 <= prc < 6, and that Emean is typically at least two or more orders of magnitude less than Emabs. Thus quantized values with precisions as low as prc = 1 still yield highly significant statistics by contemporary scientific standards. Chapter 3: Shared Features 119
Testing shows that PPC quantization enhances compression of typical climate datasets. The degree of enhancement depends, of course, on the required precision. Model results are often computed as NC_DOUBLE then archived as NC_FLOAT to save space. This table summarizes the performance of lossless and lossy compression on two typical, or at least random, netCDF data files. The files were taken from representative model-simulated and satellite-retrieved datasets. Only floating-point data were compressed. No attempt was made to compress integer-type variables as they occupy an insignificant fraction of every dataset. The columns are
Type File-type: N3 for netCDF CLASSIC, N4 for NETCDF4, N7 for NETCDF4_CLASSIC (which comprises netCDF3 data types and structures with netCDF4 storage features like compression), H4 for HDF4, and H5 for HDF5. N4/7 means results apply to both N4 and N7 filetypes. LLC Type of lossless compression employed, if any. Bare numbers refer to the strength of the DEFLATE algorithm employed internally by netCDF4/HDF5, while numbers prefixed with B refer to the block size employed by the Burrows- Wheeler algorithm in bzip2. PPC Number of significant digits retained by the precision-preserving compression NSD algorithm. Pck Y if the default ncpdq packing algorithm (convert floating-point types to NC_ SHORT) was employed. Size Resulting filesize in MB. % Compression ratio, i.e., resulting filesize relative to original size, in percent. In some cases the original files is already losslessly compressed. The compression ratios reported are relative to the size of the original file as distributed, not as optimally losslessly compressed.
A dash (-) indicates the associated compression feature was not employed. # dstmch90_clm.nc Type LLC PPC Pck Size % Flags and Notes N3 - - - 34.7 100.0 Original is not compressed N3 B1 - - 28.9 83.2 bzip2 -1 N3 B9 - - 29.3 84.4 bzip2 -9 N7 - - - 35.0 101.0 N7 1 - - 28.2 81.3 -L 1 N7 9 - - 28.0 80.8 -L 9 N7 - - Y 17.6 50.9 ncpdq -L 0 N7 1 - Y 7.9 22.8 ncpdq -L 1 N7 1 7 - 28.2 81.3 --ppc default=7 N7 1 6 - 27.9 80.6 --ppc default=6 N7 1 5 - 25.9 74.6 --ppc default=5 N7 1 4 - 22.3 64.3 --ppc default=4 N7 1 3 - 18.9 54.6 --ppc default=3 N7 1 2 - 14.5 43.2 --ppc default=2 N7 1 1 - 10.0 29.0 --ppc default=1 120 NCO 5.0.2-alpha07 User Guide
# b1850c5cn_doe_polar_merged_0_cesm1_2_0_HD+MAM4+tun2b.hp.e003.cam.h0.0001-01.nc Type LLC PPC Pck Size % Flags and Notes N3 - - - 119.8 100.0 Original is not compressed N3 B1 - - 84.2 70.3 bzip2 -1 N3 B9 - - 84.8 70.9 bzip2 -9 N7 - - - 120.5 100.7 N7 1 - - 82.6 69.0 -L 1 N7 9 - - 82.1 68.6 -L 9 N7 - - Y 60.7 50.7 ncpdq -L 0 N7 1 - Y 26.0 21.8 ncpdq -L 1 N7 1 7 - 82.6 69.0 --ppc default=7 N7 1 6 - 81.9 68.4 --ppc default=6 N7 1 5 - 77.2 64.5 --ppc default=5 N7 1 4 - 69.0 57.6 --ppc default=4 N7 1 3 - 59.3 49.5 --ppc default=3 N7 1 2 - 49.5 41.3 --ppc default=2 N7 1 1 - 38.2 31.9 --ppc default=1
# MERRA300.prod.assim.inst3_3d_asm_Cp.20130601.hdf Type LLC PPC Pck Size % Flags and Notes H4 5 - - 244.3 100.0 Original is compressed H4 B1 - - 244.7 100.1 bzip2 -1 N4 5 - - 214.5 87.8 N7 5 - - 210.6 86.2 N4 B1 - - 215.4 88.2 bzip2 -1 N4 B9 - - 214.8 87.9 bzip2 -9 N3 - - - 617.1 252.6 N4/7 - - - 694.0 284.0 -L 0 N4/7 1 - - 223.2 91.3 -L 1 N4/7 9 - - 207.3 84.9 -L 9 N4/7 - - Y 347.1 142.1 ncpdq -L 0 N4/7 1 - Y 133.6 54.7 ncpdq -L 1 N4/7 1 7 - 223.1 91.3 --ppc default=7 N4/7 1 6 - 225.1 92.1 --ppc default=6 N4/7 1 5 - 221.4 90.6 --ppc default=5 N4/7 1 4 - 201.4 82.4 --ppc default=4 N4/7 1 3 - 185.3 75.9 --ppc default=3 N4/7 1 2 - 150.0 61.4 --ppc default=2 N4/7 1 1 - 100.8 41.3 --ppc default=1
# OMI-Aura_L2-OMIAuraSO2_2012m1222-o44888_v01-00-2014m0107t114720.h5 Type LLC PPC Pck Size % Flags and Notes H5 5 - - 29.5 100.0 Original is compressed H5 B1 - - 29.3 99.6 bzip2 -1 N4 5 - - 29.5 100.0 N4 B1 - - 29.3 99.6 bzip2 -1 Chapter 3: Shared Features 121
N4 B9 - - 29.3 99.4 bzip2 -9 N4 - - - 50.7 172.3 -L 0 N4 1 - - 29.8 101.3 -L 1 N4 9 - - 29.4 99.8 -L 9 N4 - - Y 27.7 94.0 ncpdq -L 0 N4 1 - Y 12.9 43.9 ncpdq -L 1 N4 1 7 - 29.7 100.7 --ppc default=7 N4 1 6 - 29.7 100.8 --ppc default=6 N4 1 5 - 27.3 92.8 --ppc default=5 N4 1 4 - 23.8 80.7 --ppc default=4 N4 1 3 - 20.3 69.0 --ppc default=3 N4 1 2 - 15.1 51.2 --ppc default=2 N4 1 1 - 9.9 33.6 --ppc default=1
A selective, per-variable approach to PPC yields the best balance of precision and com- pression yet requires the dataset producer to understand the intrinsic precision of each variable. Such a specification for a GCM dataset might look like this (using names for the NCAR CAM model):
# Be conservative on non-explicit quantities, so default=5 # Some quantities deserve four significant digits # Many quantities, such as aerosol optical depths and burdens, are # highly uncertain and only useful to three significant digits. ncks -7 -O \ --ppc default=5 \ --ppc AN.?,AQ.?=4 \ --ppc AER.?,AOD.?,ARE.?,AW.?,BURDEN.?=3 \ ncar_cam.nc ~/foo.nc
3.33 Deflation
Availability: ncap2, ncbo, ncclimo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ¨ ncremap, ncwa Short options: ‘-L’ Long options: ‘--dfl_lvl’, ‘--deflate’
© All NCO operators that define variables support the netCDF4 feature of storing variables compressed with the lossless DEFLATE compression algorithm. DEFLATE combines the Lempel-Ziv encoding with Huffman coding. The specific version used by HDF5netCDF4/ is that implemented in the zlib library used by gzip. Activate deflation with the -L dfl_lvl short option (or with the same argument to the ‘--dfl_lvl’ or ‘--deflate’ long options). Specify the deflation level dfl lvl on a scale from no deflation (dfl lvl = 0) to maximum deflation (dfl lvl = 9). Under the hood, this selects the compression blocksize. Minimal deflationdfl ( lvl = 1) achieves considerable storage compression with little time penalty. Higher deflation levels require more time for compression. File sizes resulting from minimal 122 NCO 5.0.2-alpha07 User Guide
(dfl lvl = 1) and maximal (dfl lvl = 9) deflation levels typically differ by less than 10%in size. To compress an entire file using deflation, use ncks -4 -L 0 in.nc out.nc # No deflation (fast, no time penalty) ncks -4 -L 1 in.nc out.nc # Minimal deflation (little time penalty) ncks -4 -L 9 in.nc out.nc # Maximal deflation (much slower) Unscientific testing shows that deflation compresses typical climate datasets by 30-60%. Packing, a lossy compression technique available for all netCDF files (see Section 3.38 [Packed data], page 126), can easily compress files by 50%. Packed data may be deflated to squeeze datasets by about 80%: ncks -4 -L 1 in.nc out.nc # Minimal deflation (~30-60% compression) ncks -4 -L 9 in.nc out.nc # Maximal deflation (~31-63% compression) ncpdq in.nc out.nc # Standard packing (~50% compression) ncpdq -4 -L 9 in.nc out.nc # Deflated packing (~80% compression) ncks prints deflation parameters, if any, to screen (see Section 4.8 [ncks netCDF Kitchen Sink], page 263).
3.34 MD5 digests
Availability: ncecat, ncks, ncrcat ¨ Short options: Long options: ‘--md5_dgs’, ‘--md5_digest’, ‘--md5_wrt_att’, ‘--md5_write_attribute’
As of NCO version 4.1.0 (April, 2012), NCO supports data integrity verification using the © MD5 digest algorithm. This support is currently implemented in ncks and in the multi-file concatenators ncecat and ncrcat. Activate it with the ‘--md5_dgs’ or ‘--md5_digest’ long options. As of NCO version 4.3.3 (July, 2013), NCO will write the MD5 digest of each variable as an NC_CHAR attribute named MD5. This support is currently implemented in ncks and in the multi-file concatenators ncecat and ncrcat. Activate it with the ‘--md5_wrt_att’ or ‘--md5_write_attribute’ long options. The behavior and verbosity of the MD5 digest is operator-dependent. MD5 digests may be activated in both ncks invocation types, the one-filename argument mode for printing sub- setted and hyperslabbed data, and the two-filename argument mode for copying that data to a new file. Both modes will incur minor overhead from performing the hash algorithm for each variable read, and each variable written will have an additional attribute named MD5. When activating MD5 digests with ncks it is assumed that the user wishes to print the digest of every variable when the debugging level exceeds one. ncks displays an MD5 digest as a 32-character hexadecimal string in which each two characters represent one byte of the 16-byte digest: > ncks --trd -D 2 -C --md5 -v md5_a,md5_abc ~/nco/data/in.nc ... Chapter 3: Shared Features 123
ncks: INFO MD5(md5_a) = 0cc175b9c0f1b6a831c399e269772661 md5_a = ’a’ ncks: INFO MD5(md5_abc) = 900150983cd24fb0d6963f7d28e17f72 lev[0]=100 md5_abc[0--2]=’abc’ > ncks --trd -D 2 -C -d lev,0 --md5 -v md5_a,md5_abc ~/nco/data/in.nc ... ncks: INFO MD5(md5_a) = 0cc175b9c0f1b6a831c399e269772661 md5_a = ’a’ ncks: INFO MD5(md5_abc) = 0cc175b9c0f1b6a831c399e269772661 lev[0]=100 md5_abc[0--0]=’a’ In fact these examples demonstrate the validity of the hash algorithm since the MD5 hashes of the strings “a” and “abc” are widely known. The second example shows that the hyperslab of variable md5_abc (= “abc”) consisting of only its first letter (= “a”) has the same hash as the variable md5_a (“a”). This illustrates that MD5 digests act only on variable data, not on metadata. When activating MD5 digests with ncecat or ncrcat it is assumed that the user wishes to verify that every variable written to disk has the same MD5 digest as when it is subsequently read from disk. This incurs the major additional overhead of reading in each variable after it is written and performing the hash algorithm again on that to compare to the original hash. Moreover, it is assumed that such operations are generally done in “production mode” where the user is not interested in actually examining the digests herself. The digests proceed silently unless the debugging level exceeds three: > ncecat -O -D 4 --md5 -p ~/nco/data in.nc in.nc ~/foo.nc | grep MD5 ... ncecat: INFO MD5(wnd_spd) = bec190dd944f2ce2794a7a4abf224b28 ncecat: INFO MD5 digests of RAM and disk contents for wnd_spd agree > ncrcat -O -D 4 --md5 -p ~/nco/data in.nc in.nc ~/foo.nc | grep MD5 ... ncrcat: INFO MD5(wnd_spd) = 74699bb0a72b7f16456badb2c995f1a1 ncrcat: INFO MD5 digests of RAM and disk contents for wnd_spd agree Regardless of the debugging level, an error is returned when the digests of the variable read from the source file and from the output file disagree. These rules may further evolve as NCO pays more attention to data integrity. We welcome feedback and suggestions from users.
3.35 Buffer sizes
Availability: All operators ¨ Short options: Long options: ‘--bfr_sz_hnt’, ‘--buffer_size_hint’
As of NCO version 4.2.0 (May, 2012), NCO allows the user to request specific buffer sizes © to allocate for reading and writing files. This buffer size determines how many system calls 124 NCO 5.0.2-alpha07 User Guide
the netCDF layer must invoke to read and write files. By default, netCDF uses the preferred I/O block size returned as the ‘st_blksize’ member of the ‘stat’ structure returned by the stat() system call27. Otherwise, netCDF uses twice the system pagesize. Larger sizes can increase access speed by reducing the number of system calls netCDF makes to read/write data from/to disk. Because netCDF cannot guarantee the buffer size request will be met, the actual buffer size granted by the system is printed as an INFO statement. # Request 2 MB file buffer instead of default 8 kB buffer > ncks -O -D 3 --bfr_sz=2097152 ~/nco/data/in.nc ~/foo.nc ... ncks: INFO nc__open() will request file buffer size = 2097152 bytes ncks: INFO nc__open() opened file with buffer size = 2097152 bytes ...
3.36 RAM disks
Availability: All operators (works with netCDF3 files only) ¨ Short options: Long options: ‘--ram_all’, ‘--create_ram’, ‘--open_ram’, ‘--diskless_all’
As of NCO version 4.2.1 (August, 2012), NCO supports the use of diskless files, aka RAM © disks, for access and creation of netCDF3 files (these options have no effect on netCDF4 files). Two independent switches,--open_ram ‘ ’ and ‘--create_ram’, control this feature. Before describing the specifics of these switches, we describe why many NCO operations will not benefit from them. Essentially, reading/writing from/to RAM rather than disk only hastens the task when reads/writes to disk are avoided. Most NCO operations are simple enough that they require a single read-from/write-to disk for every block of input/output. Diskless access does not change this, but it does add an extra read-from/write-to RAM. However this extra RAM write/read does avoid contention for limited system resources like disk-head access. Operators which may benefit from RAM disks include ncwa, which may need to read weighting variables multiple times, the multi-file operators ncra, ncrcat, and ncecat, which may try to write output at least once per input file, and ncap2 scripts which may be arbitrarily long and convoluted. The ‘--open_ram’ switch causes input files to copied to RAM when opened. All further metadata and data access occurs in RAM and thus avoids access time delays caused by disk- head movement. Usually input data is read at most once so it is unlikely that requesting input files be stored in RAM will save much time. The likeliest exceptions are files that are accessed numerous times, such as those repeatedly analyzed by ncap2. Invoking ‘--open_ram’, ‘--ram_all’, or ‘--diskless_all’ uses much more system mem- ory. To copy the input file to RAM increases the sustained memory use by exactly the on-disk filesize of the input file, i.e., MS+ = FT . For large input files this can be a huge memory burden that starves the rest of the NCO analysis of sufficient RAM. To be safe,
27 On modern Linux systems the block size defaults to 8192 B. The GLADE filesystem at NCAR has a block size of 512 kB. Chapter 3: Shared Features 125
use ‘--open_ram’, ‘--ram_all’, or ‘--diskless_all’ only on files that are much (say at least a factor of four) smaller than your available system RAM. See Section 2.9 [Memory Requirements], page 24, for further details. The ‘--create_ram’ switch causes output files to be created in RAM, rather than on disk. These files are copied to disk only when closed, i.e., when the operator completes. Creating files in RAM may save time, especially with ncap2 computations that are iterative, e.g., loops, and for multi-file operators that write output every record (timestep) orfile. RAM files provide many of the same benefits as RAM variables in such cases (see Section 4.1.13 [RAM variables], page 178). Two switches, ‘--ram_all’ and ‘--diskless_all’, are convenient shortcuts for specify- ing both ‘--create_ram’ and ‘--open_ram’. Thus ncks in.nc out.nc # Default: Open in.nc on disk, write out.nc to disk ncks --open_ram in.nc out.nc # Open in.nc in RAM, write out.nc to disk ncks --create_ram in.nc out.nc # Create out.nc in RAM, write to disk # Open in.nc in RAM, create out.nc in RAM, then write out.nc to disk ncks --open_ram --create_ram in.nc out.nc ncks --ram_all in.nc out.nc # Same as above ncks --diskless_all in.nc out.nc # Same as above It is straightforward to demonstrate the efficacy of RAM disks. For NASA we constructed a test that employs ncecat an arbitrary number (set to one hundred thousand) of files that are all symbolically linked to the same file. Everything is on the local filesystem (not DAP). # Create symbolic links for benchmark cd ${DATA}/nco # Do all work here for idx in {1..99999}; do idx_fmt=‘printf "%05d" ${idx}‘ /bin/ln -s ${DATA}/nco/LPRM-AMSR_E_L3_D_SOILM3_V002-20120512T111931Z_20020619.nc \ ${DATA}/nco/${idx_fmt}.nc done # Benchmark time to ncecat one hundred thousand files time ncecat --create_ram -O -u time -v ts -d Latitude,40.0 \ -d Longitude,-105.0 -p ${DATA}/nco -n 99999,5,1 00001.nc ~/foo.nc Run normally on a laptop in 201303, this completes in 21 seconds. The ‘--create_ram’ reduces the elapsed time to 9 seconds. Some of this speed may be due to using symlinks and caching. However, the efficacy of--create_ram ‘ ’ is clear. Placing the output file in RAM avoids thousands of disk writes. It is not unreasonable to for NCO to process a million files like this in a few minutes. However, there is no substitute for benchmarking withreal files. A completely independent way to reduce time spent writing files is to refrain from writing temporary output files. This is accomplished with--no_tmp_fl the‘ ’ switch (see Section 2.3 [Temporary Output Files], page 17).
3.37 Unbuffered I/O 126 NCO 5.0.2-alpha07 User Guide
Availability: All operators (works on netCDF3 files only) ¨ Short options: Long options: ‘--share_all’, ‘--create_share’, ‘--open_share’, ‘--unbuffered_io’, ‘--uio’
© As of NCO version 4.9.4 (July, 2020), NCO supports unbuffered I/O with netCDF3 files when requested with the ‘--unbuffered_io’ flag, or its synonyms--uio ‘ ’ or ‘--share_all’. (Note that these options work only with netCDF3 files and have no affect on netCDF4 files). These flags turn-off the default I/O buffering mode for both newly created and existing datasets. For finer-grained control, use the --create_share switch to request unbuffered I/O only for newly created datasets, and the --open_share switch to request unbuffered I/O only for existing datasets. Typically these options only significantly reduce throughput time when large record variables are written or read. Normal I/O buffering copies the data to be read/written into an intermediate buffer in order to avoid numerous small reads/writes. Unbuffered I/O avoids this intermediate step and can therefore execute (sometimes much) faster when read/write lengths are large.
3.38 Packed data
Availability: ncap2, ncbo, nces, ncflint, ncpdq, ncra, ncwa ¨ Short options: None Long options: ‘--hdf_upk’, ‘--hdf_unpack’
© The phrase packed data refers to data which are stored in the standard netCDF3 lossy linear packing format. See Section 4.8 [ncks netCDF Kitchen Sink], page 263, for a descrip- tion of deflation, a lossless compression technique available with netCDF4 only. Packed data may be deflated to save additional space.
Standard Packing Algorithm Packing The standard netCDF linear packing algorithm (described here (http://www. unidata.ucar.edu/software/netcdf/docs/netcdf/Attribute-Conventions. html)) produces packed data with the same dynamic range as the original but which re- quires no more than half the space to store. NCO will always use this algorithm for packing. Like all packing algorithms, linear packing is lossy. Just how lossy depends on the values themselves, especially their range. The packed variable is stored (usually) as type NC_SHORT with the two attributes required to unpack the variable, scale_factor and add_offset, stored at the original (unpacked) precision of the variable28. Let min and max be the
28 Although not a part of the standard, NCO enforces the policy that the _FillValue attribute, if any, of a packed variable is also stored at the original precision. Chapter 3: Shared Features 127
minimum and maximum values of x. scale factor = (max − min)/ndrv add offset = (min + max)/2 pck = (upk − add offset)/scale factor ndrv × [upk − (min + max)/2] = max − min where ndrv is the number of discrete representable values for given type of packed variable. The theoretical maximum value for ndrv is two raised to the number of bits used to store the packed variable. Thus if the variable is packed into type NC_SHORT, a two-byte datatype, then there are at most 216 = 65536 distinct values representable. In practice, the number of discretely representible values is taken to be two less than the theoretical maximum. This leaves space for a missing value and solves potential problems with rounding that may occur during the unpacking of the variable. Thus for NC_SHORT, ndrv = 65536 − 2 = 65534. Less often, the variable may be packed into type NC_CHAR, where ndrv = 28 − 2 = 256 − 2 = 254, or type NC_INT where where ndrv = 232 − 2 = 4294967295 − 2 = 4294967293. One useful feature of the (lossy) netCDF packing algorithm is that lossless packing algorithms perform well on top of it. Standard (Default) Unpacking Algorithm Unpacking The unpacking algorithm depends on the presence of two attributes, scale_ factor and add_offset. If scale_factor is present for a variable, the data are multiplied by the value scale factor after the data are read. If add_offset is present for a variable, then the add offset value is added to the data after the data are read. If both scale_factor and add_offset attributes are present, the data are first scaled by scale factor before the offset add offset is added. upk = scale factor × pck + add offset pck × (max − min) min + max = + ndrv 2 NCO will use this algorithm for unpacking unless told otherwise as described below. When scale_factor and add_offset are used for packing, the associated variable (containing the packed data) is typically of type byte or short, whereas the unpacked values are intended to be of type int, float, or double. An attribute’s scale_factor and add_offset and _FillValue, if any, should all be of the type intended for the unpacked data, i.e., int, float or double. Non-Standard Packing and Unpacking Algorithms Many (most?) files originally written in HDF4 format use poorly documented pack- ing/unpacking algorithms that are incompatible and easily confused with the netCDF pack- ing algorithm described above. The unpacking component of the “conventional” HDF algo- rithm (described here (http://www.hdfgroup.org/HDF5/doc/UG/UG_frame10Datasets. html) and in Section 3.10.6 of the HDF4 Users Guide here (http://www.hdfgroup.org/ release4/doc/UsrGuide_html/UG_PDF.pdf), and in the FAQ for MODIS MOD08 data here (http://modis-atmos.gsfc.nasa.gov/MOD08_D3/faq.html)) is upk = scale factor × (pck − add offset) 128 NCO 5.0.2-alpha07 User Guide
The unpacking component of the HDF algorithm employed for MODIS MOD13 data is
upk = (pck − add offset)/scale factor
The unpacking component of the HDF algorithm employed for MODIS MOD04 data is the same as the netCDF algorithm. Confusingly, the (incompatible) netCDF and HDF algorithms both store their parameters in attributes with the same names (scale_factor and add_offset). Data packed with one algorithm should never be unpacked with the other; doing so will result in incorrect answers. Unfortunately, few users are aware that their datasets may be packed, and fewer know the details of the packing algorithm employed. This is what we in the “bizness” call an interoperability issue because it hampers data analysis performed on heterogeneous systems. As described below, NCO automatically unpacks data before performing arithmetic. This automatic unpacking occurs silently since there is usually no reason to bother users with these details. There is as yet no generic way for NCO to know which packing convention was used, so NCO assumes the netCDF convention was used. NCO uses the same convention for unpacking unless explicitly told otherwise with the ‘--hdf_upk’ (also ‘--hdf_unpack’) switch. Until and unless a method of automatically detecting the packing method is devised, it must remain the user’s responsibility to tell NCO when to use the HDF convention instead of the netCDF convention to unpack. If your data originally came from an HDF file (e.g., NASA EOS) then it was likely packed with the HDF convention and must be unpacked with the same convention. Our recom- mendation is to only request HDF unpacking when you are certain. Most packed datasets encountered by NCO will have used the netCDF convention. Those that were not will hope- fully produce noticeably weird values when unpacked by the wrong algorithm. Before or after panicking, treat this as a clue to re-try your commands with the ‘--hdf_upk’ switch. See Section 4.9 [ncpdq netCDF Permute Dimensions Quickly], page 289, for an easy tech- nique to unpack data packed with the HDF convention, and then re-pack it with the netCDF convention. Handling of Packed Data by Other Operators All NCO arithmetic operators understand packed data. The operators automatically unpack any packed variable in the input file which will be arithmetically processed. For example, ncra unpacks all record variables, and ncwa unpacks all variable which contain a dimension to be averaged. These variables are stored unpacked in the output file. On the other hand, arithmetic operators do not unpack non-processed variables. For example, ncra leaves all non-record variables packed, and ncwa leaves packed all variables lacking an averaged dimension. These variables (called fixed variables) are passed unaltered from the input to the output file. Hence fixed variables which are packed in input files remain packed in output files. Completely packing and unpacking files is easily accomplished with ncpdq (see Section 4.9 [ncpdq netCDF Permute Dimensions Quickly], page 289). Pack and Chapter 3: Shared Features 129
unpack individual variables with ncpdq and the ncap2 pack() and unpack() functions (see Section 4.1.12 [Methods and functions], page 175).
3.39 Operation Types
Availability: ncap2, ncra, nces, ncwa ¨ Short options: ‘-y’ Long options: ‘--operation’, ‘--op_typ’
The ‘-y op_typ’ switch allows specification of many different types of operations Set op typ © to the abbreviated key for the corresponding operation: avg Mean value sqravg Square of the mean avgsqr Mean of sum of squares max Maximum value min Minimum value mabs Maximum absolute value mebs Mean absolute value mibs Minimum absolute value rms Root-mean-square (normalized by N) rmssdn Root-mean square (normalized by N-1) sqrt Square root of the mean tabs Sum of absolute values ttl Sum of values
NCO assumes coordinate variables represent grid axes, e.g., longitude. The only rank- reduction which makes sense for coordinate variables is averaging. Hence NCO implements the operation type requested with ‘-y’ on all non-coordinate variables, not on coordinate variables. When an operation requires a coordinate variable to be reduced in rank, i.e., from one dimension to a scalar or from one dimension to a degenerate (single value) array, then NCO always averages the coordinate variable regardless of the arithmetic operation type performed on the non-coordinate variables. The mathematical definition of each arithmetic operation is given below. See Section 4.14 [ncwa netCDF Weighted Averager], page 347, for additional information on masks and normalization. If an operation type is not specified with ‘-y’ then the operator performs an arithmetic average by default. Averaging is described first so the terminology for the other operations is familiar. 130 NCO 5.0.2-alpha07 User Guide
The masked, weighted average of a variable x can be generally represented as
Pi=N µ m w x x¯ = i=1 i i i i j Pi=N i=1 µimiwi wherex ¯j is the j’th element of the output hyperslab, xi is the i’th element of the input hyperslab, µi is 1 unless xi equals the missing value, mi is 1 unless xi is masked, and wi is the weight. This formiddable formula represents a simple weighted average whose bells and whistles are all explained below. It is not too early to note, however, that when
µi = mi = wi = 1, the generic averaging expression above reduces to a simple arithmetic average. Furthermore, mi = wi = 1 for all operators except ncwa. These variables are included in the discussion below for completeness, and for possible future use in other operators. The size J of the output hyperslab for a given variable is the product of all the dimensions of the input variable which are not averaged over. The size N of the input hyperslab contributing to eachx ¯j is simply the product of the sizes of all dimensions which are averaged over (i.e., dimensions specified with ‘-a’). Thus N is the number of input elements which potentially contribute to each output element. An input element xi contributes to the output element xj except in two conditions:
1. xi equals the missing value (see Section 3.30 [Missing Values], page 103) for the variable.
2. xi is located at a point where the mask condition (see Section 4.14.1 [Mask condition], page 348) is false.
Points xi in either of these two categories do not contribute to xj—they are ignored. We now define these criteria more rigorously.
Each xi has an associated Boolean weight µi whose value is 0 or 1 (false or true). The value of µi is 1 (true) unless xi equals the missing value (see Section 3.30 [Missing Values], page 103) for the variable. Thus, for a variable with no _FillValue attribute, µi is always 1. All NCO arithmetic operators (ncbo, ncra, nces, ncflint, ncwa) treat missing values analogously. Besides (weighted) averaging, ncwa, ncra, and nces also compute some common non- linear operations which may be specified with the-y ‘ ’ switch (see Section 3.39 [Operation Types], page 129). The other rank-reducing operations are simple variations of the generic weighted mean described above. The total value of x (-y ttl) is
i=N X x¯j = µimiwixi i=1
Note that the total is the same as the numerator of the mean of x, and may also be obtained in ncwa by using the ‘-N’ switch (see Section 4.14 [ncwa netCDF Weighted Averager], page 347). The minimum value of x (-y min) is
x¯j = min[µ1m1w1x1, µ2m2w2x2, . . . , µN mN wN xN ] Chapter 3: Shared Features 131
Analogously, the maximum value of x (-y max) is
x¯j = max[µ1m1w1x1, µ2m2w2x2, . . . , µN mN wN xN ]
Thus the minima and maxima are determined after any weights are applied. The total absolute value of x (-y tabs) is
i=N X x¯j = µimiwi|xi| i=1
The minimum absolute value of x (-y mibs) is
x¯j = min[µ1m1w1|x1|, µ2m2w2|x2|, . . . , µN mN wN |xN |]
Analogously, the maximum absolute value of x (-y mabs) is
x¯j = max[µ1m1w1|x1|, µ2m2w2|x2|, . . . , µN mN wN |xN |]
Thus the minimum and maximum absolute values are determined after any weights are applied. The mean absolute value of x (-y mebs) is
Pi=N µ m w |x | x¯ = i=1 i i i i j Pi=N i=1 µimiwi
The square of the mean value of x (-y sqravg) is
!2 Pi=N µ m w x x¯ = i=1 i i i i j Pi=N i=1 µimiwi
The mean of the sum of squares of x (-y avgsqr) is
Pi=N µ m w x2 x¯ = i=1 i i i i j Pi=N i=1 µimiwi
If x represents a deviation from the mean of another variable, xi = yi−y¯ (possibly created by ncbo in a previous step), then applying avgsqr to x computes the approximate variance of y. Computing the true variance of y requires subtracting 1 from the denominator, discussed below. For a large sample size however, the two results will be nearly indistinguishable. The root mean square of x (-y rms) is
v uPi=N 2 u µimiwix x¯ = t i=1 i j Pi=N i=1 µimiwi
Thus rms simply computes the squareroot of the quantity computed by avgsqr. 132 NCO 5.0.2-alpha07 User Guide
The root mean square of x with standard-deviation-like normalization (-y rmssdn) is implemented as follows. When weights are not specified, this function is the same asthe root mean square of x except one is subtracted from the sum in the denominator
v u Pi=N 2 u µimix x¯ = t i=1 i j Pi=N −1 + i=1 µimi
If x represents the deviation from the mean of another variable, xi = yi − y¯, then applying rmssdn to x computes the standard deviation of y. In this case the −1 in the denominator compensates for the degree of freedom already used in computingy ¯ in the numerator. Consult a statistics book for more details. When weights are specified it is unclear how to compensate for this extra degree of freedom. Weighting the numerator and denominator of the above by wi and subtracting one from the denominator is only appropriate when all the weights are 1.0. When the weights are arbitrary (e.g., Gaussian weights), subtracting one from the sum in the denominator does not necessarily remove one degree of freedom. Therefore when -y rmssdn is requested and weights are specified, ncwa actually implements the rms procedure. nces and ncra, which do not allow weights to be specified, always implement the rmssdn procedure when asked. The square root of the mean of x (-y sqrt) is
v uPi=N u µimiwixi x¯ = t i=1 j Pi=N i=1 µimiwi
The definitions of some of these operations are not universally useful. Mostly theywere chosen to facilitate standard statistical computations within the NCO framework. We are open to redefining and or adding to the above. If you are interested in having other statistical quantities defined in NCO please contact the NCO project (see Section 1.7 [Help Requests and Bug Reports], page 15). EXAMPLES Suppose you wish to examine the variable prs_sfc(time,lat,lon) which contains a time series of the surface pressure as a function of latitude and longitude. Find the minimum value of prs_sfc over all dimensions: ncwa -y min -v prs_sfc in.nc foo.nc Find the maximum value of prs_sfc at each time interval for each latitude: ncwa -y max -v prs_sfc -a lon in.nc foo.nc Find the root-mean-square value of the time-series of prs_sfc at every gridpoint: ncra -y rms -v prs_sfc in.nc foo.nc ncwa -y rms -v prs_sfc -a time in.nc foo.nc The previous two commands give the same answer but ncra is preferred because it has a smaller memory footprint. A dimension of size one is said to be degenerate. By default, Chapter 3: Shared Features 133
ncra leaves the (degenerate) time dimension in the output file (which is usually useful) whereas ncwa removes the time dimension (unless ‘-b’ is given). These operations work as expected in multi-file operators. Suppose that prs_sfc is stored in multiple timesteps per file across multiple files, say jan.nc, feb.nc, march.nc. We can now find the three month maximum surface pressure at every point. nces -y max -v prs_sfc jan.nc feb.nc march.nc out.nc It is possible to use a combination of these operations to compute the variance and standard deviation of a field stored in a single file or across multiple files. The procedure tocompute the temporal standard deviation of the surface pressure at all points in a single file in.nc involves three steps. ncwa -O -v prs_sfc -a time in.nc out.nc ncbo -O -v prs_sfc in.nc out.nc out.nc ncra -O -y rmssdn out.nc out.nc First construct the temporal mean of prs_sfc in the file out.nc. Next overwrite out.nc with the anomaly (deviation from the mean). Finally overwrite out.nc with the root-mean- square of itself. Note the use of ‘-y rmssdn’ (rather than ‘-y rms’) in the final step. This ensures the standard deviation is correctly normalized by one fewer than the number of time samples. The procedure to compute the variance is identical except for the use of ‘-y avgsqr’ instead of ‘-y rmssdn’ in the final step. ncap2 can also compute statistics like standard deviations. Brute-force implementation of formulae is one option, e.g., ncap2 -s ’prs_sfc_sdn=sqrt((prs_sfc-prs_sfc.avg($time)^2). \ total($time)/($time.size-1))’ in.nc out.nc The operation may, of course, be broken into multiple steps in order to archive interme- diate quantities, such as the time-anomalies ncap2 -s ’prs_sfc_anm=prs_sfc-prs_sfc.avg($time)’ \ -s ’prs_sfc_sdn=sqrt((prs_sfc_anm^2).total($time)/($time.size-1))’ \ in.nc out.nc ncap2 supports intrinsic standard deviation functions (see Section 3.39 [Operation Types], page 129) which simplify the above expression to ncap2 -s ’prs_sfc_sdn=(prs_sfc-prs_sfc.avg($time)).rmssdn($time)’ in.nc out.nc These instrinsic functions compute the answer quickly and concisely. The procedure to compute the spatial standard deviation of a field in a single file in.nc involves three steps. ncwa -O -v prs_sfc,gw -a lat,lon -w gw in.nc out.nc ncbo -O -v prs_sfc,gw in.nc out.nc out.nc ncwa -O -y rmssdn -v prs_sfc -a lat,lon -w gw out.nc out.nc First the spatially weighted (by ‘-w gw’) mean values are written to the output file, as are the mean weights. The initial output file is then overwritten with the gridpoint deviations from the spatial mean. It is important that the output file after the second line contain the 134 NCO 5.0.2-alpha07 User Guide
original, non-averaged weights. This will be the case if the weights are named so that NCO treats them like a coordinate (see Section 3.45 [CF Conventions], page 146). One such name is gw, and any variable whose name begins with msk_ (for “mask”) or wgt_ (for “weight”) will likewise be treated as a coordinate, and will be copied (not differenced) straight from in.nc to out.nc in the second step. When using weights to compute standard deviations one must remember to include the weights in the initial output files so that they may be used again in the final step. Finally the root-mean-square of the appropriately weighted spatial deviations is taken. No elegant ncap2 solution exists to compute weighted standard deviations. Those brave of heart may try to formulate one. A general formula should allow weights to have fewer than and variables to have more than the minimal spatial dimensions (latitude and longitude). The procedure to compute the standard deviation of a time-series across multiple files involves one extra step since all the input must first be collected into one file. ncrcat -O -v tpt in.nc in.nc foo1.nc ncwa -O -a time foo1.nc foo2.nc ncbo -O -v tpt foo1.nc foo2.nc foo3.nc ncra -O -y rmssdn foo3.nc out.nc The first step assembles all the data into a single file. Though this may consume alot of temporary disk space, it is more or less required by the ncbo operation in the third step.
3.40 Type Conversion
Availability (automatic type conversion): ncap2, ncbo, nces, ncflint, ncra, ncwa ¨ Short options: None (it’s automatic) Availability (manual type conversion): nces, ncra, ncwa Short options: None Long options: ‘--dbl’, ‘--flt’, ‘--rth_dbl’, ‘--rth_flt’
Type conversion refers to the casting or coercion of one fundamental or atomic data © type to another, e.g., converting NC_SHORT (two bytes) to NC_DOUBLE (eight bytes). Type conversion always promotes or demotes the range and/or precision of the values a variable can hold. Type conversion is automatic when the language carries out this promotion according to an internal set of rules without explicit user intervention. In contrast, manual type conversion refers to explicit user commands to change the type of a variable or attribute. Most type conversion happens automatically, yet there are situations in which manual type conversion is advantageous. 3.40.1 Automatic type conversion There are at least two reasons to avoid type conversions. First, type conversions are expen- sive since they require creating (temporary) buffers and casting each element of a variable from its storage type to some other type and then, often, converting it back. Second, a dataset’s creator perhaps had a good reason for storing data as, say, NC_FLOAT rather than NC_DOUBLE. In a scientific framework there is no reason to store data with more precision Chapter 3: Shared Features 135
than the observations merit. Normally this is single-precision, which guarantees 6–9 digits of precision. Reasons to engage in type conversion include avoiding rounding errors and out-of-range limitations of less-precise types. This is the case with most integers. Thus NCO defaults to automatically promote integer types to floating-point when performing lengthy arithmetic, yet NCO defaults to not promoting single to double-precision floats. Before discussing the more subtle floating-point issues, we first examine integer promo- tion. We will show how following parsimonious conversion rules dogmatically can cause problems, and what NCO does about that. That said, there are situations in which implicit conversion of single- to double-precision is also warranted. Understanding the narrowness of these situations takes time, and we hope the reader appreciates the following detailed discussion. Consider the average of the two NC_SHORTs 17000s and 17000s. A straightforward average without promotion results in garbage since the intermediate value which holds their sum is also of type NC_SHORT and thus overflows on (i.e., cannot represent) values greater than 32,76729. There are valid reasons for expecting this operation to succeed and the NCO philosophy is to make operators do what you want, not what is purest. Thus, unlike C and Fortran, but like many other higher level interpreted languages, NCO arithmetic operators will perform automatic type conversion on integers when all the following conditions are met30: 1. The requested operation is arithmetic. This is why type conversion is limited to the operators ncap2, ncbo, nces, ncflint, ncra, and ncwa. 2. The arithmetic operation could benefit from type conversion. Operations that could benefit include averaging, summation, or any “hard” arithmetic that could overflowor underflow. Larger representable sums help avoid overflow, and more precision helpsto avoid underflow. Type conversion does not benefit searching for minima and maxima (‘-y min’, or ‘-y max’). 3. The variable on disk is of type NC_BYTE, NC_CHAR, NC_SHORT, or NC_INT. Type NC_ DOUBLE is not promoted because there is no type of higher precision. Conversion of type NC_FLOAT is discussed in detail below. When it occurs, it follows the same procedure (promotion then arithmetic then demotion) as conversion of integer types.
When these criteria are all met, the operator promotes the variable in question to type NC_DOUBLE, performs all the arithmetic operations, casts the NC_DOUBLE type back to the original type, and finally writes the result to disk. The result written to disk maynotbe what you expect, because of incommensurate ranges represented by different types, and because of (lack of) rounding. First, continuing the above example, the average (e.g., ‘-y avg’) of 17000s and 17000s is written to disk as 17000s. The type conversion feature of NCO makes this possible since the arithmetic and intermediate values are stored as NC_DOUBLEs, i.e., 34000.0d and only the final result must be represented asan NC_SHORT. Without the type conversion feature of NCO, the average would have been garbage (albeit predictable garbage near -15768s). Similarly, the total (e.g., ‘-y ttl’) of 17000s and 17000s written to
29 32767 = 215 − 1 30 Operators began performing automatic type conversions before arithmetic in NCO version 1.2, August, 2000. Previous versions never performed unnecessary type conversion for arithmetic. 136 NCO 5.0.2-alpha07 User Guide
disk is garbage (actually -31536s) since the final result (the true total) of 34000 is outside the range of type NC_SHORT. After arithmetic is computed in double-precision for promoted variables, the intermediate double-precision values must be demoted to the variables’ original storage type (e.g., from NC_DOUBLE to NC_SHORT). NCO has handled this demotion in three ways in its history. Prior to October, 2011 (version 4.0.8), NCO employed the C library truncate function, trunc()31. Truncation rounds x to the nearest integer not larger in absolute value. For example, truncation rounds 1.0d, 1.5d, and 1.8d to the same value, 1s. Clearly, truncation does not round floating-point numbers to the nearest integer! Yet truncation is howthe C language performs implicit conversion of real numbers to integers.
NCO stopped using truncation for demotion when an alert user (Neil Davis) informed us that this caused a small bias in the packing algorithm employed by ncpdq. This led to NCO adopting rounding functions for demotion. Rounding functions eliminated the small bias in the packing algorithm.
From February, 2012 through March, 2013 (versions 4.0.9–4.2.6), NCO employed the C library family of rounding functions, lround(). These functions round x to the nearest integer, halfway cases away from zero. The problem with lround() is that it always rounds real values ending in .5 away from zero. This rounds, for example, 1.5d and 2.5d to 2s and 3s, respectively.
Since April, 2013 (version 4.3.0), NCO has employed the other C library family of round- ing functions, lrint(). This algorithm rounds x to the nearest integer, using the current rounding direction. Halfway cases are rounded to the nearest even integer. This rounds, for example, both 1.5d and 2.5d to the same value, 2s, as recommended by the IEEE. This rounding is symmetric: up half the time, down half the time. This is the current and hopefully final demotion algorithm employed by NCO.
Hence because of automatic conversion, NCO will compute the average of 2s and 3s in double-precision arithmetic as (2.0d+3.0d)/2.0d) = 2.5d. It then demotes this intermediate result back to NC_SHORT and stores it on disk as trunc(2.5d) = 2s (versions up to 4.0.8), lround(2.5d) = 3s (versions 4.0.9–4.2.6), and lrint(2.5d) = 2s (versions 4.3.0 and later).
3.40.2 Promoting Single-precision to Double Promotion of real numbers from single- to double-precision is fundamental to scientific computing. When it should occur depends on the precision of the inputs and the number of operations. Single-precision (four-byte) numbers contain about seven significant figures, while double-precision contain about sixteen. More, err, precisely, the IEEE single-precision representation gives from 6 to 9 significant decimal digits precision32. And the IEEE double-
31 The actual type conversions with trunction were handled by intrinsic type conversion, so the trunc() function was never explicitly called, although the results would be the same if it were. 32 According to Wikipedia’s summary of IEEE standard 754, “If a decimal string with at most 6 significant digits is converted to IEEE 754 single-precision and then converted back to the same number of significant decimal, then the final string should match the original; and ifan IEEE 754 single-precision is converted to a decimal string with at leastn 9 significant decimal and then converted back to single, then thefinal number must match the original”. Chapter 3: Shared Features 137
precision representation gives from 15 to 17 significant decimal digits precision33. Hence double-precision numbers represent about nine digits more precision than single-precision numbers. Given these properties, there are at least two possible arithmetic conventions for the treatment of real numbers: 1. Conservative, aka Fortran Convention Automatic type conversion during arithmetic in the Fortran language is, by default, performed only when necessary. All operands in an operation are converted to the most precise type involved the operation before the arithmetic operation. Expressions which involve only single-precision numbers are computed entirely in single-precision. Expressions involving mixed precision types are computed in the type of higher precision. NCO by default employs the Fortan Convention for promotion. 2. Aggressive, aka C Convention The C language is by default much more aggressive (and thus wasteful) than Fortran, and will always implicitly convert single- to double- precision numbers, even when there is no good reason. All real-number standard C library functions are double-precision, and C programmers must take extra steps to only utilize single precision arithmetic. The high-level interpreted data analysis languages IDL, Matlab, and NCL all adopt the C Convention.
NCO does not automatically promote NC_FLOAT because, in our judgement, the perfor- mance penalty of always doing so would outweigh the potential benefits. The now-classic text “Numerical Recipes in C” discusses this point under the section “Implicit Conversion of Float to Double”34. That said, such promotion is warranted in some circumstances. For example, rounding errors can accumulate to worrisome levels during arithmetic per- formed on large arrays of single-precision floats. This use-case occurs often in geoscientific studies of climate where thousands-to-millions of gridpoints may contribute to a single av- erage. If the inputs are all single-precision, then so should be the output. However the intermediate results where running sums are accumulated may suffer from too much round- ing or from underflow unless computed in double-precision. The order of operations matters to floating-point math even when the analytic expres- sions are equal. Cautious users feel disquieted when results from equally valid analyses differ in the final bits instead of agreeing bit-for-bit. For example, averaging arraysin multiple stages produces different answers than averaging them in one step. This iseasily seen in the computation of ensemble averages by two different methods. The NCO test file in.nc contains single- and double-precision representations of the same temperature
33 According to Wikipedia’s summary of IEEE standard 754, “If a decimal string with at most 15 significant digits is converted to IEEE 754 double-precision representation and then converted back to a string with the same number of significant digits, then the final string should match the original; andifan IEEE 754 double precision is converted to a decimal string with at least 17 significant digits and then converted back to double, then the final number must match the original”. 34 See page 21 in Section 1.2 of the First edition for this gem: One does not need much experience in scientific computing to recognize that the implicit conversion rules are, in fact, sheer madness! In effect, they make it impossible to write efficient numerical programs. 138 NCO 5.0.2-alpha07 User Guide
timeseries as tpt_flt and tpt_dbl. Pretend each datapoint in this timeseries represents a monthly-mean temperature. We will mimic the derivation of a fifteen-year ensemble-mean January temperature by concatenating the input file five times, and then averaging the datapoints representing January two different ways. In Method 1 we derive the 15-year ensemble January average in two steps, as the average of three five-year averages. This method is naturally used when each input file contains multiple years and multiple input files are needed35. In Method 2 we obtain 15-year ensemble January average in a single step, by averaging all 15 Januaries at one time: # tpt_flt and tpt_dbl are identical except for precision ncks -C -v tpt_flt,tpt_dbl ~/nco/data/in.nc # tpt_dbl = 273.1, 273.2, 273.3, 273.4, 273.5, 273.6, 273.7, 273.8, 273.9, 274 # tpt_flt = 273.1, 273.2, 273.3, 273.4, 273.5, 273.6, 273.7, 273.8, 273.9, 274 # Create file with five "ten-month years" (i.e., 50 timesteps) of temperature data ncrcat -O -v tpt_flt,tpt_dbl -p ~/nco/data in.nc in.nc in.nc in.nc in.nc ~/foo.nc # Average 1st five "Januaries" (elements 1, 11, 21, 31, 41) ncra --flt -O -F -d time,1,,10 ~/foo.nc ~/foo_avg1.nc # Average 2nd five "Januaries" (elements 2, 12, 22, 32, 42) ncra --flt -O -F -d time,2,,10 ~/foo.nc ~/foo_avg2.nc # Average 3rd five "Januaries" (elements 3, 13, 23, 33, 43) ncra --flt -O -F -d time,3,,10 ~/foo.nc ~/foo_avg3.nc # Method 1: Obtain ensemble January average by averaging the averages ncra --flt -O ~/foo_avg1.nc ~/foo_avg2.nc ~/foo_avg3.nc ~/foo_avg_mth1.nc # Method 2: Obtain ensemble January average by averaging the raw data # Employ ncra’s "subcycle" feature (http://nco.sf.net/nco.html#ssc) ncra --flt -O -F -d time,1,,10,3 ~/foo.nc ~/foo_avg_mth2.nc # Difference the two methods ncbo -O ~/foo_avg_mth1.nc ~/foo_avg_mth2.nc ~/foo_avg_dff.nc ncks ~/foo_avg_dff.nc # tpt_dbl = 5.6843418860808e-14 ; # tpt_flt = -3.051758e-05 ; Although the two methods are arithmetically equivalent, they produce slightly different answers due to the different order of operations. Moreover, it appears at first glance that the single-precision answers suffer from greater error than the double-precision answers. In fact both precisions suffer from non-zero rounding errors. The answers differ negligibly to machine precision, which is about seven significant figures for single precision floats (tpt_ flt), and sixteen significant figures for double precisiontpt_dbl ( ). The input precision determines the answer precision.
IEEE arithmetic guarantees that two methods will produce bit-for-bit identical answers only if they compute the same operations in the same order. Bit-for-bit identical answers may also occur by happenstance when rounding errors exactly compensate one another. This is demonstrated by repeating the example above with the ‘--dbl’ (or ‘--rth_dbl’ for clarity) option which forces conversion of single-precision numbers to double-precision prior to arithmetic. Now ncra will treat the first value of tpt_flt, 273.1000f, as
35 For example, the CMIP5 archive tends to distribute monthly average timeseries in 50-year chunks. Chapter 3: Shared Features 139
273.1000000000000d. Arithmetic on tpt_flt then proceeds in double-precision until the final answer, which is converted back to single-precision for final storage. # Average 1st five "Januaries" (elements 1, 11, 21, 31, 41) ncra --dbl -O -F -d time,1,,10 ~/foo.nc ~/foo_avg1.nc # Average 2nd five "Januaries" (elements 2, 12, 22, 32, 42) ncra --dbl -O -F -d time,2,,10 ~/foo.nc ~/foo_avg2.nc # Average 3rd five "Januaries" (elements 3, 13, 23, 33, 43) ncra --dbl -O -F -d time,3,,10 ~/foo.nc ~/foo_avg3.nc # Method 1: Obtain ensemble January average by averaging the averages ncra --dbl -O ~/foo_avg1.nc ~/foo_avg2.nc ~/foo_avg3.nc ~/foo_avg_mth1.nc # Method 2: Obtain ensemble January average by averaging the raw data # Employ ncra’s "subcycle" feature (http://nco.sf.net/nco.html#ssc) ncra --dbl -O -F -d time,1,,10,3 ~/foo.nc ~/foo_avg_mth2.nc # Difference the two methods ncbo -O ~/foo_avg_mth1.nc ~/foo_avg_mth2.nc ~/foo_avg_dff.nc # Show differences ncks ~/foo_avg_dff.nc # tpt_dbl = 5.6843418860808e-14 ; # tpt_flt = 0 ; The ‘--dbl’ switch has no effect on the results computed from double-precision inputs. But now the two methods produce bit-for-bit identical results from the single-precision inputs! This is due to the happenstance of rounding along with the effects of the ‘--dbl’ switch. The ‘--flt’ and ‘--rth_flt’ switches are provided for symmetry. They enforce the traditional NCO and Fortran convention of keeping single-precision arithmetic in single- precision unless a double-precision number is explicitly involved. We have shown that forced promotion of single- to double-precision prior to arith- metic has advantages and disadvantages. The primary disadvantages are speed and size. Double-precision arithmetic is 10–60% slower than, and requires twice the memory of single- precision arithmetic. The primary advantage is that rounding errors in double-precision are much less likely to accumulate to values near the precision of the underlying geophysical variable. For example, if we know temperature to five significant digits, then a rounding error of 1-bit could affect the least precise digit of temperature after 1,000–10,000 consecutive one- sided rounding errors under the worst possible scenario. Many geophysical grids have tens- of-thousands to millions of points that must be summed prior to normalization to compute an average. It is possible for single-precision rouding errors to accumulate and degrade the precision in such situtations. Double-precision arithmetic mititgates this problem, so ‘--dbl’ would be warranted. This can be seen with another example, averaging a global surface temperature field with ncwa. The input contains a single-precision global temperature field (stored in TREFHT) produced by the CAM3 general circulation model (GCM) run and stored at 1.9 by 2.5 degrees resolution. This requires 94 latitudes and 144 longitudes, or 13, 824 total surface gridpoints, a typical GCM resolution in 2008–2013. These input characteristics are provided only to show the context to the interested reader, equivalent results would be found in statistics 140 NCO 5.0.2-alpha07 User Guide
of any dataset of comparable size. Models often represent Earth on a spherical grid where global averages must be created by weighting each gridcell by its latitude-dependent weight (e.g., a Gaussian weight stored in gw), or by the surface area of each contributing gridpoint (stored in area).
Like many geophysical models and most GCMs, CAM3 runs completely in double- precision yet stores its archival output in single-precision to save space. In prac- tice such models usually save multi-dimensional prognostic and diagnostic fields (like TREFHT(lat,lon)) as single-precision, while saving all one-dimensional coordinates and weights (here lat, lon, and gw(lon)) as double-precision. The gridcell area area(lat,lon) is an extensive grid property that should be, but often is not, stored as double-precision. To obtain pure double-precision arithmetic and storage of the globla mean temperature, we first create and store double-precision versions of the single-precision fields: ncap2 -O -s ’TREFHT_dbl=double(TREFHT);area_dbl=double(area)’ in.nc in.nc The single- and double-precision temperatures may each be averaged globally using four permutations for the precision of the weight and of the intermediate arithmetic representa- tion: 1. Single-precision weight (area), single-precision arithmetic 2. Double-precision weight (gw), single-precision arithmetic 3. Single-precision weight (area), double-precision arithmetic 4. Double-precision weight (gw), double-precision arithmetic # NB: Values below are printed with C-format %5.6f using # ncks -H -C -s ’%5.6f’ -v TREFHT,TREFHT_dbl out.nc # Single-precision weight (area), single-precision arithmetic ncwa --flt -O -a lat,lon -w area in.nc out.nc # TREFHT = 289.246735 # TREFHT_dbl = 289.239964 # Double-precision weight (gw), single-precision arithmetic ncwa --flt -O -a lat,lon -w gw in.nc out.nc # TREFHT = 289.226135 # TREFHT_dbl = 289.239964 # Single-precision weight (area), double-precision arithmetic ncwa --dbl -O -a lat,lon -w area in.nc out.nc # TREFHT = 289.239960 # TREFHT_dbl = 289.239964 # Double-precision weight (gw), double-precision arithmetic ncwa --dbl -O -a lat,lon -w gw in.nc out.nc # TREFHT = 289.239960 # TREFHT_dbl = 289.239964 First note that the TREFHT_dbl average never changes because TREFHT_dbl(lat,lon) is double-precision in the input file. As described above, NCO automatically converts all operands involving to the highest precision involved in the operation. So specifying ‘--dbl’ is redundant for double-precision inputs. Chapter 3: Shared Features 141
Second, the single-precision arithmetic averages of the single-precision input TREFHT differ by 289.246735 − 289.226135 = 0.0206 from eachother, and, more importantly, by as much as 289.239964 − 289.226135 = 0.013829 from the correct (double-precision) answer. These averages differ in the fifth digit, i.e., they agree only to four significant figures!Given that climate scientists are concerned about global temperature variations of a tenth of a degree or less, this difference is large. Global mean temperature changes significant to climate scientists are comparable in size to the numerical artifacts produced by the averaging procedure. Why are the single-precision numerical artifacts so large? Each global average is the result of multiplying almost 15,000 elements each by its weight, summing those, and then dividing by the summed weights. Thus about 50,000 single-precision floating-point opera- tions caused the loss of two to three significant digits of precision. The net error of a series of independent rounding errors is a random walk phenomena36. Successive rounding errors displace the answer further from the truth. An ensemble of such averages will, on average, have no net bias. In other words, the expectation value of a series of IEEE rounding errors is zero. And the error of any given sequence of rounding errors obeys, for large series, a Gaussian distribution centered on zero. Single-precision numbers use three of their four eight-bit bytes to represent the mantissa so the smallest representable single-precision mantissa is ≡ 2−23 = 1.19209 × 10−7. This is the smallest x such that 1.0 + x 6= 1.0. This is the rounding error for non-exact precision- numbers. Applying random walk theory to rounding, it can be shown that the expected rounding error after n inexact operations is p2n/π for large n. The expected (i.e., mean absolute) rounding error in our example with 13, 824 additions is about p2 × 13824/π = 91.96. Hence, addition alone of about fifteen thousand single-precision floats is expected to consume about two significant digits of precision. This neglects the error due to theinner product (weights times values) and normalization (division by tally) aspects of a weighted average. The ratio of two numbers each containing a numerical bias can magnify the size of the bias. In summary, a global mean number computed from about 15,000 gridpoints each with weights can be expected to lose up to three significant digits. Since single-precision starts with about seven significant digits, we should not expect to retain more than four significant digits after computing weighted averages in single-precision. The above example with TREFHT shows the expected four digits of agreement. The NCO results have been independently validated to the extent possible in three other languages: C, Matlab, and NCL. C and NCO are the only languages that permit single- precision numbers to be treated with single precision arithmetic: # Double-precision weight (gw), single-precision arithmetic (C) ncwa_3528514.exe # TREFHT = 289.240112 # Double-precision weight (gw), double-precision arithmetic (C) # TREFHT = 289.239964 # Single-precision weight (area), double-precision arithmetic (Matlab) # TREFHT = 289.239964 # Double-precision weight (gw), double-precision arithmetic (Matlab)
36 Thanks to Michael J. Prather for explaining this to me. 142 NCO 5.0.2-alpha07 User Guide
# TREFHT = 289.239964 # Single-precision weight (area), double-precision arithmetic (NCL) ncl < ncwa_3528514.ncl # TREFHT = 289.239960 # TREFHT_dbl = 289.239964 # Double-precision weight (gw), double-precision arithmetic (NCL) # TREFHT = 289.239960 # TREFHT_dbl = 289.239964 All languages tested (C, Matlab, NCL, and NCO) agree to machine precision with double- precision arithmetic. Users are fortunate to have a variety of high quality software that liberates them from the drudgery of coding their own. Many packages are free (as in beer)! As shown above NCO permits one to shift to their float-promotion preferences as desired. No other language allows this with a simple switch. To summarize, until version 4.3.6 (September, 2013), the default arithmetic convention of NCO adhered to Fortran behavior, and automatically promoted single-precision to double- precision in all mixed-precision expressions, and left-alone pure single-precision expressions. This is faster and more memory efficient than other conventions. However, pure single- precision arithmetic can lose too much precision when used to condense (e.g., average) large arrays. Statistics involving about n = 10, 000 single-precision inputs will lose about 2–3 digits if not promoted to double-precision prior to arithmetic. The loss scales with the squareroot of n. For larger n, users should promote floats with the ‘--dbl’ option if they want to preserve more than four significant digits in their results. The ‘--dbl’ and ‘--flt’ switches are only available with the NCO arithmetic operators that could potentially perform more than a few single-precision floating-point operations per result. These are nces, ncra, and ncwa. Each is capable of thousands to millions or more operations per result. By contrast, the arithmetic operators ncbo and ncflint perform at most one floating-point operation per result. Providing the‘--dbl’ option for such trivial operations makes little sense, so the option is not currently made available. We are interested in users’ opinions on these matters. The default behavior was changed from ‘--flt’ to ‘--dbl’ with the release of NCO version 4.3.6 (October 2013). We will change the default back to ‘--flt’ if users prefer. Or we could set a threshold (e.g., n ≥ 10000) after which single- to double-precision promotion is automatically invoked. Or we could make the default promotion convention settable via an environment variable (GSL does this a lot). Please let us know what you think of the selected defaults and options.
3.40.3 Manual type conversion ncap2 provides intrinsic functions for performing manual type conversions. This, for exam- ple, converts variable tpt to external type NC_SHORT (a C-type short), and variable prs to external type NC_DOUBLE (a C-type double). ncap2 -s ’tpt=short(tpt);prs=double(prs)’ in.nc out.nc With ncap2 there also is the convert() method that takes an integer argument. For example the above statements become: ncap2 -s ’tpt=tpt.convert(NC_SHORT);prs=prs.convert(NC_DOUBLE)’ in.nc out.nc Chapter 3: Shared Features 143
Can also use convert() in combination with type() so to make variable ilev_new the same type as ilev just do: ncap2 -s ’ilev_new=ilev_new.convert(ilev.type())’ in.nc out.nc See Section 4.1 [ncap2 netCDF Arithmetic Processor], page 154, for more details.
3.41 Batch Mode
Availability: All operators ¨ Short options: ‘-O’, ‘-A’ Long options: ‘--ovr’, ‘--overwrite’, ‘--apn’, ‘--append’
If the output-file specified for a command is a pre-existing file, then the operator will © prompt the user whether to overwrite (erase) the existing output-file, attempt to append to it, or abort the operation. However, interactive questions reduce productivity when processing large amounts of data. Therefore NCO also implements two ways to override its own safety features, the ‘-O’ and ‘-A’ switches. Specifying ‘-O’ tells the operator to overwrite any existing output-file without prompting the user interactively. Specifying ‘-A’ tells the operator to attempt to append to any existing output-file without prompting the user interactively. These switches are useful in batch environments because they suppress interactive keyboard input. NB: As of 20120515, ncap2 is unable to append to files that already contain the appended dimensions.
3.42 Global Attribute Addition
Availability: All operators ¨ Short options: None Long options: ‘--glb’, ‘--gaa’, ‘--glb_att_add’ ‘--glb att_nm=att_val’ (multiple invocations allowed)
All operators can add user-specified global attributes to output files. Asof NCO version © 4.5.2 (July, 2015), NCO supports multiple uses of the ‘--glb’ (or equivalent ‘--gaa’ or ‘--glb_att_add’) switch. The option ‘--gaa’ (and its long option equivalents such as ‘--glb_att_add’) indicates the argument syntax will be key=val. As such, ‘--gaa’ and its synonyms are indicator options that accept arguments supplied one-by-one like ‘--gaa key1=val1 --gaa key2=val2’, or aggregated together in multi-argument format like ‘--gaa key1=val1#key2=val2’ (see Section 3.4.2 [Multi-arguments], page 32). The switch takes mandatory arguments ‘--glb att_nm=att_val’ where att nm is the desired name of the global attribute to add, and att val is its value. Currently only text attributes are supported (recorded as type NC_CHAR), and regular expressions are not al- lowed (unlike see Section 4.2 [ncatted netCDF Attribute Editor], page 218). Attributes are added in “Append” mode, meaning that values are appended to pre-existing values, if any. 144 NCO 5.0.2-alpha07 User Guide
Multiple invocations can simplify the annotation of output file at creation (or modification) time:
ncra --glb machine=${HOSTNAME} --glb created_by=${USER} in*.nc out.nc
As of NCO version 4.6.2 (October, 2016), one may instead combine the separate invoca- tions into a single list of invocations separated by colons:
ncra --glb machine=${HOSTNAME}:created_by=${USER} in*.nc out.nc
The list may contain any number of key-value pairs. Special care must be taken should a key or value contain a delimiter (i.e., a colon) otherwise NCO will interpret the colon as a delimiter and will attempt to create a new attribute. To protect a colon from being interpreted as an argument delimiter, precede it with a backslash.
The global attribution addition feature helps to avoid the performance penalty incurred by using ncatted separately to annotate large files. Should users emit a loud hue and cry, we will consider ading the functionality of ncatted to the front-end of all operators, i.e., accepting valid ncatted arguments to modify attributes of any type and to apply regular expressions.
3.43 History Attribute
Availability: All operators ¨ Short options: ‘-h’ Long options: ‘--hst’, ‘--history’
© All operators automatically append a history global attribute to any file they create or modify. The history attribute consists of a timestamp and the full string of the invocation command to the operator, e.g., ‘Mon May 26 20:10:24 1997: ncks in.nc out.nc’. The full contents of an existing history attribute are copied from the first input-file to the output- file. The timestamps appear in reverse chronological order, with the most recent timestamp appearing first in the history attribute. Since NCO adheres to the history convention, the entire data processing path of a given netCDF file may often be deduced from examination of its history attribute. As of May, 2002, NCO is case-insensitive to the spelling of the history attribute name. Thus attributes named History or HISTORY (which are non- standard and not recommended) will be treated as valid history attributes. When more than one global attribute fits the case-insensitive search for “history”, the first one foundis used. To avoid information overkill, all operators have an optional switch (‘-h’, ‘--hst’, or ‘--history’) to override automatically appending the history attribute (see Section 4.2 [ncatted netCDF Attribute Editor], page 218). Note that the ‘-h’ switch also turns off writing the nco_input_file_list-attribute for multi-file operators (see Section 3.44 [File List Attributes], page 145). Chapter 3: Shared Features 145
As of NCO version 4.5.0 (June, 2015), NCO supports its own convention to retain the history-attribute contents of all files that were appended to a file37. This convention stores those contents in the history_of_appended_files attribute, which complements the history-attribute to provide a more complete provenance. These attributes may appear something like this in output: // global attributes: :history = "Thu Jun 4 14:19:04 2015: ncks -A /home/zender/foo3.nc /home/zender/tmp.nc\n", "Thu Jun 4 14:19:04 2015: ncks -A /home/zender/foo2.nc /home/zender/tmp.nc\n", "Thu Jun 4 14:19:04 2015: ncatted -O -a att1,global,o,c,global metadata only in foo1 /home/zender/foo1.nc\n", "original history from the ur-file serving as the basis for subsequent appends." ; :history_of_appended_files = "Thu Jun 4 14:19:04 2015: Appended file \ /home/zender/foo3.nc had following \"history\" attribute:\n", "Thu Jun 4 14:19:04 2015: ncatted -O -a att2,global,o,c,global metadata only in foo3 /home/zender/foo3.nc\n", "history from foo3 from which data was appended to foo1 after data from foo2 was appended\n", "Thu Jun 4 14:19:04 2015: Appended file /home/zender/foo2.nc had following \"history\" attribute:\n", "Thu Jun 4 14:19:04 2015: ncatted -O -a att2,global,o,c,global metadata only in foo2 /home/zender/foo2.nc\n", "history of some totally different file foo2 from which data was appended to foo1 before foo3 was appended\n", :att1 = "global metadata only in foo1" ; Note that the history_of_appended_files-attribute is only created, and will only exist, in a file that is, or descends from a file that was, appended to. The optional-h switch‘ ’ (or ‘--hst’ or ‘--history’) also overrides automatically appending the history_of_appended_ files attribute.
3.44 File List Attributes
Availability: nces, ncecat, ncra, ncrcat ¨ Short options: ‘-H’ Long options: ‘--fl_lst_in’, ‘--file_list’
Many methods of specifying large numbers of input file names pass these names via © pipes, encodings, or argument transfer programs (see Section 2.7 [Large Numbers of Files], page 21). When these methods are used, the input file list is not explicitly passed onthe command line. This results in a loss of information since the history attribute no longer contains the exact command by which the file was created. NCO solves this dilemma by archiving input file list attributes. When the input file list to a multi-file operator is specified via stdin, the operator, by default, attaches two global attributes to any file they create or modify. The nco_input_file_number global attribute contains the number of input files, and nco_input_file_list contains the file names, specified as standard input to the multi-file operator. This information helps toverifythat all input files the user thinks were piped through stdin actually arrived. Without the nco_
37 Note that before version 4.5.0, NCO could, in append (‘-A’) mode only, inadvertently overwrite the global metadata (including history) of the output file with that of the input file. This is opposite the behavior most would want. 146 NCO 5.0.2-alpha07 User Guide
input_file_list attribute, the information is lost forever and the “chain of evidence” would be broken. The ‘-H’ switch overrides (turns off) the default behavior of writing the input filelist global attributes when input is from stdin. The ‘-h’ switch does this too, and turns off the history attribute as well (see Section 3.43 [History Attribute], page 144). Hence both switches allows space-conscious users to avoid storing what may amount to many thousands of filenames in a metadata attribute.
3.45 CF Conventions
Availability: ncbo, nces, ncecat, ncflint, ncpdq, ncra, ncwa ¨ Short options: None
NCO recognizes some Climate and Forecast (CF) metadata conventions, and applies © special rules to such data. NCO was contemporaneous with COARDS and still contains some rules to handle older model datasets that pre-date CF, such as NCAR CCM and early CCSM datasets. Such datasets may not contain an explicit Conventions attribute (e.g., ‘CF-1.0’). Nevertheless, we refer to all such metadata collectively as CF metadata. Skip this section if you never work with CF metadata. The latest CF netCDF conventions are described here (http://cfconventions.org/ 1.7.html). Most CF netCDF conventions are transparent to NCO. There are no known pitfalls associated with using any NCO operator on files adhering to these conventions. NCO applies some rules that are not in CF, or anywhere else, because experience shows that they simplify data analysis, and stay true to the NCO mantra to do what users want. Here is a general sense of NCO’s CF-support: • Understand and implement NUG recommendations such as the history attribute, pack- ing conventions, and attention to units. • Special handling of variables designated as coordinates, bounds, or ancillary variables, so that users subsetting a certain variable automatically obtain all related variables. • Special handling and prevention of meaningless operations (e.g., the root-mean-square of latitude) so that coordinates and bounds preserve meaningful information even as normal (non-coordinate) fields are statistically transformed. • Understand units and certain calendars so that hyperslabs may be specified in physical units, and so that user needs not manually decode per-file time specifications. • Understand auxiliary coordinates so that irregular hyperslabs may be specified on com- plex geometric grids. • Check for CF-compliance on netCDF3 and netCDF4 and HDF files. • Convert netCDF4 and HDF files to netCDF3 for strict CF-compliance.
Finally, a main use of NCO is to “produce CF”, i.e., to improve CF-compliance by annotating metadata, renaming objects (attributes, variables, and dimensions), permuting and inverting dimensions, recomputing values, and data compression. Chapter 3: Shared Features 147
Currently, NCO determines whether a datafile is a CF output datafile simply by checking (case-insensitively) whether the value of the global attribute Conventions (if any) equals ‘CF-1.0’ or ‘NCAR-CSM’ Should Conventions equal either of these in the (first) input-file, NCO will apply special rules to certain variables because of their usual meaning in CF files. NCO will not average the following variables often found in CF files: ntrm, ntrn, ntrk, ndbase, nsbase, nbdate, nbsec, mdt, mhisf. These variables contain scalar metadata such as the resolution of the host geophysical model and it makes no sense to change their values. Furthermore, the size and rank-preserving arithmetic operators try not to operate on certain grid properties. These operators are ncap2, ncbo, nces, ncflint, and ncpdq (when used for packing, not for permutation). These operators do not operate, by default, on (i.e., add, subtract, pack, etc.) the following variables: ORO, area, datesec, date, gw, hyai, hyam, hybi. hybm, lat_bnds, lon_bnds, msk_*, and wgt_*. These variables represent Gaussian weights, land/sea masks, time fields, hybrid pressure coefficients, and latititude/longitude boundaries. We call these fields non-coordinate grid properties. Coordinate grid properties are easy to identify because they are coordinate variables such as latitude and longitude. Users usually want all grid properties to remain unaltered in the output file. To be treated as a grid property, the variable name must exactly match a name in the above list, or be a coordinate variable. Handling of msk_* and wgt_* is exceptional in that any variable whose name starts with msk_ or wgt_ is considered to be a “mask” or a “weight” and is thus preserved (not operated on when arithmetic can be avoided). As of NCO version 4.7.7 (September, 2018), NCO began to explicitly identify files adhering to the MPAS convention. These files have a global attribute Conventions at- tribute that contains the string or ‘MPAS’. Size and rank-preserving arithmetic operators will not operate on these MPAS non-coordinate grid properties: angleEdge, areaCell, areaTriangle, cellMask, cellsOnCell, cellsOnEdge, cellsOnVertex, dcEdge, dvEdge, edgesOnCell, edgesOnEdge, edgesOnVertex, indexToCellID, indexToEdgeID, indexToVertexID, kiteAreasOnVertex, latCell, latEdge, latVertex, lonCell, lonEdge, lonVertex, maxLevelCell, meshDensity, nEdgesOnCell, nEdgesOnEdge, vertexMask, verticesOnCell, verticesOnEdge, weightsOnEdge, xCell, xEdge, xVertex, yCell, yEdge, yVertex, zCell, zEdge, and zVertex. As of NCO version 4.5.0 (June, 2015), NCO began to support behavior required for the DOE E3SM/ACME program, and we refer to these rules collectively as the E3SM/ACME con- vention. The first E3SM/ACME rule implemented is that the contents of input-file variables named date_written and time_written, if any, will be updated to the current system- supplied (with gmtime()) GMT-time as the variables are copied to the output-file. You must spoof NCO if you would like any grid properties or other special CF fields processed normally. For example rename the variables first with ncrename, or alter the Conventions attribute. As of NCO version 4.0.8 (April, 2011), NCO supports the CF bounds convention for cell boundaries described here (http://cfconventions.org/cf-conventions/ cf-conventions.html#cell-boundaries). This convention allows coordinate variables (including multidimensional coordinates) to describe the boundaries of their cells. This is done by naming the variable which contains the bounds in in the bounds attribute. Note that coordinates of rank N have bounds of rank N + 1. NCO-generated subsets of CF- 148 NCO 5.0.2-alpha07 User Guide
compliant files with bounds attributes will include the coordinates specified by the bounds attribute, if any. Hence the subsets will themselves be CF-compliant. Bounds are subject to the user-specified override switches (including-c ‘ ’ and ‘-C’) described in Section 3.13 [Subsetting Coordinate Variables], page 53. The CAM/EAM family of atmospheric models does not output a bounds variable or attribute corresponding to the lev coordinate. This prevents NCO from activating its CF bounds machinery when lev is extracted. As of version 4.7.7 (September, 2018), NCO works around this by outputting the ilev coordinate (and hyai, hybi) whenever the lev coordinate is also output. As of NCO version 4.4.9 (May, 2015), NCO supports the CF climatology con- vention for climatological statistics described here (http://cfconventions. org/Data/cf-conventions/cf-conventions-1.7/build/cf-conventions.html# climatological-statistics). This convention allows coordinate variables (including mul- tidimensional coordinates) to describe the (possibly nested) periods and statistical methods of their associated statistics. This is done by naming the variable which contains the periods and methods in the climatology attribute. Note that coordinates of rank N have climatol- ogy bounds of rank N + 1. NCO-generated subsets of CF-compliant files with climatology attributes will include the variables specified by the climatology attribute, if any. Hence the subsets will themselves be CF-compliant. Climatology variables are subject to the user- specified override switches (including-c ‘ ’ and ‘-C’) described in Section 3.13 [Subsetting Coordinate Variables], page 53. As of NCO version 4.4.5 (July, 2014), NCO supports the CF ancillary_variables convention for described here (http://cfconventions.org/cf-conventions/ cf-conventions.html#ancillary-data). This convention allows ancillary variables to be associated with one or more primary variables. NCO attaches any such variables to the extraction list along with the primary variable and its usual (one-dimensional) coordinates, if any. Ancillary variables are subject to the user-specified override switches (including ‘-c’ and ‘-C’) described in Section 3.13 [Subsetting Coordinate Variables], page 53. As of NCO version 4.6.4 (January, 2017), NCO supports the CF cell_measures con- vention described here (http://cfconventions.org/cf-conventions/cf-conventions. html#cell-measures). This convention allows variables to indicate which other variable or variables contains area or volume information about a gridcell. These measures vari- ables are pointed to by the cell_measures attribute. The CDL specification of a measures variable for area looks like orog:cell_measures = "area: areacella" where areacella is the name of the measures variable. Unless the default behavior is overridden, NCO attaches any measures variables to the extraction list along with the primary variable and other associated variables. By definition, measures variables are a subset of the rank of the variable they measure. The most common case is that the mea- sures variable for area is the same size as 2D fields (like surface air temperature) and much smaller than 3D fields (like full air temperature). In such cases the measures variable might occupy 50% of the space of a dataset consisting of only one 2D field. Extraction of mea- sures variables is subject to the user-specified override switches (including ‘-c’ and ‘-C’) described in Section 3.13 [Subsetting Coordinate Variables], page 53. To conserve space Chapter 3: Shared Features 149
without sacrificing too much metadata, NCO makes it possible to override the extraction of measures variables independent of extracting other associated variables. Override the default with ‘--no_cell_measures’ or ‘--no_cll_msr’. These options are available in all operators that perform subsetting (i.e., all operators except ncatted and ncrename). As of NCO version 4.6.4 (January, 2017), NCO supports the CF formula_terms con- vention described here (http://cfconventions.org/cf-conventions/cf-conventions. html#formula-terms). This convention encodes formulas used to construct (usually ver- tical) coordinate grids. The CDL specification of a vertical coordinate formula for looks like lev:standard_name = "atmosphere_hybrid_sigma_pressure_coordinate" lev:formula_terms = "a: hyam b: hybm p0: P0 ps: PS" where standard_name contains the standardized name of the formula variable and formula_terms contains a list of the variables used, called formula variables. Above the formula variables are hyam, hybm, P0, and PS. Unless the default behavior is overridden, NCO attaches any formula variables to the extraction list along with the primary variable and other associated variables. By definition, formula variables are a subset of the rank of the variable they define. One common case is that the formula variables for construct- ing a 3D height grid involves a 2D variable (like surface pressure, or elevation). In such cases the formula variables typically constitute only a small fraction of a dataset consisting of one 3D field. Extraction of formula variables is subject to the user-specified override switches (including ‘-c’ and ‘-C’) described in Section 3.13 [Subsetting Coordinate Vari- ables], page 53. To conserve space without sacrificing too much metadata, NCO makes it possible to override the extraction of formula variables independent of extracting other associated variables. Override the default with ‘--no_formula_terms’ or ‘--no_frm_trm’. These options are available in all operators that perform subsetting (i.e., all operators except ncatted and ncrename). As of NCO version 4.6.0 (May, 2016), NCO supports the CF grid_mapping conven- tion for described here (http://cfconventions.org/cf-conventions/cf-conventions. html#grid-mappings-and-projections). This convention allows descriptions of map- projections to be associated with variables. NCO attaches any such map-projection vari- ables to the extraction list along with the primary variable and its usual (one-dimensional) coordinates, if any. Map-projection variables are subject to the user-specified override switches (including ‘-c’ and ‘-C’) described in Section 3.13 [Subsetting Coordinate Vari- ables], page 53. As of NCO version 3.9.6 (January, 2009), NCO supports the CF coordinates con- vention described here (http://cfconventions.org/cf-conventions/cf-conventions. html#coordinate-system). This convention allows variables to specify additional coordi- nates (including mult-idimensional coordinates) in a space-separated string attribute named coordinates. NCO attaches any such coordinates to the extraction list along with the variable and its usual (one-dimensional) coordinates, if any. These auxiliary coordinates are subject to the user-specified override switches (including-c ‘ ’ and ‘-C’) described in Section 3.13 [Subsetting Coordinate Variables], page 53. Elimination of reduced dimensions from the coordinates attribute helps ensure that rank-reduced variables become completely independent from their former dimensions. As 150 NCO 5.0.2-alpha07 User Guide
of NCO version 4.4.9 (May, 2015), NCO may modify the coordinates attribute to assist this. In particular, ncwa eliminates from the coordinates attribute any dimension that it collapses, e.g., by averaging. The former presence of this dimension will usually be indi- cated by the CF cell_methods convention described here (http://cfconventions.org/ cf-conventions/cf-conventions.html#cell-methods). Hence the CF cell_methods and coordinates conventions can be said to work in tandem to characterize the state and history of a variable’s analysis.
As of NCO version 4.4.2 (February, 2014), NCO supports some of the CF cell_methods convention (http://cfconventions.org/cf-conventions/cf-conventions.html# cell-methods) to describe the analysis procedures that have been applied to data. The convention creates (or appends to an existing) cell_methods attribute a space-separated list of couplets of the form dmn: op where dmn is a comma-separated list of dimensions previously contained in the variable that have been reduced by the arithmetic operation op. For example, the cell_methods value time: mean says that the variable in question was averaged over the time dimension. In such cases time will either be a scalar variable or a degenerate dimension or coordinate. This simply means that it has been averaged-over. The value time, lon: mean lat: max says that the variable in question is the maximum zonal mean of the time averaged original variable. Which is to say that the variable was first averaged over time and longitude, and then the residual latitudinal array was reduced by choosing the maximum value. Since the cell methods convention may alter metadata in an undesirable (or possibly incorrect) fashion, we provide switches to ensure it is always or never used. Use long-options ‘--cll_mth’ or ‘--cell_methods’ to invoke the algorithm (true by default), and options ‘--no_cll_mth’ or ‘--no_cell_methods’ to turn it off. These options are only available in the operators ncwa and ncra.
3.46 ARM Conventions
Availability: ncrcat ¨ Short options: None
ncrcat has been programmed to correctly handle data files which utilize the Atmo- © spheric Radiation Measurement (ARM) Program convention (http://www.arm.gov/data/ time.stm) for time and time offsets. If you do not work with ARM data then you may skip this section. ARM data files store time information in two variables, a scalar, base_time, and a record variable, time_offset. Subtle but serious problems can arise when these type of files are blindly concatenated without CF or ARM support. NCO implements rebasing (see Section 3.28 [Rebasing Time Coordinate], page 101) as necessary on both CF and ARM files. Rebasing chains together consecutive input-files and produces an output-file which contains the correct time information. For ARM files this is expecially complex because the time coordinates are often stored as type NC_CHAR. Currently, ncrcat determines whether a datafile is an ARM datafile simply by testing for the existence of the variables base_time, time_offset, and the dimension time. If these are found in the input-file then ncrcat will automatically perform two non-standard, but hopefully useful, procedures. First, ncrcat will ensure that values of time_offset appearing in the output-file are relative to the Chapter 3: Shared Features 151
base_time appearing in the first input-file (and presumably, though not necessarily, also appearing in the output-file). Second, if a coordinate variable named time is not found in the input-files, then ncrcat automatically creates the time coordinate in the output-file. The values of time are defined by the ARM conventions time = base time + time offset. Thus, if output-file contains the time_offset variable, it will also contain the time coordi- nate. A short message is added to the history global attribute whenever these ARM-specific procedures are executed.
3.47 Operator Version
Availability: All operators ¨ Short options: ‘-r’ Long options: ‘--revision’, ‘--version’, or ‘--vrs’
All operators can be told to print their version information, library version, copyright © notice, and compile-time configuration with the-r ‘ ’ switch, or its long-option equivalent ‘revision’. The ‘--version’ or ‘--vrs’ switches print the operator version information only. The internal version number varies between operators, and indicates the most re- cent change to a particular operator’s source code. This is useful in making sure you are working with the most recent operators. The version of NCO you are using might be, e.g., 3.9.5. Using ‘-r’ on, say, ncks, produces something like ‘NCO netCDF Operators version "3.9.5" last modified 2008/05/11 built May 12 2008 on neige by zender Copyright (C) 1995--2008 Charlie Zender ncks version 20090918’. This tells you that ncks contains all patches up to version 3.9.5, which dates from May 11, 2008.
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4 Reference Manual
This chapter presents reference pages for each of the operators individually. The operators are presented in alphabetical order. All valid command line switches are included in the syntax statement. Recall that descriptions of many of these command line switches are provided only in Chapter 3 [Shared features], page 29, to avoid redundancy. Only options specific to, or most useful with, a particular operator are described in any detailinthe sections below. 154 NCO 5.0.2-alpha07 User Guide
4.1 ncap2 netCDF Arithmetic Processor
ncap2 understands a relatively full-featured language of operations, including loops, con- ¨ ditionals, arrays, and math functions. ncap2 is the most rapidly changing NCO operator and its documentation is incomplete. The distribution file data/ncap2_tst.nco contains an up-to-date overview of its syntax and capabilities. The data/*.nco distribution files (especially bin_cnt.nco, psd_wrf.nco, and rgr.nco) contain in-depth examples of ncap2 solutions to complex problems.
SYNTAX © ncap2 [-3] [-4] [-5] [-6] [-7] [-A] [-C] [-c] [-D dbg] [-F] [-f] [--glb ...] [-h] [--hdf] [--hdr_pad nbr] [--hpss] [-L dfl_lvl] [-l path] [--no_tmp_fl] [-O] [-o output-file] [-p path] [-R] [-r] [--ram_all] [-s algebra] [-S fl.nco] [-t thr_nbr] [-v] [input-file][output-file] DESCRIPTION ncap2 arithmetically processes netCDF files. ncap2 is the successor to ncap which was put into maintenance mode in November, 2006, and completely removed from NCO in March, 2018. This documentation refers to ncap2 implements its own domain-specific language to produc a powerful superset ncap-functionality. ncap2 may be renamed ncap one day! The processing instructions are contained either in the NCO script file fl.nco or in a sequence of command line arguments. The options ‘-s’ (or long options ‘--spt’ or ‘--script’) are used for in-line scripts and ‘-S’ (or long options ‘--fl_spt’, ‘--nco_script’, or ‘--script-file’) are used to provide the filename where (usually multiple) scripting commands are pre-stored. ncap2 was written to perform arbitrary algebraic transformations of data and archive the results as easily as possible. See Section 3.30 [Missing Values], page 103, for treatment of missing values. The results of the algebraic manipulations are called derived fields. Unlike the other operators, ncap2 does not accept a list of variables to be operated on as an argument to ‘-v’ (see Section 3.12 [Subsetting Files], page 48). Rather, the ‘-v’ switch takes no arguments and indicates that ncap2 should output only user-defined variables. ncap2 neither accepts nor understands the -x switch. NB: As of 20120515, ncap2 is unable to append to files that already contain the appended dimensions. Providing a name for output-file is optional if input-file is a netCDF3 format, in which case ncap2 attempts to write modifications directly to input-file (similar to the behavior of ncrename and ncatted). Format-constraints prevent this type of appending from working on a netCDF4 format input-file. In any case, reading and writing the same file can be risky and lead to unexpected consequences (since the file is being both read and written), so in normal usage we recommend providing output-file (which can be the same as input-file since the changes are first written to an intermediate file). As of NCO version 4.8.0 (released May, 2019), ncap2 does not require that input-file be specified when output-file has no dependency on it. Prior to this, ncap2 required users to Chapter 4: Reference Manual 155
specify a dummy input-file even if it was not used to construct output-file. Input files are always read by ncap2, and dummy input files were read though not used for anything nor modified. Now ncap2 -s ’quark=1’ ~/foo.nc # Create new foo.nc ncap2 -s ’print(quark)’ ~/foo.nc # Print existing foo.nc ncap2 -O -s ’quark=1’ ~/foo.nc # Overwrite old with new foo.nc ncap2 -s ’quark=1’ ~/foo.nc ~/foo.nc # Add to old foo.nc Defining new variables in terms of existing variables is a powerful featureof ncap2. De- rived fields inherit the metadata (i.e., attributes) of their ancestors, if any, inthescript or input file. When the derived field is completely new (no identically-named ancestors exist), then it inherits the metadata (if any) of the left-most variable on the right hand side of the defining expression. This metadata inheritance is called attribute propagation. At- tribute propagation is intended to facilitate well-documented data analysis, and we welcome suggestions to improve this feature. The only exception to this rule of attribute propagation is in cases of left hand casting (see Section 4.1.4 [Left hand casting], page 160). The user must manually define the proper metadata for variables defined using left hand casting.
4.1.1 Syntax of ncap2 statements Mastering ncap2 is relatively simple. Each valid statement statement consists of standard forward algebraic expression. The fl.nco, if present, is simply a list of such statements, whitespace, and comments. The syntax of statements is most like the computer language C. The following characteristics of C are preserved:
Array syntax Arrays elements are placed within [] characters; Array indexing Arrays are 0-based; Array storage Last dimension is most rapidly varying; Assignment statements A semi-colon ‘;’ indicates the end of an assignment statement. Comments Multi-line comments are enclosed within /* */ characters. Single line comments are preceded by // characters. Nesting Files may be nested in scripts using #include script. The #include command is not followed by a semi-colon because it is a pre-processor directive, not an assignment statement. The filename script is interpreted relative to the run directory. Attribute syntax The at-sign @ is used to delineate an attribute name from a variable name. 156 NCO 5.0.2-alpha07 User Guide
4.1.2 Expressions Expressions are the fundamental building block of ncap2. Expressions are composed of variables, numbers, literals, and attributes. The following C operators are “overloaded” and work with scalars and multi-dimensional arrays: Arithmetic Operators: * / % + - ^ Binary Operators: > >= < <= == != == || && >> << Unary Operators: + - ++ -- ! Conditional Operator: exp1 ? exp2 : exp3 Assign Operators: = += -= /= *= In the following section a variable also refers to a number literal which is read in as a scalar variable: Arithmetic and Binary Operators Consider var1 ’op’ var2 Precision • When both operands are variables, the result has the precision of the higher precision operand. • When one operand is a variable and the other an attribute, the result has the precision of the variable. • When both operands are attributes, the result has the precision of the more precise attribute. • The exponentiation operator “^” is an exception to the above rules. When both operands have type less than NC_FLOAT, the result is NC_FLOAT. When either type is NC_DOUBLE, the result is also NC_DOUBLE.
Rank • The Rank of the result is generally equal to Rank of the operand that has the greatest number of dimensions. • If the dimensions in var2 are a subset of the dimensions in var1 then its possible to make var2 conform to var1 through broadcasting and or dimension reordering. • Broadcasting a variable means creating data in non-existing dimensions by copying data in existing dimensions. • More specifically: If the numbers of dimensions in var1 is greater than or equal tothe number of dimensions in var2 then an attempt is made to make var2 conform to var1 ,else var1 is made to conform to var2. If conformance is not possible then an error message will be emitted and script execution will cease.
Even though the logical operators return True(1) or False(0) they are treated in the same way as the arithmetic operators with regard to precision and rank. Examples: dimensions: time=10, lat=2, lon=4 Suppose we have the two variables: Chapter 4: Reference Manual 157
double P(time,lat,lon); float PZ0(lon,lat); // PZ0=1,2,3,4,5,6,7,8;
Consider now the expression: PZ=P-PZ0
PZ0 is made to conform to P and the result is PZ0 = 1,3,5,7,2,4,6,8, 1,3,5,7,2,4,6,8, 1,3,5,7,2,4,6,8, 1,3,5,7,2,4,6,8, 1,3,5,7,2,4,6,8, 1,3,5,7,2,4,6,8, 1,3,5,7,2,4,6,8, 1,3,5,7,2,4,6,8, 1,3,5,7,2,4,6,8, 1,3,5,7,2,4,6,8,
Once the expression is evaluated then PZ will be of type double;
Consider now start=four-att_var@double_att; // start =-69 and is of type intger; four_pow=four^3.0f // four_pow=64 and is of type float three_nw=three_dmn_var_sht*1.0f; // type is now float start@n1=att_var@short_att*att_var@int_att; // start@n1=5329 and is type int Binary Operators Unlike C the binary operators return an array of values. There is no such thing as short circuiting with the AND/OR operators. Missing values are carried into the result in the same way they are with the arithmetic operators. When an expression is evaluated in an if() the missing values are treated as true. The binary operators are, in order of precedence:
! Logical Not ------<< Less Than Selection >> Greater Than Selection ------> Greater than >= Greater than or equal to < Less than <= Less than or equal to ------== Equal to 158 NCO 5.0.2-alpha07 User Guide
!= Not equal to ------&& Logical AND ------|| Logical OR ------To see all operators: see Section 4.1.29 [Operator precedence and associativity], page 215, Examples: tm1=time>2 && time <7; // tm1=0, 0, 1, 1, 1, 1, 0, 0, 0, 0 double tm2=time==3 || time>=6; // tm2=0, 0, 1, 0, 0, 1, 1, 1, 1, 1 double tm3=int(!tm1); // tm3=1, 1, 0, 0, 0, 0, 1, 1, 1, 1 int tm4=tm1 && tm2; // tm4=0, 0, 1, 0, 0, 1, 0, 0, 0, 0 double tm5=!tm4; // tm5=1, 1, 0, 1, 1, 0, 1, 1, 1, 1 double Regular Assign Operator var1 ’=’ exp1 If var1 does not already exist in Input or Output then var1 is written to Output with the values, type and dimensions from expr1. If var1 is in Input only it is copied to Output first. Once the var is in Ouptut then the only reqirement on expr1 is that the number of elements must match the number already on disk. The type of expr1 is converted as necessary to the disk type. If you wish to change the type or shape of a variable in Input then you must cast the variable. See see Section 4.1.4 [Left hand casting], page 160, time[time]=time.int(); three_dmn_var_dbl[time,lon,lat]=666L; Other Assign Operators +=,-=,*=./= var1 ’ass op’ exp1 if exp1 is a variable and it doesn’t conform to var1 then an attempt is made to make it conform to var1. If exp1 is an attribute it must have unity size or else have the same number of elements as var1. If expr1 has a different type to var1 the it is converted to the var1 type. z1=four+=one*=10 // z1=14 four=14 one=10; time-=2 // time= -1,0,1,2,3,4,5,6,7,8 Increment/Decrement Operators These work in a similar fashion to their regular C counterparts. If say the variable four is input only then the statement ++four effectively means read four from input increment each element by one, then write the new values to Output; Example: n2=++four; n2=5, four=5 n3=one--+20; n3=21 one=0; n4=--time; n4=time=0.,1.,2.,3.,4.,5.,6.,7.,8.,9.; Conditional Operator ?: exp1 ? exp2 : exp3 Chapter 4: Reference Manual 159
The conditional operator (or ternary Operator) is a succinct way of writing an if/then/else. If exp1 evaluates to true then exp2 is returned else exp3 is returned. Example: weight_avg=weight.avg(); weight_avg@units= (weight_avg == 1 ? "kilo" : "kilos"); PS_nw=PS-(PS.min() > 100000 ? 100000 : 0); Clipping Operators
<< Less-than Clipping
For arrays, the less-than selection operator selects all values in the left operand that are less than the corresponding value in the right operand. If the value of the left side is greater than or equal to the corresponding value of the right side, then the right side value is placed in the result >> Greater-than Clipping
For arrays, the greater-than selection operator selects all values in the left operand that are greater than the corresponding value in the right operand. If the value of the left side is less than or equal to the corresponding value of the right side, then the right side value is placed in the result.
Example: RDM2=RDM >> 100.0 // 100,100,100,100,126,126,100,100,100,100 double RDM2=RDM << 90s // 1, 9, 36, 84, 90, 90, 84, 36, 9, 1 int 4.1.3 Dimensions Dimensions are defined in Output using the defdim() function. defdim("cnt",10); # Dimension size is fixed by default defdim("cnt",10,NC_UNLIMITED); # Dimension is unlimited (record dimension) defdim("cnt",10,0); # Dimension is unlimited (record dimension) defdim("cnt",10,1); # Dimension size is fixed defdim("cnt",10,737); # All non-zero values indicate dimension size is fixed This dimension name must then be prefixed with a dollar-sign$ ‘ ’ when referred to in method arguments or left-hand-casting, e.g., new_var[$cnt]=time; temperature[$time,$lat,$lon]=35.5; temp_avg=temperature.avg($time); The size method allows dimension sizes to be used in arithmetic expressions: time_avg=time.total()/$time.size; Increase the size of a new variable by one and set new member to zero: defdim("cnt_new",$cnt.size+1); new_var[$cnt_new]=0.0; 160 NCO 5.0.2-alpha07 User Guide
new_var(0:($cnt_new.size-2))=old_var; To define an unlimited dimension, simply set the size tozero defdim("time2",0) Dimension Abbreviations It is possible to use dimension abbreviations as method arguments: $0 is the first dimension of a variable $1 is the second dimension of a variable $n is the n+1 dimension of a variable
float four_dmn_rec_var(time,lat,lev,lon); double three_dmn_var_dbl(time,lat,lon);
four_nw=four_dmn_rev_var.reverse($time,$lon) four_nw=four_dmn_rec_var.reverse($0,$3);
four_avg=four_dmn_rec_var.avg($lat,$lev); four_avg=four_dmn_rec_var.avg($1,$2);
three_mw=three_dmn_var_dbl.permute($time,$lon,$lat); three_mw=three_dmn_var_dbl.permute($0,$2,$1); ID Quoting If the dimension name contains non-regular characters use ID quoting: See see Section 4.1.30 [ID Quoting], page 215, defdim("a--list.A",10); A1[’$a--list.A’]=30.0; GOTCHA It is not possible to manually define in Output any dimensions that exist in Input. When a variable from Input appears in an expression or statement its dimensions in Input are automagically copied to Output (if they are not already present)
4.1.4 Left hand casting The following examples demonstrate the utility of the left hand casting ability of ncap2. Consider first this simple, artificial, example. If lat and lon are one dimensional coordinates of dimensions lat and lon, respectively, then addition of these two one-dimensional arrays is intrinsically ill-defined because whether lat lon should be dimensioned lat by lon or lon by lat is ambiguous (assuming that addition is to remain a commutative procedure, i.e., one that does not depend on the order of its arguments). Differing dimensions are said to be orthogonal to one another, and sets of dimensions which are mutually exclusive are or- thogonal as a set and any arithmetic operation between variables in orthogonal dimensional spaces is ambiguous without further information. The ambiguity may be resolved by enumerating the desired dimension ordering of the output expression inside square brackets on the left hand side (LHS) of the equals sign. This Chapter 4: Reference Manual 161
is called left hand casting because the user resolves the dimensional ordering of the RHS of the expression by specifying the desired ordering on the LHS. ncap2 -s ’lat_lon[lat,lon]=lat+lon’ in.nc out.nc ncap2 -s ’lon_lat[lon,lat]=lat+lon’ in.nc out.nc The explicit list of dimensions on the LHS, [lat,lon] resolves the otherwise ambiguous ordering of dimensions in lat lon. In effect, the LHS casts its rank properties onto the RHS. Without LHS casting, the dimensional ordering of lat lon would be undefined and, hopefully, ncap2 would print an error message. Consider now a slightly more complex example. In geophysical models, a coordinate system based on a blend of terrain-following and density-following surfaces is called a hybrid coordinate system. In this coordinate system, four variables must be manipulated to obtain the pressure of the vertical coordinate: PO is the domain-mean surface pressure offset (a scalar), PS is the local (time-varying) surface pressure (usually two horizontal spatial dimensions, i.e. latitude by longitude), hyam is the weight given to surfaces of constant density (one spatial dimension, pressure, which is orthogonal to the horizontal dimensions), and hybm is the weight given to surfaces of constant elevation (also one spatial dimension). This command constructs a four-dimensional pressure prs_mdp from the four input variables of mixed rank and orthogonality: ncap2 -s ’prs_mdp[time,lat,lon,lev]=P0*hyam+PS*hybm’ in.nc out.nc Manipulating the four fields which define the pressure in a hybrid coordinate systemis easy with left hand casting. Finally, we show how to use interface quantities to define midpoint quantities. In partic- ular, we will define interface pressures using the standard CESM output hybrid coordinate parameters, and then difference those interface pressures to obtain the pressure difference between the interfaces. The pressure difference is necessary obtain gridcell mass path and density (which are midpoint quantities). Definitions are as in the above example, with new variables hyai and hybi defined at grid cell vertical interfaces (rather than midpoints like hyam and hybm). The approach naturally fits into two lines: cat > ~/pdel.nco << ’EOF’ *prs_ntf[time,lat,lon,ilev]=P0*hyai+PS*hybi; // Requires NCO 4.5.4 and later: prs_dlt[time,lat,lon,lev]=prs_ntf(:,:,:,1:$ilev.size-1)-prs_ntf(:,:,:,0:$ilev.size-2); // Derived variable that require pressure thickness: // Divide by gravity to obtain total mass path in layer aka mpl [kg m-2] mpl=prs_dlt/grv_sfc; // Multiply by mass mixing ratio to obtain mass path of constituent mpl_CO2=mpl*mmr_CO2; EOF ncap2 -O -v -S ~/pdel.nco ~/nco/data/in.nc ~/foo.nc ncks -O -C -v prs_dlt ~/foo.nc The first line defines the four-dimensional interface pressures prs_ntf as a RAM variable because those are not desired in the output file. The second differences each pressure level from the pressure above it to obtain the pressure difference. This line employs both left-hand 162 NCO 5.0.2-alpha07 User Guide
casting and array hyperslabbing. However, this syntax only works with NCO version 4.5.4 (November, 2015) and later because earlier versions require that LHS and RHS dimension names (not just sizes) match. From the pressure differences, one can obtain the mass path in each layer as shown. Another reason to cast a variable is to modify the shape or type of a variable already in Input gds_var[gds_crd]=gds_var.double(); three_dmn_var_crd[lat,lon,lev]=10.0d; four[]=four.int(); 4.1.5 Arrays and hyperslabs Generating a regularly spaced n-dimensional array with ncap2 is simple with the array() function. The function comes in three (overloaded) forms (A) var_out=array(val_srt,val_inc,$dmn_nm); // One-dimensional output (B) var_out=array(val_srt,val_inc,var_tpl); // Multi-dimensional output (C) var_out=array(val_srt,val_inc,/$dmn1,$dmn2...,$dmnN/); // Multi-dimensional output val srt Starting value of the array. The type of the array will be the type of this starting value. val inc Spacing (or increment) between elements. var tpl Variable from which the array can derive its shape 1D or nD
One-Dimensional Arrays Use form (A) or (B) above for 1D arrays: # var_out will be NC_DOUBLE: var_out=array(10.0,2,$time) // 10.5,12.5,14.5,16.5,18.5,20.5,22.5,24.5,26.5,28.5
// var_out will be NC_UINT, and "shape" will duplicate "ilev" var_out=array(0ul,2,ilev) // 0,2,4,6
// var_out will be NC_FLOAT var_out=array(99.0f,2.5,$lon) // 99,101.5,104,106.5
// Create an array of zeros var_out=array(0,0,$time) // 0,0,0,0,0,0,0,0,0,0
// Create array of ones var_out=array(1.0,0.0,$lon) // 1.0,1.0,1.0,1.0 n-Dimensional Arrays Use form (B) or (C) for creating n-D arrays. NB: In (C) the final argument is a list of dimensions // These are equivalent var_out=array(1.0,2.0,three_dmn_var); var_out=array(1.0,2.0,/$lat,$lev,$lon/); Chapter 4: Reference Manual 163
// var_out is NC_BYTE var_out=array(20b, -4, /$lat,$lon/); // 20,16,12,8,4,0,-4,-8
srt=3.14159f; inc=srt/2.0f; var_out(srt,inc,var_2D_rrg); // 3.14159, 4.712385, 6.28318, 7.853975, 9.42477, 10.99557, 12.56636, 14.13716 ; Hyperslabs in ncap2 are more limited than hyperslabs with the other NCO operators. ncap2 does not understand the shell command-line syntax used to specify multi-slabs, wrapped co-ordinates, negative stride or coordinate value limits. However with a bit of syntactic magic they are all are possible. ncap2 accepts (in fact, it requires) N-hyperslab arguments for a variable of rank N: var1(arg1,arg2 ... argN); where each hyperslab argument is of the form start:end:stride and the arguments for different dimensions are separated by commas. If start is omitted, it defaults to zero. If end is omitted, it defaults to dimension size minus one. If stride is omitted, it defaults to one.
If a single value is present then it is assumed that that dimension collapses to a single value (i.e., a cross-section). The number of hyperslab arguments MUST equal the variable’s rank.
Hyperslabs on the Right Hand Side of an assign
A simple 1D example: ($time.size=10) od[$time]={20,22,24,26,28,30,32,34,36,38};
od(7); // 34 od(7:); // 34,36,38 od(:7); // 20,22,24,26,28,30,32,34 od(::4); // 20,28,36 od(1:6:2) // 22,26,30 od(:) // 20,22,24,26,28,30,32,34,36,38 A more complex three dimensional example: ($lat.size=2,$lon.size=4) th[$time,$lat,$lon]= {1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12,13,14,15,16, 17,18,19,20,21,22,23,24, -99,-99,-99,-99,-99,-99,-99,-99, 164 NCO 5.0.2-alpha07 User Guide
33,34,35,36,37,38,39,40, 41,42,43,44,45,46,47,48, 49,50,51,52,53,54,55,56, -99,58,59,60,61,62,63,64, 65,66,67,68,69,70,71,72, -99,74,75,76,77,78,79,-99 };
th(1,1,3); // 16 th(2,0,:); // 17, 18, 19, 20 th(:,1,3); // 8, 16, 24, -99, 40, 48, 56, 64, 72, -99 th(::5,:,0:3:2); // 1, 3, 5, 7, 41, 43, 45, 47 If hyperslab arguments collapse to a single value (a cross-section has been specified), then that dimension is removed from the returned variable. If all the values collapse then a scalar variable is returned. So, for example, the following is valid:
th_nw=th(0,:,:)+th(9,:,:); // th_nw has dimensions $lon,$lat // NB: the time dimension has become degenerate The following is invalid:
th_nw=th(0,:,0:1)+th(9,:,0:1); because the $lon dimension now only has two elements. The above can be calculated by using a LHS cast with $lon_nw as replacement dim for $lon: defdim("lon_nw",2); th_nw[$lat,$lon_nw]=th(0,:,0:1)+th(9,:,0:1); Hyperslabs on the Left Hand Side of an assign When hyperslabing on the LHS, the expression on the RHS must evaluate to a scalar or a variable/attribute with the same number of elements as the LHS hyperslab. Set all elements of the last record to zero:
th(9,:,:)=0.0; Set first element of each lon element to 1.0:
th(:,:,0)=1.0; One may hyperslab on both sides of an assign. For example, this sets the last record to the first record:
th(9,:,:)=th(0,:,:); Say th0 represents pressure at height=0 and th1 represents pressure at height=1. Then it is possible to insert these hyperslabs into the records
prs[$time,$height,$lat,$lon]=0.0; prs(:,0,:,:)=th0; prs(:,1,:,:)=th1; Chapter 4: Reference Manual 165
Reverse method Use the reverse() method to reverse a dimension’s elements in a variable with at least one dimension. This is equivalent to a negative stride, e.g., th_rv=th(1,:,:).reverse($lon); // {12,11,10,9 }, {16,15,14,13} od_rv=od.reverse($time); // {38,36,34,32,30,28,26,24,22,20} Permute methodp Use the permute() method to swap the dimensions of a variable. The number and names of dimension arguments must match the dimensions in the variable. If the first dimension in the variable is of record type then this must remain the first dimension. If you want to change the record dimension then consider using ncpdq. Consider the variable: float three_dmn_var(lat,lev,lon); three_dmn_var_prm=three_dmn_var.permute($lon,$lat,$lev); // The permuted values are three_dmn_var_prm= 0,4,8, 12,16,20, 1,5,9, 13,17,21, 2,6,10, 14,18,22, 3,7,11, 15,19,23; 4.1.6 Attributes Refer to attributes with var nm@att nm. The following are all valid statements: global@text="Test Attributes"; /* Assign a global variable attribute */ a1[$time]=time*20; a1@long_name="Kelvin"; a1@min=a1.min(); a1@max=a1.max(); a1@min++; --a1@max; a1(0)=a1@min; a1($time.size-1)=a1@max; NetCDF allows all attribute types to have a size between one NC_MAX_ATTRS. Here is the metadata for variable a1: double a1(time) ; a1:long_name = "Kelvin" ; a1:max = 199. ; a1:min = 21. ; a1:trip1 = 1, 2, 3 ; a1:triplet = 21., 110., 199. ; 166 NCO 5.0.2-alpha07 User Guide
These basic methods can be used with attributes: size(), type(), and exists(). For example, to save an attribute text string in a variable:
defdim("sng_len",a1@long_name.size()); sng_arr[$sng_len]=a1@long_name; // sng_arr now contains "Kelvin" Attributes defined in a script are stored in memory and are written to the outputfile after script completion. To stop the attribute being written use the ram_delete() method or use a bogus variable name. Attribute Propagation and Inheritance • Attribute propagation occurs in a regular assign statement. The variable being defined on the LHS gets copies of the attributes from the leftermost variable on the RHS. • Attribute Inheritance: The LHS variable “inherits” attributes from an Input variable with the same name • It is possible to have a regular assign statement for which both propagation and inher- itance occur.
// prs_mdp inherits attributes from P0: prs_mdp[time,lat,lon,lev]=P0*hyam+hybm*PS; // th_min inherits attributes from three_dmn_var_dbl: th_min=1.0 + 2*three_dmn_var_dbl.min($time); Attribute Concatenation
The push() function concatenates attributes, or appends an “expression” to a pre- existing attribute. It comes in two forms
(A) att_new=push(att_exp, expr) (B) att_size=push(&att_nm,expr) In form (A) The first argument should be an attribute identifier or an expression that evaluates to an attribute. The second argument can evalute to an attribute or a variable. The second argument is then converted to the type of att exp; and appended to att exp ; and the resulting attribute is returned.
In form (B) the first argument is a call-by-reference attribute identifier (which maynot yet exist). The second argument is then evaluated (and type-converted as needed) and appended to the call-by-reference atttribute. The final size of the attribute is then returned.
temp@range=-10.0; push(&temp@range,12.0); // temp@range=-10.0,12.0
numbers@squares=push(1,4); numbers@squares=push(numbers@squares,9); push(&number@squares,16.0); push(&number@squares,25ull); // numbers@squares=1,4,9,16,25 Chapter 4: Reference Manual 167
Now some text examples. Remember, an atttribute identifier that begins with @ implies a global attribute. For example, ’@institution’ is short for ’global@institution’. global@greetings=push("hello"," world !!"); global@greek={"alpha"s,"beta"s,"gamma"s}; // Append an NC_STRING push(&@greek,"delta"s); // Pushing an NC_CHAR to a NC_STRING attribute is allowed, it is converted to an an NC_CHAR @e="epsilon"; push(&@greek,@e); push(&@greek,"zeta");
// Pushing a single NC_STRING to an NC_CHAR is not allowed @h="hello"; push(&@h," again"s); // BAD PUSH If the attribute name contains non-regular characters use ID quoting: ’b..m1@c--lost’=23; See see Section 4.1.30 [ID Quoting], page 215.
4.1.7 Value List A value list is a special type of attribute. It can only be used on the RHS of the assign family of statements. That is =, +=, -=, *=, /= A value list CANNOT be involved in any logical, binary, or arithmetical operations (except those above). A value list CANNOT be used as a function argument. A value list CANNOT have nested value lists. The type of a value list is the type of the member with the highest type.
a1@trip={1,2,3}; a1@trip+={3,2,1}; // 4,4,4 a1@triplet={a1@min,(a1@min+a1@max)/2,a1@max}; lon[lon]={0.0,90.0,180.0,270.0}; lon*={1.0,1.1,1.2,1.3} dlon[lon]={1b,2s,3ull,4.0f}; // final type NC_FLOAT
a1@ind={1,2,3}+{4,4,4}; // BAD a1@s=sin({1.0,16.0}); // BAD One can also use a value list to create an attribute of type NC STRING. Remember, a literal string of type NC STRING has a postfix ’s’. A value list ofNC CHAR has no semantic meaning and is plain wrong. array[lon]={1.0,2.,4.0,7.0}; array@numbers={"one"s, "two"s, "four"s, "seven"s}; // GOOD 168 NCO 5.0.2-alpha07 User Guide
ar[lat]={0,20} ar@numbers={"zero","twenty"}; // BAD 4.1.8 Number literals The table below lists the postfix character(s) to add to a number literal (aka, a naked constant) for explicit type specification. The same type-specification rules are used for variables and attributes. A floating-point number without a postfix defaults to NC_DOUBLE, while an integer without a postfix defaults to type NC_INT: var[$rlev]=0.1; // Variable will be type @code{NC_DOUBLE} var[$lon_grd]=2.0; // Variable will be type @code{NC_DOUBLE} var[$gds_crd]=2e3; // Variable will be type @code{NC_DOUBLE} var[$gds_crd]=2.0f; // Variable will be type @code{NC_FLOAT} (note "f") var[$gds_crd]=2e3f; // Variable will be type @code{NC_FLOAT} (note "f") var[$gds_crd]=2; // Variable will be type @code{NC_INT} var[$gds_crd]=-3; // Variable will be type @code{NC_INT} var[$gds_crd]=2s; // Variable will be type @code{NC_SHORT} var[$gds_crd]=-3s; // Variable will be type @code{NC_SHORT} var@att=41.; // Attribute will be type @code{NC_DOUBLE} var@att=41.f; // Attribute will be type @code{NC_FLOAT} var@att=41; // Attribute will be type @code{NC_INT} var@att=-21s; // Attribute will be type @code{NC_SHORT} var@units="kelvin"; // Attribute will be type @code{NC_CHAR} There is no postfix for characters, use a quoted string instead for NC_CHAR. ncap2 interprets a standard double-quoted string as a value of type NC_CHAR. In this case, any receiving variable must be dimensioned as an array of NC_CHAR long enough to hold the value. To use the newer netCDF4 types NCO must be compiled/linked to the netCDF4 library and the output file must be of type NETCDF4: var[$time]=1UL; // Variable will be type @code{NC_UINT} var[$lon]=4b; // Variable will be type @code{NC_BYTE} var[$lat]=5ull; // Variable will be type @code{NC_UINT64} var[$lat]=5ll; // Variable will be type @code{NC_INT64} var@att=6.0d; // Attribute will be type @code{NC_DOUBLE} var@att=-666L; // Attribute will be type @code{NC_INT} var@att="kelvin"s; // Attribute will be type @code{NC_STRING} (note the "s") Use a post-quote ‘s’ for NC_STRING. Place the letter ‘s’ immediately following the double- quoted string to indicate that the value is of type NC_STRING. In this case, the receiving variable need not have any memory allocated to hold the string because netCDF4 handles that memory allocation. Suppose one creates a file containing an ensemble of model results, and wishes to label the record coordinate with the name of each model. The NC_STRING type is well-suited to this because it facilitates storing arrays of strings of arbitrary length. This is sophisticated, though easy with ncap2: Chapter 4: Reference Manual 169
% ncecat -O -u model cesm.nc ecmwf.nc giss.nc out.nc % ncap2 -4 -O -s ’model[$model]={"cesm"s,"ecmwf"s,"giss"s}’ out.nc out.nc The key here to place an ‘s’ character after each double-quoted string value to indicate an NC_STRING type. The ‘-4’ ensures the output filetype is netCDF4 in case the input filetype is not. netCDF3/4 Types b|B NC_BYTE, a signed 1-byte integer none NC_CHAR, an ISO/ASCII character s|S NC_SHORT, a signed 2-byte integer l|L NC_INT, a signed 4-byte integer f|F NC_FLOAT, a single-precision (4-byte) floating-point number d|D NC_DOUBLE, a double-precision (8-byte) floating-point number netCDF4 Types ub|UB NC_UBYTE, an unsigned 1-byte integer us|US NC_USHORT, an unsigned 2-byte integer u|U|ul|UL NC_UINT, an unsigned 4-byte integer ll|LL NC_INT64, a signed 8-byte integer ull|ULL NC_UINT64, an unsigned 8-byte integer s NC_STRING, a string of arbitrary length 4.1.9 if statement The syntax of the if statement is similar to its C counterpart. The Conditional Operator (ternary operator) has also been implemented. if(exp1) stmt1; else if(exp2) stmt2; else stmt3;
# Can use code blocks as well: if(exp1){ stmt1; stmt1a; stmt1b; }else if(exp2) stmt2; else{ stmt3; 170 NCO 5.0.2-alpha07 User Guide
stmt3a; stmt3b; } For a variable or attribute expression to be logically true all its non-missing value elements must be logically true, i.e., non-zero. The expression can be of any type. Unlike C there is no short-circuiting of an expression with the OR (||) and AND (&&) operators. The whole expression is evaluated regardless if one of the AND/OR operands are True/False. # Simple example if(time > 0) print("All values of time are greater than zero\n"); else if(time < 0) print("All values of time are less than zero\n"); else { time_max=time.max(); time_min=time.min(); print("min value of time=");print(time_min,"%f"); print("max value of time=");print(time_max,"%f"); }
# Example from ddra.nco if(fl_typ == fl_typ_gcm){ var_nbr_apx=32; lmn_nbr=1.0*var_nbr_apx*varsz_gcm_4D; /* [nbr] Variable size */ if(nco_op_typ==nco_op_typ_avg){ lmn_nbr_avg=1.0*var_nbr_apx*varsz_gcm_4D; // Block size lmn_nbr_wgt=dmnsz_gcm_lat; /* [nbr] Weight size */ } // !nco_op_typ_avg }else if(fl_typ == fl_typ_stl){ var_nbr_apx=8; lmn_nbr=1.0*var_nbr_apx*varsz_stl_2D; /* [nbr] Variable size */ if(nco_op_typ==nco_op_typ_avg){ lmn_nbr_avg=1.0*var_nbr_apx*varsz_stl_2D; // Block size lmn_nbr_wgt=dmnsz_stl_lat; /* [nbr] Weight size */ } // !nco_op_typ_avg } // !fl_typ Conditional Operator
// netCDF4 needed for this example th_nw=(three_dmn_var_sht >= 0 ? three_dmn_var_sht.uint() : \ three_dmn_var_sht.int()); 4.1.10 Print & String methods The print statement comes in a variety of forms: (A) print(variable_name, format string?); Chapter 4: Reference Manual 171
(A1) print(expression/string, format string?);
(B) sprint(expression/string, format string?); (B1) sprint4(expression/string, format string?); print()
If the variable exists in I/O then it is printed in a similar fashion to ncks -H.
print(lon); lon[0]=0 lon[1]=90 lon[2]=180 lon[3]=270
print(byt_2D) lat[0]=-90 lon[0]=0 byt_2D[0]=0 lat[0]=-90 lon[1]=90 byt_2D[1]=1 lat[0]=-90 lon[2]=180 byt_2D[2]=2 lat[0]=-90 lon[3]=270 byt_2D[3]=3 lat[1]=90 lon[0]=0 byt_2D[4]=4 lat[1]=90 lon[1]=90 byt_2D[5]=5 lat[1]=90 lon[2]=180 byt_2D[6]=6 lat[1]=90 lon[3]=270 byt_2D[7]=7 If the first argument is NOT a variable the form (A1) is invoked.
print(mss_val_fst@_FillValue); mss_val_fst@_FillValue, size = 1 NC_FLOAT, value = -999
print("This function \t is monotonic\n"); This function is monotonic
print(att_var@float_att) att_var@float_att, size = 7 NC_FLOAT, value = 73, 72, 71, 70.01, 69.001, 68.01, 67.01
print(lon*10.0) lon, size = 4 NC_DOUBLE, value = 0, 900, 1800, 2700 If the format string is specified then the results from (A) and (A1) forms are thesame
print(lon_2D_rrg,"%3.2f,"); 0.00,0.00,180.00,0.00,180.00,0.00,180.00,0.00,
print(lon*10.0,"%g,") 0,900,1800,2700,
print(att_var@float_att,"%g,") 73,72,71,70.01,69.001,68.01,67.01, 172 NCO 5.0.2-alpha07 User Guide
sprint() & sprint4()
These functions work in an identical fashion to (A1) except that sprint() outputs a regular netCDF3 NC_CHAR attribute and sprint4() outputs a netCDF4 NC_STRING attribute time@units=sprint(nDays,"%d days since 1970-1-1") bnd@num=sprint4(bnd_idx,"Band number=%d")
time@arr=sprint4(time,"%.2f,") // "1.00,2.00,3.00,4.00,5.00,6.00,7.00,8.00,9.00,10.00," You can also use sprint4() to convert a NC_CHAR string to a NC_STRING string and sprint() to convert a NC_STRING to a NC_CHAR lat_1D_rct@long_name = "Latitude for 2D rectangular grid stored as 1D arrays"; //
// convert to NC_STRING lat_1D_rct@long_name = sprint4(lat_1D_rct@long_name) hyperslab a netCDF string
Its possible to index-into a NC CHAR string. Just like a C-String. Remember an NC CHAR string is has no terminating null. You CANNOT index into a NC STRING. You have to convert to an NC CHAR first. global@greeting="hello world!!!" @h=@greeting(0:4); // "hello" @w=@greeting(6:11); // "world"
// can use negative inidices @x=@greeting(-3:-1); // "!!!"
// can use stride @n=@greeting(::2); // "hlowrd!"
// concatenation global@new_greeting=push(@h, " users !!!"); // "hello users!!!"
@institution="hotel california"s; @h=@institution(0:4); // BAD
// convert NC_STRING to NC_CHAR @is=sprint(@institution); @h=@is(0:4); // "hotel"
// convert NC_CHAR to NC_STRING @h=sprint4(@h); get vars in() & get vars out() att_lst=get_vars_in(att_regexp?) Chapter 4: Reference Manual 173
att_lst=get_vars_out(att_regexp?) These functions are used to create a list of vars in Input or Output. The optional arg ’att regexp’. Can be an NC CHAR att or a NC STRING att. If NC CHAR then only a single reg-exp can be specified. If NC STRING then multiple reg-exp can be specified. The output is allways an NC STRING att. The matching works in an identical fashion to the -v switch in ncks. if there is no arg then all vars are returned. @slist=get_vars_in("^time"); // "time", "time_bnds", "time_lon", "time_udunits" // Use NC_STRINGS @regExp={".*_bnd"s,".*_grd"s} @slist=get_vars_in(@regExp); // "lat_bnd", "lat_grd", "lev_bnd", "lon_grd", "time_bnds", "cnv_CF_grd"
4.1.11 Missing values ncap2 Missing values operate slightly differently in ncap2 Consider the expression where op is any of the following operators (excluding ’=’) Arithmetic operators ( * / % + - ^ ) Binary Operators ( >, >= <, <= ==, !=,==,||,&&, >>,<< ) Assign Operators ( +=,-=,/=, *= )
var1 ’op’ var2 If var1 has a missing value then this is the value used in the operation, otherwise the missing value for var2 is used. If during the element-by-element operation an element from either operand is equal to the missing value then the missing value is carried through. In this way missing values ’percolate’ or propagate through an expression. Missing values associated with Output variables are stored in memory and are written to disk after the script finishes. During script execution its possible (and legal) for the missing value of a variable to take on several different values. # Consider the variable: int rec_var_int_mss_val_int(time); =-999,2,3,4,5,6,7,8,-999,-999; rec_var_int_mss_val_int:_FillValue = -999;
n2=rec_var_int_mss_val_int + rec_var_int_mss_val_int.reverse($time);
n2=-999,-999,11,11,11,11,11,11,999,-999; The following methods query or manipulate missing value (aka _FillValue information associated with a variable. The methods that “manipulate” only succeed on variables in Output. set_miss(expr) The numeric argument expr becomes the new missing value, overwriting the old missing value, if any. The argument given is converted if necessary to the variable’s type. NB: This only changes the missing value attribute. Missing val- ues in the original variable remain unchanged, and thus are no long considered missing values. They are effectively “orphaned”. Thus set_miss() is normally 174 NCO 5.0.2-alpha07 User Guide
used only when creating new variables. The intrinsic function change_miss() (see below) is typically used to edit values of existing variables. change_miss(expr) Sets or changes (any pre-existing) missing value attribute and missing data values to expr. NB: This is an expensive function since all values must be examined. Use this function when changing missing values for pre-existing variables. get_miss() Returns the missing value of a variable. If the variable exists in Input and Output then the missing value of the variable in Output is returned. If the variable has no missing value then an error is returned. delete_miss() Delete the missing value associated with a variable. number_miss() Count the number of missing values a variable contains. has_miss() Returns 1 (True) if the variable has a missing value associated with it. else returns 0 (False) missing() This function creates a True/False mask array of where the missing value is set. It is syntatically equivalent to (var_in == var_in.get_miss()), except that requires deleting the missing value before-hand. th=three_dmn_var_dbl; th.change_miss(-1e10d); /* Set values less than 0 or greater than 50 to missing value */ where(th < 0.0 || th > 50.0) th=th.get_miss();
# Another example: new[$time,$lat,$lon]=1.0; new.set_miss(-997.0);
// Extract all elements evenly divisible by 3 where (three_dmn_var_dbl%3 == 0) new=three_dmn_var_dbl; elsewhere new=new.get_miss();
// Print missing value and variable summary mss_val_nbr=three_dmn_var_dbl.number_miss(); print(three_dmn_var_dbl@_FillValue); print("Number of missing values in three_dmn_var_dbl: "); print(mss_val_nbr,"%d"); print(three_dmn_var_dbl); Chapter 4: Reference Manual 175
// Find total number of missing values along dims $lat and $lon mss_ttl=three_dmn_var_dbl.missing().ttl($lat,$lon); print(mss_ttl); // 0, 0, 0, 8, 0, 0, 0, 1, 0, 2 ; simple_fill_miss(var) This function takes a variable and attempts to fill missing values using an average of up to the 4 nearest neighbour grid points. The method used is iterative (up to 1000 cycles). For very large areas of missing values results can be unpredictable. The given variable must be at least 2D; and the algorithm assumes that the last two dims are lat/lon or y/x weighted_fill_miss(var) Weighted fill miss is more sophisticated. Up to 8 nearest neighbours are used to calculate a weighted average. The weighting used is the inverse square of distance. Again the method is iterative (up to 1000 cycles). The area filled is defined by the final two dims of the variable. In addition this function assumes the existance of coordinate vars the same name as the last two dims. if it doesn’t find these dims it will gently exit with warning. 4.1.12 Methods and functions The convention within this document is that methods can be used as functions. However, functions are not and cannot be used as methods. Methods can be daisy-chained d and their syntax is cleaner than functions. Method names are reserved words and CANNOT be used as variable names. The command ncap2 -f shows the complete list of methods available on your build. n2=sin(theta) n2=theta.sin() n2=sin(theta)^2 + cos(theta)^2 n2=theta.sin().pow(2) + theta.cos()^2 This statement chains together methods to convert three dmn var sht to type double, average it, then convert this back to type short: three_avg=three_dmn_var_sht.double().avg().short();
Aggregate Methods These methods mirror the averaging types available in ncwa. The arguments to the methods are the dimensions to average over. Specifying no dimensions is equivalent to specifying all dimensions i.e., averaging over all dimensions. A masking variable and a weighting variable can be manually created and applied as needed. avg() Mean value sqravg() Square of the mean avgsqr() Mean of sum of squares max() Maximum value min() Minimum value 176 NCO 5.0.2-alpha07 User Guide
mabs() Maximum absolute value mebs() Mean absolute value mibs() Minimum absolute value rms() Root-mean-square (normalize by N) rmssdn() Root-mean square (normalize by N-1) tabs() or ttlabs() Sum of absolute values ttl() or total() or sum() Sum of values // Average a variable over time four_time_avg=four_dmn_rec_var($time);
Packing Methods For more information see see Section 3.38 [Packed data], page 126, and see Section 4.9 [ncpdq netCDF Permute Dimensions Quickly], page 289, pack() & pack_short() The default packing algorithm is applied and variable is packed to NC_SHORT pack_byte() Variable is packed to NC_BYTE pack_short() Variable is packed to NC_SHORT pack_int() Variable is packed to NC_INT unpack() The standard unpacking algorithm is applied.
NCO automatically unpacks packed data before arithmetically modifying it. After modi- fication NCO stores the unpacked data. To store it as packed data again, repack it with, e.g., the pack() function. To ensure that temperature is packed in the output file, regardless of whether it is packed in the input file, one uses, e.g., ncap2 -s ’temperature=pack(temperature-273.15)’ in.nc out.nc All the above pack functions also take the additional two arguments scale_factor, add_offset. Both arguments must be included: ncap2 -v -O -s ’rec_pck=pack(three_dmn_rec_var,-0.001,40.0);’ in.nc foo.nc Basic Methods These methods work with variables and attributes. They have no arguments. size() Total number of elements ndims() Number of dimensions in variable Chapter 4: Reference Manual 177
type() Returns the netcdf type (see previous section) exists() Return 1 (true) if var or att is present in I/O else return 0 (false) getdims() Returns an NC STRING attribute of all the dim names of a variable
Utility Methods These functions are used to manipulate missing values and RAM variables. see Section 4.1.11 [Missing values ncap2], page 173, set_miss(expr) Takes one argument the missing value. Sets or overwrites the existing missing value. The argument given is converted if necessary to the variable type change_miss(expr) Changes the missing value elements of the variable to the new missing value (n.b. an expensive function). get_miss() Returns the missing value of a variable in Input or Output delete_miss() Deletes the missing value associated with a variable. has_miss() Returns 1 (True) if the variable has a missing else returns 0 (False) number_miss Returns the number of missing values a variable contains ram_write() Writes a RAM variable to disk i.e., converts it to a regular disk type variable ram_delete() Deletes a RAM variable or an attribute
PDQ Methods See see Section 4.9 [ncpdq netCDF Permute Dimensions Quickly], page 289, reverse(dim args) Reverse the dimension ordering of elements in a variable. permute(dim args) Re-shape variables by re-ordering the dimensions. All the dimensions of the variable must be specified in the arguments. A limitation of this permute (unlike ncpdq) is that the record dimension cannot be re-assigned.
// Swap dimensions about and reorder along lon lat_2D_rrg_new=lat_2D_rrg.permute($lon,$lat).reverse($lon); lat_2D_rrg_new=0,90,-30,30,-30,30,-90,0 178 NCO 5.0.2-alpha07 User Guide
Type Conversion Methods and Functions These methods allow ncap2 to convert variables and attributes to the different netCDF types. For more details on automatic and manual type conversion see (see Section 3.40 [Type Conversion], page 134). netCDF4 types are only available if you have compiled/links NCO with the netCDF4 library and the Output file is HDF5. netCDF3/4 Types byte() convert to NC_BYTE, a signed 1-byte integer char() convert to NC_CHAR, an ISO/ASCII character short() convert to NC_SHORT, a signed 2-byte integer int() convert to NC_INT, a signed 4-byte integer float() convert to NC_FLOAT, a single-precision (4-byte) floating-point number double() convert to NC_DOUBLE, a double-precision (8-byte) floating-point number netCDF4 Types ubyte() convert to NC_UBYTE, an unsigned 1-byte integer ushort() convert to NC_USHORT, an unsigned 2-byte integer uint() convert to NC_UINT, an unsigned 4-byte integer int64() convert to NC_INT64, a signed 8-byte integer uint64() convert to NC_UINT64, an unsigned 8-byte integer
You can also use the convert() method to do type conversion. This takes an integer agument. For convenience, ncap2 defines the netCDF pre-processor tokens as RAM vari- ables. For example you may wish to convert a non-floating point variable to the same type as another variable. lon_type=lon.type(); if(time.type() != NC_DOUBLE && time.type() != NC_FLOAT) time=time.convert(lon_type); Intrinsic Mathematical Methods The list of mathematical methods is system dependant. For the full list see Section 4.1.28 [Intrinsic mathematical methods], page 213, All the mathematical methods take a single argument except atan2() and pow() which take two. If the operand type is less than float then the result will be of type float. Argu- ments of type double yield results of type double. Like the other methods, you are free to use the mathematical methods as functions. n1=pow(2,3.0f) // n1 type float n2=atan2(2,3.0) // n2 type double n3=1/(three_dmn_var_dbl.cos().pow(2))-tan(three_dmn_var_dbl)^2; // n3 type double
4.1.13 RAM variables Unlike regular variables, RAM variables are never written to disk. Hence using RAM vari- ables in place of regular variables (especially within loops) significantly increases execution Chapter 4: Reference Manual 179
speed. Variables that are frequently accessed within for or where clauses provide the great- est opportunities for optimization. To declare and define a RAM variable simply prefix the variable name with an asterisk (*) when the variable is declared/initialized. To delete RAM variables (and recover their memory) use the ram_delete() method. To write a RAM variable to disk (like a regular variable) use ram_write(). *temp[$time,$lat,$lon]=10.0; // Cast *temp_avg=temp.avg($time); // Regular assign temp.ram_delete(); // Delete RAM variable temp_avg.ram_write(); // Write Variable to output
// Create and increment a RAM variable from "one" in Input *one++; // Create RAM variables from the variables three and four in Input. // Multiply three by 10 and add it to four. *four+=*three*=10; // three=30, four=34 4.1.14 Where statement The where() statement combines the definition and application of a mask and can leadto succinct code. The syntax of a where() statement is: // Single assign (’elsewhere’ is optional) where(mask) var1=expr1; elsewhere var1=expr2;
// Multiple assigns where(mask){ var1=expr1; var2=expr2; ... }elsewhere{ var1=expr3 var2=expr4 var3=expr5; ... } • The only expression allowed in the predicate of a where is assign, i.e., ’var=expr’. This assign differs from a regular ncap2 assign. The LHS var must already exist in Input or Output. The RHS expression must evaluate to a scalar or a variable/attribute of the same size as the LHS variable. • Consider when both the LHS and RHS are variables: For every element where mask condition is True, the corresponding LHS variable element is re-assigned to its partner element on the RHS. In the elsewhere part the mask is logically inverted and the assign process proceeds as before. • If the mask dimensions are a subset of the LHS variable’s dimensions, then it is made 180 NCO 5.0.2-alpha07 User Guide
to conform; if it cannot be made to conform then script execution halts. • Missing values in the mask evaluate to False in the where code/block statement and to True in the elsewhere block/statement. • LHS variable elements set to missing value are treated just like any other elements and can be re-assigned as the mask dictates • LHS variable cannot include subscripts. If they do script execution will terminate. See below example for work-araound.
Consider the variables float lon_2D_rct(lat,lon); and float var_msk(lat,lon);. Suppose we wish to multiply by two the elements for which var_msk equals 1: where(var_msk == 1) lon_2D_rct=2*lon_2D_rct; Suppose that we have the variable int RDM(time) and that we want to set its values less than 8 or greater than 80 to 0: where(RDM < 8 || RDM > 80) RDM=0; To use where on a variable hyperslab, define and use a temporary variable, e.g., *var_tmp=var2(:,0,:,:); where (var1 < 0.5) var_tmp=1234; var2(;,0,:,;)=var_tmp; ram_delete(var_tmp); Consider irregu- larly gridded data, described using rank 2 coordinates: double lat(south_north,east_ west), double lon(south_north,east_west), double temperature(south_north,east_ west). This type of structure is often found in regional weather/climate model (such as WRF) output, and in satellite swath data. For this reason we call it “Swath-like Data”, or SLD. To find the average temperature in a region boundedlat by[ min,lat max] and [lon min,lon max]: temperature_msk[$south_north,$east_west]=0.0; where((lat >= lat_min && lat <= lat_max) && (lon >= lon_min && lon <= lon_max)) temperature_msk=temperature; elsewhere temperature_msk=temperature@_FillValue;
temp_avg=temperature_msk.avg(); temp_max=temperature.max(); For North American Regional Reanalysis (NARR) data (example dataset (http://dust. ess.uci.edu/diwg/narr_uwnd.199605.nc)) the procedure looks like this ncap2 -O -v -S ~/narr.nco ${DATA}/hdf/narr_uwnd.199605.nc ~/foo.nc where narr.nco is an ncap2 script like this: /* North American Regional Reanalysis (NARR) Statistics NARR stores grids with 2-D latitude and longitude, aka Swath-like Data (SLD) Here we work with three variables: Chapter 4: Reference Manual 181
lat(y,x), lon(y,x), and uwnd(time,level,y,x); To study sub-regions of SLD, we use masking techniques: 1. Define mask as zero times variable to be masked Then mask automatically inherits variable attributes And average below will inherit mask attributes 2. Optionally, create mask as RAM variable (as below with asterisk *) NCO does not write RAM variable to output Masks are often unwanted, and can be big, so this speeds execution 3. Example could be extended to preserve mean lat and lon of sub-region Follow uwnd example to do this: lat_msk=0.0*lat ... lat_avg=lat.avg($y,$x) */ *uwnd_msk=0.0*uwnd; where((lat >= 35.6 && lat <= 37.0) && (lon >= -100.5 && lon <= -99.0)) uwnd_msk=uwnd; elsewhere uwnd_msk=uwnd@_FillValue;
// Average only over horizontal dimensions x and y (preserve level and time) uwnd_avg=uwnd_msk.avg($y,$x); Stripped of comments and formatting, this example is a three-statement script executed by a one-line command. NCO needs only this meagre input to unpack and copy the in- put data and attributes, compute the statistics, and then define and write the output file. Unless the comments pointed out that wind variable (uwnd) was four-dimensional and the latitude/longitude grid variables were both two-dimensional, there would be no way to tell. This shows how NCO hides from the user the complexity of analyzing multi-dimensional SLD. We plan to extend such SLD features to more operators soon. 4.1.15 Loops ncap2 supplies for() loops and while() loops. They are completely unoptimized so use them only with RAM variables unless you want thrash your disk to death. To break out of a loop use the break command. To iterate to the next cycle use the continue command. // Set elements in variable double temp(time,lat) // If element < 0 set to 0, if element > 100 set to 100 *sz_idx=$time.size; *sz_jdx=$lat.size;
for(*idx=0;idx
// Are values of co-ordinate variable double lat(lat) monotonic? *sz=$lat.size;
for(*idx=1;idx if(idx == sz) print("lat co-ordinate is monotonic\n"); else print("lat co-ordinate is NOT monotonic\n"); // Sum odd elements *idx=0; *sz=$lat_nw.size; *sum=0.0; while(idx ram_write(sum); print("Total of odd elements ");print(sum);print("\n"); 4.1.16 Include files The syntax of an include-file is: #include "script.nco" #include "/opt/SOURCES/nco/data/tst.nco" If the filename is relative and not absolute then the directory searched is relative tothe run-time directory. It is possible to nest include files to an arbitrary depth. A handy useof inlcude files is to store often used constants. Use RAM variables if you do not want these constants written to nc-file. output-file. // script.nco // Sample file to #include in ncap2 script *pi=3.1415926535; // RAM variable, not written to output *h=6.62607095e-34; // RAM variable, not written to output e=2.71828; // Regular (disk) variable, written to output As of NCO version 4.6.3 (December, 2016), The user can specify the directory(s) to be searched by specifing them in the UNIX environment var NCO_PATH. The format used is identical to the UNIX PATH. The directory(s) are only searched if the include filename is relative. export NCO_PATH=":/home/henryb/bin/:/usr/local/scripts:/opt/SOURCES/nco/data:" 4.1.17 sort methods In ncap2 there are multiple ways to sort data. Beginning with NCO 4.1.0 (March, 2012), ncap2 support six sorting functions: var_out=sort(var_in,&srt_map); // Ascending sort var_out=asort(var_in,&srt_map); // Accending sort var_out=dsort(var_in,&srt_map); // Desending sort var_out=remap(var_in,srt_map); // Apply srt_map to var_in Chapter 4: Reference Manual 183 var_out=unmap(var_in,srt_map); // Reverse what srt_map did to var_in dsr_map=invert_map(srt_map); // Produce "de-sort" map that inverts srt_map The first two functions, sort() and asort() sort, in ascending order, all the elements of var in (which can be a variable or attribute) without regard to any dimensions. The third function, dsort() does the same but sorts in descending order. Remember that ascending and descending sorts are specified by asort() and dsort(), respectively. These three functions are overloaded to take a second, optional argument called the sort map srt map, which should be supplied as a call-by-reference variable, i.e., preceded with an ampersand. If the sort map does not yet exist, then it will be created and returned as an integer type the same shape as the input variable. The output var out of each sort function is a sorted version of the input, var in. The output var out of the two mapping functions the result of applying (with remap() or un- applying (with unmap()) the sort map srt map to the input var in. To apply the sort map with remap() the size of the variable must be exactly divisible by the size of the sort map. The final function invert_map() returns the so-called de-sorting map dsr map which is the inverse of the input map srt map. This gives the user access to both the forward and inverse sorting maps: a1[$time]={10,2,3,4,6,5,7,3,4,1}; a1_sort=sort(a1); print(a1_sort); // 1, 2, 3, 3, 4, 4, 5, 6, 7, 10; a2[$lon]={2,1,4,3}; a2_sort=sort(a2,&a2_map); print(a2); // 1, 2, 3, 4 print(a2_map); // 1, 0, 3, 2; If the map variable does not exist prior to the sort() call, then it will be created with the same shape as the input variable and be of type NC_INT. If the map variable already exists, then the only restriction is that it be of at least the same size as the input variable. To apply a map use remap(var_in,srt_map). defdim("nlat",5); a3[$lon]={2,5,3,7}; a4[$nlat,$lon]={ 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12, 13,14,15,16, 17,18,19,20}; a3_sort=sort(a3,&a3_map); 184 NCO 5.0.2-alpha07 User Guide print(a3_map); // 0, 2, 1, 3; a4_sort=remap(a4,a3_map); print(a4_sort); // 1, 3, 2, 4, // 5, 7, 6, 8, // 9,11,10,12, // 13,15,14,16, // 17,19,18,20; a3_map2[$nlat]={4,3,0,2,1}; a4_sort2=remap(a4,a3_map2); print(a4_sort2); // 3, 5, 4, 2, 1 // 8, 10, 9,7, 6, // 13,15,14,12,11, // 18,20,19,17,16 As in the above example you may create your own sort map. To sort in descending order, apply the reverse() method after the sort(). Here is an extended example of how to use ncap2 features to hyperslab an irregular region based on the values of a variable not a coordinate. The distinction is crucial: hyperslabbing based on dimensional indices or coordinate values is straightforward. Using the values of single or multi-dimensional variable to define a hyperslab is quite different. cat > ~/ncap2_foo.nco << ’EOF’ // Purpose: Save irregular 1-D regions based on variable values // Included in NCO User Guide at http://nco.sf.net/nco.html#sort /* NB: Single quotes around EOF above turn off shell parameter expansion in "here documents". This in turn prevents the need for protecting dollarsign characters in NCO scripts with backslashes when the script is cut-and-pasted (aka "moused") from an editor or e-mail into a shell console window */ /* Copy coordinates and variable(s) of interest into RAM variable(s) Benefits: 1. ncap2 defines writes all variables on LHS of expression to disk Only exception is RAM variables, which are stored in RAM only Repeated operations on regular variables takes more time, because changes are written to disk copy after every change. RAM variables are only changed in RAM so script works faster RAM variables can be written to disk at end with ram_write() 2. Script permutes variables of interest during processing Chapter 4: Reference Manual 185 Safer to work with copies that have different names This discourages accidental, mistaken use of permuted versions 3. Makes this script a more generic template: var_in instead of specific variable names everywhere */ *var_in=one_dmn_rec_var; *crd_in=time; *dmn_in_sz=$time.size; // [nbr] Size of input arrays /* Create all other "intermediate" variables as RAM variables to prevent them from cluttering the output file. Mask flag and sort map are same size as variable of interest */ *msk_flg=var_in; *srt_map=var_in; /* In this example we mask for all values evenly divisible by 3 This is the key, problem-specific portion of the template Replace this where() condition by that for your problem Mask variable is Boolean: 1=Meets condition, 0=Fails condition */ where(var_in % 3 == 0) msk_flg=1; elsewhere msk_flg=0; // print("msk_flg = ");print(msk_flg); // For debugging... /* The sort() routine is overloaded, and takes one or two arguments The second argument (optional) is the "sort map" (srt_map below) Pass the sort map by reference, i.e., prefix with an ampersand If the sort map does not yet exist, then it will be created and returned as an integer type the same shape as the input variable. The output of sort(), on the LHS, is a sorted version of the input msk_flg is not needed in its original order after sort() Hence we use msk_flg as both input to and output from sort() Doing this prevents the need to define a new, unneeded variable */ msk_flg=sort(msk_flg,&srt_map); // Count number of valid points in mask by summing the one’s *msk_nbr=msk_flg.total(); // Define output dimension equal in size to number of valid points defdim("crd_out",msk_nbr); /* Now sort the variable of interest using the sort map and remap() The output, on the LHS, is the input re-arranged so that all points meeting the mask condition are contiguous at the end of the array Use same srt_map to hyperslab multiple variables of the same shape Remember to apply srt_map to the coordinate variables */ crd_in=remap(crd_in,srt_map); var_in=remap(var_in,srt_map); 186 NCO 5.0.2-alpha07 User Guide /* Hyperslab last msk_nbr values of variable(s) of interest */ crd_out[crd_out]=crd_in((dmn_in_sz-msk_nbr):(dmn_in_sz-1)); var_out[crd_out]=var_in((dmn_in_sz-msk_nbr):(dmn_in_sz-1)); /* NB: Even though we created all variables possible as RAM variables, the original coordinate of interest, time, is written to the ouput. I’m not exactly sure why. For now, delete it from the output with: ncks -O -x -v time ~/foo.nc ~/foo.nc */ EOF ncap2 -O -v -S ~/ncap2_foo.nco ~/nco/data/in.nc ~/foo.nc ncks -O -x -v time ~/foo.nc ~/foo.nc ncks ~/foo.nc Here is an extended example of how to use ncap2 features to sort multi-dimensional arrays based on the coordinate values along a single dimension. cat > ~/ncap2_foo.nco << ’EOF’ /* Purpose: Sort multi-dimensional array based on coordinate values This example sorts the variable three_dmn_rec_var(time,lat,lon) based on the values of the time coordinate. */ // Included in NCO User Guide at http://nco.sf.net/nco.html#sort // Randomize the time coordinate time=10.0*gsl_rng_uniform(time); //print("original randomized time = \n");print(time); /* The sort() routine is overloaded, and takes one or two arguments The first argument is a one dimensional array The second argument (optional) is the "sort map" (srt_map below) Pass the sort map by reference, i.e., prefix with an ampersand If the sort map does not yet exist, then it will be created and returned as an integer type the same shape as the input variable. The output of sort(), on the LHS, is a sorted version of the input */ time=sort(time,&srt_map); //print("sorted time (ascending order) and associated sort map =\n");print(time);print(srt_map); /* sort() always sorts in ascending order The associated sort map therefore re-arranges the original, randomized time array into ascending order. There are two methods to obtain the descending order the user wants 1) We could solve the problem in ascending order (the default) and then apply the reverse() method to re-arrange the results. 2) We could change the sort map to return things in descending order of time and solve the problem directly in descending order. */ Chapter 4: Reference Manual 187 // Following shows how to do method one: /* Expand the sort map to srt_map_3d, the size of the data array 1. Use data array to provide right shape for the expanded sort map 2. Coerce data array into an integer so srt_map_3d is an integer 3. Multiply data array by zero so 3-d map elements are all zero 4. Add the 1-d sort map to the 3-d sort map (NCO automatically resizes) 5. Add the spatial (lat,lon) offsets to each time index 6. de-sort using the srt_map_3d 7. Use reverse to obtain descending in time order Loops could accomplish the same thing (exercise left for reader) However, loops are slow for large datasets */ /* Following index manipulation requires understanding correspondence between 1-d (unrolled, memory order of storage) and access into that memory as a multidimensional (3-d, in this case) rectangular array. Key idea to understand is how dimensionality affects offsets */ // Copy 1-d sort map into 3-d sort map srt_map_3d=(0*int(three_dmn_rec_var))+srt_map; // Multiply base offset by factorial of lesser dimensions srt_map_3d*=$lat.size*$lon.size; lon_idx=array(0,1,$lon); lat_idx=array(0,1,$lat)*$lon.size; lat_lon_idx[$lat,$lon]=lat_idx+lon_idx; srt_map_3d+=lat_lon_idx; print("sort map 3d =\n");print(srt_map_3d); // Use remap() to re-map the data three_dmn_rec_var=remap(three_dmn_rec_var,srt_map_3d); // Finally, reverse data so time coordinate is descending time=time.reverse($time); //print("sorted time (descending order) =\n");print(time); three_dmn_rec_var=three_dmn_rec_var.reverse($time); // Method two: Key difference is srt_map=$time.size-srt_map-1; EOF ncap2 -O -v -S ~/ncap2_foo.nco ~/nco/data/in.nc ~/foo.nc 4.1.18 UDUnits script As of NCO version 4.6.3 (December, 2016), ncap2 includes support for UDUnits conversions. The function is called udunits. Its syntax is varOut=udunits(varIn,"UnitsOutString") The udunits() function looks for the attribute of varIn@units and fails if it is not found. A quirk of this function that due to attribute propagation varOut@units will be 188 NCO 5.0.2-alpha07 User Guide overwritten by varIn@units. It is best to re-initialize this attribute AFTER the call. In addition if varIn@units is of the form "time_interval since basetime" then the calendar attribute varIn@calendar will read it. If it does not exist then the calendar used defaults to mixed Gregorian/Julian as defined by UDUnits. If varIn is not a floating-point type then it is promoted to NC_DOUBLE for the system call in the UDUnits library, and then demoted back to its original type after. T[lon]={0.0,100.0,150.0,200.0}; T@units="Celsius"; // Overwrite variable T=udunits(T,"kelvin"); print(T); // 273.15, 373.15, 423.15, 473.15 ; T@units="kelvin"; // Rebase coordinate days to hours timeOld=time; print(timeOld); // 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ; timeOld@units="days since 2012-01-30"; @units="hours since 2012-02-01 01:00"; timeNew=udunits(timeOld, @units); timeNew@units=@units; print(timeNew); // -25, -1, 23, 47, 71, 95, 119, 143, 167, 191 ; tOld=time; // nb in this calendar NO Leap year tOld@calendar="365_day"; tOld@units="minutes since 2012-02-28 23:58:00.00"; @units="seconds since 2012-03-01 00:00"; tNew=udunits(tOld, @units); tNew@units=@units; print(tNew); // -60, 0, 60, 120, 180, 240, 300, 360, 420, 480 strftime() The var_str=strtime(var_time,fmt_sng) method takes a time-based variable and a format string and returns an NC STRING variable (of the same shape as var time) of time-stamps in the form specified by ’fmt sng’. In order to run this command output type must be netCDF-4. ncap2 -4 -v -O -s ’time_str=strftime(time,"%Y-%m-%d");’ in.nc foo.nc time_str="1964-03-13", "1964-03-14", "1964-03-15", "1964-03-16", "1964-03-17", "1964-03-18", "1964-03-19", "1964-03-20", "1964-03-21", "1964-03-22" ; Chapter 4: Reference Manual 189 Under the hood there are a few steps invoved: First the method reads var_time@units and var_time@calendar (if present) then converts var_time to seconds since 1970-01- 01. It then converts these possibly UTC seconds to the standard struture struct *tm. Finally strftime() is called with fmt_sng and the *tm struct. The C-standard strftime() is used as defined in time.h. If the method is called without fmt sng then the following default is used: "%Y-%m-%d %H:%M:%S". The method regular takes a single var argument and uses the above default string. ncap2 -4 -v -O -s ’time_str=regular(time);’ in.nc foo.nc time_str = "1964-03-13 21:09:00", "1964-03-14 21:09:00", "1964-03-15 21:09:00", "1964-03-16 21:09:00", "1964-03-17 21:09:00", "1964-03-18 21:09:00", "1964-03-19 21:09:00", "1964-03-20 21:09:00", "1964-03-21 21:09:00", "1964-03-22 21:09:00" ; Another working example ncap2 -v -O -s ’ts=strftime(frametime(0),"%Y-%m-%d/envlog_netcdf_L1_ua-mac_%Y-%m-%d.nc");’ in.nc out.nc ts="2017-08-11/envlog_netcdf_L1_ua-mac_2017-08-11.nc" 4.1.19 Vpointer A variable-pointer or vpointer is a pointer to a variable or attribute. It is most useful when one needs to apply a set of operations on a list of variables. For example, after regular processing one may wish to set the _FillValue of all NC_FLOAT variables to a particular value, or to create min/max attributes for all 3D variables of type NC_DOUBLE. A vpointer is not a ’pointer’ to a memory location in the C/C++ sense. Rather the vpointer is a text attribute that contains the name of a variable. To use the pointer simply prefix the pointer with *. Then, most places where you use VAR_ID you can use *vpointer nm. There are a variety of ways to maintain a list of strings in ncap2. The easiest method is to use an NC_STRING attribute. Below is a simple illustration that uses a vpointer of type NC_CHAR. Remember an attribute starting with @ implies ’global’, e.g., @vpx is short for global@vpx. idx=9; idy=20; t2=time; global@vpx="idx"; // Increment idx by one *global@vpx++; print(idx); // Multiply by 5 *@vpx*=5; // idx now 50 print(idx); 190 NCO 5.0.2-alpha07 User Guide // Add 200 (long method) *@vpx=*@vpx+200; //idx now 250 print(idx); @vpy="idy"; // Add idx idy to get idz idz=*@vpx+*@vpy; // idz == 270 print(idz); // We can also reference variables in the input file // Can use an existing attribute pointer since attributes are not written // to the netCDF file until after the script has finished. @vpx="three_dmn_var"; // We can convert this variable to type NC_DOUBLE and // write it to ouptut all at once *@vpx=*@vpx.double(); The following script writes to the output files all variables that are of type NC_DOUBLE and that have at least two dimensions. It then changes their _FillValue to 1.0E-9. The function get_vars_in() creates an NC_STRING attribute that contains all of the variable names in the input file. Note that a vpointer must be a plain attribute, NOT an aattribute expression. Thus in the below script using *all(idx) would be a fundamental mistake. In the below example the vpointer var_nm is of type NC_STRING. @all=get_vars_in(); *[email protected](); *idx=0; for(idx=0;idx if(*@var_nm.type() == NC_DOUBLE && *@var_nm.ndims() >= 2){ *@var_nm=*@var_nm; *@var_nm.change_miss(1e-9d); } } The following script writes to the output file all 3D/4D variables of type NC_FLOAT. Then for each variable it calculates a range attribute that contains the maximum and minimum values, and a total attribute that is the sum of all the elements. In this example vpointers are used to ’point’ to attributes. @all=get_vars_in(); *[email protected](); Chapter 4: Reference Manual 191 for(*idx=0;idx for(*idx=0;idx // We can work with attribute pointers as well // sprint - ouptut is of type NC_CHAR @att_total=sprint(@var_nm,"%s@total"); @att_range=sprint(@var_nm,"%s@range"); // If you are still confused then print out the atts print(@att_total); print(@att_range); *@att_total= *@var_nm.total(); *@att_range={ min(*@var_nm), max(*@var_nm)}; } This is an ncdump of one of the variables that has been processed by the above script float three_dmn_var_int(time, lat, lon) ; three_dmn_var_int:_FillValue = -99.f ; three_dmn_var_int:long_name = "three dimensional record variable of type int" ; three_dmn_var_int:range = 1.f, 80.f ; three_dmn_var_int:total = 2701.f ; three_dmn_var_int:units = "watt meter-2" ; 4.1.20 Irregular Grids NCO is capable of analyzing datasets for many different underlying coordinate grid types. netCDF was developed for and initially used with grids comprised of orthogonal dimen- sions forming a rectangular coordinate system. We call such grids standard grids. It is increasingly common for datasets to use metadata to describe much more complex grids. Let us first define three important coordinate grid properties: regularity, rectangularity, and structure. Grids are regular if the spacing between adjacent is constant. For example, a 4-by-5 degree latitude-longitude grid is regular because the spacings between adjacent latitudes (4 degrees) are constant as are the (5 degrees) spacings between adjacent longitudes. Spac- 192 NCO 5.0.2-alpha07 User Guide ing in irregular grids depends on the location along the coordinate. Grids such as Gaussian grids have uneven spacing in latitude (points cluster near the equator) and so are irregular. Grids are rectangular if the number of elements in any dimension is not a function of any other dimension. For example, a T42 Gaussian latitude-longitude grid is rectangular because there are the same number of longitudes (128) for each of the (64) latitudes. Grids are non-rectangular if the elements in any dimension depend on another dimension. Non- rectangular grids present many special challenges to analysis software like NCO. Grids are structured if they are represented as functions of two horizontal spatial di- mensions. For example, grids with latitude and longitude dimensions are structured, and so are curvilinear grids with along-track and cross-track dimensions. A grid with a single dimension is unstructured. For example, icosohedral grids are usually unstructured, as are MPAS grids. Wrapped coordinates (see Section 3.22 [Wrapped Coordinates], page 74), such as longi- tude, are independent of these grid properties (regularity, rectangularity, structure). The preferred NCO technique to analyze data on non-standard coordinate grids is to create a region mask with ncap2, and then to use the mask within ncap2 for variable-specific processing, and/or with other operators (e.g., ncwa, ncdiff) for entire file processing. Before describing the construction of masks, let us review how irregularly gridded geo- science data are described. Say that latitude and longitude are stored as R-dimensional arrays and the product of the dimension sizes is the total number of elements N in the other variables. Geoscience applications tend to use R = 1, R = 2, and R = 3. If the grid is has no simple representation (e.g., discontinuous) then it makes sense to store all coordinates as 1D arrays with the same size as the number of grid points. These gridpoints can be completely independent of all the other (own weight, area, etc.). R=1: lat(number of gridpoints) and lon(number of gridpoints) If the horizontal grid is time-invariant then R=2 is common: R=2: lat(south north,east west) and lon(south north,east west) The Weather and Research Forecast (WRF) model uses R=3: R=3: lat(time,south north,east west), lon(time,south north,east west) and so supports grids that change with time. Grids with R > 1 often use missing values to indicated empty points. For example, so-called “staggered grids” will use fewer east west points near the poles and more near the equator. netCDF only accepts rectangular arrays so space must be allocated for the maximum number of east west points at all latitudes. Then the application writes missing values into the unused points near the poles. We demonstrate the ncap2 analysis technique for irregular regions by constructing a mask for an R=2 grid. We wish to find, say, the mean temperature withinlat [ min,lat max] and [lon min,lon max]: ncap2 -s ’mask_var= (lat >= lat_min && lat <= lat_max) && \ (lon >= lon_min && lon <= lon_max);’ in.nc out.nc Chapter 4: Reference Manual 193 Arbitrarily shaped regions can be defined by more complex conditional statements. Once defined, masks can be applied to specific variables, and to entirefiles: ncap2 -s ’temperature_avg=(temperature*mask_var).avg()’ in.nc out.nc ncwa -a lat,lon -m mask_var -w area in.nc out.nc Crafting such commands on the command line is possible though unwieldy. In such cases, a script is often cleaner and allows you to document the procedure: cat > ncap2.in << ’EOF’ mask_var = (lat >= lat_min && lat <= lat_max) && (lon >= lon_min && > lon <= lon_max); if(mask_var.total() > 0){ // Check that mask contains some valid values temperature_avg=(temperature*mask_var).avg(); // Average temperature temperature_max=(temperature*mask_var).max(); // Maximum temperature } EOF ncap2 -S ncap2.in in.nc out.nc Grids like those produced by the WRF model are complex because one must use global metadata to determine the grid staggering and offsets to translate XLAT and XLONG into real latitudes, longitudes, and missing points. The WRF grid documentation should describe this. For WRF files creating regional masks looks, in general, like mask_var = (XLAT >= lat_min && XLAT <= lat_max) && (XLONG >= lon_min && XLONG <= lon_max); A few notes: Irregular regions are the union of arrays of lat/lon min/max’s. The mask procedure is identical for all R. 4.1.21 Bilinear interpolation As of version 4.0.0 NCO has internal routines to perform bilinear interpolation on gridded data sets. In mathematics, bilinear interpolation is an extension of linear interpolation for interpolating functions of two variables on a regular grid. The idea is to perform linear interpolation first in one direction, and then again in the other direction. Suppose we have an irregular grid of data temperature[lat,lon], with co-ordinate vars lat[lat], lon[lon]. We wish to find the temperature at an arbitary pointX [ ,Y ] within the grid. If we can locate lat min,lat max and lon min,lon max such that lat_min <= X <= lat_max and lon_min <= Y <= lon_max then we can interpolate in two dimensions the temperature at [X,Y ]. The general form of the ncap2 interpolation function is var_out=bilinear_interp(grid_in,grid_out,grid_out_x,grid_out_y,grid_in_x,grid_in_y) where grid_in Input function data. Usually a two dimensional variable. It must be of size grid_in_x.size()*grid_in_y.size() grid_out This variable is the shape of var_out. Usually a two dimensional variable. It must be of size grid_out_x.size()*grid_out_y.size() grid_out_x X output values 194 NCO 5.0.2-alpha07 User Guide grid_out_y Y output values grid_in_x X input values values. Must be monotonic (increasing or decreasing). grid_in_y Y input values values. Must be monotonic (increasing or decreasing). Prior to calculations all arguments are converted to type NC_DOUBLE. After calculations var_out is converted to the input type of grid_in. Suppose the first part of an ncap2 script is defdim("X",4); defdim("Y",5); // Temperature T_in[$X,$Y]= {100, 200, 300, 400, 500, 101, 202, 303, 404, 505, 102, 204, 306, 408, 510, 103, 206, 309, 412, 515.0 }; // Coordinate variables x_in[$X]={0.0,1.0,2.0,3.01}; y_in[$Y]={1.0,2.0,3.0,4.0,5}; Now we interpolate with the following variables: defdim("Xn",3); defdim("Yn",4); T_out[$Xn,$Yn]=0.0; x_out[$Xn]={0.0,0.02,3.01}; y_out[$Yn]={1.1,2.0,3,4}; var_out=bilinear_interp(T_in,T_out,x_out,y_out,x_in,y_in); print(var_out); // 110, 200, 300, 400, // 110.022, 200.04, 300.06, 400.08, // 113.3, 206, 309, 412 ; It is possible to interpolate a single point: var_out=bilinear_interp(T_in,0.0,3.0,4.99,x_in,y_in); print(var_out); // 513.920594059406 Wrapping and Extrapolation The function bilinear_interp_wrap() takes the same arguments as bilinear_interp() but performs wrapping (Y ) and extrapolation (X) for points off the edge of the grid. If the given range of longitude is say (25-335) and we have a point at 20 degrees, then the Chapter 4: Reference Manual 195 endpoints of the range are used for the interpolation. This is what wrapping means. For wrapping to occur Y must be longitude and must be in the range (0,360) or (-180,180). There are no restrictions on the longitude (X) values, though typically these are in the range (-90,90). This ncap2 script illustrates both wrapping and extrapolation of end points: defdim("lat_in",6); defdim("lon_in",5); // Coordinate input vars lat_in[$lat_in]={-80,-40,0,30,60.0,85.0}; lon_in[$lon_in]={30, 110, 190, 270, 350.0}; T_in[$lat_in,$lon_in]= {10,40,50,30,15, 12,43,52,31,16, 14,46,54,32,17, 16,49,56,33,18, 18,52,58,34,19, 20,55,60,35,20.0 }; defdim("lat_out",4); defdim("lon_out",3); // Coordinate variables lat_out[$lat_out]={-90,0,70,88.0}; lon_out[$lon_out]={0,190,355.0}; T_out[$lat_out,$lon_out]=0.0; T_out=bilinear_interp_wrap(T_in,T_out,lat_out,lon_out,lat_in,lon_in); print(T_out); // 13.4375, 49.5, 14.09375, // 16.25, 54, 16.625, // 19.25, 58.8, 19.325, // 20.15, 60.24, 20.135 ; 4.1.22 GSL special functions As of version 3.9.6 (released January, 2009), NCO can link to the GNU Scientific Li- brary (GSL). ncap2 can access most GSL special functions including Airy, Bessel, error, gamma, beta, hypergeometric, and Legendre functions and elliptical integrals. GSL must be version 1.4 or later. To list the GSL functions available with your NCO build, use ncap2 -f | grep ^gsl. The function names used by ncap2 mirror their GSL names. The NCO wrappers for GSL functions automatically call the error-handling version of the GSL function when available1. 1 These are the GSL standard function names postfixed with _e. NCO calls these functions automatically, without the NCO command having to specifically indicate the _e function suffix. 196 NCO 5.0.2-alpha07 User Guide This allows NCO to return a missing value when the GSL library encounters a domain error or a floating-point exception. The slow-down due to calling the error-handling version of the GSL numerical functions was found to be negligible (please let us know if you find otherwise). Consider the gamma function. The GSL function prototype is int gsl_sf_gamma_e(const double x, gsl_sf_result * result) The ncap2 script would be: lon_in[lon]={-1,0.1,0,2,0.3}; lon_out=gsl_sf_gamma(lon_in); lon_out= _, 9.5135, 4.5908, 2.9915 The first value is set to _FillValue since the gamma function is undefined for negative integers. If the input variable has a missing value then this value is used. Otherwise, the default double fill value is used (defined in the netCDF header netcdf.h as NC_FILL_DOUBLE = 9.969e+36). Consider a call to a Bessel function with GSL prototype int gsl_sf_bessel_Jn_e(int n, double x, gsl_sf_result * result) An ncap2 script would be lon_out=gsl_sf_bessel_Jn(2,lon_in); lon_out=0.11490, 0.0012, 0.00498, 0.011165 This computes the Bessel function of order n=2 for every value in lon_in. The Bessel order argument, an integer, can also be a non-scalar variable, i.e., an array. n_in[lon]={0,1,2,3}; lon_out=gsl_sf_bessel_Jn(n_in,0.5); lon_out= 0.93846, 0.24226, 0.03060, 0.00256 Arguments to GSL wrapper functions in ncap2 must conform to one another, i.e., they must share the same sub-set of dimensions. For example: three_out=gsl_sf_bessel_ Jn(n_in,three_dmn_var_dbl) is valid because the variable three_dmn_var_dbl has a lon dimension, so n_in in can be broadcast to conform to three_dmn_var_dbl. However time_ out=gsl_sf_bessel_Jn(n_in,time) is invalid. Consider the elliptical integral with prototype int gsl_sf_ellint_RD_e(double x, double y, double z, gsl_mode_t mode, gsl_sf_result * result) three_out=gsl_sf_ellint_RD(0.5,time,three_dmn_var_dbl); The three arguments are all conformable so the above ncap2 call is valid. The mode argument in the function prototype controls the convergence of the algorithm. It also appears in the Airy Function prototypes. It can be set by defining the environment variable GSL_PREC_MODE. If unset it defaults to the value GSL_PREC_DOUBLE. See the GSL manual for more details. export GSL_PREC_MODE=0 // GSL_PREC_DOUBLE export GSL_PREC_MODE=1 // GSL_PREC_SINGLE export GSL_PREC_MODE=2 // GSL_PREC_APPROX Chapter 4: Reference Manual 197 The ncap2 wrappers to the array functions are slightly different. Consider the following GSL prototype int gsl_sf_bessel_Jn_array(int nmin, int nmax, double x, double *result_array) b1=lon.double(); x=0.5; status=gsl_sf_bessel_Jn_array(1,4,x,&b1); print(status); b1=0.24226,0.0306,0.00256,0.00016; This calculates the Bessel function of x=0.5 for n=1 to 4. The first three arguments are scalar values. If a non-scalar variable is supplied as an argument then only the first value is used. The final argument is the variable where the results are stored (NB:the & indicates this is a call by reference). This final argument must be of type double and must be of least size nmax-nmin+1. If either of these conditions is not met then then the function returns an error message. The function/wrapper returns a status flag. Zero indicates success. Consider another array function int gsl_sf_legendre_Pl_array(int lmax, double x, double *result_array); a1=time.double(); x=0.3; status=gsl_sf_legendre_Pl_array(a1.size()-1, x,&a1); print(status); This call calculates P l(0.3) for l=0..9. Note that |x|<=1, otherwise there will be a domain error. See the GSL documentation for more details. The GSL functions implemented in NCO are listed in the table below. This table is correct for GSL version 1.10. To see what functions are available on your build run the command ncap2 -f |grep ^gsl . To see this table along with the GSL C-function prototypes look at the spreadsheet doc/nco gsl.ods. GSL NAME I NCAP FUNCTION CALL gsl sf airy Ai e Y gsl sf airy Ai(dbl expr) gsl sf airy Bi e Y gsl sf airy Bi(dbl expr) gsl sf airy Ai scaled e Y gsl sf airy Ai scaled(dbl expr) gsl sf airy Bi scaled e Y gsl sf airy Bi scaled(dbl expr) gsl sf airy Ai deriv e Y gsl sf airy Ai deriv(dbl expr) gsl sf airy Bi deriv e Y gsl sf airy Bi deriv(dbl expr) gsl sf airy Ai deriv scaled e Y gsl sf airy Ai deriv scaled(dbl expr) gsl sf airy Bi deriv scaled e Y gsl sf airy Bi deriv scaled(dbl expr) gsl sf airy zero Ai e Y gsl sf airy zero Ai(uint expr) gsl sf airy zero Bi e Y gsl sf airy zero Bi(uint expr) gsl sf airy zero Ai deriv e Y gsl sf airy zero Ai deriv(uint expr) gsl sf airy zero Bi deriv e Y gsl sf airy zero Bi deriv(uint expr) gsl sf bessel J0 e Y gsl sf bessel J0(dbl expr) gsl sf bessel J1 e Y gsl sf bessel J1(dbl expr) 198 NCO 5.0.2-alpha07 User Guide gsl sf bessel Jn e Y gsl sf bessel Jn(int expr,dbl expr) gsl sf bessel Jn array Y status=gsl sf bessel Jn array(int,int,double,&var out) gsl sf bessel Y0 e Y gsl sf bessel Y0(dbl expr) gsl sf bessel Y1 e Y gsl sf bessel Y1(dbl expr) gsl sf bessel Yn e Y gsl sf bessel Yn(int expr,dbl expr) gsl sf bessel Yn array Y gsl sf bessel Yn array gsl sf bessel I0 e Y gsl sf bessel I0(dbl expr) gsl sf bessel I1 e Y gsl sf bessel I1(dbl expr) gsl sf bessel In e Y gsl sf bessel In(int expr,dbl expr) gsl sf bessel In array Y status=gsl sf bessel In array(int,int,double,&var out) gsl sf bessel I0 scaled e Y gsl sf bessel I0 scaled(dbl expr) gsl sf bessel I1 scaled e Y gsl sf bessel I1 scaled(dbl expr) gsl sf bessel In scaled e Y gsl sf bessel In scaled(int expr,dbl expr) gsl sf bessel In scaled array Y staus=gsl sf bessel In scaled array(int,int,double,&var out) gsl sf bessel K0 e Y gsl sf bessel K0(dbl expr) gsl sf bessel K1 e Y gsl sf bessel K1(dbl expr) gsl sf bessel Kn e Y gsl sf bessel Kn(int expr,dbl expr) gsl sf bessel Kn array Y status=gsl sf bessel Kn array(int,int,double,&var out) gsl sf bessel K0 scaled e Y gsl sf bessel K0 scaled(dbl expr) gsl sf bessel K1 scaled e Y gsl sf bessel K1 scaled(dbl expr) gsl sf bessel Kn scaled e Y gsl sf bessel Kn scaled(int expr,dbl expr) gsl sf bessel Kn scaled array Y status=gsl sf bessel Kn scaled array(int,int,double,&var out) gsl sf bessel j0 e Y gsl sf bessel J0(dbl expr) gsl sf bessel j1 e Y gsl sf bessel J1(dbl expr) gsl sf bessel j2 e Y gsl sf bessel j2(dbl expr) gsl sf bessel jl e Y gsl sf bessel jl(int expr,dbl expr) gsl sf bessel jl array Y status=gsl sf bessel jl array(int,double,&var out) gsl sf bessel jl steed array Y gsl sf bessel jl steed array gsl sf bessel y0 e Y gsl sf bessel Y0(dbl expr) gsl sf bessel y1 e Y gsl sf bessel Y1(dbl expr) gsl sf bessel y2 e Y gsl sf bessel y2(dbl expr) gsl sf bessel yl e Y gsl sf bessel yl(int expr,dbl expr) gsl sf bessel yl array Y status=gsl sf bessel yl array(int,double,&var out) gsl sf bessel i0 scaled e Y gsl sf bessel I0 scaled(dbl expr) gsl sf bessel i1 scaled e Y gsl sf bessel I1 scaled(dbl expr) gsl sf bessel i2 scaled e Y gsl sf bessel i2 scaled(dbl expr) gsl sf bessel il scaled e Y gsl sf bessel il scaled(int expr,dbl expr) gsl sf bessel il scaled array Y status=gsl sf bessel il scaled array(int,double,&var out) gsl sf bessel k0 scaled e Y gsl sf bessel K0 scaled(dbl expr) gsl sf bessel k1 scaled e Y gsl sf bessel K1 scaled(dbl expr) gsl sf bessel k2 scaled e Y gsl sf bessel k2 scaled(dbl expr) gsl sf bessel kl scaled e Y gsl sf bessel kl scaled(int expr,dbl expr) gsl sf bessel kl scaled array Y status=gsl sf bessel kl scaled array(int,double,&var out) gsl sf bessel Jnu e Y gsl sf bessel Jnu(dbl expr,dbl expr) gsl sf bessel Ynu e Y gsl sf bessel Ynu(dbl expr,dbl expr) gsl sf bessel sequence Jnu e N gsl sf bessel sequence Jnu gsl sf bessel Inu scaled e Y gsl sf bessel Inu scaled(dbl expr,dbl expr) Chapter 4: Reference Manual 199 gsl sf bessel Inu e Y gsl sf bessel Inu(dbl expr,dbl expr) gsl sf bessel Knu scaled e Y gsl sf bessel Knu scaled(dbl expr,dbl expr) gsl sf bessel Knu e Y gsl sf bessel Knu(dbl expr,dbl expr) gsl sf bessel lnKnu e Y gsl sf bessel lnKnu(dbl expr,dbl expr) gsl sf bessel zero J0 e Y gsl sf bessel zero J0(uint expr) gsl sf bessel zero J1 e Y gsl sf bessel zero J1(uint expr) gsl sf bessel zero Jnu e N gsl sf bessel zero Jnu gsl sf clausen e Y gsl sf clausen(dbl expr) gsl sf hydrogenicR 1 e N gsl sf hydrogenicR 1 gsl sf hydrogenicR e N gsl sf hydrogenicR gsl sf coulomb wave FG e N gsl sf coulomb wave FG gsl sf coulomb wave F array N gsl sf coulomb wave F array gsl sf coulomb wave FG array N gsl sf coulomb wave FG array gsl sf coulomb wave FGp array N gsl sf coulomb wave FGp array gsl sf coulomb wave sphF array N gsl sf coulomb wave sphF array gsl sf coulomb CL e N gsl sf coulomb CL gsl sf coulomb CL array N gsl sf coulomb CL array gsl sf coupling 3j e N gsl sf coupling 3j gsl sf coupling 6j e N gsl sf coupling 6j gsl sf coupling RacahW e N gsl sf coupling RacahW gsl sf coupling 9j e N gsl sf coupling 9j gsl sf coupling 6j INCORRECT eN gsl sf coupling 6j INCORRECT gsl sf dawson e Y gsl sf dawson(dbl expr) gsl sf debye 1 e Y gsl sf debye 1(dbl expr) gsl sf debye 2 e Y gsl sf debye 2(dbl expr) gsl sf debye 3 e Y gsl sf debye 3(dbl expr) gsl sf debye 4 e Y gsl sf debye 4(dbl expr) gsl sf debye 5 e Y gsl sf debye 5(dbl expr) gsl sf debye 6 e Y gsl sf debye 6(dbl expr) gsl sf dilog e N gsl sf dilog gsl sf complex dilog xy e N gsl sf complex dilog xy e gsl sf complex dilog e N gsl sf complex dilog gsl sf complex spence xy e N gsl sf complex spence xy e gsl sf multiply e N gsl sf multiply gsl sf multiply err e N gsl sf multiply err gsl sf ellint Kcomp e Y gsl sf ellint Kcomp(dbl expr) gsl sf ellint Ecomp e Y gsl sf ellint Ecomp(dbl expr) gsl sf ellint Pcomp e Y gsl sf ellint Pcomp(dbl expr,dbl expr) gsl sf ellint Dcomp e Y gsl sf ellint Dcomp(dbl expr) gsl sf ellint F e Y gsl sf ellint F(dbl expr,dbl expr) gsl sf ellint E e Y gsl sf ellint E(dbl expr,dbl expr) gsl sf ellint P e Y gsl sf ellint P(dbl expr,dbl expr,dbl expr) gsl sf ellint D e Y gsl sf ellint D(dbl expr,dbl expr,dbl expr) gsl sf ellint RC e Y gsl sf ellint RC(dbl expr,dbl expr) gsl sf ellint RD e Y gsl sf ellint RD(dbl expr,dbl expr,dbl expr) gsl sf ellint RF e Y gsl sf ellint RF(dbl expr,dbl expr,dbl expr) gsl sf ellint RJ e Y gsl sf ellint RJ(dbl expr,dbl expr,dbl expr,dbl expr) 200 NCO 5.0.2-alpha07 User Guide gsl sf elljac e N gsl sf elljac gsl sf erfc e Y gsl sf erfc(dbl expr) gsl sf log erfc e Y gsl sf log erfc(dbl expr) gsl sf erf e Y gsl sf erf(dbl expr) gsl sf erf Z e Y gsl sf erf Z(dbl expr) gsl sf erf Q e Y gsl sf erf Q(dbl expr) gsl sf hazard e Y gsl sf hazard(dbl expr) gsl sf exp e Y gsl sf exp(dbl expr) gsl sf exp e10 e N gsl sf exp e10 gsl sf exp mult e Y gsl sf exp mult(dbl expr,dbl expr) gsl sf exp mult e10 e N gsl sf exp mult e10 gsl sf expm1 e Y gsl sf expm1(dbl expr) gsl sf exprel e Y gsl sf exprel(dbl expr) gsl sf exprel 2 e Y gsl sf exprel 2(dbl expr) gsl sf exprel n e Y gsl sf exprel n(int expr,dbl expr) gsl sf exp err e Y gsl sf exp err(dbl expr,dbl expr) gsl sf exp err e10 e N gsl sf exp err e10 gsl sf exp mult err e N gsl sf exp mult err gsl sf exp mult err e10 e N gsl sf exp mult err e10 gsl sf expint E1 e Y gsl sf expint E1(dbl expr) gsl sf expint E2 e Y gsl sf expint E2(dbl expr) gsl sf expint En e Y gsl sf expint En(int expr,dbl expr) gsl sf expint E1 scaled e Y gsl sf expint E1 scaled(dbl expr) gsl sf expint E2 scaled e Y gsl sf expint E2 scaled(dbl expr) gsl sf expint En scaled e Y gsl sf expint En scaled(int expr,dbl expr) gsl sf expint Ei e Y gsl sf expint Ei(dbl expr) gsl sf expint Ei scaled e Y gsl sf expint Ei scaled(dbl expr) gsl sf Shi e Y gsl sf Shi(dbl expr) gsl sf Chi e Y gsl sf Chi(dbl expr) gsl sf expint 3 e Y gsl sf expint 3(dbl expr) gsl sf Si e Y gsl sf Si(dbl expr) gsl sf Ci e Y gsl sf Ci(dbl expr) gsl sf atanint e Y gsl sf atanint(dbl expr) gsl sf fermi dirac m1 e Y gsl sf fermi dirac m1(dbl expr) gsl sf fermi dirac 0 e Y gsl sf fermi dirac 0(dbl expr) gsl sf fermi dirac 1 e Y gsl sf fermi dirac 1(dbl expr) gsl sf fermi dirac 2 e Y gsl sf fermi dirac 2(dbl expr) gsl sf fermi dirac int e Y gsl sf fermi dirac int(int expr,dbl expr) gsl sf fermi dirac mhalf e Y gsl sf fermi dirac mhalf(dbl expr) gsl sf fermi dirac half e Y gsl sf fermi dirac half(dbl expr) gsl sf fermi dirac 3half e Y gsl sf fermi dirac 3half(dbl expr) gsl sf fermi dirac inc 0 e Y gsl sf fermi dirac inc 0(dbl expr,dbl expr) gsl sf lngamma e Y gsl sf lngamma(dbl expr) gsl sf lngamma sgn e N gsl sf lngamma sgn gsl sf gamma e Y gsl sf gamma(dbl expr) gsl sf gammastar e Y gsl sf gammastar(dbl expr) gsl sf gammainv e Y gsl sf gammainv(dbl expr) Chapter 4: Reference Manual 201 gsl sf lngamma complex e N gsl sf lngamma complex gsl sf taylorcoeff e Y gsl sf taylorcoeff(int expr,dbl expr) gsl sf fact e Y gsl sf fact(uint expr) gsl sf doublefact e Y gsl sf doublefact(uint expr) gsl sf lnfact e Y gsl sf lnfact(uint expr) gsl sf lndoublefact e Y gsl sf lndoublefact(uint expr) gsl sf lnchoose e N gsl sf lnchoose gsl sf choose e N gsl sf choose gsl sf lnpoch e Y gsl sf lnpoch(dbl expr,dbl expr) gsl sf lnpoch sgn e N gsl sf lnpoch sgn gsl sf poch e Y gsl sf poch(dbl expr,dbl expr) gsl sf pochrel e Y gsl sf pochrel(dbl expr,dbl expr) gsl sf gamma inc Q e Y gsl sf gamma inc Q(dbl expr,dbl expr) gsl sf gamma inc P e Y gsl sf gamma inc P(dbl expr,dbl expr) gsl sf gamma inc e Y gsl sf gamma inc(dbl expr,dbl expr) gsl sf lnbeta e Y gsl sf lnbeta(dbl expr,dbl expr) gsl sf lnbeta sgn e N gsl sf lnbeta sgn gsl sf beta e Y gsl sf beta(dbl expr,dbl expr) gsl sf beta inc e N gsl sf beta inc gsl sf gegenpoly 1 e Y gsl sf gegenpoly 1(dbl expr,dbl expr) gsl sf gegenpoly 2 e Y gsl sf gegenpoly 2(dbl expr,dbl expr) gsl sf gegenpoly 3 e Y gsl sf gegenpoly 3(dbl expr,dbl expr) gsl sf gegenpoly n e N gsl sf gegenpoly n gsl sf gegenpoly array Y gsl sf gegenpoly array gsl sf hyperg 0F1 e Y gsl sf hyperg 0F1(dbl expr,dbl expr) gsl sf hyperg 1F1 int e Y gsl sf hyperg 1F1 int(int expr,int expr,dbl expr) gsl sf hyperg 1F1 e Y gsl sf hyperg 1F1(dbl expr,dbl expr,dbl expr) gsl sf hyperg U int e Y gsl sf hyperg U int(int expr,int expr,dbl expr) gsl sf hyperg U int e10 e N gsl sf hyperg U int e10 gsl sf hyperg U e Y gsl sf hyperg U(dbl expr,dbl expr,dbl expr) gsl sf hyperg U e10 e N gsl sf hyperg U e10 gsl sf hyperg 2F1 e Y gsl sf hyperg 2F1(dbl expr,dbl expr,dbl expr,dbl expr) gsl sf hyperg 2F1 conj e Y gsl sf hyperg 2F1 conj(dbl expr,dbl expr,dbl expr,dbl expr) gsl sf hyperg 2F1 renorm e Y gsl sf hyperg 2F1 renorm(dbl expr,dbl expr,dbl expr,dbl expr) gsl sf hyperg 2F1 conj renorm e Y gsl sf hyperg 2F1 conj renorm(dbl expr,dbl expr,dbl expr,dbl expr) gsl sf hyperg 2F0 e Y gsl sf hyperg 2F0(dbl expr,dbl expr,dbl expr) gsl sf laguerre 1 e Y gsl sf laguerre 1(dbl expr,dbl expr) gsl sf laguerre 2 e Y gsl sf laguerre 2(dbl expr,dbl expr) gsl sf laguerre 3 e Y gsl sf laguerre 3(dbl expr,dbl expr) gsl sf laguerre n e Y gsl sf laguerre n(int expr,dbl expr,dbl expr) gsl sf lambert W0 e Y gsl sf lambert W0(dbl expr) gsl sf lambert Wm1 e Y gsl sf lambert Wm1(dbl expr) gsl sf legendre Pl e Y gsl sf legendre Pl(int expr,dbl expr) gsl sf legendre Pl array Y status=gsl sf legendre Pl array(int,double,&var out) gsl sf legendre Pl deriv array N gsl sf legendre Pl deriv array gsl sf legendre P1 e Y gsl sf legendre P1(dbl expr) gsl sf legendre P2 e Y gsl sf legendre P2(dbl expr) 202 NCO 5.0.2-alpha07 User Guide gsl sf legendre P3 e Y gsl sf legendre P3(dbl expr) gsl sf legendre Q0 e Y gsl sf legendre Q0(dbl expr) gsl sf legendre Q1 e Y gsl sf legendre Q1(dbl expr) gsl sf legendre Ql e Y gsl sf legendre Ql(int expr,dbl expr) gsl sf legendre Plm e Y gsl sf legendre Plm(int expr,int expr,dbl expr) gsl sf legendre Plm array Y status=gsl sf legendre Plm array(int,int,double,&var out) gsl sf legendre Plm deriv array N gsl sf legendre Plm deriv array gsl sf legendre sphPlm e Y gsl sf legendre sphPlm(int expr,int expr,dbl expr) gsl sf legendre sphPlm array Y status=gsl sf legendre sphPlm array(int,int,double,&var out) gsl sf legendre sphPlm deriv arrayN gsl sf legendre sphPlm deriv array gsl sf legendre array size N gsl sf legendre array size gsl sf conicalP half e Y gsl sf conicalP half(dbl expr,dbl expr) gsl sf conicalP mhalf e Y gsl sf conicalP mhalf(dbl expr,dbl expr) gsl sf conicalP 0 e Y gsl sf conicalP 0(dbl expr,dbl expr) gsl sf conicalP 1 e Y gsl sf conicalP 1(dbl expr,dbl expr) gsl sf conicalP sph reg e Y gsl sf conicalP sph reg(int expr,dbl expr,dbl expr) gsl sf conicalP cyl reg e Y gsl sf conicalP cyl reg(int expr,dbl expr,dbl expr) gsl sf legendre H3d 0 e Y gsl sf legendre H3d 0(dbl expr,dbl expr) gsl sf legendre H3d 1 e Y gsl sf legendre H3d 1(dbl expr,dbl expr) gsl sf legendre H3d e Y gsl sf legendre H3d(int expr,dbl expr,dbl expr) gsl sf legendre H3d array N gsl sf legendre H3d array gsl sf legendre array size N gsl sf legendre array size gsl sf log e Y gsl sf log(dbl expr) gsl sf log abs e Y gsl sf log abs(dbl expr) gsl sf complex log e N gsl sf complex log gsl sf log 1plusx e Y gsl sf log 1plusx(dbl expr) gsl sf log 1plusx mx e Y gsl sf log 1plusx mx(dbl expr) gsl sf mathieu a array N gsl sf mathieu a array gsl sf mathieu b array N gsl sf mathieu b array gsl sf mathieu a N gsl sf mathieu a gsl sf mathieu b N gsl sf mathieu b gsl sf mathieu a coeff N gsl sf mathieu a coeff gsl sf mathieu b coeff N gsl sf mathieu b coeff gsl sf mathieu ce N gsl sf mathieu ce gsl sf mathieu se N gsl sf mathieu se gsl sf mathieu ce array N gsl sf mathieu ce array gsl sf mathieu se array N gsl sf mathieu se array gsl sf mathieu Mc N gsl sf mathieu Mc gsl sf mathieu Ms N gsl sf mathieu Ms gsl sf mathieu Mc array N gsl sf mathieu Mc array gsl sf mathieu Ms array N gsl sf mathieu Ms array gsl sf pow int e N gsl sf pow int gsl sf psi int e Y gsl sf psi int(int expr) gsl sf psi e Y gsl sf psi(dbl expr) gsl sf psi 1piy e Y gsl sf psi 1piy(dbl expr) gsl sf complex psi e N gsl sf complex psi gsl sf psi 1 int e Y gsl sf psi 1 int(int expr) Chapter 4: Reference Manual 203 gsl sf psi 1 e Y gsl sf psi 1(dbl expr) gsl sf psi n e Y gsl sf psi n(int expr,dbl expr) gsl sf synchrotron 1 e Y gsl sf synchrotron 1(dbl expr) gsl sf synchrotron 2 e Y gsl sf synchrotron 2(dbl expr) gsl sf transport 2 e Y gsl sf transport 2(dbl expr) gsl sf transport 3 e Y gsl sf transport 3(dbl expr) gsl sf transport 4 e Y gsl sf transport 4(dbl expr) gsl sf transport 5 e Y gsl sf transport 5(dbl expr) gsl sf sin e N gsl sf sin gsl sf cos e N gsl sf cos gsl sf hypot e N gsl sf hypot gsl sf complex sin e N gsl sf complex sin gsl sf complex cos e N gsl sf complex cos gsl sf complex logsin e N gsl sf complex logsin gsl sf sinc e N gsl sf sinc gsl sf lnsinh e N gsl sf lnsinh gsl sf lncosh e N gsl sf lncosh gsl sf polar to rect N gsl sf polar to rect gsl sf rect to polar N gsl sf rect to polar gsl sf sin err e N gsl sf sin err gsl sf cos err e N gsl sf cos err gsl sf angle restrict symm e N gsl sf angle restrict symm gsl sf angle restrict pos e N gsl sf angle restrict pos gsl sf angle restrict symm err e N gsl sf angle restrict symm err gsl sf angle restrict pos err e N gsl sf angle restrict pos err gsl sf zeta int e Y gsl sf zeta int(int expr) gsl sf zeta e Y gsl sf zeta(dbl expr) gsl sf zetam1 e Y gsl sf zetam1(dbl expr) gsl sf zetam1 int e Y gsl sf zetam1 int(int expr) gsl sf hzeta e Y gsl sf hzeta(dbl expr,dbl expr) gsl sf eta int e Y gsl sf eta int(int expr) gsl sf eta e Y gsl sf eta(dbl expr) 4.1.23 GSL interpolation As of version 3.9.9 (released July, 2009), NCO has wrappers to the GSL interpolation func- tions. Given a set of data points (x1,y1)...(xn, yn) the GSL functions computes a continuous interpolating function Y(x) such that Y(xi) = yi. The interpolation is piecewise smooth, and its behavior at the end-points is determined by the type of interpolation used. For more information consult the GSL manual. Interpolation with ncap2 is a two stage process. In the first stage, a RAM variable is created from the chosen interpolating function and the data set. This RAM variable holds in memory a GSL interpolation object. In the second stage, points along the interpolating function are calculated. If you have a very large data set or are interpolating many sets then consider deleting the RAM variable when it is redundant. Use the command ram_delete(var_nm). 204 NCO 5.0.2-alpha07 User Guide A simple example x_in[$lon]={1.0,2.0,3.0,4.0}; y_in[$lon]={1.1,1.2,1.5,1.8}; // Ram variable is declared and defined here gsl_interp_cspline(&ram_sp,x_in,y_in); x_out[$lon_grd]={1.1,2.0,3.0,3.1,3.99}; y_out=gsl_spline_eval(ram_sp,x_out); y2=gsl_spline_eval(ram_sp,1.3); y3=gsl_spline_eval(ram_sp,0.0); ram_delete(ram_sp); print(y_out); // 1.10472, 1.2, 1.4, 1.42658, 1.69680002 print(y2); // 1.12454 print(y3); // ’_’ Note in the above example y3 is set to ’missing value’ because 0.0 isn’t within the input X range. GSL Interpolation Types All the interpolation functions have been implemented. These are: gsl interp linear() gsl interp polynomial() gsl interp cspline() gsl interp cspline periodic() gsl interp akima() gsl interp akima periodic() Evaluation of Interpolating Types Implemented gsl spline eval() Unimplemented gsl spline deriv() gsl spline deriv2() gsl spline integ() 4.1.24 GSL least-squares fitting Least Squares fitting is a method of calculating a straight line through a set of experimental data points in the XY plane. Data may be weighted or unweighted. For more information please refer to the GSL manual. Chapter 4: Reference Manual 205 These GSL functions fall into three categories: A) Fitting data to Y=c0+c1*X B) Fitting data (through the origin) Y=c1*X C) Multi-parameter fitting (not yet implemented) Section A status=gsl fit linear (data_x,stride_x,data_y,stride_y,n,&co,&c1,&cov00,&cov01,&cov11,&sumsq) Input variables: data x, stride x, data y, stride y, n From the above variables an X and Y vector both of length ’n’ are derived. If data x or data y is less than type double then it is converted to type double. It is up to you to do bounds checking on the input data. For example if stride x=3 and n=8 then the size of data x must be at least 24 Output variables: c0, c1, cov00, cov01, cov11,sumsq The ’&’ prefix indicates that these are call-by-reference variables. If any of theoutput variables don’t exist prior to the call then they are created on the fly as scalar variables of type double. If they already exist then their existing value is overwritten. If the function call is successful then status=0. status= gsl fit wlinear(data_x,stride_x,data_w,stride_w,data_y,stride_ y,n,&co,&c1,&cov00,&cov01,&cov11,&chisq) Similar to the above call except it creates an additional weighting vector from the variables data w, stride w, n data_y_out=gsl fit linear est(data_x,c0,c1,cov00,cov01,cov11) This function calculates y values along the line Y=c0+c1*X Section B status=gsl fit mul(data_x,stride_x,data_y,stride_y,n,&c1,&cov11,&sumsq) Input variables: data x, stride x, data y, stride y, n From the above variables an X and Y vector both of length ’n’ are derived. If data x or data y is less than type double then it is converted to type double. Output variables: c1,cov11,sumsq status= gsl fit wmul(data_x,stride_x,data_w,stride_w,data_y,stride_ y,n,&c1,&cov11,&sumsq) Similar to the above call except it creates an additional weighting vector from the variables data w, stride w, n data_y_out=gsl fit mul est(data_x,c0,c1,cov11) 206 NCO 5.0.2-alpha07 User Guide This function calculates y values along the line Y=c1*X The below example shows gsl fit linear() in action defdim("d1",10); xin[d1]={1,2,3,4,5,6,7,8,9,10.0}; yin[d1]={3.1,6.2,9.1,12.2,15.1,18.2,21.3,24.0,27.0,30.0}; gsl_fit_linear(xin,1,yin,1,$d1.size,&c0,&c1,&cov00,&cov01,&cov11,&sumsq); print(c0); // 0.2 print(c1); // 2.98545454545 defdim("e1",4); xout[e1]={1.0,3.0,4.0,11}; yout[e1]=0.0; yout=gsl_fit_linear_est(xout,c0,c1,cov00,cov01,cov11,sumsq); print(yout); // 3.18545454545, 9.15636363636, 12.1418181818, 33.04 The following code does linear regression of sst(time,lat,lon) for each time-step // Declare variables c0[$lat, $lon]=0.; // Intercept c1[$lat, $lon]=0.; // Slope sdv[$lat, $lon]=0.; // Standard deviation covxy[$lat, $lon]=0.; // Covariance for (i=0;i<$lat.size;i++) // Loop over lat { for (j=0;j<$lon.size;j++) // Loop over lon { // Linear regression function gsl_fit_linear(time,1,sst(:, i, j),1,$time.size,&tc0,&tc1,&cov00,&cov01,&cov11,&sumsq); c0(i,j)=tc0; // Output results c1(i,j)=tc1; // Output results // Covariance function covxy(i,j)=gsl_stats_covariance(time,1,$time.size,double(sst(:,i,j)),1,$time.size); // Standard deviation function sdv(i,j)=gsl_stats_sd(sst(:,i,j),1,$time.size); } } // slope (c1) missing values are set to ’0’, change to -999. (variable c0 intercept value) where(c0 == -999) c1=-999; 4.1.25 GSL statistics Wrappers for most of the GSL Statistical functions have been implemented. The GSL function names include a type specifier (except for type double functions). To obtain the equivalent NCO name simply remove the type specifier; then depending on the data type Chapter 4: Reference Manual 207 the appropriate GSL function is called. The weighed statistical functions e.g., gsl_stats_ wvariance() are only defined in GSL for floating-point types; so your data must of type float or double otherwise ncap2 will emit an error message. To view the implemented functions use the shell command ncap2 -f|grep _stats GSL Functions short gsl_stats_max (short data[], size_t stride, size_t n); double gsl_stats_int_mean (int data[], size_t stride, size_t n); double gsl_stats_short_sd_with_fixed_mean (short data[], size_t stride, size_t n, double mean); double gsl_stats_wmean (double w[], size_t wstride, double data[], size_t stride, size_t n); double gsl_stats_quantile_from_sorted_data (double sorted_data[], size_t stride, size_t n, double f) ; Equivalent ncap2 wrapper functions short gsl_stats_max (var_data, data_stride, n); double gsl_stats_mean (var_data, data_stride, n); double gsl_stats_sd_with_fixed_mean (var_data, data_stride, n, var_mean); double gsl_stats_wmean (var_weight, weight_stride, var_data, data_stride, n, var_mean); double gsl_stats_quantile_from_sorted_data (var_sorted_data, data_stride, n, var_f) ; GSL has no notion of missing values or dimensionality beyond one. If your data has missing values which you want ignored in the calculations then use the ncap2 built in aggregate functions(Section 4.1.12 [Methods and functions], page 175). The GSL functions operate on a vector of values created from the var data/stride/n arguments. The ncap wrappers check that there is no bounding error with regard to the size of the data and the final value in the vector. a1[time]={1,2,3,4,5,6,7,8,9,10}; a1_avg=gsl_stats_mean(a1,1,10); print(a1_avg); // 5.5 a1_var=gsl_stats_variance(a1,4,3); print(a1_var); // 16.0 // bounding error, vector attempts to access element a1(10) a1_sd=gsl_stats_sd(a1,5,3); For functions with the signature func nm(var data,data stride,n), one may omit the sec- ond or third arguments. The default value for stride is 1. The default value for n is 1+(data.size()-1)/stride. // Following statements are equvalent n2=gsl_stats_max(a1,1,10) n2=gsl_stats_max(a1,1); n2=gsl_stats_max(a1); // Following statements are equvalent n3=gsl_stats_median_from_sorted_data(a1,2,5); n3=gsl_stats_median_from_sorted_data(a1,2); 208 NCO 5.0.2-alpha07 User Guide // Following statements are NOT equvalent n4=gsl_stats_kurtosis(a1,3,2); n4=gsl_stats_kurtosis(a1,3); //default n=4 The following example illustrates some of the weighted functions. The data are randomly generated. In this case the value of the weight for each datum is either 0.0 or 1.0 defdim("r1",2000); data[r1]=1.0; // Fill with random numbers [0.0,10.0) data=10.0*gsl_rng_uniform(data); // Create a weighting variable weight=(data>4.0); wmean=gsl_stats_wmean(weight,1,data,1,$r1.size); print(wmean); wsd=gsl_stats_wsd(weight,1,data,1,$r1.size); print(wsd); // number of values in data that are greater than 4 weight_size=weight.total(); print(weight_size); // print min/max of data dmin=data.gsl_stats_min(); dmax=data.gsl_stats_max(); print(dmin);print(dmax); 4.1.26 GSL random number generation The GSL library has a large number of random number generators. In addition there are a large set of functions for turning uniform random numbers into discrete or continuous probabilty distributions. The random number generator algorithms vary in terms of quality numbers output, speed of execution and maximum number output. For more information see the GSL documentation. The algorithm and seed are set via environment variables, these are picked up by the ncap2 code. Setup The number algorithm is set by the environment variable GSL_RNG_TYPE. If this variable isn’t set then the default rng algorithm is gsl rng 19937. The seed is set with the environ- ment variable GSL_RNG_SEED. The following wrapper functions in ncap2 provide information about the chosen algorithm. gsl_rng_min() the minimum value returned by the rng algorithm. Chapter 4: Reference Manual 209 gsl_rng_max() the maximum value returned by the rng algorithm. Uniformly Distributed Random Numbers gsl_rng_get(var_in) This function returns var in with integers from the chosen rng algorithm. The min and max values depend uoon the chosen rng algorthm. gsl_rng_uniform_int(var_in) This function returns var in with random integers from 0 to n-1. The value n must be less than or equal to the maximum value of the chosen rng algorithm. gsl_rng_uniform(var_in) This function returns var in with double-precision numbers in the range [0.0,1). The range includes 0.0 and excludes 1.0. gsl_rng_uniform_pos(var_in) This function returns var in with double-precision numbers in the range (0.0,1), excluding both 0.0 and 1.0. Below are examples of gsl_rng_get() and gsl_rng_uniform_int() in action. export GSL_RNG_TYPE=ranlux export GSL_RNG_SEED=10 ncap2 -v -O -s ’a1[time]=0;a2=gsl_rng_get(a1);’ in.nc foo.nc // 10 random numbers from the range 0 - 16777215 // a2=9056646, 12776696, 1011656, 13354708, 5139066, 1388751, 11163902, 7730127, 15531355, 10387694 ; ncap2 -v -O -s ’a1[time]=21;a2=gsl_rng_uniform_int(a1).sort();’ in.nc foo.nc // 10 random numbers from the range 0 - 20 a2 = 1, 1, 6, 9, 11, 13, 13, 15, 16, 19 ; The following example produces an ncap2 runtime error. This is because the chose rng algo- rithm has a maximum value greater than NC_MAX_INT=2147483647; the wrapper functions to gsl_rng_get() and gsl_rng_uniform_int() return variable of type NC_INT. Please be aware of this when using random number distribution functions functions from the GSL library which return unsigned int. Examples of these are gsl_ran_geometric() and gsl_ ran_pascal(). export GSL_RNG_TYPE=mt19937 ncap2 -v -O -s ’a1[time]=0;a2=gsl_rng_get(a1);’ in.nc foo.nc To find the maximum value of the chosen rng algorithm use the following code snippet. ncap2 -v -O -s ’rng_max=gsl_rng_max();print(rng_max)’ in.nc foo.nc Random Number Distributions The GSL library has a rich set of random number disribution functions. The library also provides cumulative distribution functions and inverse cumulative distribution functions sometimes referred to a quantile functions. To see whats available on your build use the shell command ncap2 -f|grep -e _ran -e _cdf. 210 NCO 5.0.2-alpha07 User Guide The following examples all return variables of type NC_INT defdim("out",15); a1[$out]=0.5; a2=gsl_ran_binomial(a1,30).sort(); //a2 = 10, 11, 12, 12, 13, 14, 14, 15, 15, 16, 16, 16, 16, 17, 22 ; a3=gsl_ran_geometric(a2).sort(); //a2 = 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 4, 5 ; a4=gsl_ran_pascal(a2,50); //a5 = 37, 40, 40, 42, 43, 45, 46, 49, 52, 58, 60, 62, 62, 65, 67 ; The following all return variables of type NC_DOUBLE; defdim("b1",1000); b1[$b1]=0.8; b2=gsl_ran_exponential(b1); b2_avg=b2.avg(); print(b2_avg); // b2_avg = 0.756047976787 b3=gsl_ran_gaussian(b1); b3_avg=b3.avg(); b3_rms=b3.rms(); print(b3_avg); // b3_avg = -0.00903446534258; print(b3_rms); // b3_rms = 0.81162979889; b4[$b1]=10.0; b5[$b1]=20.0; b6=gsl_ran_flat(b4,b5); b6_avg=b6.avg(); print(b6_avg); // b6_avg=15.0588129413 4.1.27 Examples ncap2 See the ncap.in and ncap2.in scripts released with NCO for more complete demonstrations of ncap2 functionality (script available on-line at http://nco.sf.net/ncap2.in). Define new attribute new for existing variable one as twice the existing attribute dou- ble att of variable att var: ncap2 -s ’one@new=2*att_var@double_att’ in.nc out.nc Average variables of mixed types (result is of type double): ncap2 -s ’average=(var_float+var_double+var_int)/3’ in.nc out.nc Multiple commands may be given to ncap2 in three ways. First, the commands may be placed in a script which is executed, e.g., tst.nco. Second, the commands may be Chapter 4: Reference Manual 211 individually specified with multiple-s ‘ ’ arguments to the same ncap2 invocation. Third, the commands may be chained into a single ‘-s’ argument to ncap2. Assuming the file tst.nco contains the commands a=3;b=4;c=sqrt(a^2+b^2);, then the following ncap2 invocations produce identical results: ncap2 -v -S tst.nco in.nc out.nc ncap2 -v -s ’a=3’ -s ’b=4’ -s ’c=sqrt(a^2+b^2)’ in.nc out.nc ncap2 -v -s ’a=3;b=4;c=sqrt(a^2+b^2)’ in.nc out.nc The second and third examples show that ncap2 does not require that a trailing semi- colon ‘;’ be placed at the end of a ‘-s’ argument, although a trailing semi-colon ‘;’ is always allowed. However, semi-colons are required to separate individual assignment statements chained together as a single ‘-s’ argument. ncap2 may be used to “grow” dimensions, i.e., to increase dimension sizes without al- tering existing data. Say in.nc has ORO(lat,lon) and the user wishes a new file with new_ORO(new_lat,new_lon) that contains zeros in the undefined portions of the new grid. defdim("new_lat",$lat.size+1); // Define new dimension sizes defdim("new_lon",$lon.size+1); new_ORO[$new_lat,$new_lon]=0.0f; // Initialize to zero new_ORO(0:$lat.size-1,0:$lon.size-1)=ORO; // Fill valid data The commands to define new coordinate variables new_lat and new_lon in the output file follow a similar pattern. One would might store these commands inascript grow.nco and then execute the script with ncap2 -v -S grow.nco in.nc out.nc Imagine you wish to create a binary flag based on the value of an array. The flag should have value 1.0 where the array exceeds 1.0, and value 0.0 elsewhere. This example creates the binary flag ORO_flg in out.nc from the continuous array named ORO in in.nc. ncap2 -s ’ORO_flg=(ORO > 1.0)’ in.nc out.nc Suppose your task is to change all values of ORO which equal 2.0 to the new value 3.0: ncap2 -s ’ORO_msk=(ORO==2.0);ORO=ORO_msk*3.0+!ORO_msk*ORO’ in.nc out.nc This creates and uses ORO_msk to mask the subsequent arithmetic operation. Values of ORO are only changed where ORO_msk is true, i.e., where ORO equals 2.0 Using the where statement the above code simplifies to : ncap2 -s ’where(ORO == 2.0) ORO=3.0;’ in.nc foo.nc This example uses ncap2 to compute the covariance of two variables. Let the variables u and v be the horizontal wind components. The covariance of u and v is defined as the time mean product of the deviations of u and v from their respective time means. Symbolically, the covariance [u0v0] = [uv] − [u][v] where [x] denotes the time-average of x, 1 R t=τ 0 [x] ≡ τ t=0 x(t) dt and x denotes the deviation from the time-mean. The covariance tells us how much of the correlation of two signals arises from the signal fluctuations versus the mean signals. Sometimes this is called the eddy covariance. We will store the covariance in the variable uprmvprm. 212 NCO 5.0.2-alpha07 User Guide ncwa -O -a time -v u,v in.nc foo.nc # Compute time mean of u,v ncrename -O -v u,uavg -v v,vavg foo.nc # Rename to avoid conflict ncks -A -v uavg,vavg foo.nc in.nc # Place time means with originals ncap2 -O -s ’uprmvprm=u*v-uavg*vavg’ in.nc in.nc # Covariance ncra -O -v uprmvprm in.nc foo.nc # Time-mean covariance The mathematically inclined will note that the same covariance would be obtained by replacing the step involving ncap2 with ncap2 -O -s ’uprmvprm=(u-uavg)*(v-vavg)’ foo.nc foo.nc # Covariance As of NCO version 3.1.8 (December, 2006), ncap2 can compute averages, and thus co- variances, by itself: ncap2 -s ’uavg=u.avg($time);vavg=v.avg($time);uprmvprm=u*v-uavg*vavg’ \ -s ’uprmvrpmavg=uprmvprm.avg($time)’ in.nc foo.nc We have not seen a simpler method to script and execute powerful arithmetic than ncap2. ncap2 utilizes many meta-characters (e.g., ‘$’, ‘?’, ‘;’, ‘()’, ‘[]’) that can confuse the command-line shell if not quoted properly. The issues are the same as those which arise in utilizing extended regular expressions to subset variables (see Section 3.12 [Subsetting Files], page 48). The example above will fail with no quotes and with double quotes. This is because shell globbing tries to interpolate the value of $time from the shell environment unless it is quoted: ncap2 -s ’uavg=u.avg($time)’ in.nc foo.nc # Correct (recommended) ncap2 -s uavg=u.avg(’$time’) in.nc foo.nc # Correct (and dangerous) ncap2 -s uavg=u.avg($time) in.nc foo.nc # Wrong ($time = ’’) ncap2 -s "uavg=u.avg($time)" in.nc foo.nc # Wrong ($time = ’’) Without the single quotes, the shell replaces $time with an empty string. The command ncap2 receives from the shell is uavg=u.avg(). This causes ncap2 to average over all dimensions rather than just the time dimension, and unintended consequence. We recommend using single quotes to protect ncap2 command-line scripts from the shell, even when such protection is not strictly necessary. Expert users may violate this rule to exploit the ability to use shell variables in ncap2 command-line scripts (see Chapter 9 [CCSM Example], page 387). In such cases it may be necessary to use the shell backslash character ‘\’ to protect the ncap2 meta-character. A dimension of size one is said to be degenerate. Whether a degenerate record dimension is desirable or not depends on the application. Often a degenerate time dimension is useful, e.g., for concatentating, but it may cause problems with arithmetic. Such is the case in the above example, where the first step employs ncwa rather than ncra for the time-averaging. Of course the numerical results are the same with both operators. The difference is that, unless ‘-b’ is specified, ncwa writes no time dimension to the output file, while ncra defaults to keeping time as a degenerate (size 1) dimension. Appending u and v to the output file would cause ncks to try to expand the degenerate time axis of uavg and vavg to the size of the non-degenerate time dimension in the input file. Thus the appendncks ( -A) command would be undefined (and should fail) in this case. Equally important-C isthe‘ ’ argument Chapter 4: Reference Manual 213 (see Section 3.13 [Subsetting Coordinate Variables], page 53) to ncwa to prevent any scalar time variable from being written to the output file. Knowing when to use ncwa -a time rather than the default ncra for time-averaging takes, well, time. 4.1.28 Intrinsic mathematical methods ncap2 supports the standard mathematical functions supplied with most operating systems. Standard calculator notation is used for addition +, subtraction -, multiplication *, division /, exponentiation ^, and modulus %. The available elementary mathematical functions are: abs(x) Absolute value Absolute value of x, |x|. Example: abs(−1) = 1 acos(x) Arc-cosine Arc-cosine of x where x is specified in radians. Example: acos(1.0) = 0.0 acosh(x) Hyperbolic arc-cosine Hyperbolic arc-cosine of x where x is specified in radians. Example: acosh(1.0) = 0.0 asin(x) Arc-sine Arc-sine of x where x is specified in radians. Example: asin(1.0) = 1.57079632679489661922 asinh(x) Hyperbolic arc-sine Hyperbolic arc-sine of x where x is specified in radians. Example: asinh(1.0) = 0.88137358702 atan(x) Arc-tangent Arc-tangent of x where x is specified in radians between −π/2 and π/2. Example: atan(1.0) = 0.78539816339744830961 atan2(y,x) Arc-tangent2 Arc-tangent of y/x atanh(x) Hyperbolic arc-tangent Hyperbolic arc-tangent of x where x is specified in radi- ans between −π/2 and π/2. Example: atanh(3.14159265358979323844) = 1.0 ceil(x) Ceil Ceiling of x. Smallest integral value not less than argument. Example: ceil(0.1) = 1.0 cos(x) Cosine Cosine of x where x is specified in radians. Example: cos(0.0) = 1.0 cosh(x) Hyperbolic cosine Hyperbolic cosine of x where x is specified in radians. Ex- ample: cosh(0.0) = 1.0 erf(x) Error function Error function of x where x is specified between −1 and 1. Example: erf(1.0) = 0.842701 erfc(x) Complementary error function Complementary error function of x where x is specified between −1 and 1. Example: erfc(1.0) = 0.15729920705 exp(x) Exponential Exponential of x, ex. Example: exp(1.0) = 2.71828182845904523536 floor(x) Floor Floor of x. Largest integral value not greater than argument. Example: floor(1.9) = 1 gamma(x) Gamma function Gamma function of x, Γ(x). The√ well-known and loved continuous factorial function. Example: gamma(0.5) = π 214 NCO 5.0.2-alpha07 User Guide gamma_inc_P(x) Incomplete Gamma function Incomplete Gamma function of parameter a and variable x, P (a, x). One of the four incomplete gamma functions. Example: gamma inc P(1, 1) = 1 − e−1 ln(x) Natural Logarithm Natural logarithm of x, ln(x). Example: ln(2.71828182845904523536) = 1.0 log(x) Natural Logarithm Exact synonym for ln(x). log10(x) Base 10 Logarithm Base 10 logarithm of x, log10(x). Example: log(10.0) = 1.0 nearbyint(x) Round inexactly Nearest integer to x is returned in floating-point format. No exceptions are raised for inexact conversions. Example: nearbyint(0.1) = 0.0 pow(x,y) Power Value of x is raised to the power of y. Exceptions are raised for do- main errors. Due to type-limitations in the C language pow function, integer arguments are promoted (see Section 3.40 [Type Conversion], page 134) to type NC_FLOAT before evaluation. Example: pow(2, 3) = 8 rint(x) Round exactly Nearest integer to x is returned in floating-point format. Ex- ceptions are raised for inexact conversions. Example: rint(0.1) = 0.0 round(x) Round Nearest integer to x is returned in floating-point format. Round halfway cases away from zero, regardless of current IEEE rounding direction. Example: round(0.5) = 1.0 sin(x) Sine Sine of x where x is specified in radians. Example: sin(1.57079632679489661922) = 1.0 sinh(x) Hyperbolic sine Hyperbolic sine of x where x is specified in radians. Example: sinh(1.0) = 1.1752 √ sqrt(x) Square Root Square Root of x, x. Example: sqrt(4.0) = 2.0 tan(x) Tangent Tangent of x where x is specified in radians. Example: tan(0.78539816339744830961) = 1.0 tanh(x) Hyperbolic tangent Hyperbolic tangent of x where x is specified in radians. Example: tanh(1.0) = 0.761594155956 trunc(x) Truncate Nearest integer to x is returned in floating-point format. Round half- way cases toward zero, regardless of current IEEE rounding direction. Example: trunc(0.5) = 0.0 The complete list of mathematical functions supported is platform-specific. Functions man- dated by ANSI C are guaranteed to be present and are indicated with an asterisk 2. and 2 ANSI C compilers are guaranteed to support double-precision versions of these functions. These func- tions normally operate on netCDF variables of type NC_DOUBLE without having to perform intrinsic conversions. For example, ANSI compilers provide sin for the sine of C-type double variables. The ANSI standard does not require, but many compilers provide, an extended set of mathematical functions that apply to single (float) and quadruple (long double) precision variables. Using these functions (e.g., sinf for float, sinl for long double), when available, is (presumably) more efficient than casting variables to type double, performing the operation, and then re-casting. NCO uses the faster intrinsic functions when they are available, and uses the casting method when they are not. Chapter 4: Reference Manual 215 are indicated with an asterisk. Use the ‘-f’ (or ‘fnc_tbl’ or ‘prn_fnc_tbl’) switch to print a complete list of functions supported on your platform. 3 4.1.29 Operator precedence and associativity This page lists the ncap2 operators in order of precedence (highest to lowest). Their asso- ciativity indicates in what order operators of equal precedence in an expression are applied. Operator Description Associativity ++ -- Postfix Increment/Decrement Right to Left () Parentheses (function call) . Method call ++ -- Prefix Increment/Decrement Right to Left + - Unary Plus/Minus ! Logical Not ^ Power of Operator Right to Left */% Multiply/Divide/Modulus Left To Right + - Addition/Subtraction Left To Right >> << Fortran style array clipping Left to Right < <= Less than/Less than or equal to Left to Right > >= Greater than/Greater than or equal to == != Equal to/Not equal to Left to Right && Logical AND Left to Right || Logical OR Left to Right ?: Ternary Operator Right to Left = Assignment Right to Left += -= Addition/subtraction assignment *= /= Multiplication/division assignment 4.1.30 ID Quoting In this section a name refers to a variable, attribute, or dimension name. The allowed characters in a valid netCDF name vary from release to release. (See end section). To use metacharacters in a name, or to use a method name as a variable name, the name must be quoted wherever it occurs. The default NCO name is specified by the regular expressions: DGT: (’0’..’9’); LPH: ( ’a’..’z’ | ’A’..’Z’ | ’_’ ); name: (LPH)(LPH|DGT)+ The first character of a valid name must be alphabetic or the underscore. Subsequent characters must be alphanumeric or underscore, e.g., a1, 23, hell is 666. The valid characters in a quoted name are specified by the regular expressions: LPHDGT: ( ’a’..’z’ | ’A’..’Z’ | ’_’ | ’0’..’9’); name: (LPHDGT|’-’|’+’|’.’|’(’|’)’|’:’ )+ ; 3 Linux supports more of these intrinsic functions than other OSs. 216 NCO 5.0.2-alpha07 User Guide Quote a variable: ’avg’ , ’10 +10’,’set miss’ ’+-90field’ , ’–test’=10.0d Quote a attribute: ’three@10’, ’set mss@+10’, ’666@hell’, ’t1@+units’="kelvin" Quote a dimension: ’$10’, ’$t1–’, ’$–odd’, c1[’$10’,’$t1–’]=23.0d The following comments are from the netCDF library definitions and detail the naming conventions for each release. netcdf-3.5.1 netcdf-3.6.0-p1 netcdf-3.6.1 netcdf-3.6.2 /* * ( [a-zA-Z]|[0-9]|’_’|’-’|’+’|’.’|’|’:’|’@’|’(’|’)’ )+ * Verify that name string is valid CDL syntax, i.e., all characters are * alphanumeric, ’-’, ’_’, ’+’, or ’.’. * Also permit ’:’, ’@’, ’(’, or ’)’ in names for chemists currently making * use of these characters, but don’t document until ncgen and ncdump can * also handle these characters in names. */ netcdf-3.6.3 netcdf-4.0 Final 2008/08/28 /* * Verify that a name string is valid syntax. The allowed name * syntax (in RE form) is: * * ([a-zA-Z_]|{UTF8})([^\x00-\x1F\x7F/]|{UTF8})* * * where UTF8 represents a multibyte UTF-8 encoding. Also, no * trailing spaces are permitted in names. This definition * must be consistent with the one in ncgen.l. We do not allow ’/’ * because HDF5 does not permit slashes in names as slash is used as a * group separator. If UTF-8 is supported, then a multi-byte UTF-8 * character can occur anywhere within an identifier. We later * normalize UTF-8 strings to NFC to facilitate matching and queries. */ 4.1.31 make bounds() function The ncap2 custom function ’make bounds()’ takes any monotonic 1D coordinate variable with regular or irregular (e.g., Gaussian) spacing and creates a bounds variable. Chapter 4: Reference Manual 217 Typical usage: defdim("nv",2); longitude_bounds=make_bounds(longitude,$nv,"longitude_bounds"); Another common CF convention: defdim("nv",2); climatology_bounds=make_bounds(time,$nv,"climatology_bounds"); 4.1.32 solar zenith angle function // Assume we are at Equator latitude=0.0; // 32.05428, 27.61159, 24.55934, 23.45467, 24.55947, 27.61184, 32.05458, 37.39353, 43.29914, 49.55782 ; zenith=solar_zenith_angle(my_time,latitude); 218 NCO 5.0.2-alpha07 User Guide 4.2 ncatted netCDF Attribute Editor SYNTAX ncatted [-a att_dsc] [-a ...] [-D dbg] [-h] [--hdr_pad nbr] [--hpss] [-l path] [-O] [-o output-file] [-p path] [-R] [-r] [--ram_all] [-t] input-file [[output-file]] DESCRIPTION ncatted edits attributes in a netCDF file. If you are editing attributes then you are spending too much time in the world of metadata, and ncatted was written to get you back out as quickly and painlessly as possible. ncatted can append, create, delete, modify, and overwrite attributes (all explained below). ncatted allows each editing operation to be applied to every variable in a file. This saves time when changing attribute conventions throughout a file. ncatted is for writing attributes. To read attribute values in plain text, use ncks -m -M, or define something like ncattget as a shell command (see Section 4.8.2 [Filters for ncks], page 283). Because repeated use of ncatted can considerably increase the size of the history global attribute (see Section 3.43 [History Attribute], page 144), the ‘-h’ switch is provided to override automatically appending the command to the history global attribute in the output-file. According to the netCDF User Guide, altering metadata in netCDF files does not incur the penalty of recopying the entire file when the new metadata occupies less space than the old metadata. Thus ncatted may run much faster (at least on netCDF3 files) if judicious use of header padding (see Section 3.2 [Metadata Optimization], page 29) was made when producing the input-file. Similarly, using the ‘--hdr_pad’ option with ncatted helps ensure that future metadata changes to output-file occur as swiftly as possible. When ncatted is used to change the _FillValue attribute, it changes the associated missing data self-consistently. If the internal floating-point representation of a missing value, e.g., 1.0e36, differs between two machines then netCDF files produced on those machines will have incompatible missing values. This allows ncatted to change the missing values in files from different machines to a single value so that the files may then be concatenated, e.g., by ncrcat, without losing information. See Section 3.30 [Missing Values], page 103, for more information. To master ncatted one must understand the meaning of the structure that describes the attribute modification, att dsc specified by the required option ‘-a’ or ‘--attribute’. This option is repeatable and may be used multiple time in a single ncatted invocation to increase the efficiency of altering multiple attributes. Each att dsc contains five elements. This makes using ncatted somewhat complicated, though powerful. The att dsc fields are in the following order: att dsc = att nm, var nm, mode, att type, att val Chapter 4: Reference Manual 219 att nm Attribute name. Example: units As of NCO 4.5.1 (July, 2015), ncatted ac- cepts regular expressions (see Section 3.12 [Subsetting Files], page 48) for at- tribute names (it has “always” accepted regular expressions for variable names). Regular expressions will select all matching attribute names. var nm Variable name. Example: pressure, ’^H2O’. Regular expressions (see Section 3.12 [Subsetting Files], page 48) are accepted and will select all match- ing variable (and/or group) names. The names global and group have special meaning. mode Edit mode abbreviation. Example: a. See below for complete listing of valid values of mode. att type Attribute type abbreviation. Example: c. See below for complete listing of valid values of att type. att val Attribute value. Example: pascal. There should be no empty space between these five consecutive arguments. The description of these arguments follows in their order of appearance. The value of att nm is the name of the attribute to edit. The meaning of this should be clear to all ncatted users. Both att nm and var nm may be specified as regular expres- sions. If att nm is omitted (i.e., left blank) and Delete mode is selected, then all attributes associated with the specified variable will be deleted. The value of var nm is the name of the variable containing the attribute (named att nm) that you want to edit. There are three very important and useful exceptions to this rule. The value of var nm can also be used to direct ncatted to edit global attributes, or to repeat the editing operation for every group or variable in a file. A value of var nm of global indicates that att nm refers to a global (i.e., root-level) attribute, rather than to a particular variable’s attribute. This is the method ncatted supports for editing global attributes. A value of var nm of group indicates that att nm refers to all groups, rather than to a particular variable’s or group’s attribute. The operation will proceed to edit group metadata for every group. Finally, if var nm is left blank, then ncatted attempts to perform the editing operation on every variable in the file. This option may be convenient to use if you decide to change the conventions you use for describing the data. As of NCO 4.6.0 (May, 2016), ncatted accepts the ‘-t’ (or long-option equivalent ‘--typ_mch’ or ‘--type_match’) option. This causes ncatted to perform the editing operation only on variables that are the same type as the specified attribute. The value of mode is a single character abbreviation (a, c, d, m, n, or o) standing for one of five editing modes: a Append. Append value att val to current var nm attribute att nm value att val, if any. If var nm does not already have an existing attribute att nm, it is created with the value att val. c Create. Create variable var nm attribute att nm with att val if att nm does not yet exist. If var nm already has an attribute att nm, there is no effect, so the existing attribute is preserved without change. 220 NCO 5.0.2-alpha07 User Guide d Delete. Delete current var nm attribute att nm. If var nm does not have an attribute att nm, there is no effect. If att nm is omitted (left blank), then all at- tributes associated with the specified variable are automatically deleted. When Delete mode is selected, the att type and att val arguments are superfluous and may be left blank. m Modify. Change value of current var nm attribute att nm to value att val. If var nm does not have an attribute att nm, there is no effect. n Nappend. Append value att val to var nm attribute att nm value att val if att nm already exists. If var nm does not have an attribute att nm, there is no effect. In other words, if att nm already exist, Nappend behaves like Append otherwise it does nothing. The mnenomic is “non-create append”. Nappend mode was added to ncatted in version 4.6.0 (May, 2016). o Overwrite. Write attribute att nm with value att val to variable var nm, over- writing existing attribute att nm, if any. This is the default mode. The value of att type is a single character abbreviation (f, d, l, i, s, c, b, u) or a short string standing for one of the twelve primitive netCDF data types: f Float. Value(s) specified in att val will be stored as netCDF intrinsic type NC_FLOAT. d Double. Value(s) specified in att val will be stored as netCDF intrinsic type NC_DOUBLE. i, l Integer or (its now deprecated synonym) Long. Value(s) specified in att val will be stored as netCDF intrinsic type NC_INT. s Short. Value(s) specified in att val will be stored as netCDF intrinsic type NC_SHORT. c Char. Value(s) specified in att val will be stored as netCDF intrinsic type NC_CHAR. b Byte. Value(s) specified in att val will be stored as netCDF intrinsic type NC_BYTE. ub Unsigned Byte. Value(s) specified in att val will be stored as netCDF intrinsic type NC_UBYTE. us Unsigned Short. Value(s) specified in att val will be stored as netCDF intrinsic type NC_USHORT. u, ui, ul Unsigned Int. Value(s) specified in att val will be stored as netCDF intrinsic type NC_UINT. ll, int64 Int64. Value(s) specified in att val will be stored as netCDF intrinsic type NC_INT64. ull, uint64 Uint64. Value(s) specified in att val will be stored as netCDF intrinsic type NC_UINT64. Chapter 4: Reference Manual 221 sng, string String. Value(s) specified in att val will be stored as netCDF intrinsic type NC_STRING. Note that ncatted handles type NC_STRING attributes correctly beginning with version 4.3.3 released in July, 2013. Earlier versions fail when asked to handle NC_STRING attributes. In Delete mode the specification of att type is optional (and is ignored if supplied). The value of att val is what you want to change attribute att nm to contain. The specification of att val is optional in Delete (and is ignored) mode. Attribute values for all types besides NC_CHAR must have an attribute length of at least one. Thus att val may be a single value or one-dimensional array of elements of type att_type. If the att val is not set or is set to empty space, and the att type is NC_CHAR, e.g., -a units,T,o,c,"" or -a units,T,o,c,, then the corresponding attribute is set to have zero length. When specifying an array of values, it is safest to enclose att val in single or double quotes, e.g., -a levels,T,o,s,"1,2,3,4" or -a levels,T,o,s,’1,2,3,4’. The quotes are strictly unnecessary around att val except when att val contains characters which would confuse the calling shell, such as spaces, commas, and wildcard characters. NCO processing of NC_CHAR attributes is a bit like Perl in that it attempts to do what you want by default (but this sometimes causes unexpected results if you want unusual data storage). If the att type is NC_CHAR then the argument is interpreted as a string and it may contain C-language escape sequences, e.g., \n, which NCO will interpret before writing anything to disk. NCO translates valid escape sequences and stores the appropriate ASCII code instead. Since two byte escape sequences, e.g., \n, represent one-byte ASCII codes, e.g., ASCII 10 (decimal), the stored string attribute is one byte shorter than the input string length for each embedded escape sequence. The most frequently used C-language escape sequences are \n (for linefeed) and \t (for horizontal tab). These sequences in particular allow convenient editing of formatted text attributes. The other valid ASCII codes are \a, \b, \f, \r, \v, and \\. See Section 4.8 [ncks netCDF Kitchen Sink], page 263, for more examples of string formatting (with the ncks ‘-s’ option) with special characters. Analogous to printf, other special characters are also allowed by ncatted if they are “protected” by a backslash. The characters ", ’, ?, and \ may be input to the shell as \", \’, \?, and \\. NCO simply strips away the leading backslash from these characters before editing the attribute. No other characters require protection by a backslash. Backslashes which precede any other character (e.g., 3, m, $, |, &, @, %, {, and }) will not be filtered and will be included in the attribute. Note that the NUL character \0 which terminates C language strings is assumed and need not be explicitly specified. If \0 is input, it is translated to the NUL character. However, this will make the subsequent portion of the string, if any, invisible to C standard library string functions. And that may cause unintended consequences. Because of these context-sensitive rules, one must use ncatted with care in order to store data, rather than text strings, in an attribute of type NC_CHAR. Note that ncatted interprets character attributes (i.e., attributes of type NC_CHAR) as strings. EXAMPLES Append the string Data version 2.0.\n to the global attribute history: 222 NCO 5.0.2-alpha07 User Guide ncatted -a history,global,a,c,’Data version 2.0\n’ in.nc Note the use of embedded C language printf()-style escape sequences. Change the value of the long_name attribute for variable T from whatever it currently is to “temperature”: ncatted -a long_name,T,o,c,temperature in.nc Many model and observational datasets use missing values that are not annotated in the standard manner. For example, at the time (2015–2018) of this writing, the MPAS ocean and ice models use −9.99999979021476795361e + 33 as the missing value, yet do not store a _FillValue attribute with any variables. To prevent arithmetic from treating these values as normal, designate this value as the _FillValue attribute: ncatted -a _FillValue,,o,d,-9.99999979021476795361e+33 in.nc ncatted -t -a _FillValue,,o,d,-9.99999979021476795361e+33 in.nc ncatted -t -a _FillValue,,o,d,-9.99999979021476795361e+33 \ -a _FillValue,,o,f,1.0e36 -a _FillValue,,o,i,-999 in.nc The first example adds the attribute to all variables. The‘-t’ switch causes the second example to add the attribute only to double precision variables. This is often more useful, and can be used to provide distinct missing value attributes to each numeric type, as in the third example. NCO arithmetic operators may not work as expected on IEEE NaN (short for Not-a- Number) and NaN-like numbers such as positive infinity and negative infinity1. One way to work-around this problem is to change IEEE NaNs to normal missing values. As of NCO 4.1.0 (March, 2012), ncatted works with NaNs (though none of the arithmetic operators do). This limited support enables users to change NaN to a normal number before performing arithmetic or propagating a NaN-tainted dataset. First set the missing value (i.e., the value of the _FillValue attribute) for the variable(s) in question to the IEEE NaN value. ncatted -a _FillValue,,o,f,NaN in.nc Then change the missing value from the IEEE NaN value to a normal IEEE number, like 1.0e36 (or to whatever the original missing value was). ncatted -a _FillValue,,m,f,1.0e36 in.nc Some NASA MODIS datasets provide a real-world example. ncatted -O -a _FillValue,,m,d,1.0e36 -a missing_value,,m,d,1.0e36 \ 1 NaN is a special floating point value (not a string). Arithmetic comparisons to NaN and NaN-like numbers always return False, contrary to the behavior of all other numbers. This behavior is difficult to intuit, yet IEEE 754 mandates it. To correctly handle NaNs during arithmetic, code must use special math library macros (e.g., isnormal()) to determine whether any operand is special. If so, additional special logic must handle the arithmetic. This is in addition to the normal handling incurred to correctly handle missing values. Handling field and missing values (either or both of which may be NaN) inbinary operators thus incurs four-to-eight extra code paths. Each code path slows down arithmetic relative to normal numbers. This makes supporting NaN arithmetic costly and inefficient. Hence NCO supports NaN only to the extent necessary to replace it with a normal number. Although using NaN for the missing value (or any value) in datasets is legal in netCDF, we strongly discourage it. We recommend avoiding NaN entirely. Chapter 4: Reference Manual 223 MODIS_L2N_20140304T1120.nc MODIS_L2N_20140304T1120_noNaN.nc Delete all existing units attributes: ncatted -a units,,d,, in.nc The value of var nm was left blank in order to select all variables in the file. The values of att type and att val were left blank because they are superfluous in Delete mode. Delete all attributes associated with the tpt variable, and delete all global attributes ncatted -a ,tpt,d,, -a ,global,d,, in.nc The value of att nm was left blank in order to select all attributes associated with the variable. To delete all global attributes, simply replace tpt with global in the above. Modify all existing units attributes to meter second-1: ncatted -a units,,m,c,’meter second-1’ in.nc Add a units attribute of kilogram kilogram-1 to all variables whose first three char- acters are ‘H2O’: ncatted -a units,’^H2O’,c,c,’kilogram kilogram-1’ in.nc Overwrite the quanta attribute of variable energy to an array of four integers. ncatted -a quanta,energy,o,s,’010,101,111,121’ in.nc As of NCO 3.9.6 (January, 2009), ncatted accepts extended regular expressions as ar- guments for variable names, and, since NCO 4.5.1 (July, 2015), for attribute names. ncatted -a isotope,’^H2O*’,c,s,’18’ in.nc ncatted -a ’.?_iso19115$’,’^H2O*’,d,, in.nc The first example creates isotope attributes for all variables whose names contain ‘H2O’. The second deletes all attributes whose names end in _iso19115 from all variables whose names contain ‘H2O’. See Section 3.12 [Subsetting Files], page 48, for more details on using regular expressions. As of NCO 4.3.8 (November, 2013), ncatted accepts full and partial group paths in names of attributes, variables, dimensions, and groups. # Overwrite units attribute of specific ’lon’ variable ncatted -O -a units,/g1/lon,o,c,’degrees_west’ in_grp.nc # Overwrite units attribute of all ’lon’ variables ncatted -O -a units,lon,o,c,’degrees_west’ in_grp.nc # Delete units attribute of all ’lon’ variables ncatted -O -a units,lon,d,, in_grp.nc # Overwrite units attribute with new type for specific ’lon’ variable ncatted -O -a units,/g1/lon,o,sng,’degrees_west’ in_grp.nc # Add new_att attribute to all variables ncatted -O -a new_att,,c,sng,’new variable attribute’ in_grp.nc # Add new_grp_att group attribute to all groups ncatted -O -a new_grp_att,group,c,sng,’new group attribute’ in_grp.nc # Add new_grp_att group attribute to single group 224 NCO 5.0.2-alpha07 User Guide ncatted -O -a g1_grp_att,g1,c,sng,’new group attribute’ in_grp.nc # Add new_glb_att global attribute to root group ncatted -O -a new_glb_att,global,c,sng,’new global attribute’ in_grp.nc Note that regular expressions work well in conjuction with group path support. In other words, the variable name (including group path component) and the attribute names may both be extended regular expressions. Demonstrate input of C-language escape sequences (e.g., \n) and other special characters (e.g., \") ncatted -h -a special,global,o,c, ’\nDouble quote: \"\nTwo consecutive double quotes: \"\"\n Single quote: Beyond my shell abilities!\nBackslash: \\\n Two consecutive backslashes: \\\\\nQuestion mark: \?\n’ in.nc Note that the entire attribute is protected from the shell by single quotes. These outer single quotes are necessary for interactive use, but may be omitted in batch scripts. Although ncatted accepts multiple ‘-a att_dst’ options simultaneously, modifying lengthy commands can become unwieldy. To preserve simplicity in storing/modifying mul- tiple attribute edits, consider storing the options separately in a text file and assembling them at run-time to generate and submit the correct command. One such method uses the xargs command to intermediate between an on-disk list attributes to change and the ncatted command. For example, use an intermediate file named options.txt to store one option per line thusly cat > opt.txt << EOF -a institution,global,m,c,\"Super Cool University\" -a source,global,c,c,\"My Awesome Radar\" -a contributors,global,c,c,\"Enrico Fermi, Galileo Galilei, Leonardo Da Vinci\" ... EOF The backslashes preserve the whitespace in the individual attributes for correct parsing by the shell. Simply substituting the expansion of this file through xargs directly on the command line fails to work (why?). However, a simple workaround is to use xargs to construct the command string, and execute that string with eval: opt=$(cat opt.txt | xargs) cmd="ncatted -O ${opt} in.nc out.nc" eval $cmd This procedure can by modified to employ more complex option pre-processing using other tools such as Awk, Perl, or Python. Chapter 4: Reference Manual 225 4.3 ncbo netCDF Binary Operator SYNTAX ncbo [-3] [-4] [-5] [-6] [-7] [-A] [-C] [-c] [--cnk_byt sz_byt] [--cnk_csh sz_byt] [--cnk_dmn nm,sz_lmn] [--cnk_map map] [--cnk_min sz_byt] [--cnk_plc plc] [--cnk_scl sz_lmn] [-D dbg] [-d dim,[min][,[max][,[stride]]] [-F] [--fl_fmt fl_fmt] [-G gpe_dsc] [-g grp[,...]] [--glb ...] [-h] [--hdr_pad nbr] [--hpss] [-L dfl_lvl] [-l path] [--no_cll_msr] [--no_frm_trm] [--no_tmp_fl] [-O] [-o file_3] [-p path] [-R] [-r] [--ram_all] [-t thr_nbr] [--unn] [-v var[,...]] [-X ...] [-x] [-y op_typ] file_1 file_2 [file_3] DESCRIPTION ncbo performs binary operations on variables in file 1 and the corresponding variables (those with the same name) in file 2 and stores the results in file 3. The binary operation operates on the entire files (modulo any excluded variables). See Section 3.30 [Missing Values], page 103, for treatment of missing values. One of the four standard arithmetic binary operations currently supported must be selected with the ‘-y op_typ’ switch (or long options ‘--op_typ’ or ‘--operation’). The valid binary operations for ncbo, their definitions, corresponding values of the op typ key, and alternate invocations are: Addition Definition: file 3 = file 1 + file 2 Alternate invocation: ncadd op typ key values: ‘add’, ‘+’, ‘addition’ Examples: ‘ncbo --op_typ=add 1.nc 2.nc 3.nc’, ‘ncadd 1.nc 2.nc 3.nc’ Subtraction Definition: file 3 = file 1 - file 2 Alternate invocations: ncdiff, ncsub, ncsubtract op typ key values: ‘sbt’, ‘-’, ‘dff’, ‘diff’, ‘sub’, ‘subtract’, ‘subtraction’ Examples: ‘ncbo --op_typ=- 1.nc 2.nc 3.nc’, ‘ncdiff 1.nc 2.nc 3.nc’ Multiplication Definition: file 3 = file 1 * file 2 Alternate invocations: ncmult, ncmultiply op typ key values: ‘mlt’, ‘*’, ‘mult’, ‘multiply’, ‘multiplication’ Examples: ‘ncbo --op_typ=mlt 1.nc 2.nc 3.nc’, ‘ncmult 1.nc 2.nc 3.nc’ Division Definition: file 3 = file 1 / file 2 Alternate invocation: ncdivide op typ key values: ‘dvd’, ‘/’, ‘divide’, ‘division’ Examples: ‘ncbo --op_typ=/ 1.nc 2.nc 3.nc’, ‘ncdivide 1.nc 2.nc 3.nc’ 226 NCO 5.0.2-alpha07 User Guide Care should be taken when using the shortest form of key values, i.e., ‘+’, ‘-’, ‘*’, and ‘/’. Some of these single characters may have special meanings to the shell 1. Place these characters inside quotes to keep them from being interpreted (globbed) by the shell2. For example, the following commands are equivalent ncbo --op_typ=* 1.nc 2.nc 3.nc # Dangerous (shell may try to glob) ncbo --op_typ=’*’ 1.nc 2.nc 3.nc # Safe (’*’ protected from shell) ncbo --op_typ="*" 1.nc 2.nc 3.nc # Safe (’*’ protected from shell) ncbo --op_typ=mlt 1.nc 2.nc 3.nc ncbo --op_typ=mult 1.nc 2.nc 3.nc ncbo --op_typ=multiply 1.nc 2.nc 3.nc ncbo --op_typ=multiplication 1.nc 2.nc 3.nc ncmult 1.nc 2.nc 3.nc # First do ’ln -s ncbo ncmult’ ncmultiply 1.nc 2.nc 3.nc # First do ’ln -s ncbo ncmultiply’ No particular argument or invocation form is preferred. Users are encouraged to use the forms which are most intuitive to them. Normally, ncbo will fail unless an operation type is specified with-y ‘ ’ (equivalent to ‘--op_typ’). You may create exceptions to this rule to suit your particular tastes, in conformance with your site’s policy on symbolic links to executables (files of a different name point to the actual executable). For many years, ncdiff was the main binary file operator. As a result, many users prefer to continue invoking ncdiff rather than memorizing a new command (‘ncbo -y sbt’) which behaves identically to the original ncdiff command. However, from a software maintenance standpoint, maintaining a distinct executable for each binary operation (e.g., ncadd) is untenable, and a single executable, ncbo, is desirable. To maintain backward compatibility, therefore, NCO automatically creates a symbolic link from ncbo to ncdiff. Thus ncdiff is called an alternate invocation of ncbo. ncbo supports many additional alternate invocations which must be manually activated. Should users or system adminitrators decide to activate them, the procedure is simple. For example, to use ‘ncadd’ instead of ‘ncbo --op_typ=add’, simply create a symbolic link from ncbo to ncadd3. The alternatate invocations supported for each operation type are listed above. Alternatively, users may always define ‘ncadd’ as an alias to ‘ncbo --op_typ=add’4. It is important to maintain portability in NCO scripts. Therefore we recommend that site-specfic invocations (e.g.,ncadd ‘ ’) be used only in interactive sessions from the command-line. For scripts, we recommend using the full invocation (e.g., ‘ncbo --op_typ=add’). This ensures portability of scripts between users and sites. ncbo operates (e.g., adds) variables in file 2 with the corresponding variables (those with the same name) in file 1 and stores the results in file 3. Variables in file 1 or file 2 1 A naked (i.e., unprotected or unquoted) ‘*’ is a wildcard character. A naked ‘-’ may confuse the com- mand line parser. A naked ‘+’ and ‘/’ are relatively harmless. 2 The widely used shell Bash correctly interprets all these special characters even when they are not quoted. That is, Bash does not prevent NCO from correctly interpreting the intended arithmetic operation when the following arguments are given (without quotes) to ncbo:‘--op_typ=+’, ‘--op_typ=-’, ‘--op_typ=*’, and ‘--op_typ=/’ 3 The command to do this is ‘ln -s -f ncbo ncadd’ 4 The command to do this is ‘alias ncadd=’ncbo --op_typ=add’’ Chapter 4: Reference Manual 227 are broadcast to conform to the corresponding variable in the other input file if necessary5. Now ncbo is completely symmetric with respect to file 1 and file 2, i.e., file1 − file2 = −(file2 − file1). Broadcasting a variable means creating data in non-existing dimensions by copying data in existing dimensions. For example, a two dimensional variable in file 2 can be subtracted from a four, three, or two (not one or zero) dimensional variable (of the same name) in file_1. This functionality allows the user to compute anomalies from the mean. In the future, we will broadcast variables in file 1, if necessary to conform to their counterparts in file 2. Thus, presently, the number of dimensions, or rank, of any processed variable in file 1 must be greater than or equal to the rank of the same variable in file 2. Of course, the size of all dimensions common to both file 1 and file 2 must be equal. When computing anomalies from the mean it is often the case that file 2 was created by applying an averaging operator to a file with initially the same dimensions as file 1 (often file 1 itself). In these cases, creating file 2 with ncra rather than ncwa will cause the ncbo operation to fail. For concreteness say the record dimension in file_1 is time. If file 2 was created by averaging file 1 over the time dimension with the ncra operator (rather than with the ncwa operator), then file 2 will have a time dimension of size 1 rather than having no time dimension at all 6. In this case the input files to ncbo, file 1 and file 2, will have unequally sized time dimensions which causes ncbo to fail. To prevent this from occurring, use ncwa to remove the time dimension from file 2. See the example below. ncbo never operates on coordinate variables or variables of type NC_CHAR or NC_STRING. This ensures that coordinates like (e.g., latitude and longitude) are physically meaningful in the output file, file 3. This behavior is hardcoded. ncbo applies special rules to some CF-defined (and/or NCAR CCSM or NCAR CCM fields) such as ORO. See Section 3.45 [CF Conventions], page 146, for a complete description. Finally, we note that ncflint (see Section 4.7 [ncflint netCDF File Interpolator], page) 260 is designed for file interpolation. As such, it also performs file subtraction, addition, multiplication, albeit in a more convoluted way than ncbo. Beginning with NCO version 4.3.1 (May, 2013), ncbo supports group broadcasting. Group broadcasting means processing data based on group patterns in the input file(s) and automatically transferring or transforming groups to the output file. Consider the case where file 1 contains multiple groups each with the variable v1, while file 2 contains v1 only in its top-level (i.e., root) group. Then ncbo will replicate the group structure of file 1 in the output file, file 3. Each group in file 3 contains the output of the corresponding group in file 1 operating on the data in the single group in file 2. An example is provided below. EXAMPLES Say files 85_0112.nc and 86_0112.nc each contain 12 months of data. Compute the change in the monthly averages from 1985 to 1986: 5 Prior to NCO version 4.3.1 (May, 2013), ncbo would only broadcast variables in file 2 to conform to file 1. Variables in file 1 were never broadcast to conform to the dimensions in file 2. 6 This is because ncra collapses the record dimension to a size of 1 (making it a degenerate dimension), but does not remove it, while, unless ‘-b’ is given, ncwa removes all averaged dimensions. In other words, by default ncra changes variable size though not rank, while, ncwa changes both variable size and rank. 228 NCO 5.0.2-alpha07 User Guide ncbo 86_0112.nc 85_0112.nc 86m85_0112.nc ncdiff 86_0112.nc 85_0112.nc 86m85_0112.nc ncbo --op_typ=sub 86_0112.nc 85_0112.nc 86m85_0112.nc ncbo --op_typ=’-’ 86_0112.nc 85_0112.nc 86m85_0112.nc These commands are all different ways of expressing the same thing. The following examples demonstrate the broadcasting feature of ncbo. Say we wish to compute the monthly anomalies of T from the yearly average of T for the year 1985. First we create the 1985 average from the monthly data, which is stored with the record dimension time. ncra 85_0112.nc 85.nc ncwa -O -a time 85.nc 85.nc The second command, ncwa, gets rid of the time dimension of size 1 that ncra left in 85.nc. Now none of the variables in 85.nc has a time dimension. A quicker way to accomplish this is to use ncwa from the beginning: ncwa -a time 85_0112.nc 85.nc We are now ready to use ncbo to compute the anomalies for 1985: ncdiff -v T 85_0112.nc 85.nc t_anm_85_0112.nc Each of the 12 records in t_anm_85_0112.nc now contains the monthly deviation of T from the annual mean of T for each gridpoint. Say we wish to compute the monthly gridpoint anomalies from the zonal annual mean. A zonal mean is a quantity that has been averaged over the longitudinal (or x) direction. First we use ncwa to average over longitudinal direction lon, creating 85_x.nc, the zonal mean of 85.nc. Then we use ncbo to subtract the zonal annual means from the monthly gridpoint data: ncwa -a lon 85.nc 85_x.nc ncdiff 85_0112.nc 85_x.nc tx_anm_85_0112.nc This examples works assuming 85_0112.nc has dimensions time and lon, and that 85_x.nc has no time or lon dimension. Group broadcasting simplifies evaluation of multiple models against observations. Con- sider the input file cmip5.nc which contains multiple top-level groups cesm, ecmwf, and giss, each of which contains the surface air temperature field tas. We wish to compare these models to observations stored in obs.nc which contains tas only in its top-level (i.e., root) group. It is often the case that many models and/or model simulations exist, whereas only one observational dataset does. We evaluate the models and obtain the bias (difference) between models and observations by subtracting obs.nc from cmip5.nc. Then ncbo “broadcasts” (i.e., replicates) the observational data to match the group structure of cmip5.nc, subtracts, and then stores the results in the output file, bias.nc which has the same group structure as cmip5.nc. % ncbo -O cmip5.nc obs.nc bias.nc % ncks -H -v tas -d time,3 bias.nc /cesm/tas Chapter 4: Reference Manual 229 time[3] tas[3]=-1 /ecmwf/tas time[3] tas[3]=0 /giss/tas time[3] tas[3]=1 As a final example, say we have five years of monthly data (i.e., 60 months) storedin 8501_8912.nc and we wish to create a file which contains the twelve month seasonal cycle of the average monthly anomaly from the five-year mean of this data. The following method is just one permutation of many which will accomplish the same result. First use ncwa to create the five-year mean: ncwa -a time 8501_8912.nc 8589.nc Next use ncbo to create a file containing the difference of each month’s data fromthe five-year mean: ncbo 8501_8912.nc 8589.nc t_anm_8501_8912.nc Now use ncks to group together the five January anomalies in one file, anduse ncra to create the average anomaly for all five Januarys. These commands are embedded in ashell loop so they are repeated for all twelve months: for idx in {1..12}; do # Bash Shell (version 3.0+) idx=‘printf "%02d" ${idx}‘ # Zero-pad to preserve order ncks -F -d time,${idx},,12 t_anm_8501_8912.nc foo.${idx} ncra foo.${idx} t_anm_8589_${idx}.nc done for idx in 01 02 03 04 05 06 07 08 09 10 11 12; do # Bourne Shell ncks -F -d time,${idx},,12 t_anm_8501_8912.nc foo.${idx} ncra foo.${idx} t_anm_8589_${idx}.nc done foreach idx (01 02 03 04 05 06 07 08 09 10 11 12) # C Shell ncks -F -d time,${idx},,12 t_anm_8501_8912.nc foo.${idx} ncra foo.${idx} t_anm_8589_${idx}.nc end Note that ncra understands the stride argument so the two commands inside the loop may be combined into the single command ncra -F -d time,${idx},,12 t_anm_8501_8912.nc foo.${idx} Finally, use ncrcat to concatenate the 12 average monthly anomaly files into one twelve- record file which contains the entire seasonal cycle of the monthly anomalies: ncrcat t_anm_8589_??.nc t_anm_8589_0112.nc 230 NCO 5.0.2-alpha07 User Guide 4.4 ncclimo netCDF Climatology Generator SYNTAX ncclimo [-3] [-4] [-5] [-6] [-7] [-a dec_md] [-C clm_md] [-c caseid] [-d dbg_lvl] [--d2f] [--dpf=dpf] [--dpt_fl=dpt_fl] [-E yr_prv] [-e yr_end] [-f fml_nm] [--fl_fmt=fl_fmt] [--glb_avg] [-h hst_nm] [-i drc_in] [-j job_nbr] [-L dfl_lvl] [-l lnk_flg] [-m mdl_nm] [--mth_end=mth_end] [--mth_srt=mth_srt] [-n nco_opt] [--no_cll_msr] [--no_frm_trm] [--no_ntv_tms] [--no_stg_grd] [--no_stdin] [-O drc_rgr] [-o drc_out] [-P prc_typ] [-p par_typ] [--ppc=ppc_prc] [-R rgr_opt] [-r rgr_map] [-S yr_prv] [-s yr_srt] [--seasons=csn_lst] [--sgs_frc=sgs_frc] [-t thr_nbr] [--tpd=tpd] [--uio] [-v var_lst] [--var_xtr=var_xtr] [--version] [--vrt_fl=vrt_fl] [--vrt_xtr=vrt_xtr] [-X drc_xtn] [-x drc_prv] [--xcl_var] [-Y rgr_xtn] [-y rgr_prv] [--ypf=ypf_max] DESCRIPTION In climatology generation mode, ncclimo ingests “raw” data consisting of interannual sets of files, each containing sub-daily (diurnal), daily, monthly, or yearly averages, andfrom these produces climatological daily, monthly, seasonal, and/or annual means. Alternatively, in timeseries reshaping (aka “splitter”) mode, ncclimo will subset and temporally split the input raw data timeseries into per-variable files spanning the entire period. ncclimo can optionally (call ncremap to) regrid all output files in either mode. Unlike the restof NCO, ncclimo and ncremap are shell scripts, not compiled binaries1. As of NCO 4.9.2 (February, 2020), the ncclimo and ncremap scripts export the environment variable HDF5_USE_FILE_ LOCKING with a value of FALSE. This prevents failures of these operators that can occur with some versions of the underlying HDF library that attempt to lock files on file systems that cannot, or do not, support it. There are five (usually) required options-c (‘ ’, ‘-s’, ‘-e’, ‘-i’, and ‘-o’)) to generate climatologies, and many more options are available to customize the processing. Options are similar to ncremap options. Standard ncclimo usage for climatology generation looks like ncclimo -c caseid -s srt_yr -e end_yr -i drc_in -o drc_out ncclimo -m mdl_nm -c caseid -s srt_yr -e end_yr -i drc_in -o drc_out ncclimo -v var_lst -c caseid -s srt_yr -e end_yr -i drc_in -o drc_out ncclimo --case=caseid --start=srt_yr --end=end_yr --input=drc_in --output=drc_out In climatology generation mode, ncclimo constructs the list of input filenames from the arguments to the caseid, date, and model-type options. As of NCO version 4.9.4 (Septem- ber, 2020), ncclimo can produce climatologies of high-frequency input data supplied via 1 This means that newer (including user-modified) versions of ncclimo work fine without re-compiling NCO. Re-compiling is only necessary to take advantage of new features or fixes in the NCO binaries, not to improve ncclimo. One may download and give executable permissions to the latest source at https://github.com/nco/nco/tree/master/data/ncclimo without re-installing the rest of NCO. Chapter 4: Reference Manual 231 standard input, positional command-line options, or directory contents, all input methods traditionally supported only in splitter mode. Instead of using the caseid option to help generate the input filenames as it does for normal (monthly) climos, ncclimo uses the caseid option, when provided, to rename the output files for high-frequency climos. # Generate diurnal climos from high-frequency CMIP6 timeseries cd ${drc_in};ls ${caseid}*.h4.nc | ncclimo --clm_md=hfc \ -c ${caseid} --yr_srt=2001 --yr_end=2002 --drc_out=${HOME} ncclimo automatically switches to timeseries reshaping mode if it receives a list of files from stdin, or, alternatively, placed as positional arguments (after the last command-line option), or if neither of these is done and no caseid is specified, in which case it assumes all *.nc files in drc in constitute the input file list. # Split monthly timeseries into CMIP-like timeseries cd ${drc_in};ls ${caseid}*.h4.nc | ncclimo -v=T \ --ypf=1 --yr_srt=56 --yr_end=76 --drc_out=${HOME} # Split high-frequency timeseries into CMIP-like timeseries cd ${drc_in};ls ${caseid}*.h4.nc | ncclimo --clm_md=hfs -v=T \ --ypf=1 --yr_srt=56 --yr_end=76 --drc_out=${HOME} Options for ncclimo and ncremap come in both short (single-letter) and long forms. The handful of long-option synonyms for each option allows the user to imbue the commands with a level of verbosity and precision that suits her taste. A complete description of all options is given below, in alphabetical order of the short option letter. Long option synonyms are given just after the letter. When invoked without options, ncclimo and ncremap print a succinct table of all options and some examples. All valid options for both operators are listed in their command syntax above but, for brevity, options that ncclimo passes straight through to ncremap are only fully described in the table of ncremap options. -a dec_md (--dec_md, --dcm_md, --december_mode, --dec_mode) December mode determines the start and end months of the climatology and the type of NH winter seasonal average. Two valid arguments are scd (default, or synonyms djf and DJF) and sdd (or synonyms jfd and JFD). SCD-mode stands for “Seasonally Continuous December”. The first month used is December of the year before the start year specified with-s ‘ ’. The last month is November of the end year specified with-e ‘ ’. In SCD-mode the Northern Hemisphere winter seasonal climatology will be computed with sets of the three consecutive months December, January, and February (DJF) where the calendar year of the December months is always one less than the calendar year of January and February. SDD-mode stands for “Seasonally Discontinuous December”. The first month used is January of the specified start year. The lastmonth is December of the end year specified with-e ‘ ’. In SDD-mode the Northern Hemisphere winter seasonal climatology will be computed with sets of the three non-consecutive months January, February, and December (JFD) from each calendar year. -C clm_md (--clm_md, --climatology_mode, --mode, --climatology) Climatology mode. Valid values for clm md are ann (or synonyms annual, annual, yearly, or year) for annual-mode climatologies, dly (or synonyms 232 NCO 5.0.2-alpha07 User Guide daily, doy, or day) for daily-mode climatologies, hfc (or synonyms high_ frequency_climo or hgh_frq_clm) for high-frequency (diurnally resolved) climos, hfs (or synonyms high_frequency_splitter or hgh_frq_spl) for high-frequency splitting, and mth (or synonyms month or monthly) for monthly climotologies. The value indicates the timespan of each input file for annual and monthly climatologies. The default mode is ‘mth’, which means input files are monthly averages. Use ‘ann’ when the input files are a series of annual means (a common temporal resolution for ice-sheet simulations). The value ‘dly’ is used only input files whose temporal resolution is daily or finer, and when thedesired output is a day-of-year climatology where the means are output for each day of a 365 day year. Day-of-year climatologies are uncommon, yet useful for showing daily variability. The value ‘hfc’ indicates a high-frequency climatology where the output will be a traditional set of climatological monthly, seasonal, or an- nual means similar to monthly climos, except that each file will have the same number of timesteps-per-day as the input data to resolve the diurnal cycle. The value ‘hfs’ indicates a high-frequency splitting operation where an interannual input timeseries will be split into regular size segments of a given number of years, similar to CMIP timeseries. The climatology generator and splitter do not require that daily-mode input files begin or end on daily boundaries. These tools hyperslab the inputfiles using the date information required to performed their analysis. This facilitates analyzing datasets with varying numbers of days per input file. Explicitly specifying ‘--clm_md=mth’ serves a secondary purpose, namely invok- ing the default setting on systems that control stdin. When ncclimo detects that stdin is not attached to the terminal (keyboard) it automatically expects a list of files on stdin. Some environments, however, hijack stdin for their purposes and thereby confuse ncclimo into expecting a list argument. Users have encountered this issue when attempting to run ncclimo in Python paral- lel environments, via inclusion in crontab, and in nohup-mode (whatever that is!). In such cases, explicitly specify ‘--clm_md=mth’ (or ann or day) to persuade ncclimo to run a normal climatology. -c caseid (--case, --caseid, --case_id) Simulation name, or any input filename for non-CESM’ish files. The use of caseid is required in climate generation mode (unless equivalent information is provided through other options), where caseid is used to construct both in- put and output filenames. For CESM’ish input files like famipc5_ne30_v0.3_ 00001.cam.h0.1980-01.nc, specify ‘-c famipc5_ne30_v0.3_00001’. The ‘.cam.’ and ‘.h0.’ bits are added internally to produce the input filenames. Modify these via the -m mdl_nm and -h hst_nm switches if needed. For in- put files named slightly differently than standard CESM’ish names, supply the filename (excluding the path component) as the caseid and then ncclimo will attempt to parse that by matching to a database of known regular expres- sions common to model output. These are all of the format prefix[.-]YYYY [- ]MM[-]DD.suffix. The particular formats current supported, as of NCO ver- sion 4.7.3 (March, 2018) are: prefix_YYYYMM.suffix, prefix.YYYY-MM.suffix, and prefix.YYYY-MM-01.suffix. For example, input files like merra2_198001.nc Chapter 4: Reference Manual 233 (i.e., the six digits that precede the suffix are YYYYMM-format), specify ‘-c merra2_198001.nc’ and the prefixmerra2 ( ) will be automatically abstracted and used to template and generate all the filenames based on the specified yr srt and yr end. Please tell us any dataset filename regular expressions that you would like added to ncclimo’s internal database. The ‘--caseid=caseid’ option is not mandatory in the High-Frequency-Splitter (clm md=hfs) and High-Frequency-Climatology (clm md=hfc) modes. Those modes expect all input filenames to be entered from the command-line so there is no internal need to create filenames from the caseid variable. Instead, when caseid is specified in a high-freqency mode, its value is used to name the output files in a similar manner to the‘-f fml_nm’ option. -D dbg_lvl (--dbg_lvl, --dbg, --debug, --debug_level) Specifies a debugging level similar to the restof NCO. If dbg lvl = 1, ncclimo prints more extensive diagnostics of its behavior. If dbg lvl = 2, ncclimo prints the commands it would execute at any higher or lower debugging level, but does not execute these commands. If dbg lvl > 2, ncclimo prints the diagnostic information, executes all commands, and passes-through the debugging level to the regridder (ncks) for additional diagnostics. --d2f (--d2f, --d2s, --dbl_flt, --dbl_sgl, --double_float) This switch (which takes no argument) causes ncclimo to invoke ncremap with the same switch, so that ncremap converts all double precision non-coordinate variables to single precision in the regridded file. This switch has no effect on files that are not regridded. To demote the precision in such files,use ncpdq to apply the dbl_flt packing map to the file directly. --dpf=dpf (--dpf, --days_per_file) The number of days-per-file in files ingested by ncclimo. It can sometimes be difficult for ncclimo to infer the number of days-per-file in high-frequency input files, i.e., those with 1 or more timesteps-per-day. In such cases, users may override the inferred value by explicitly specifying --dpf=dpf. --dpt_fl=dpt_fl (--dpt_fl, --depth_file, --mpas_fl, --mpas_depth) The ‘--dpt_fl=dpt_fl’ triggers the addition of a depth coordinate to MPAS ocean datasets that will undergo regridding. ncclimo passes this option through to ncremap, and this option has no effect when ncclimo does not invoke ncremap. The ncremap documentation contains the full description of this option. -e end_yr (--end_yr, --yr_end, --end_year, --year_end, --end) End year (example: 2000). By default, the last month is December of the specified end year.-a If‘ scd’ is specified, the last month used is November of the specified end year. -f fml_nm (--fml_nm, --fml, --family, --family_name) Family name (nickname) of output files. In climate generation mode, output climo file names are constructed by default with thesame caseid as the input files. The fml nm, if supplied, replaces caseid in output climo names, which are of the form fml nm XX YYYYMM YYYYMM.nc where XX is the month 234 NCO 5.0.2-alpha07 User Guide or seasonal abbreviation. Use ‘-f fml_nm’ to simplify long names, avoid over- lap, etc. Example values of fml nm are ‘control’, ‘experiment’, and (for a single-variable climo) ‘FSNT’. In timeseries reshaping mode, fml nm will be used, if supplied, as an additional string in the output filename. For example, specifying ‘-f control’ would cause T_000101_000912.nc to be instead named T_control_000101_000912.nc. -h hst_nm (--hst_nm, --history_name, --history) History volume name of file used to generate climatologies. This referring tothe hst nm character sequence used to construct input file names: caseid.mdl_ nm.hst nm.YYYY-MM.nc. By default input climo file names are constructed from the caseid of the input files, together with the model name mdl nm (speci- fied with-m ‘ ’) and the date range. Use ‘-h hst_nm’ to specify alternative history volumes. Examples include ‘h0’ (default, works for CAM, CLM/CTSM/ELM), ‘h1’, and ‘h’ (for CISM). -i drc_in (--drc_in, --in_drc, --dir_in, --input) Directory containing all monthly mean files to read as input to the climatology. The use of drc in is mandatory in climate generation mode and is optional in timeseries reshaping mode. In timeseries reshaping mode, ncclimo uses all netCDF files (meaning files with suffixes .nc, .nc3, .nc4, .nc5, .nc6, .nc7, .cdf, .hdf, .he5, or .h5) in drc in to create the list of input files when no list is provided through stdin or as positional arguments to the command-line. -j job_nbr (--job_nbr, --job_number, --jobs) The job nbr parameter controls the parallelism granularity of both timeseries reshaping (aka splitting) and climatology generation. These modes parallelize over different types of tasks, so we describe the effects of job nbr separately, first for climatologies, then for splitting. However, for both modes, job nbr specifies the total number of simultaneous processes to run during in parallel either on the local node for Background parallelism, or across all the nodes for MPI parallelism (i.e., job nbr is the total across all nodes, it is not the number per node). For climatology generation, job nbr specifies the number of averaging tasks to perform simultaneously on the local node for Background parallelism, or spread across all nodes for MPI-parallelism. By default ncclimo sets job nbr = 12 for both parallelism modes. This number ensures that monthly averages for all individual months complete more-or-less simultaneously, so that all seasonal averages can then be computed. However, many nodes are too small to simul- taneously average multiple distinct months (January, February, etc.). Hence job nbr may be set to any factor of 12, i.e., 1, 2, 3, 4, 6, or 12. For Back- ground parallelism, setting job nbr = 4 causes four-months to be averaged at one time. After three batches of four-months complete, the climatology gener- ator then moves on to seasonal averaging and regridding. For MPI-parallelism, set job nbr >= nd nbr otherwise some nodes will be idle for the entire time. For the biggest jobs, when a single-month nearly exhausts the RAM on a node, set job nbr = nd nbr so that each node gets only one job at a time. If a node can handle average three distinct months simultaneously, then try Chapter 4: Reference Manual 235 job nbr = 3 ∗ nd nbr. Never set job nbr > 12 in climatology modes, since there are at most only twelve jobs that can be performed in parallel. For splitting, job nbr specifies the number of simultaneous subsetting processes to spawn during parallel execution for both Background and MPI-parallelism. In both parallelism modes ncclimo spawns processes in batches of job nbr jobs, then waits for those processes to complete. Once a batch finishes, ncclimo spawns the next batch. For Background-parallelism, all jobs are spawned to the local node. For MPI-parallelism, all jobs are spawned in round-robin fashion to all available nodes until job nbr jobs are running. Rinse, lather, repeat until all variables have been split. The splitter chooses its default value of job nbr based on on the parallelism mode. For Background parallelism, job nbr defaults to the number of variables to be split, so that not specifying job nbr results in launching var nbr simultaneous splitter tasks. This scales well to over a hundred variables in our tests2. In practice, splitting timeseries consumes minimal memory, since ncrcat (which underlies the splitter) only holds one record (timestep) of a variable in memory Section 2.9 [Memory Requirements], page 24. However, if splitting consumes so much RAM (e.g., because variables are large and/or the number of jobs is large) that a single node can perform only one or a few subsetting jobs at a time, then it is reasonable value to employ MPI to split the datasets. For MPI-parallelism, job nbr defaults to the number of nodes requested. This helps prevent users from overloading nodes with too many jobs. Usually, however, nodes can usually subset (and then regrid, if requested) multiple variables simultaneously. In summary, by default job nbr = var nbr in Background mode, and job nbr = node nbr in MPI mode. Subject to the availability of adequate RAM, expand the number of jobs per node by increasing job nbr until overall throughput peaks. The main throughput bottleneck in timeseries reshaping mode is I/O. Increasing job nbr may reduce throughput once the maximum I/O bandwidth of the node is reached, due to contention for I/O resources. Regridding requires math that can relieve some I/O contention and allows for some throughput gains with increasing job nbr. One strategy that seems sensible is to set job nbr equal to the number of nodes times the number of cores per node, and increase or decrease as necessary until throughput peaks. -L (--dfl_lvl, --dfl, --deflate) Activate deflation (i.e., lossless compress, see Section 3.33 [Deflation], page 121) with the -L dfl_lvl short option (or with the same argument to the ‘--dfl_lvl’ or ‘--deflate’ long options). Specify deflation level dfl lvl on a scale from no deflationdfl ( lvl = 0, the default) to maximum deflationdfl ( lvl = 9). 2 At least one known environment (the E3SM-Unified Anaconda environment at NERSC) prevents users from spawning scores of processes and may report OpenBLAS/pthread or RLIMIT_NPROC-related errors. A solution seems to be executing ‘ulimit -u unlimited’ 236 NCO 5.0.2-alpha07 User Guide -l (--lnk_flg, --link_flag) --no_amwg_link (--no_amwg_link, --no_amwg_links, --no_amwg, --no_AMWG_link, --no_AMWG_links) --amwg_link (--amwg_link, --amwg_links, --AMWG_link, --AMWG_links) These options turn-on or turn-off the linking of E3SM/ACME-climo to AMWG- climo filenames. AMWG omits the YYYYMM components of climo filenames, resulting in shorter names. By default ncclimo symbolically links the full E3SM/ACME filename (which is always) created to a file with the shorter (AMWG) name whose creation is optional. AMWG diagnostics scripts can pro- duce plots directly from the linked AMWG filenames. The ‘-l’ (and ‘--lnk_flg’ and ‘--link_flag’ long-option synonmyms) are true options that require an argument of either ‘Yes’ or ‘No’. The remaining synonyms are switches that take no arguments. The ‘--amwg_link’ switch and its synonyms cause the cre- ation of symbolic links with AMWG filenames. The ‘--no_amwg_link’ switch and its synonyms prevent the creation of symbolic links with AMWG filenames. If you do not need AMWG filenames, turn-off linking to reduce file proliferation in the output directories. -m mdl_nm (--mdl_nm, --mdl, --model_name, --model) Model name (as embedded in monthly input filenames). Default iscam ‘ ’. Other options are ‘clm2’, ‘ocn’, ‘ice’, ‘cism’, ‘cice’, ‘pop’. -n nco_opt (nco_opt, nco, nco_options) Specifies a string of options to pass-through unaltered to ncks. nco opt defaults to ‘--no_tmp_fl’. Note that ncclimo passes its nco opt to ncremap. This can cause unexpected results, so use the front-end options to ncclimo when possible, rather than attempting to subvert them with nco opt. -O drc_rgr (--drc_rgr, --rgr_drc, --dir_rgr, --regrid) Directory to hold regridded climo files. Regridded climos are placed in drc out unless a separate directory for them is specified with-O ‘ ’ (NB: capital “O”). --no_cll_msr (--no_cll_msr, --no_cll, --no_cell_measures, --no_area) This switch (which takes no argument) controls whether ncclimo and ncremap add measures variables to the extraction list along with the primary variable and other associated variables. See Section 3.45 [CF Conventions], page 146, for a detailed description. --no_frm_trm (--no_frm_trm, --no_frm, --no_formula_terms) This switch (which takes no argument) controls whether ncclimo and ncremap add formula variables to the extraction list along with the primary variable and other associated variables. See Section 3.45 [CF Conventions], page 146, for a detailed description. --glb_avg (--glb_avg, --global_average) As of NCO version 4.9.1 (released December, 2019), this switch (which takes no argument) tells the splitter to output horizontally spatially averaged time- series files instead of raw, native-grid timeseries. This switch only has effect in timeseries splitting mode. This is useful, for example, to quickly diagnose the behavior of ongoing model simulations prior to a full-blown analysis. Thus Chapter 4: Reference Manual 237 the spatial mean files will be in the same location and have the same nameas the native grid timeseries would have been and had, respectively. Note that this switch does not alter the capability of also outputting the full regridded timeseries, if requested, at the same time. --no_ntv_tms (--no_ntv_tms, --no_ntv, --no_native, --remove_native) This switch (which takes no argument) controls whether the splitter retains native grid split files, which it does by default, or deletes them. ncclimo can split model output from multi-variable native grid files into per-variable time- series files and regrid those onto a so-called analysis grid. That is thetypical format in which Model Intercomparison Projects (MIPs) request and dissemi- nate contributions. When the data producer has no use for the split timeseries on the native grid, he/she can invoke this flag to cause ncclimo to delete the native grid timeseries (not the raw native grid datafiles). This functionality is implemented by first creating the native grid timeseries, regridding it,and then overwriting the native grid timeseries with the regridded timeseries. Thus the regridded files will be in the same location and have the same name asthe native grid timeseries would have been and had, respectively. --no_stg_grd (--no_stg_grd, --no_stg, --no_stagger, --no_staggered_grid) This switch (which takes no argument) controls whether regridded output will contain the staggered grid coordinates slat, slon, and w_stag (see Section 3.25 [Regridding], page 86). By default the staggered grid is output for all files regridded from a Cap (aka FV) grid, except when the regridding is performed as part of splitting (reshaping) into timeseries. -o drc_out (--drc_out, --out_drc, --dir_out, --output) Directory to hold computed (output) native grid climo files. Regridded climos are also placed here unless a separate directory for them is specified with-O ‘ ’ (NB: capital “O”). -p par_typ (--par_typ, --par_md, --parallel_type, --parallel_mode, --parallel) Specifies the parallelism mode desired. The options are serial-p mode(‘ srl’, ‘-p serial’, or ‘-p nil’), background mode parallelism (‘-p bck’ or ‘-p background’)), and MPI parallelism (‘-p mpi’ or ‘-p MPI’). The default is background-mode parallelism. The default par typ is ‘background’, which means ncclimo spawns up to twelve (one for each month) parallel processes at a time. See discussion below under Memory Considerations. --ppc=ppc_prc (--ppc, --ppc_prc, --precision, --quantize) Specifies the precision of the Precision-Preserving Compression algorithm (see Section 3.32.2 [Precision-Preserving Compression], page 112). A positive in- teger is interpreted as the Number of Significant Digits for the Bit-Grooming algorithm, and is equivalent to specifying ‘--ppc default=ppc_prc’ to a bi- nary operator. A positive or negative integer preceded by a period, e.g., ‘.-2’ is interpreted as the number of Decimal Significant Digits for the rounding al- gorithm and is equivalent to specifying ‘--ppc default=.ppc_prc’ to a binary operator. This option applies one precision algorithm and a uniform precision for the entire file. To specify variable-by-variable precision options, pass the 238 NCO 5.0.2-alpha07 User Guide desired options as a quoted string directly with ‘-n nco_opt’, e.g., ‘-n ’--ppc FSNT,TREFHT=4 --ppc CLOUD=2’’. -R rgr_opt (rgr_opt, regrid_options) Specifies a string of options to pass-through unaltered to ncks. rgr opt defaults to ‘-O --no_tmp_fl’. -r rgr_map (--rgr_map, --regrid_map, --map) Regridding map. Unless ‘-r’ is specified ncclimo produces only a climatology on the native grid of the input datasets. The rgr map specifies how to (quickly) transform the native grid into the desired analysis grid. ncclimo will (call ncremap to) apply the given map to the native grid climatology and produce a second climatology on the analysis grid. Options intended exclusively for the regridder may be passed as arguments to the ‘-R’ switch. See below the discussion on regridding. --mth_srt=mth_srt (--mth_srt, --srt_mth, --month_start, --start_month) --mth_end=mth_end (--mth_end, --end_mth, --month_end, --end_month) Start month (example: 4), and end month (example: 11). The starting month of monthly timeseries extracted by the splitter defaults to January of the specified start year, and the ending month defaults to December of the specified end year. As of NCO version 4.9.8, released in March, 2021, the splitter mode of ncclimo accepts user-specified start and end months with--mth_srt the‘ ’ and ‘--mth_end’ options, respectively. Months are input as one-based integers so January is 1 and December is is 12. To extract 14-month timeseries from individual monthly input files one could use, e.g., ncclimo --yr_srt=1 --yr_end=2 --mth_srt=4 --mth_end=5 ... Note that mth_srt and mth_end only affect the splitter, and that they play no role in climatology generation. -s srt_yr (--srt_yr, --yr_srt, --start_year, --year_start, --start) Start year (example: 1980). By default, the first month used is January of the specified start year.-a If‘ scd’ is specified, the first month used willbe December of the year before the start year (to allow for contiguous DJF climos). --seasons=csn_lst (--seasons, --csn_lst, --csn) Seasons for ncclimo to compute in monthly climatology generation mode. The list of seasons, csn lst, is a comma-separated, case-insensitive, unordered subset of the abbreviations for the eleven (so far) defined seasons: jfm, amj, jas, ond, on, fm, djf, mam, jja, son, and ann. By default csn_lst=mam,jja,son,djf. Moreover, ncclimo automatically computes the climatological annual mean, ANN, is always computed when MAM, JJA, SON, and DJF are all requested (which is the default). The ANN computed automatically is the time-weighted average of the four seasons, rather than as the time-weighted average of the twelve monthly climatologies. Users who need ANN but not DJF, MAM, JJA, and SON should instead explicitly specify ANN as a season in csn lst. The ANN computed as a season is the time-weighted average of the twelve monthly climatologies, rather than the time-weighted average of four seasonal climatologies. Specifying the four seasons and ANN in csn lst (e.g., csn_ lst=mam,jja,son,djf,ann) is legal though redundant and wasteful. It cause Chapter 4: Reference Manual 239 ANN to be computed twice, first as the average of the twelve monthly climatolo- gies, then as the average of the four seasons. The special value csn_lst=none turns-off computation of seasonal (and annual) climatologies. ncclimo --seasons=none ... # Produce only monthly climos ncclimo --seasons=mam,jja,son,djf ... # Monthly + MAM,JJA,SON,DJF,ANN ncclimo --seasons=jfm,jas,ann ... # Monthly + JFM,JAS,ANN ncclimo --seasons=fm,on ... # Monthly + FM,ON --stdin (--stdin, --inp_std, --std_flg, --redirect, --standard_input) This switch (which takes no argument) explicitly indicates that input file lists are provided via stdin, i.e., standard input. In interactive environments, ncclimo and ncremap can automatically (i.e., without any switch) detect whether input is provided via stdin. This switch is never required for jobs run in an interactive shell. However, non-interactive batch jobs (such as those submitted to the SLURM and PBS schedulers) make it impossible to unambigu- ously determine whether input has been provided via stdin. Specifically, the ‘--stdin’ switch must be used in non-interactive batch jobs on PBS when the input files are piped to stdin, and on SLURM when the input files are redirected from a file to stdin. Using this switch in any other context (e.g., interactive shells) is optional. In some other non-interactive environments (e.g., crontab, nohup, Azure CI, CWL), ncclimo and ncremap may mistakenly expect input to be provided on stdin simply because the environment is using stdin for other purposes. In such cases users may disable checking stdin by explicitly invoking the ‘--clm_md’ option (this works, as described above, only for ncclimo), or by invoking the ‘--no_stdin’ flag (described next), which works for both ncclimo and ncremap. --no_stdin (--no_stdin, --no_inp_std, --no_redirect, --no_standard_input) First introduced in NCO version 4.8.0 (released May, 2019), this switch (which takes no argument) disables checking standard input (aka stdin) for input files. This is useful because ncclimo and ncremap may mistakenly expect input to be provided on stdin in environments that use stdin for other purposes. Some non-interactive environments (e.g., crontab, nohup, Azure CI, CWL), may use standard input for their own purposes, and thus confuse NCO into thinking that you provided the input files names via the stdin mechanism. In such cases users may disable the automatic checks for standard input by explicitly invoking the ‘--no_stdin’ flag. This switch is usually not required for jobsin an interactive shell. Interactive SLURM shells can also commandeer stdin, as is the case on the DOE machine named Chrysalis. This behavior appears to vary depending on the SLURM implementation. ncclimo --no_stdin -v T -s 2000 -e 2001 --ypf=10 -i in -o out -t thr_nbr (--thr_nbr, --thr, --thread_number, --threads) Specifies the number of threads used per regridding process (see Section 3.3 [OpenMP Threading], page 30). The NCO regridder scales well to 8–16 threads. However, regridding with the maximum number of threads can interfere with 240 NCO 5.0.2-alpha07 User Guide climatology generation in parallel climatology mode (i.e., when par typ = mpi or bck). Hence ncclimo defaults to thr nbr=2. --tpd=tpd (--tpd_out, --tpd, --timesteps_per_day) Normally, the number of timesteps-per-day in files ingested by ncclimo. It can sometimes be difficult for ncclimo to infer the number of timesteps-per-day in high-frequency input files, i.e., those with 1 or more timesteps-per-day. In such cases, users may override the inferred value by explicitly specifying --tpd=tpd. The value of tpd out in daily-average climatology mode clm_md=dly (which is generally not used outside of ice-sheet models) is different, and actually refers to the number of timesteps per day that ncclimo will output, regardless of its value in the input files. Hence in daily-average mode (only), we refer tothis variable as tpd out. The climatology output from input files at daily or sub-daily resolution is, byde- fault, averaged to daily resolution, i.e., tpd out=1. If the number of timesteps per day in each input file is tpd in, then the user may select any value of tpd out that is smaller than and integrally divides tpd in. For example, an in- put timeseries with tpd in=8 (i.e., 3-hourly resolution), can be used to produce climatological output at 3, 6, or 12-hourly resolution by setting tpd out to 8, 4, or 2, respectively. This option only takes effect in daily-average climatology mode. For full generality, the --tpd option should probably be split into separate options --tpd_in and --tpd_out. However, because it is unlikely that anyone will need to specify these to different values, we leave only one option. If this hinders you, please let us know and we will split the options. -v var_lst (--var_lst, --var, --vars, --variables, --variable_list) Variables to subset or to split. Same behavior as Section 3.12 [Subsetting Files], page 48. The use of var lst is optional in clim-generation mode. We suggest using this feature to test whether an ncclimo command, especially one that is lengthy and/or time-consuming, works as intended on one or a few variables with, e.g., ‘-v T,FSNT’ before generating the full climatology (by omitting this option). Invoking this switch was required in the original splitter released in ver- sion 4.6.5 (March, 2017), and became optional as of version 4.6.6 (May, 2017). This option is recommended in timeseries reshaping mode to prevent inadver- tently copying the results of an entire model simulation. Regular expressions are allowed so, e.g., ‘PREC.?’ extracts the variables ‘PRECC,PRECL,PRECSC,PRECSL’ if present. Currently in reshaping mode all matches to a regular expression are placed in the same output file. We hope to remove this limitation in the future. --var_xtr=var_xtr (--var_xtr, --var_xtr, --var_extra, --variables_extra, --extra_variables) The ‘--var_xtr’ option causes ncclimo to include the extra variables list in var xtr in every timeseries split from the raw data. This is useful when extra variables are desired in timeseries. There are no limits on the extra variables— they may be of any rank and may be timeseries themselves. One useful appli- cation of this option, is to ensure that the area variable is included with each timeseries, e.g., ‘--var_xtr=area’. Chapter 4: Reference Manual 241 --version (--version, --vrs, --config, --configuration, --cnf) This switch (which takes no argument) causes the operator to print its version and configuration. This includes the copyright notice, URLs to the BSD and NCO license, directories from which the NCO scripts and binaries are running, and the locations of any separate executables that may be used by the script. --xcl_var (--xcl_var, --xcl, --exclude, --exclude_variables) This flag (which takes no argument) changes var lst, as set by the --var_lst option, from an extraction list to an exclusion list so that variables in var lst will not be processed, and variables not in var lst will be processed. Thus the option ‘-v var_lst’ must also be present for this flag to take effect. Variables explicitly specified for exclusion--xcl by‘ --vars=var_lst[,...]’ need not be present in the input file. --ypf_max ypf_max (--ypf, --years, --years_per_file) Specifies the maximum number of years-per-file output by ncclimo’s splitting operation. When ncclimo subsets and splits a collection of input files span- ning a timerseries, it places each subset variable in its own output file. The maximum length, in years, of each output file is ypf max, which defaults to ypf max=50. If an input timeseries spans 237 years and ypf max=50, then ncclimo will generate four output files of length 50 years and one output fileof length 37 years. Note that invoking this option causes ncclimo to enter time- series reshaping mode. In fact, one must use ‘--ypf’ to turn-on splitter mode when the input files are specified by using the‘-i drc_in’ method. Otherwise it would be ambiguous whether to generate a climatology from or to split the input files. Timeseries Reshaping mode, aka Splitting This section of the ncclimo documentation applies only to resphaping mode, whereas all subsequent sections apply to climatology generation mode. As mentioned above, ncclimo automatically switches to timeseries reshaping mode if it receives a list of files through stdin, or, alternatively, placed as positional arguments (after the last command-line op- tion), or if neither of these is done and no caseid is specified, in which case it assumes all *.nc files in drc in constitute the input file list. These examples invoke reshaping modein the three possible ways: # Pipe list to stdin cd $drc_in;ls *mdl*000[1-9]*.nc | ncclimo -v T,Q,RH -s 1 -e 9 -o $drc_out # Redirect list from file to stdin cd $drc_in;ls *mdl*000[1-9]*.nc > foo;ncclimo -v T,Q,RH -s 1 -e 9 -o $drc_out < foo # List as positional arguments ncclimo -v T,Q,RH -s 1 -e 9 -o $drc_out $drc_in/*mdl*000[1-9]*.nc # Glob directory ncclimo -v T,Q,RH -s 1 -e 9 -i $drc_in -o $drc_out Assuming each input file is a monthly average comprising the variables T, Q, and RH, then the output will be files T_000101_000912.nc, Q_000101_000912.nc, and RH_000101_ 000912.nc. ncclimo reshapes the input so that the outputs are continuous timeseries of each variable taken from all input files. When necessary, the output is split into seg- 242 NCO 5.0.2-alpha07 User Guide ments each containing no more than ypf max (default 50) years of input, i.e., T_000101_ 005012.nc, T_005101_009912.nc, T_010001_014912.nc, etc. MPAS-O/I/L considerations MPAS ocean and ice models currently have their own (non-CESM’ish) naming convention that guarantees output files have the same names for all simulations. By default ncclimo analyzes the “timeSeriesStatsMonthly” analysis member output (tell us if you want options for other analysis members). ncclimo and ncremap recognize input files as being MPAS- style when invoked with ‘-P mpas’ or with the more expressive synonym ‘--prc_typ=mpas’. While this works for generating climatologies for any MPAS model, some regridder options are model-specific and therefore it is smarter to specify which MPAS model produced the input data. ‘-m mpasocean’, (or ‘-m mpaso’ for short), ‘-m mpasseaice’, (or ‘-m mpassi’ for short), or ‘-m mpaslandice’ (or ‘-m mpasli’ for short), like this: ncclimo -m mpaso -s 1980 -e 1983 -i $drc_in -o $drc_out # MPAS-O ncclimo -m mpassi -s 1980 -e 1983 -i $drc_in -o $drc_out # MPAS-SI ncclimo -m mpasli -s 1980 -e 1983 -i $drc_in -o $drc_out # MPAS-LI Raw output data from all MPAS models does not contain missing value attributes3. These attributes must be manually added before sending the data as input to ncclimo or ncremap. We recommend that simulation producers annotate all floating point variables with the appropriate _FillValue prior to invoking ncclimo. Run something like this once in the history-file directory: for fl in ‘ls hist.*‘ ; do ncatted -O -t -a _FillValue,,o,d,-9.99999979021476795361e+33 ${fl} done If/when MPAS-O/I generates the _FillValue attributes itself, this step can and should be skipped. All other ncclimo features like regridding (below) are invoked identically for MPAS as for CAM/CLM users although under-the-hood ncclimo does do some spe- cial pre-processing (dimension permutation, metadata annotation) for MPAS. A five-year oEC60to30 MPAS-O climo with regridding to T62 takes less than 10 minutes on the machine rhea. Annual climos Not all model or observed history files are created as monthly means. To create a climatolog- ical annual mean from a series of annual mean inputs, select ncclimo’s annual climatology mode with the ‘-C ann’ option: ncclimo -C ann -m cism -h h -c caseid -s 1851 -e 1900 -i drc_in -o drc_out The options ‘-m mdl_nm’ and ‘-h hst_nm’ (that default to cam and h0, respectively) tell ncclimo how to construct the input filenames. The above formula names the files caseid.cism.h.1851-01-01-00000.nc, caseid.cism.h.1852-01-01-00000.nc, and so 3 We submitted pull-requests to implement the _FillValue attribute in all MPAS-ocean output in July, 2020. The status of this PR may be tracked at https://github.com/MPAS-Dev/MPAS-Model/pull/677. Once this PR is merged to master, we will do the same for the MPAS-Seaice and MPAS-Landice models. Chapter 4: Reference Manual 243 on. Annual climatology mode produces a single output file (or two if regridding is selected), and in all other respects behaves the same as monthly climatology mode. Regridding Climos and Other Files ncclimo will (optionally) regrid during climatology generation and produce climatology files on both native and analysis grids. This regridding is virtually free, because itis performed on idle nodes/cores after monthly climatologies have been computed and while seasonal climatologies are being computed. This load-balancing can save half-an-hour on ne120 datasets. To regrid, simply pass the desired mapfile name with ‘-r map.nc’, e.g., ‘-r maps/map_ne120np4_to_fv257x512_aave.20150901.nc’. Although this should not be necessary for normal use, you may pass any options specific to regridding with ‘-R opt1 opt2’. Specifying ‘-O drc_rgr’ (NB: uppercase ‘O’) causes ncclimo to place the regridded files in the directory drc rgr. These files have the same names as the native grid climos from which they were derived. There is no namespace conflict because they are in separate directories. These files also have symbolic links to their AMWG filenames. If-O ‘ drc_rgr’ is not specified, ncclimo places all regridded files in the native grid climo output directory, drc out, specified by-o ‘ drc_out’ (NB: lowercase ‘o’). To avoid namespace conflicts when both climos are stored in the same directory, the names of regridded files are suffixed bythe destination geometry string obtained from the mapfile, e.g., *_climo_fv257x512_bilin.nc. These files also have symbolic links to their AMWG filenames. ncclimo -c amip_xpt -s 1980 -e 1983 -i drc_in -o drc_out ncclimo -c amip_xpt -s 1980 -e 1983 -i drc_in -o drc_out -r map_fl ncclimo -c amip_xpt -s 1980 -e 1983 -i drc_in -o drc_out -r map_fl -O drc_rgr The above commands perform a climatology without regridding, then with regridding (all climos stored in drc out), then with regridding and storing regridded files separately. Paths specified by drc in, drc out, and drc rgr may be relative or absolute. An alternative to regridding during climatology generation is to regrid afterwards with ncremap, which has more special features built-in for regridding. To use ncremap to regrid a climatology in drc out and place the results in drc rgr, use something like ncremap -I drc_out -m map.nc -O drc_rgr ls drc_out/*climo* | ncremap -m map.nc -O drc_rgr See Section 4.12 [ncremap netCDF Remapper], page 304, for more details (including MPAS!). Extended Climatologies ncclimo supports two methods for generating extended climatologies: Binary and Incremen- tal. Both methods lengthen a climatology without requiring access to all the raw monthly data spanning the time period. The binary method combines, with appropriate weighting, two previously computed climatologies into a single climatology. No raw monthly data are employed. The incremental method computes a climatology from raw monthly data and (with appropriate weighting) combines that with a previously computed climatology that ends the month prior to raw data. The incremental method was introduced in NCO version 244 NCO 5.0.2-alpha07 User Guide 4.6.1 (released August, 2016), and the binary method was introduced in NCO version 4.6.3 (released December, 2016). Both methods, binary and incremental, compute the so-called “extended climo” as a weighted mean of two shorter climatologies, called the “previous” and “current” climos. The incremental method uses the original monthly input to compute the curent climo, which must immediately follow in time the previous climo which has been pre-computed. The binary method use pre-computed climos for both the previous and current climos, and these climos need not be sequential nor chronological. Both previous and current climos for both binary and incremental methods may be of any length (in years); their weights will be automatically adjusted in computing the extended climo. The use of pre-computed climos permits ongoing simulations (or lengthy observations) to be analyzed in shorter segments combined piecemeal, instead of requiring all raw, native-grid data to be simultaneously accessible. Without extended climatology capability, generating a one-hundred year climatology requires that one-hundred years of monthly data be available on disk. Disk-space requirements for large datasets may make this untenable. Extended climo methods permits a one-hundred year climo to be generated as the weighted mean of, say, the current ten year climatology (weighted at 10%) combined with the pre-computed climatology of the previous 90-years (weighted at 90%). The 90-year climo could itself have been generated incrementally or binary-wise, and so on. Climatologies occupy at most 17/(12N) the amount of space of N years of monthly data, so the extended methods vastly reduce disk-space requirements. Incremental mode is selected by specifying ‘-S’, the start year of the pre-computed, previous climo. The argument to ‘-S’) is the previous climo start year. That, together with the current climo end year, determines the extended climo range. ncclimo assumes that the previous climo ends the month before the current climo begins. In incremental mode, ncclimo first generates the current climatology from the current monthly input filesthen weights that current climo with the previous climo to produce the extended climo. Binary mode is selected by specifying both ‘-S’ and ‘-E’, the end year of the pre- computed, previous climo. In binary mode, the previous and current climatologies can be of any length, and from any time-period, even overlapping. Most users will run extended clmos the same way they run regular climos in terms of parallelism and regridding, although that is not required. Both climos must treat Decembers same way (or else previous climo files will not be found), and if subsetting-v (i.e.,‘ var_lst’) is performed, then the subset must remain the same, and if nicknames (i.e., ‘-f fml_nm’) are employed, then the nickname must remain the same. As of 20161129, the climatology_bounds attributes of extended climos are incorrect. This is a work in progress... Options: -E yr_end_prv (--yr_end_prv, --prv_yr_end, --previous_end) The ending year of the previous climo. This argument is required to trigger binary climatologies, and should not be used for incremental climatologies. Chapter 4: Reference Manual 245 -S yr_srt_prv (--yr_srt_prv, --prv_yr_srt, --previous_start) The starting year of the previous climo. This argument is required to trigger incremental climatologies, and is also mandatory for binary climatologies. -X drc_xtn (--drc_xtn, --xtn_drc, --extended) Directory in which the extended native grid climo files will be stored for an extended climatology. Default value is drc prv. Unless a separate directory is specified (with-Y ‘ ’) for the extended climo on the analysis grid, it will be stored in drc xtn, too. -x drc_prv (--drc_prv, --prv_drc, --previous) Directory in which the previous native grid climo files reside for an incremental climatology. Default value is drc out. Unless a separate directory is specified (with ‘-y’) for the previous climo on the analysis grid, it is assumed to reside in drc prv, too. -Y drc_rgr_xtn (--drc_rgr_xtn, --drc_xtn_rgr, --extended_regridded, --regridded_extended) Directory in which the extended analysis grid climo files will be stored in an incremental climatology. Default value is drc xtn. -y drc_rgr_prv (--drc_rgr_prv, --drc_prv_rgr, --regridded_previous, --previous_regridded) Directory in which the previous climo on the analysis grid resides in an incre- mental climatology. Default value is drc prv. Incremental method climatologies can be as simple as providing a start year for the previous climo, e.g., ncclimo -v FSNT,AODVIS -c caseid -s 1980 -e 1981 -i raw -o clm - r map.nc ncclimo -v FSNT,AODVIS -c caseid -s 1982 -e 1983 -i raw -o clm - r map.nc -S 1980 By default ncclimo stores all native and analysis grid climos in one directory so the above “just works”. There are no namespace clashes because all climos are for distinct years, and regridded files have a suffix based on their grid resolution. However, therecan be only one set of AMWG filename links due to AMWG filename convention. Thus AMWG filename links, if any, point to the latest extended climo in a given directory. Many researchers segregate (with ‘-O drc_rgr’) native-grid from analysis-grid climos. Incrementally generated climos must be consistent in this regard. In other words, all climos contributing to an extended climo must have their native-grid and analysis-grid files in the same (per-climo) directory, or all climos must segregate their native from their analysis grid files. Do not segregate the grids in one climo, and combine them in another. Such climos cannot be incrementally aggregated. Thus incrementing climos can require from zero to four additional options that specify all the previous and extended climatologies for both native and analysis grids. The example below constructs the current climo in crr, then combines the weighted average of that with the previous climo in prv, and places the resulting extended climatology in xtn. Here the native and analysis climos are combined in one directory per climo: 246 NCO 5.0.2-alpha07 User Guide ncclimo -v FSNT,AODVIS -c caseid -s 1980 -e 1981 -i raw -o prv -r map.nc ncclimo -v FSNT,AODVIS -c caseid -s 1982 -e 1983 -i raw -o clm -r map.nc \ -S 1980 -x prv -X xtn If the native and analysis grid climo directories are segregated, then those directories must be specified, too: ncclimo -v FSNT,AODVIS -c caseid -s 1980 -e 1981 -i raw -o prv -O rgr_prv -r map.nc ncclimo -v FSNT,AODVIS -c caseid -s 1982 -e 1983 -i raw -o clm -O rgr -r map.nc \ -S 1980 -x prv -X xtn -y rgr_prv -Y rgr_xtn ncclimo does not know whether a pre-computed climo is on a native grid or an analysis grid, i.e., whether it has been regridded. In binary mode, ncclimo may be pointed to two pre-computed native grid climatologies, or to two pre-computed analysis grid climatologies. In other words, it is not necessary to maintain native grid climatologies for use in creating extended climatologies. It is sufficient to generate climatologies on the analysis grid, and feed them to ncclimo in binary mode, without a mapping file: ncclimo -c caseid -S 1980 -E 1981 -x prv -s 1980 -e 1981 -i crr -o clm Coupled Runs ncclimo works on all E3SM/ACME and CESM models. It can simultaneously generate cli- matologies for a coupled run, where climatologies mean both native and regridded monthly, seasonal, and annual averages as per E3SM/ACME specifications (which mandate the inclu- sion of certain helpful metadata and provenance information). Here are template commands for a recent simulation: caseid=20160121.A_B2000ATMMOD.ne30_oEC.titan.a00 drc_in=/scratch/simulations/$caseid/run drc_out=${DATA}/acme map_atm=${DATA}/maps/map_ne30np4_to_fv129x256_aave.20150901.nc map_lnd=$map_atm map_ocn=${DATA}/maps/map_oEC60to30_to_t62_bilin.20160301.nc map_ice=$map_ocn ncclimo -p mpi -c $caseid -m cam -s 2 -e 5 -i $drc_in -r $map_atm -o $drc_out/atm ncclimo -c $caseid -m clm2 -s 2 -e 5 -i $drc_in -r $map_lnd -o $drc_out/lnd ncclimo -p mpi -m mpaso -s 2 -e 5 -i $drc_in -r $map_ocn -o $drc_out/ocn ncclimo -m mpassi -s 2 -e 5 -i $drc_in -r $map_ice -o $drc_out/ice Atmosphere and ocean model output is typically larger than land and ice model output. These commands recognize that by using different parallelization strategies thatrhea may( standard queue) or may not (cooley, or rhea’s bigmem queue) be required, depending on the fatness of the analysis nodes, as explained below. Memory Considerations It is important to employ the optimal ncclimo parallelization strategy for your computer hardware resources. Select from the three available choices with the -p par_typ switch. The options are serial mode (‘-p srl’, ‘-p serial’, or ‘-p nil’), background mode parallelism (‘-p bck’, or ‘-p background’), and MPI parallelism (‘-p mpi’ or ‘-p MPI’). The default is background-mode parallelism. This is appropriate for lower resolution (e.g., ne30L30) Chapter 4: Reference Manual 247 simulations on most nodes at high-performance computer centers. Use (or at least start with) serial mode on personal laptops/workstations. Serial mode requires twelve times less RAM than the parallel modes, and is much less likely to deadlock or cause OOM (out- of-memory) conditions on your personal computer. If the available RAM (plus swap) is < 12 ∗ 4∗sizeof(monthly input file), then try serial mode first (12 is the optimal number of parallel processes for monthly climos, the computational overhead is a factor of four). CAM-SE ne30L30 output is about 1 GB/month so each month requires about 4 GB of RAM. CAM-SE ne30L72 output (with LINOZ) is about 10 GB/month so each month requires about 40 GB RAM. CAM-SE ne120 output is about 12 GB/month so each month requires about 48 GB RAM. The computer does not actually use all this memory at one time, and many kernels compress RAM usage to below what top reports, so the actual physical usage is hard to pin-down, but may be a factor of 2.5–3.0 (rather than a factor of four) times the size of the input file. For instance, my 16 GB 2014 MacBookPro successfully runs an ne30L30 climatology (that requests 48 GB RAM) in background mode. However the laptop is slow and unresponsive for other uses until it finishes (in 6–8 minutes) the climos. Experiment and choose the parallelization option that performs best. Serial-mode, as its name implies, uses one core at a time for climos, and proceeds se- quentially from months to seasons to annual climatologies. Serial mode means that climos are performed serially, while regridding still employs OpenMP threading (up to 16 cores) on platforms that support it. By design each month and each season is independent of the oth- ers, so all months can be computed in parallel, then each season can be computed in parallel (using monthly climatologies), from which annual average is computed. Background paral- lelization mode exploits this parallelism and executes the climos in parallel as background processes on a single node, so that twelve cores are simultaneously employed for monthly climatologies, four for seasonal, and one for annual. The optional regridding will employ, by default, up to two cores per process. The MPI parallelism mode executes the climatologies on different nodes so that up to (optimally) twelve nodes compute monthly climos. The full memory of each node is available for each individual climo. The optional regridding employs, by default, up to eight cores per node in MPI-mode. MPI-mode or serial-mode must be used to process ne30L72 and ne120L30 climos on all but the fattest DOE nodes. An ne120L30 climo in background mode on rhea (i.e., on one 128 GB compute node) fails due to OOM. (Unfortunately OOM errors do not produce useful return codes so if your climo processes die without printing useful information, the cause may be OOM). However the same climo in background-mode succeeds when executed on a single big-memory (1 TB) node on rhea (use ‘-lpartition=gpu’, as shown below). Or MPI-mode can be used for any climatology. The same ne120L30 climo will also finish blazingly fast in background mode on cooley (i.e., on one 384 GB compute node), so MPI-mode is unnecessary on cooley. In general, the fatter the memory, the better the performance. Single, Dedicated Nodes at LCFs The basic approach above (running the script from a standard terminal window) that works well for small cases can be unpleasantly slow on login nodes of LCFs and for longer or higher resolution (e.g., ne120) climatologies. As a baseline, generating a climatology of 5 years of ne30 (~1x1 degree) CAM-SE output with ncclimo takes 1–2 minutes on rhea (at a time with little contention), and 6–8 minutes on a 2014 MacBook Pro. To make things a bit faster at 248 NCO 5.0.2-alpha07 User Guide LCFs, request a dedicated node (this only makes sense on supercomputers or clusters with job-schedulers). On rhea or titan, which use the PBS scheduler, do this with # Standard node (128 GB), PBS scheduler qsub -I -A CLI115 -V -l nodes=1 -l walltime=00:10:00 -N ncclimo # Bigmem node (1 TB), PBS scheduler qsub -I -A CLI115 -V -l nodes=1 -l walltime=00:10:00 -lpartition=gpu -N ncclimo The equivalent requests on cooley or mira (Cobalt scheduler) and cori or titan (SLURM scheduler) are: # Cooley node (384 GB) with Cobalt qsub -I -A HiRes_EarthSys --nodecount=1 --time=00:10:00 --jobname=ncclimo # Cori node (128 GB) with SLURM salloc -A acme --nodes=1 --partition=debug --time=00:10:00 --job-name=ncclimo Flags used and their meanings: -I Submit in interactive mode. This returns a new terminal shell rather than running a program. --time How long to keep this dedicated node for. Unless you kill the shell created by the qsub command, the shell will exist for this amount of time, then die suddenly. In the above examples, 10 minutes is requested. -l nodes=1 PBS syntax (e.g., on rhea) for nodes. --nodecount 1 Cobalt syntax (e.g., on cooley) for nodes. --nodes=1 SLURM syntax (e.g., on cori or edison) for nodes. These scheduler-dependent variations request a quantity of nodes. Request 1 node for Serial or Background- mode, and up to 12 nodes for MPI-mode parallelism. In all cases ncclimo will use multiple cores per node if available. -V Export existing environmental variables into the new interactive shell. This may not actually be needed. -q name Queue name. This is needed for locations like edison that have multiple queues with no default queue. -A Name of account to charge for time used. Acquiring a dedicated node is useful for any workflow, not just creating climos. This command returns a prompt once nodes are assigned (the prompt is returned in your home directory so you may then have to cd to the location you meant to run from). Then run your code with the basic ncclimo invocation. The is faster because the node is exclusively dedicated to ncclimo. Again, ne30L30 climos only require < 2 minutes, so the 10 minutes requested in the example is excessive and conservative. Tune it with experience. Chapter 4: Reference Manual 249 12 node MPI-mode Jobs The above parallel approaches will fail when a single node lacks enough RAM (plus swap) to store all twelve monthly input files, plus extra RAM for computations. One should employ MPI multinode parallelism ‘-p mpi’ on nodes with less RAM than 12 ∗ 3∗sizeof(input). The longest an ne120 climo will take is less than half an hour (~25 minutes on edison or rhea), so the simplest method to run MPI jobs is to request 12-interactive nodes using the above commands (though remember to add ‘-p mpi’), then execute the script at the command line. It is also possible, and sometimes preferable, to request non-interactive compute nodes in a batch queue. Executing an MPI-mode climo (on machines with job scheduling and, optimally, 12 nodes) in a batch queue can be done in two commands. First, write an executable file which calls the ncclimo script with appropriate arguments. We do this below by echoing to a file, ncclimo.pbs. echo "ncclimo -p mpi -c $caseid -s 1 -e 20 -i $drc_in -o $drc_out" > ncclimo.pbs The only new argument here is ‘-p mpi’ that tells ncclimo to use MPI parallelism. Then execute this command file with a 12 node non-interactive job: qsub -A CLI115 -V -l nodes=12 -l walltime=00:30:00 -j oe -m e -N ncclimo \ -o ncclimo.out ncclimo.pbs This script adds new flags:-j ‘ oe’ (combine output and error streams into standard error), ‘-m e’ (send email to the job submitter when the job ends), ‘-o ncclimo.out’ (write all output to ncclimo.out). The above commands are meant for PBS schedulers like on rhea. Equivalent commands for cooley/mira (Cobalt) and cori/edison (SLURM) are # Cooley (Cobalt scheduler) /bin/rm -f ncclimo.err ncclimo.out echo ’#!/bin/bash’ > ncclimo.cobalt echo "ncclimo -p mpi -c $caseid -s 1 -e 20 -i $drc_in -o $drc_out" >> ncclimo.cobalt chmod a+x ncclimo.cobalt qsub -A HiRes_EarthSys --nodecount=12 --time=00:30:00 --jobname ncclimo \ --error ncclimo.err --output ncclimo.out --notify [email protected] ncclimo.cobalt # Cori/Edison (SLURM scheduler) echo "ncclimo -p mpi -c $caseid -s 1 -e 20 -i $drc_in -o $drc_out -r $map_fl" \ > ncclimo.pbs chmod a+x ncclimo.slurm sbatch -A acme --nodes=12 --time=03:00:00 --partition=regular --job-name=ncclimo \ --mail-type=END --error=ncclimo.err --output=ncclimo.out ncclimo.slurm Notice that Cobalt and SLURM require the introductory shebang-interpreter line (#!/bin/bash) which PBS does not need. Set only the scheduler batch queue parameters mentioned above. In MPI-mode, ncclimo determines the appropriate number of tasks- per-node based on the number of nodes available and script internals (like load-balancing for regridding). Hence do not set a tasks-per-node parameter with scheduler configuration parameters as this could cause conflicts. 250 NCO 5.0.2-alpha07 User Guide What does ncclimo do? For monthly climatologies (e.g., JAN), ncclimo passes the list of all relevant January monthly files to NCO’s ncra command, which averages each variable in these monthly files over their time-dimension (if it exists) or copies the value from the first monthun- changed (if no time-axis exists). Seasonal climos are then created by taking the average of the monthly climo files using ncra. To account for differing numbers of days per month, the ncra ‘-w’ flag is used, followed by the number of days in the relevant months. Forex- ample, the MAM climo is computed with ‘ncra -w 31,30,31 MAR_climo.nc APR_climo.nc MAY_climo.nc MAM_climo.nc’ (details about file names and other optimization flags have been stripped here to make the concept easier to follow). The annual (ANN) climo is then computed as a weighted average of the seasonal climos. Assumptions, Approximations, and Algorithms (AAA) Employed: A climatology embodies many algorithmic choices, and regridding from the native to the analysis grid involves still more choices. A separate method should reproduce the ncclimo and NCO answers to round-off precision if it implements the same algorithmic choices. For example, ncclimo agrees to round-off with AMWG diagnostics when making the same (sometimes questionable) choices. The most important choices have to do with converting single- to double-precision (SP and DP, respectively), treatment of missing values, and generation/application of regridding weights. For concreteness and clarity we describe the algorithmic choices made in processing a CAM-SE monthly output into a climatological annual mean (ANN) and then regridding that. Other climatologies (e.g., daily to monthly, or annual-to-climatological) involve similar choices. E3SM/ACME (and CESM) computes fields in DP and outputs history (not restart) files as monthly means in SP. The NCO climatology generator (ncclimo) processes these data in four stages. Stage N accesses input only from stage N − 1, never from stage N − 2 or earlier. Thus the (on-disk) files from stage N determine the highest precision achievable by stage N + 1. The general principal is to perform math (addition, weighting, normalization) in DP and output results to disk in the same precision in which they were input from disk (usually SP). In Stage 1, NCO ingests Stage 0 monthly means (raw CAM-SE output), converts SP input to DP, performs the average across all years, then converts the answer from DP to SP for storage on-disk as the climatological monthly mean. In Stage 2, NCO ingests Stage 1 climatological monthly means, converts SP input to DP, performs the average across all months in the season (e.g., DJF), then converts the answer from DP to SP for storage on- disk as the climatological seasonal mean. In Stage 3, NCO ingests Stage 2 climatological seasonal means, converts SP input to DP, performs the average across all four seasons (DJF, MAM, JJA, SON), then converts the answer from DP to SP for storage on-disk as the climatological annual mean. Stage 2 weights each input month by its number of days (e.g., 31 for January), and Stage 3 weights each input season by its number of days (e.g., 92 for MAM). E3SM/ACME runs CAM-SE with a 365-day calendar, so these weights are independent of year and never change. The treatment of missing values in Stages 1–3 is limited by the lack of missing value tallies provided by Stage 0 (model) output. Stage 0 records a value as missing if it is missing for the entire month, and present if the value is valid for one or more timesteps. Stage 0 does not record the missing value tally (number of valid timesteps) for each spatial Chapter 4: Reference Manual 251 point. Thus a point with a single valid timestep during a month is weighted the same in Stages 1–4 as a point with 100% valid timesteps during the month. The absence of tallies inexorably degrades the accuracy of subsequent statistics by an amount that varies in time and space. On the positive side, it reduces the output size (by a factor of two) and complexity of analyzing fields that contain missing values. Due to the ambiguous nature of missing values, it is debatable whether they merit efforts to treat them more exactly. The vast majority of fields undergo three promotion/demotion cycles between CAM-SE and ANN. No promotion/demotion cycles occur for history fields that CAM-SE outputs in DP rather than SP, nor for fields without a time dimension. Typically these fields aregrid coordinates (e.g., longitude, latitude) or model constants (e.g., CO2 mixing ratio). NCO never performs any arithmetic on grid coordinates or non-time-varying input, regardless of whether they are SP or DP. Instead, NCO copies these fields directly from the first input file. Stage 4 uses a mapfile to regrid climos from the native to the desired analysisgrid. E3SM/ACME currently uses mapfiles generated by ESMF_RegridWeightGen (ERWG) and by TempestRemap. The algorithmic choices, approximations, and commands used to generate mapfiles from input gridfiles are separate issues. We mention only some of these issues here for brevity. Input gridfiles used by E3SM/ACME until ~20150901, and by CESM (then and currently, at least for Gaussian grids) contained flaws that effectively reduced their precision, especially at regional scales, and especially for Gaussian grids. E3SM/ACME (and CESM) mapfiles continue to approximate grids as connected by great circles, whereas most analysis grids (and some models) use great circles for longitude and small circles for latitude. The great circle assumption may be removed in the future. Constraints imposed by ERWG during weight-generation ensure that global integrals of fields undergoing conservative regridding are exactly conserved. Application of weights from the mapfile to regrid the native data to the analysis gridis straightforward. Grid fields (e.g., latitude, longitude, area) are not regridded. Instead they are copied (and area is reconstructed if absent) directly from the mapfile. NCO ingests all other native grid (source) fields, converts SP to DP, and accumulates destination gridcell values as the sum of the DP weight (from the sparse matrix in the mapfile) times the (usually SP-promoted-to-DP) source values. Fields without missing values are then stored to disk in their original precision. Fields with missing values are treated (by default) with what NCO calls the “conservative” algorithm. This algorithm uses all valid data from the source grid on the destination grid once and only once. Destination cells receive the weighted valid values of the source cells. This is conservative because the global integrals of the source and destination fields are equal. See Section 4.12 [ncremap netCDF Remapper], page 304, for more description of the conservative and of the optional (“renormalized”) algorithm. EXAMPLES How to create a climo from a collection of monthly non-CESM’ish files? This is a two-step procedure: First be sure the names are arranged with a YYYYMM-format date preceding the suffix (usually ‘.nc’). Then give any monthly input filename to ncclimo. Consider the MERRA2 collection, for example. As retrieved from NASA, MERRA2 files have names like svc_MERRA2_300.tavgM_2d_aer_Nx.200903.nc4. While the sub-string ‘200903’ is easy to recognize as a month in YYYYMM format, other parts (specifically the ‘300’ code) of the 252 NCO 5.0.2-alpha07 User Guide filename also change with date. We can use Bash regular expressions to extract datesand create symbolic links to simpler filenames with regularly patterned YYYYMM strings like merra2_200903.nc4: for fl in ‘ls *.nc4‘ ; do # Convert svc_MERRA2_300.tavgM_2d_aer_Nx.YYYYMM.nc4 to merra2_YYYYMM.nc4 sfx_out=‘expr match "${fl}" ’.*_Nx.\(.*.nc4\)’‘ fl_out="merra2_${sfx_out}" ln -s ${fl} ${fl_out} done Then call ncclimo with any standard format filename, e.g., merra2_200903.nc4, as as the caseid: ncclimo -c merra2_200903.nc4 -s 1980 -e 2016 -i $drc_in -o $drc_out In the default monthly climo generation mode, ncclimo expects each input file to contain one single record that is the monthly average of all fields. Another example of of wrangling observed datasets into a CESMish format is ECMWF Integrated Forecasting System (IFS) output that contains twelve months per file, rather than the one month per file that ncclimo expects. for yr in {1979..2016}; do # Convert ifs_YYYY01-YYYY12.nc to ifs_YYYYMM.nc yyyy=‘printf "%04d" $yr‘ for mth in {1..12}; do mm=‘printf "%02d" $mth‘ ncks -O -F -d time,${mth} ifs_${yyyy}01-${yyyy}12.nc ifs_${yyyy}${mm}.nc done done Then call ncclimo with ifs_197901.nc as caseid: ncclimo -c ifs_197901.nc -s 1979 -e 2016 -i $drc_in -o $drc_out ncclimo does not recognize all combinations imaginable of records per file and files per year. However, support can be added for the most prevalent combinations so that ncclimo, rather than the user, does any necessary data wrangling. Contact us if there is a common input data format you would like supported as a custom option. Often one wishes to create a climatology of a single variable. The ‘-f fml_nm’ option to ncclimo makes this easy. Consider a series of single-variable climos for the fields FSNT, and FLNT ncclimo -v FSNT -f FSNT -c amip_xpt -s 1980 -e 1983 -i drc_in -o drc_out ncclimo -v FLNT -f FLNT -c amip_xpt -s 1980 -e 1983 -i drc_in -o drc_out These climos use the ‘-f’ option and so their output files will have no namespace conflicts. Moreover, the climatologies can be generated in parallel. Chapter 4: Reference Manual 253 4.5 ncecat netCDF Ensemble Concatenator SYNTAX ncecat [-3] [-4] [-5] [-6] [-7] [-A] [-C] [-c] [--cnk_byt sz_byt] [--cnk_csh sz_byt] [--cnk_dmn nm,sz_lmn] [--cnk_map map] [--cnk_min sz_byt] [--cnk_plc plc] [--cnk_scl sz_lmn] [-D dbg] [-d dim,[min][,[max][,[stride]]] [-F] [--fl_fmt fl_fmt] [-G gpe_dsc] [-g grp[,...]] [--gag] [--glb ...] [-h] [--hdf] [--hdr_pad nbr] [--hpss] [-L dfl_lvl] [-l path] [-M] [--md5_digest] [--mrd] [-n loop] [--no_cll_msr] [--no_frm_trm] [--no_tmp_fl] [-O] [-o output-file] [-p path] [--ppc ...] [-R] [-r] [--ram_all] [-t thr_nbr] [-u ulm_nm] [--unn] [-v var[,...]] [-X ...] [-x] [input-files][output-file] DESCRIPTION ncecat aggregates an arbitrary number of input files into a single output file using using one of two methods. Record AGgregation (RAG), the traditional method employed on (flat) netCDF3 files and still the default method, stores input-files as consecutive records in the output-file. Group AGgregation (GAG) stores input-files as top-level groups in the netCDF4 output-file. Record Aggregation (RAG) makes numerous assumptions about the structure of input files whereas Group Aggregation (GAG) makes none. Both methods are described in detail below. Since ncecat aggregates all the contents of the input files, it can easily produce large output files so it is often helpful to invoke subsetting simultaneously (see Section 3.12 [Subsetting Files], page 48). RAG makes each variable (except coordinate variables) in each input file into a single record of the same variable in the output file. Coordinate variables are not concatenated, they are instead simply copied from the first input file to the output-file. All input-files must contain all extracted variables (or else there would be “gaps” in the output file). A new record dimension is the glue which binds together the input file data. The new record dimension is defined in the root group of the output file so it is visible toallsub- groups. Its name is, by default, “record”. This default name can be overridden with the ‘-u ulm_nm’ short option (or the ‘--ulm_nm’ or ‘rcd_nm’ long options). Each extracted variable must be constant in size and rank across all input-files. The only exception is that ncecat allows files to differ in the record dimension size if the requested record hyperslab (see Section 3.16 [Hyperslabs], page 63) resolves to the same size for all files. This allows easier gluing/averaging of unequal length timeseries from simulation ensembles (e.g., the CMIP archive). Classic (i.e., all netCDF3 and NETCDF4_CLASSIC) output files can contain only one record dimension. ncecat makes room for the new glue record dimension by changing the pre- existing record dimension, if any, in the input files into a fixed dimension in the output file. netCDF4 output files may contain any number of record dimensions, so ncecat need not and does not alter the record dimensions, if any, of the input files as it copies them to the output file. 254 NCO 5.0.2-alpha07 User Guide Group AGgregation (GAG) stores input-files as top-level groups in the output-file. No assumption is made about the size or shape or type of a given object (variable or dimension or group) in the input file. The entire contents of the extracted portion of each input file is placed in its own top-level group in output-file, which is automatically made as a netCDF4-format file. GAG has two methods to specify group names for the output-file. The ‘-G’ option, or its long-option equivalent ‘--gpe’, takes as argument a group path editing description gpe dsc of where to place the results. Each input file needs a distinct output group name to avoid namespace conflicts in the output-file. Hence ncecat automatically creates unique output group names based on either the input filenames or the gpe dsc arguments. When the user provides gpe dsc (i.e., with ‘-G’), then the output groups are formed by enumerating sequential two-digit numeric suffixes starting with zero, and appending them to the specified group path (see Section 3.14 [Group Path Editing], page 53). When gpe dsc is not provided (i.e., user requests GAG with ‘--gag’ instead of ‘-G’), then ncecat forms the output groups by stripping the input file name of any type-suffix (e.g., .nc), and all but the final component of the full filename. ncecat --gag 85.nc 86.nc 87.nc 8587.nc # Output groups 85, 86, 87 ncecat -G 85_ a.nc b.nc c.nc 8589.nc # Output groups 85_00, 85_01, 85_02 ncecat -G 85/ a.nc b.nc c.nc 8589.nc # Output groups 85/00, 85/01, 85/02 With both RAG and GAG the output-file size is the sum of the sizes of the extracted variables in the input files. See Section 2.6 [Statistics vs. Concatenation], page 20, for a description of the distinctions between the various statistics tools and concatenators. As a multi-file operator, ncecat will read the list of input-files from stdin if they are not specified as positional arguments on the command line (see Section 2.7 [Large Numbers of Files], page 21). Suppress global metadata copying. By default NCO’s multi-file operators copy the global metadata from the first input file into output-file. This helps to preserve the provenance of the output data. However, the use of metadata is burgeoning and sometimes one en- counters files with excessive amounts of extraneous metadata. Extracting small bitsof data from such files leads to output files which are much larger than necessary dueto the automatically copied metadata. ncecat supports turning off the default copying of global metadata via the ‘-M’ switch (or its long option equivalents, ‘--no_glb_mtd’ and ‘--suppress_global_metadata’). Consider five realizations, 85a.nc, 85b.nc,... 85e.nc of 1985 predictions from the same climate model. Then ncecat 85?.nc 85_ens.nc glues together the individual realizations into the single file, 85_ens.nc. If an input variable was dimensioned [lat,lon], it will by default have dimensions [record,lat,lon] in the output file. A restriction of ncecat is that the hyperslabs of the processed variables must be the same from file to file. Normally this means all the input files are the same size, and contain data on different realizations ofthe same variables. Concatenating a variable packed with different scales across multiple datasets isbe- yond the capabilities of ncecat (and ncrcat, the other concatenator (Section 2.6.1 [Con- catenation], page 20). ncecat does not unpack data, it simply copies the data from the input-files, and the metadata from the first input-file, to the output-file. This means that Chapter 4: Reference Manual 255 data compressed with a packing convention must use the identical packing parameters (e.g., scale_factor and add_offset) for a given variable across all input files. Otherwise the concatenated dataset will not unpack correctly. The workaround for cases where the pack- ing parameters differ across input-files requires three steps: First, unpack the data using ncpdq. Second, concatenate the unpacked data using ncecat, Third, re-pack the result with ncpdq. EXAMPLES Consider a model experiment which generated five realizations of one year of data, say 1985. You can imagine that the experimenter slightly perturbs the initial conditions of the problem before generating each new solution. Assume each file contains all twelve months (a seasonal cycle) of data and we want to produce a single file containing all the seasonal cycles. Here the numeric filename suffix denotes the experimentnot number( the month): ncecat 85_01.nc 85_02.nc 85_03.nc 85_04.nc 85_05.nc 85.nc ncecat 85_0[1-5].nc 85.nc ncecat -n 5,2,1 85_01.nc 85.nc These three commands produce identical answers. See Section 3.6 [Specifying Input Files], page 34, for an explanation of the distinctions between these methods. The output file, 85.nc, is five times the size as a single input-file. It contains 60 months of data. One often prefers that the (new) record dimension have a more descriptive, context- based name than simply “record”. This is easily accomplished with the ‘-u ulm_nm’ switch. To add a new record dimension named “time” to all variables ncecat -u time in.nc out.nc To glue together multiple files with a new record variable named “realization” ncecat -u realization 85_0[1-5].nc 85.nc Users are more likely to understand the data processing history when such descriptive coordinates are used. Consider a file with an existing record dimension named time. and suppose the user wishes to convert time from a record dimension to a non-record dimension. This may be useful, for example, when the user has another use for the record variable. The simplest method is to use ‘ncks --fix_rec_dmn’, and another possibility is to use ncecat followed by ncwa: ncecat in.nc out.nc # Convert time to non-record dimension ncwa -a record in.nc out.nc # Remove new degenerate record dimension The second step removes the degenerate record dimension. See Section 4.9 [ncpdq netCDF Permute Dimensions Quickly], page 289, and Section 4.8 [ncks netCDF Kitchen Sink], page 263, for other methods of of changing variable dimensionality, including the record dimension. 256 NCO 5.0.2-alpha07 User Guide 4.6 nces netCDF Ensemble Statistics SYNTAX nces [-3] [-4] [-5] [-6] [-7] [-A] [-C] [-c] [--cb y1,y2,m1,m2,tpd] [--cnk_byt sz_byt] [--cnk_csh sz_byt] [--cnk_dmn nm,sz_lmn] [--cnk_map map] [--cnk_min sz_byt] [--cnk_plc plc] [--cnk_scl sz_lmn] [-D dbg] [-d dim,[min][,[max][,[stride]]] [-F] [-G gpe_dsc] [-g grp[,...]] [--glb ...] [-h] [--hdf] [--hdr_pad nbr] [--hpss] [-L dfl_lvl] [-l path] [-n loop] [--no_cll_msr] [--no_frm_trm] [--no_tmp_fl] [--nsm_fl|grp] [--nsm_sfx sfx] [-O] [-o output- file] [-p path] [--ppc ...] [-R] [-r] [--ram_all] [--rth_dbl|flt] [-t thr_nbr] [--unn] [-v var[,...]] [-w wgt] [-X ...] [-x] [-y op_typ] [input-files][output-file] DESCRIPTION nces performs gridpoint statistics (including, but not limited to, averages) on variables across an arbitrary number (an ensemble) of input-files and/or of input groups within each file. Each file (or group) receives an equal weight by default. nces was formerly (until NCO version 4.3.9, released December, 2013) known as ncea (netCDF Ensemble Averager)1. For example, nces will average a set of files or groups, weighting each file or group evenly by default. This is distinct from ncra, which performs statistics only over the record dimension(s) (e.g., time), and weights each record in each record dimension evenly. The file or group is the logical unit of organization for the results of many scientific studies. Often one wishes to generate a file or group which is the statistical product (e.g., average) of many separate files or groups. This may be to reduce statistical noiseby combining the results of a large number of experiments, or it may simply be a step in a procedure whose goal is to compute anomalies from a mean state. In any case, when one desires to generate a file whose statistical properties are influenced by all the inputs, then use nces. As of NCO version 4.9.4, released in July, 2020, nces accepts user-specified weights with the ‘-w’ (or long-option equivalent ‘--wgt’, ‘--wgt_var’, or ‘--weight’) switch. The user must specify one weight per input file on the command line, or the name of a (scalar or degenerate 1-D array) variable in each input file that contains a single value to weight that file. When no weight is specified, nces weights each file (e.g., ensemble) in the input-files equally. Variables in the output-file are the same size as the variable hyperslab in each input file or group, and each input file or group must be the same size after hyperslabbing2 nces does allow files to differ in the input record dimension size if the requested record hyperslab(see 1 The old ncea command was deprecated in NCO version 4.3.9, released December, 2013. NCO will attempt to maintain back-compatibility and work as expected with invocations of ncea for as long as possible. Please replace ncea by nces in all future work. 2 As of NCO version 4.4.2 (released February, 2014) nces allows hyperslabs in all dimensions so long as the hyperslabs resolve to the same size. The fixed (i.e., non-record) dimensions should be the same size in all ensemble members both before and after hyperslabbing, although the hyperslabs may (and usually Chapter 4: Reference Manual 257 Section 3.16 [Hyperslabs], page 63) resolves to the same size for all files. nces recomputes the record dimension hyperslab limits for each input file so that coordinate limits maybe used to select equal length timeseries from unequal length files. This simplifies analysis of unequal length timeseries from simulation ensembles (e.g., the CMIP3 IPCC AR4 archive). nces works in one of two modes, file ensembles or group ensembles. File ensembles are the default (equivalent to the old ncea) and may also be explicitly specified by the ‘--nsm_fl’ or ‘--ensemble_file’ switches. To perform statistics on ensembles of groups, a newer feature, use ‘--nsm_grp’ or ‘--ensemble_group’. Members of a group ensemble are groups that share the same structure, parent group, and nesting level. Members must be leaf groups, i.e., not contain any sub-groups. Their contents usually have different values because they are realizations of replicated experiments. In group ensemble mode nces computes the statistics across the ensemble, which may span multiple input files. Files may contain members of multiple, distinct ensembles. However, all ensembles must have at least one member in the first input file. Group ensembles behave as an unlimited dimension of datasets: they may contain an arbitrary and extensible number of realizations in each file, and may be composed from multiple files. Output statistics in group ensemble mode are stored in the parent group by default. If the ensemble members are /cesm/cesm_01 and /cesm/cesm_02, then the computed statistic will be in /cesm in the output file. The--nsm_sfx ‘ ’ option instructs nces to instead store output in a new child group of the parent created by attaching the suffix to the parent group’s name, e.g., ‘--nsm_sfx=’_avg’’ would store results in the output group /cesm/cesm_avg: nces --nsm_grp mdl1.nc mdl2.nc mdl3.nc out.nc nces --nsm_grp --nsm_sfx=’_avg’ mdl1.nc mdl2.nc mdl3.nc out.nc See Section 2.6 [Statistics vs. Concatenation], page 20, for a description of the distinc- tions between the statistics tools and concatenators. As a multi-file operator, nces will read the list of input-files from stdin if they are not specified as positional arguments on the command line (see Section 2.7 [Large Numbers of Files], page 21). Like ncra and ncwa, nces treats coordinate variables as a special case. Coordinate variables are assumed to be the same in all ensemble members, so nces simply copies the coordinate variables that appear in ensemble members directly to the output file. This has the same effect as averaging the coordinate variable across the ensemble, yet doesnot incur the time- or precision- penalties of actually averaging them. ncra and ncwa allow coordinate variables to be processed only by the linear average operation, regardless of the arithmetic operation type performed on the non-coordinate variables (see Section 3.39 [Operation Types], page 129). Thus it can be said that the three operators (ncra, ncwa, and nces) all average coordinate variables (even though nces simply copies them). All other requested arithmetic operations (e.g., maximization, square-root, RMS) are applied only to non-coordinate variables. In these cases the linear average of the coordinate variable will be returned. EXAMPLES do) change the size of the dimensions from the input to the output files. Prior to this, nces was only guaranteed to work on hyperslabs in the record dimension that resolved to the same size. 258 NCO 5.0.2-alpha07 User Guide Consider a model experiment which generated five realizations of one year of data, say 1985. Imagine that the experimenter slightly perturbs the initial conditions of the problem before generating each new solution. Assume each file contains all twelve months (a seasonal cycle) of data and we want to produce a single file containing the ensemble average (mean) seasonal cycle. Here the numeric filename suffix denotes the realizationnot number( the month): nces 85_01.nc 85_02.nc 85_03.nc 85_04.nc 85_05.nc 85.nc nces 85_0[1-5].nc 85.nc nces -n 5,2,1 85_01.nc 85.nc These three commands produce identical answers. See Section 3.6 [Specifying Input Files], page 34, for an explanation of the distinctions between these methods. The output file, 85.nc, is the same size as the inputs files. It contains 12 months of data (which mightor might not be stored in the record dimension, depending on the input files), but each value in the output file is the average of the five values in the input files. In the previous example, the user could have obtained the ensemble average values in a particular spatio-temporal region by adding a hyperslab argument to the command, e.g., nces -d time,0,2 -d lat,-23.5,23.5 85_??.nc 85.nc In this case the output file would contain only three slices of data inthe time dimension. These three slices are the average of the first three slices from the input files. Additionally, only data inside the tropics is included. As of NCO version 4.3.9 (released December, 2013) nces also works with groups (rather than files) as the fundamental unit of the ensemble. Consider two ensembles, /ecmwf and /cesm stored across three input files mdl1.nc, mdl2.nc, and mdl3.nc. Ensemble members would be leaf groups with names like /ecmwf/01, /ecmwf/02 etc. and /cesm/01, /cesm/02, etc. These commands average both ensembles: nces --nsm_grp mdl1.nc mdl2.nc mdl3.nc out.nc nces --nsm_grp --nsm_sfx=’_min’ --op_typ=min -n 3,1,1 mdl1.nc out.nc nces --nsm_grp -g cesm -v tas -d time,0,3 -n 3,1,1 mdl1.nc out.nc nces --nsm_grp mdl1.nc mdl2.nc mdl3.nc out.nc nces --nsm_grp --nsm_sfx=’_min’ --op_typ=min -n 3,1,1 mdl1.nc out.nc nces --nsm_grp -g cesm -v tas -d time,0,3 -n 3,1,1 mdl1.nc out.nc The first command stores averages in the output groups /cesm and /ecmwf, while the second stores minima in the output groups /cesm/cesm_min and /ecmwf/ecmwf_min: The third command demonstrates that sub-setting and hyperslabbing work as expected. Note that each input file may contain different numbers of members of each ensemble, aslong all distinct ensembles contain at least one member in the first file. As of NCO version 4.9.4, released in July, 2020, nces accepts user-specified weights with the ‘-w’ (or long-option equivalent ‘--wgt’, ‘--wgt_var’, or ‘--weight’) switch: # Construct input variables with values of 1 and 2 ncks -O -M -v one ~/nco/data/in.nc ~/1.nc ncrename -O -v one,var ~/1.nc ncap2 -O -s ’var=2’ ~/1.nc ~/2.nc Chapter 4: Reference Manual 259 # Three methods of weighting input files unevenly # 1. Old-method: specify input files multiple times # 2. New-method: specify one weight per input file # 3. New-method: specify weight variable in each input file nces -O ~/1.nc ~/2.nc ~/2.nc ~/out.nc # Clumsy, limited to integer weights nces -O -w 1,2 ~/1.nc ~/2.nc ~/out.nc # Flexible, works for any weight nces -O -w var ~/1.nc ~/2.nc ~/out.nc # Flexible, works for any weight # All three methods produce same answer: var=(1*1+2*2)/3=5/3=1.67 ncks ~/out.nc 260 NCO 5.0.2-alpha07 User Guide 4.7 ncflint netCDF File Interpolator SYNTAX ncflint [-3] [-4] [-5] [-6] [-7] [-A] [-C] [-c] [--cnk_byt sz_byt] [--cnk_csh sz_byt] [--cnk_dmn nm,sz_lmn] [--cnk_map map] [--cnk_min sz_byt] [--cnk_plc plc] [--cnk_scl sz_lmn] [-D dbg] [-d dim,[min][,[max][,[stride]]] [--fl_fmt fl_fmt] [-F] [--fix_rec_crd] [-G gpe_dsc] [-g grp[,...]] [--glb ...] [-h] [--hdr_pad nbr] [--hpss] [-i var,val3] [-L dfl_lvl] [-l path] [-N] [--no_cll_msr] [--no_frm_trm] [--no_tmp_fl] [-O] [-o file_3] [-p path] [--ppc ...] [-R] [-r] [--ram_all] [-t thr_nbr] [--unn] [-v var[,...]] [-w wgt1[,wgt2]] [-X ...] [-x] file_1 file_2 [file_3] DESCRIPTION ncflint creates an output file that is a linear combination of the input files. This linear combination is a weighted average, a normalized weighted average, or an interpolation of the input files. Coordinate variables are not acted upon in any case, they are simply copied from file 1. There are two conceptually distinct methods of using ncflint. The first method is to specify the weight each input file contributes to the output file. In this method, the value val3 of a variable in the output file file 3 is determined from its values val1 and val2 in the two input files according to val3 = wgt1 × val1 + wgt2 × val2 . Here at least wgt1, and, optionally, wgt2, are specified on the command line with the‘-w’ (or ‘--weight’ or ‘--wgt_var’) switch. If only wgt1 is specified then wgt2 is automatically computed as wgt2 = 1−wgt1. Note that weights larger than 1 are allowed. Thus it is possible to specify wgt1 = 2 and wgt2 = −3. One can use this functionality to multiply all values in a given file by a constant. As of NCO version 4.6.1 (July, 2016), the ‘-N’ switch (or long-option equivalents ‘--nrm’ or ‘--normalize’) implements a variation of this method. This switch instructs ncflint to internally normalize the two supplied (or one supplied and one inferred) weights so that wgt1 = wgt1/(wgt1+wgt2) and wgt2 = wgt2/(wgt1+wgt2) . This allows the user to input integral weights, say, and to delegate the chore of normalizing them to ncflint. Be careful that ‘-N’ means what you think, since the same switch means something quite different in ncwa. The second method of using ncflint is to specify the interpolation option with ‘-i’ (or with the ‘--ntp’ or ‘--interpolate’ long options). This is the inverse of the first method in the following sense: When the user specifies the weights directly, ncflint has no work to do besides multiplying the input values by their respective weights and adding together the results to produce the output values. It makes sense to use this when the weights are known a priori. Another class of problems has the arrival value (i.e., val3) of a particular variable var known a priori. In this case, the implied weights can always be inferred by examining the values of var in the input files. This results in one equation in two unknowns, wgt1 and Chapter 4: Reference Manual 261 wgt2: val3 = wgt1 × val1 + wgt2 × val2 . Unique determination of the weights requires imposing the additional constraint of normalization on the weights: wgt1 + wgt2 = 1. Thus, to use the interpolation option, the user specifies var and val3 with the ‘-i’ option. ncflint then computes wgt1 and wgt2, and uses these weights on all variables to generate the output file. Although var may have any number of dimensions in the input files, it must represent a single, scalar value. Thus any dimensions associated with var must be degenerate, i.e., of size one. If neither ‘-i’ nor ‘-w’ is specified on the command line, ncflint defaults to weighting each input file equally in the output file. This is equivalent to specifying-w ‘ 0.5’ or ‘-w 0.5,0.5’. Attempting to specify both ‘-i’ and ‘-w’ methods in the same command is an error. ncflint does not interpolate variables of type NC_CHAR and NC_STRING. This behavior is hardcoded. By default ncflint interpolates or multiplies record coordinate variables (e.g., time is often stored as a record coordinate) not other coordinate variables (e.g., latitude and longi- tude). This is because ncflint is often used to time-interpolate between existing files, but is rarely used to spatially interpolate. Sometimes however, users wish to multiply entire files by a constant that does not multiply any coordinate variables.--fix_rec_crd The‘ ’ switch was implemented for this purpose in NCO version 4.2.6 (March, 2013). It prevents ncflint from multiplying or interpolating any coordinate variables, including record coor- dinate variables. Depending on your intuition, ncflint may treat missing values unexpectedly. Consider a point where the value in one input file, say val1, equals the missing value mss val 1 and, at the same point, the corresponding value in the other input file val2 is not misssing (i.e., does not equal mss val 2). There are three plausible answers, and this creates ambiguity. Option one is to set val3 = mss val 1. The rationale is that ncflint is, at heart, an interpolator and interpolation involving a missing value is intrinsically undefined. ncflint currently implements this behavior since it is the most conservative and least likely to lead to misinterpretation. Option two is to output the weighted valid data point, i.e., val3 = wgt2 × val2 . The rationale for this behavior is that interpolation is really a weighted average of known points, so ncflint should weight the valid point. Option three is to return the unweighted valid point, i.e., val3 = val2. This behavior would appeal to those who use ncflint to estimate data using the closest available data. When a point is not bracketed by valid data on both sides, it is better to return the known datum than no datum at all. The current implementation uses the first approach, Option one. If you have strong opinions on this matter, let us know, since we are willing to implement the other approaches as options if there is enough interest. EXAMPLES Although it has other uses, the interpolation feature was designed to interpolate file 3 to a time between existing files. Consider input files 85.nc and 87.nc containing variables 262 NCO 5.0.2-alpha07 User Guide describing the state of a physical system at times time = 85 and time = 87. Assume each file contains its timestamp in the scalar variable time. Then, to linearly interpolate to a file 86.nc which describes the state of the system at time at time = 86, we would use ncflint -i time,86 85.nc 87.nc 86.nc Say you have observational data covering January and April 1985 in two files named 85_01.nc and 85_04.nc, respectively. Then you can estimate the values for February and March by interpolating the existing data as follows. Combine 85_01.nc and 85_04.nc in a 2:1 ratio to make 85_02.nc: ncflint -w 0.667 85_01.nc 85_04.nc 85_02.nc ncflint -w 0.667,0.333 85_01.nc 85_04.nc 85_02.nc Multiply 85.nc by 3 and by −2 and add them together to make tst.nc: ncflint -w 3,-2 85.nc 85.nc tst.nc This is an example of a null operation, so tst.nc should be identical (within machine precision) to 85.nc. Multiply all the variables except the coordinate variables in the file emissions.nc by by 0.8: ncflint --fix_rec_crd -w 0.8,0.0 emissions.nc emissions.nc scaled_emissions.nc The use of ‘--fix_rec_crd’ ensures, e.g., that the time coordinate, if any, is not scaled (i.e., multiplied). Add 85.nc to 86.nc to obtain 85p86.nc, then subtract 86.nc from 85.nc to obtain 85m86.nc ncflint -w 1,1 85.nc 86.nc 85p86.nc ncflint -w 1,-1 85.nc 86.nc 85m86.nc ncdiff 85.nc 86.nc 85m86.nc Thus ncflint can be used to mimic some ncbo operations. However this is not a good idea in practice because ncflint does not broadcast (see Section 4.3 [ncbo netCDF Binary Operator], page 225) conforming variables during arithmetic. Thus the final two commands would produce identical results except that ncflint would fail if any variables needed to be broadcast. Rescale the dimensional units of the surface pressure prs_sfc from Pascals to hectopas- cals (millibars) ncflint -C -v prs_sfc -w 0.01,0.0 in.nc in.nc out.nc ncatted -a units,prs_sfc,o,c,millibar out.nc Chapter 4: Reference Manual 263 4.8 ncks netCDF Kitchen Sink SYNTAX ncks [-3] [-4] [-5] [-6] [-7] [-A] [-a] [--area_wgt] [-b fl_bnr] [-C] [-c] [--cdl] [--chk_map] [--chk_nan] [--cnk_byt sz_byt] [--cnk_csh sz_byt] [--cnk_dmn nm,sz_lmn] [--cnk_map map] [--cnk_min sz_byt] [--cnk_plc plc] [--cnk_scl sz_lmn] [-D dbg] [-d dim,[min][,[max][,[stride]]] [-F] [--fix_rec_dmn dim] [--fl_fmt fl_fmt] [--fmt_val format] [-G gpe_dsc] [-g grp[,...]] [--glb ...] [--grp_xtr_var_xcl] [-H] [-h] [--hdn] [--hdr_pad nbr] [--hpss] [--jsn] [--jsn_fmt lvl] [-L dfl_lvl] [-l path] [-M] [-m] [--map map-file] [--md5] [--mk_rec_dmn dim] [--no_blank] [--no_cll_msr] [--no_frm_trm] [--no_tmp_fl] [-O] [-o output-file] [-P] [-p path] [--ppc ...] [--prn_fl print-file] [-Q] [-q] [-R] [-r] [--rad] [--ram_all] [--rgr ...] [--rnr=wgt] [-s format] [-u] [--unn] [-V] [-v var[,...]] [--vrt vrt-file] [-X ...] [-x] [--xml] input-file [[output-file]] DESCRIPTION The nickname “kitchen sink” is a catch-all because ncks combines most features of ncdump and nccopy with extra features to extract, hyperslab, multi-slab, sub-set, and translate into one versatile utility. ncks extracts (a subset of the) data from input-file, regrids it according to map-file if specified, then writes in netCDF format to output-file, and optionally writes it in flat binary format to fl_bnr, and optionally prints it to screen. ncks prints netCDF input data in ASCII, CDL, JSON, or NcML/XML text formats to stdout, like (an extended version of) ncdump. By default ncks prints CDL format. Option ‘-s’ (or long options ‘--sng_fmt’ and ‘--string’) permits the user to format data using C- style format strings, while option ‘--cdl’ outputs CDL, option ‘--jsn’ (or ‘json’) outputs JSON, option ‘--trd’ (or ‘traditional’) outputs “traditional” format, and option ‘--xml’ (or ‘ncml’) outputs NcML. The “traditional” tabular format is intended to be easy to search for the data you want, one datum per screen line, with all dimension subscripts and coordinate values (if any) preceding the datum. ncks exposes many flexible controls over printed output, including CDL, JSON, and NcML. Options ‘-a’, ‘--cdl’, ‘-F’, ‘--fmt_val’, ‘-H’, ‘--hdn’, ‘--jsn’, ‘-M’, ‘-m’, ‘-P’, ‘--prn_fl’, ‘-Q’, ‘-q’, ‘-s’, ‘--trd’, ‘-u’, ‘-V’, and ‘--xml’ (and their long option coun- terparts) control the presence of data and metadata and their formatted location and ap- pearance when printed. ncks extracts (and optionally creates a new netCDF file comprised of) only selected variables from the input file (similar to the old ncextr specification). Only variables and coordinates may be specifically included or excluded—all global attributes and any attribute associated with an extracted variable are copied to the screen and/or output netCDF file. Options ‘-c’, ‘-C’, ‘-v’, and ‘-x’ (and their long option synonyms) control which variables are extracted. 264 NCO 5.0.2-alpha07 User Guide ncks extracts hyperslabs from the specified variablesncks ( implements the original nccut specification). Option ‘-d’ controls the hyperslab specification. Input dimensions that are not associated with any output variable do not appear in the output netCDF. This feature removes superfluous dimensions from netCDF files. ncks will append variables and attributes from the input-file to output-file if output- file is a pre-existing netCDF file whose relevant dimensions conform to dimension sizesof input-file. The append features of ncks are intended to provide a rudimentary means of adding data from one netCDF file to another, conforming, netCDF file. If naming conflicts exist between the two files, data in output-file is usually overwritten by the corresponding data from input-file. Thus, when appending, the user should backup output-file in case valuable data are inadvertantly overwritten. If output-file exists, the user will be queried whether to overwrite, append, or exit the ncks call completely. Choosing overwrite destroys the existing output-file and create an entirely new one from the output of the ncks call. Append has differing effects depending on the uniqueness of the variables and attributes output by ncks: If a variable or attribute extracted from input-file does not have a name conflict with the members of output-file then it will be added to output-file without overwriting any of the existing contents of output- file. In this case the relevant dimensions must agree (conform) between the two files; new dimensions are created in output-file as required. When a name conflict occurs, a global attribute from input-file will overwrite the corresponding global attribute from output-file. If the name conflict occurs for a non-record variable, then the dimensions and typeofthe variable (and of its coordinate dimensions, if any) must agree (conform) in both files. Then the variable values (and any coordinate dimension values) from input-file will overwrite the corresponding variable values (and coordinate dimension values, if any) in output-file1. Since there can only be one record dimension in a file, the record dimension must have the same name (though not necessarily the same size) in both files if a record dimension variable is to be appended. If the record dimensions are of differing sizes, the record dimension of output-file will become the greater of the two record dimension sizes, the record variable from input-file will overwrite any counterpart in output-file and fill values will be written to any gaps left in the rest of the record variables (I think). In all cases variable attributes in output-file are superseded by attributes of the same name from input-file, and left alone if there is no name conflict. Some users may wish to avoid interactive ncks queries about whether to overwrite ex- isting data. For example, batch scripts will fail if ncks does not receive responses to its queries. Options ‘-O’ and ‘-A’ are available to force overwriting existing files, and appending existing variables, respectively. Options specific to ncks The following summarizes features unique to ncks. Features common to many operators are described in Chapter 3 [Shared features], page 29. 1 Those familiar with netCDF mechanics might wish to know what is happening here: ncks does not attempt to redefine the variable in output-file to match its definition in input-file, ncks merely copies the values of the variable and its coordinate dimensions, if any, from input-file to output-file. Chapter 4: Reference Manual 265 ‘-a’ Switches ‘-a’, ‘--abc’, and ‘--alphabetize’ turn-off the default alphbetization of extracted fields in ncks only. These switches are misleadingly named and were deprecated in ncks as of NCO version 4.7.1 (December, 2017). This is the default behavior so these switches are no-ops included only for completeness. By default, NCO extracts, prints, and writes specified output variables to disk in alphabetical order. This tends to make long output lists easier to search for particular variables. Again, no option is necessary to write output in alphabetical order. Until NCO version 4.7.1 (December, 2017), ncks used the -a, --abc, or --alphabetize switches to turn-off the default alpha- betization. These names were counter-intuitive and needlessly confusing. As of NCO version 4.7.1, ncks uses the new switches --no_abc, --no-abc, --no_ alphabetize, or --no-alphabetize, all of which are equivalent. The --abc and --alphabetize switches are now no-ops, i.e., they write the output in the unsorted order of the input. The -a switch is now completely deprecated in favor of the clearer long option switches. ‘-b file’ Activate native machine binary output writing to binary file file. Also ‘--fl_bnr’ and ‘--binary-file’. Writing packed variables in binary format is not supported. Metadata is never output to the binary file. Examine the netCDF output file to see the variables in the binary file.-C Usethe‘ ’ switch, if necessary, to avoid wanting unwanted coordinates to the binary file: % ncks -O -v one_dmn_rec_var -b bnr.dat -p ~/nco/data in.nc out.nc % ls -l bnr.dat | cut -d ’ ’ -f 5 # 200 B contains time and one_dmn_rec_var 200 % ls -l bnr.dat % ncks -C -O -v one_dmn_rec_var -b bnr.dat -p ~/nco/data in.nc out.nc % ls -l bnr.dat | cut -d ’ ’ -f # 40 B contains one_dmn_rec_var only 40 ‘--cal’ As of NCO version 4.6.5 (March, 2017), ncks can print human-legible calendar strings corresponding to time values with UDUnits-compatible date units of the form time-since-basetime, e.g., ‘days since 2000-01-01’ and a CF calendar attribute, if any. Enact this with the ‘--calendar’ (also ‘--cln’, ‘--prn_lgb’, and ‘--datestamp’) option when printing in any mode. Invoking this option when dbg lvl >= 1 in CDL mode prints both the value and the calendar string (one in comments): zender@aerosol:~$ ncks -D 1 --cal -v tm_365 ~/nco/data/in.nc ... variables: double tm_365 ; tm_365:units = "days since 2013-01-01" ; // char tm_365:calendar = "365_day" ; // char data: tm_365 = "2013-03-01"; // double value: 59 ... zender@aerosol:~$ ncks -D 1 -v tm_365 ~/nco/data/in.nc 266 NCO 5.0.2-alpha07 User Guide ... tm_365 = 59; // calendar format: "2013-03-01" ... This option is similar to the ncdump ‘-t’ option. As of NCO version 4.6.8 (Au- gust, 2017), ncks CDL printing supports finer-grained control of date formats with the ‘--dt_fmt=dt_fmt’ (or ‘--date_format’) option. The dt fmt is an enumerated integer from 0–3. Values dt fmt = 0 or 1 correspond to the short format for dates that are the default. The value dt fmt = 2 requests the “reg- ular” format for dates, dt fmt = 3 requests the full ISO-8601 format with the “T” separator and the comma: ncks -H -m -v time_bnds -C --dt_fmt=value ~/nco/data/in.nc # Value: Output: # 0,1 1964-03-13 09:08:16 # Default, short format # 2 1964-03-13 09:08:16.000000 # Regular format # 3 1964-03-13T09:08:16.000000 # ISO8601 ’T’ format Note that ‘--dt_fmt’ automatically implies ‘--cal’ makes that options super- fluous. As of NCO version 4.9.4 (September, 2020), invoking the ‘--dt_fmt’ option now applies equally well to JSON and XML output as to CDL output: % ncks -d time,0 -v time --cdl --dt_fmt=3 ~/nco/data/in.nc ... time = "1964-03-13T21:09:0.000000" ; ... % ncks -d time,0 -v time --json --dt_fmt=3 ~/nco/data/in.nc ... "data": ["1964-03-13T21:09:0.000000"] ... % ncks -d time,0 -v time --xml --dt_fmt=3 ~/nco/data/in.nc ... $ ncks --chk_map --area_wgt map.nc # Area-weighted statistics The map-checker performs numerous checks and reports numerous statistics, probably more than you care about. Be assured that each piece of provided information has in the past proved useful to developers of weight-generation and regridding algorithms. Most of the time, users can learn whether the examined map is of sufficient quality for their purposes by examing only a few ofthese statistics. Before defining these primary statistics, it is helpful to understand the meaning of the weight-array S (stored in a map-file as the variable S), and the terminology of rows and columns. A remapping (aka regridding) transforms a field on an input grid to an output grid while conserving to the extent possible or desired the local and global properties of the field. The map S is a matrix of M rows and N columns of weights, where M is the number of gridcells (or degrees of freedom, DOFs) in the destination grid, and N is the number of gridcells (or DOFs) in the source grid. An individual weight S(m,n) represents the fractional contribution to destination gridcell m by source gridcell n. By convention the weights are normalized to sum to unity in each row (destination gridcell) that completely overlaps the input grid. Thus the weights in a single row are all equivalent to the fractional destination areas that the same destination gridcell (we will drop the DOF terminology hereafter for conciseness) receives from each source gridcell. Regardless of the values of the individual weights, it is intuitive that their row- sum should never exceed unity because that would be physically equivalent to an output gridcell receiving more than its own area from the source grid. Map- files typically store these row-sum statistics for each destination gridcell inthe frac_b variable described further below. Likewise the weights in a single column represent the fractional destination areas that a single source gridcell contributes to every output gridcell. Each output gridcell in a column may have a different area so column-sums need not, and in general do not, sum to unity. However, a source gridcell ought to contribute to the destination grid a total area equal to its own area. Thus a constraint on column-sums is that their weights, themselves weighted by the destination gridcell area corresponding to each row, should sum exactly to the source grid- cell area. In other words, the destination-area-weighted column-sum divided by the source gridcell area would be unity (in a perfect first order map) for every source gridcell that completely overlaps valid destination gridcells. Map-files typically store these area-weighted-column-sum-ratio statistics for each gridcell in the frac_a variable described further below. Storing the entire weight-matrix S is unnecessary because only a relative handful of gridcells in the source grid contribute to a given destination gridcell, and visa versa. Instead, map-files store only the non-zero S(m,n), and encode them as a sparse-matrix. Storing S as a sparse matrix rather than a full matrix reduces overall storage sizes by a factor on the order of the ratio of the product of the grid sizes to their sum, or about 10,000 for grids with horizontal resolution near one degree, and more for finer resolutions. The sparse-matrix representation isa one-dimensional array of weights S, together with two ancillary arrays, row and column, that contain the one-dimensional row and column indices, respectively, 268 NCO 5.0.2-alpha07 User Guide corresponding to the destination and source gridcells of the associated weight. By convention, map-files store the row and column indices using the 1-based convention in common use in the 1990s when regridding software was all written in Fortran. The map-checker prints cell locations with 1-based indices as well: % ncks --chk_map map_ne30np4_to_cmip6_180x360_nco.20190601.nc Characterization of map-file map_ne30np4_to_cmip6_180x360_nco.20190601.nc Cell triplet elements : [Fortran (1-based) index, center latitude, center longitude] Sparse matrix size n_s: 246659 Weight min S(190813): 5.1827201764857658e-25 from cell \ [33796,-45.7998,+136.437] to [15975,-45.5,+134.5] Weight max S( 67391): 1.0000000000000000e+00 from cell \ [33671,-54.4442,+189.645] to [12790,-54.5,+189.5] Ignored weights (S=0.0): 0 ... Here the map-file weights span twenty-five orders of magnitude. This may seem large though in practice is typical for high-resolution intersection grids. The Fortran-convention index of each weight extreme is followed by its geographic latitude and longitude. Reporting the locations of extrema, and of gridcells whose metrics miss their target values by more than a specificied tolerance, are prime map-checker features. As mentioned above, the two statistics most telling about map quality are the weighted column-sums frac a and the row-sums frac b. The short-hand names for what these metrics quantify are Conservation and Consistency, respectively. Conservation means the total fraction of an input gridcell that contributes to the output grid. For global input and output grids that completely tile the sphere, the entirety of each input gridcell should contribute (i.e., map to) the output grid. The same concept that applies locally to conservation of a gridcell value applies globally to the overall conservation of an input field. Thus a perfectly conservative mapping between global grids that tile the sphere would have frac a = 1.0 for every input gridcell, and for the mean of all input gridcells. The map-checker computes Conservation (frac a) from the stored variables S, row, column, area_a, and area_b in the map-file, and then compares those values to the frac_a values (if any) on-disk, and warns of any disagreements. By definition, conservation is perfect to first order if the sum of the destination- gridcell-area-weighted weights (which is an area) equals the source gridcell area, and so their ratio (frac a) is unity. Computing the area-weighted-column-sum- ratios and comparing those frac a to the stored frac_a catches any discrep- ancies. The analysis sounds an alarm when discrepancies exceed a tolerance (currently 5.0e-16). More importantly, the map-checker reports the summary statistics of the computed frac a metrics and their imputed errors, including the grid mean, minimum, maximum, mean-absolute bias, root-mean-square bias, and standard deviation. % ncks --chk_map map_ne30np4_to_cmip6_180x360_nco.20190601.nc ... Conservation metrics (column-sums of area_b-weighted weights normalized by area_a) and errors--- Perfect metrics for global Grid B are avg = min = max = 1.0, mbs = rms = sdn = 0.0: Chapter 4: Reference Manual 269 frac_a avg: 1.0000000000000000 = 1.0-0.0e+00 // Mean frac_a min: 0.9999999999991109 = 1.0-8.9e-13 // Minimum in grid A cell [45328,+77.3747,+225] frac_a max: 1.0000000000002398 = 1.0+2.4e-13 // Maximum in grid A cell [47582,+49.8351,+135] frac_a mbs: 0.0000000000000096 = 9.6e-15 // Mean absolute bias from 1.0 frac_a rms: 0.0000000000000167 = 1.7e-14 // RMS relative to 1.0 frac_a sdn: 0.0000000000000167 = 1.7e-14 // Standard deviation ... The values of the frac_a metric are generally imperfect (not 1.0) for global grids. The bias is the deviation from the target metric shown in the second floating-point column in each row above (e.g., 8.9e-13). These biases should be vanishingly small with respect to unity. Mean biases as large as 1.0e-08 may be considered acceptable for off-line analyses (i.e., a single regridding ofraw data) though the acceptable tolerance should be more stringent for on-line use such as in a coupler where forward and reverse mappings may be applied tens of thousands of times. The mean biases for such on-line regridding should be close to 1.0e-15 in order for tens-of-thousands of repetitions to still conserve mass/energy to full double-precision. The minimum and maximum gridcell biases indicate the worst performing lo- cations of the mapping. These are generally much (a few orders of magni- tude) greater than the mean biases. Observe that the minimum and maximum biases in the examples above and below occur at longitudes that are multi- ples of 45 degrees. This is characteristic of mappings to/from for cube-square grids whose faces have edges, and thus additional complexity, at multiples of 45 degrees. This illustrates how intersection grid geometry influences biases. More complex, finer-scale structures, produce greater biases. The Root-Mean- Square (RMS) and standard deviation metrics characterize the distribution of biases throughout the entire intersection grid, and are thus complementary in- formation to the minimum and maximum biases. Consistency expresses the total fraction of an output gridcell that receives con- tributions from the input grid. Thus Consistency is directly analogous to Con- servation, only applied to the output grid. Conservation is the extent to which the mapping preserves the local and grid-wide integrals of input fields, while Consistency is the extent to which the mapping correctly aligns the input and output grids so that each destination cell receives the appropriate proportion of the input integrals. The mapping will produce an acceptably faithful repro- duction of the input on the output grid only if all local and global Conservation and Consistency metrics meet the acceptable error tolerances. The map-checker computes the Consistency (frac b) as row-sums of the weights stored in S and compares these to the stored values of frac_b. (Note how the definition of weights S(m,n) as the fractional contribution to destination gridcell m by source gridcell n makes calculation of frac b almost trivial in comparison to frac a). Nevertheless, frac_b in the file may differ from the computed row-sum for example if the map-file generator artificially limits the stored frac_b value for any cell to 1.0 for those row-sums that exceed 1.0. The map-checker raises an alarm when discrepancies between computed and stored frac_b exceed a tolerance (currently 5.0e-16). There are semi-valid reasons a 270 NCO 5.0.2-alpha07 User Guide map-generator might do this, so this does not necessarily indicate an error. The alarm simply informs the user that applying the weights will lead to a slightly different Consistency than indicated by the stored frac_b. As with frac_a, the values of frac_b are generally imperfect (not 1.0) for global grids: % ncks --chk_map map_ne30np4_to_cmip6_180x360_nco.20190601.nc ... Consistency metrics (row-sums of weights) and errors--- Perfect metrics for global Grid A are avg = min = max = 1.0, mbs = rms = sdn = 0.0: frac_b avg: 0.9999999999999999 = 1.0-1.1e-16 // Mean frac_b min: 0.9999999999985523 = 1.0-1.4e-12 // Minimum in grid B cell [59446,+75.5,+45.5] frac_b max: 1.0000000000004521 = 1.0+4.5e-13 // Maximum in grid B cell [63766,+87.5,+45.5] frac_b mbs: 0.0000000000000065 = 6.5e-15 // Mean absolute bias from 1.0 frac_b rms: 0.0000000000000190 = 1.9e-14 // RMS relative to 1.0 frac_b sdn: 0.0000000000000190 = 1.9e-14 // Standard deviation ... This example shows that frac b has the greatest local errors at similar bound- aries (multiples of 45 degrees longitude) as frac a. It is typical for Conservation and Consistency to degrade in intricate areas of the intersection grid, and these areas occur at multiples of 45 degrees longitude for cubed-sphere mappings. The map-checker will produce area-weighted metrics when invoked with the --area_wgt flag, e.g., ‘ncks --area_wgt in.nc’. Area-weighted statistics show the exact local and global results to expect with real-world grids in which large consistency/conservation errors in small gridcells may be less important than smaller errors in larger gridcells. Global-weighted mean statistics will of course differ from unweighted statistics, although the minimum and maximum donot change: % ncks --area_wgt map_ne30np4_to_cmip6_180x360_nco.20190601.nc ... Conservation metrics (column-sums of area_b-weighted weights normalized by area_a) and errors--- Perfect metrics for global Grid B are avg = min = max = 1.0, mbs = rms = sdn = 0.0: frac_a avg: 1.0000000000000009 = 1.0+8.9e-16 // Area-weighted mean frac_a min: 0.9999999999999236 = 1.0-7.6e-14 // Minimum in grid A cell [12810,+3.44654,+293.25] frac_a max: 1.0000000000001146 = 1.0+1.1e-13 // Maximum in grid A cell [16203,-45.7267,+272.31] frac_a mbs: 0.0000000000000067 = 6.7e-15 // Area-weighted mean absolute bias from 1.0 frac_a rms: 0.0000000000000102 = 1.0e-14 // Area-weighted RMS relative to 1.0 frac_a sdn: 0.0000000000000103 = 1.0e-14 // Standard deviation Consistency metrics (row-sums of weights) and errors--- Perfect metrics for global Grid A are avg = min = max = 1.0, mbs = rms = sdn = 0.0: frac_b avg: 1.0000000000000047 = 1.0+4.7e-15 // Area-weighted mean frac_b min: 0.9999999999998442 = 1.0-1.6e-13 // Minimum in grid B cell [48415,+44.5,+174.5] frac_b max: 1.0000000000002611 = 1.0+2.6e-13 // Maximum in grid B cell [16558,-44.5,+357.5] frac_b mbs: 0.0000000000000065 = 6.5e-15 // Area-weighted mean absolute bias from 1.0 frac_b rms: 0.0000000000000129 = 1.3e-14 // Area-weighted RMS relative to 1.0 frac_b sdn: 0.0000000000000133 = 1.3e-14 // Standard deviation Chapter 4: Reference Manual 271 ... The examples above show no outstanding differences (besides rounding) be- tween the unweighted and area-weighted statistics. The absence of degradation between the global unweighted statistics (further up the page) and the global weighted statistics (just above) demonstrates there are no important correla- tions between local weight biases and gridcell areas. The area-weighted mean frac b statistic deserves special mention. Its value is the exact factor by which the mapping will shift the global mean of a spatially uniform input field. This metric is, therefore, first among equals when evaluating the quality ofmaps under consideration for use in time-stepping models where global conservation (e.g., of mass or energy) is crucial. As of NCO version 4.9.2 (March, 2020), adding the ‘--frac_b_nrm’ flag changes the map-checker into a read-write algorithm that first diagnoses the map-file statistics described above and then re-writes the weights (and weight-derived statistics frac a and frac b) to compensate or “fix” issues that poor-quality input grids can cause. Input grids can and often do have regions that are not tiled by any portion of any input gridcell. For example, many FV ocean grids (such as MPAS) are empty (have no gridcells) in land regions beyond the coasts. Some FV ocean grids have gridcells everywhere and mask (i.e., screen-out) the non-ocean gridcells by setting the mask value to zero. Both these designs are perfectly legal. What is illegal, yet sometimes encountered in practice, is overlapping gridcells on the same input grid. Such an input grid is said to be self-overlapping. The surface topography dataset grid SCRIPgrid_1km-merge-10min_HYDRO1K- merge-nomask_c130402.nc (hereafter the HYDRO1K grid for short) used by E3SM and CESM is self-overlapping. Weight-generators that receive the same input location twice might (if they do not take precaustions to idenfity the issue, which no known weight-generators do) double-weight the self-overlapped re- gion(s). In other words, self-overlapping input grids can lead weight-generators to produce values frac b >> 1.0. Applying these weights would lead to exag- gerated values on the destination grid. The best solution to this issue is to adjust the input grid to avoid self-overlap. However, this solution may be difficult or impractical where the original data, producer, or algorithm are unavailable or unclear. In such cases, the --frac_b_ nrm flag provides a workaround. Please understand thatncks ‘ --frac_b_nrm map.nc’ is designed to alter map.nc in-xsplace, so backup the original file first. % ncks --frac_b_nrm map_hydro1k_to_ne1024np4_nco.20200301.nc ...... ‘--chk_nan’ As of NCO version 4.8.0 (May, 2019), ncks can locate NaN of NaNf in double- and single-precision floating-point variables, respectively. If a NaN is encountered, NCO prints its location and then exits with an error code. Thanks to Matthew Thompson of NASA for this suggestion. $ ncks --chk_nan ~/nco/data/in.nc 272 NCO 5.0.2-alpha07 User Guide ‘--fix_rec_dmn’ Change record dimension dim in the input file into a fixed dimension inthe output file. Also ‘--no_rec_dmn’. Before NCO version 4.2.5 (January, 2013), the syntax for --fix_rec_dmn did not permit or require the specification of the dimension name dim. This is because the feature only worked on netCDF3 files, which support only one record dimension, so specifying its namewas unnecessary. netCDF4 files allow an arbitrary number of record dimensions, so the user must specify which record dimension to fix. The decision was made that starting with NCO version 4.2.5 (January, 2013), it is always required to specify the dimension name to fix regardless of the netCDF file type. This keeps the code simple, and is symmetric with the syntax for --mk_rec_dmn, described next. As of NCO version 4.4.0 (January, 2014), the argument all may be given to ‘--fix_rec_dmn’ to convert all record dimensions to fixed dimensions in the output file. Previously,--fix_rec_dmn ‘ ’ only allowed one option, the name of a single record dimension to be fixed. Now it is simple to simultaneously fix all record dimensions. This is useful (and nearly mandatory) when flattening netCDF4 files that have multiple record dimensions per group into netCDF3 files (which are limited to at most one record dimension) (see Section 3.14 [Group Path Editing], page 53). ‘--hdn’ As of NCO version 4.4.0 (January, 2014), the ‘--hdn’ or ‘--hidden’ options print hidden (aka special) attributes. This is equivalent to ‘ncdump -s’. Hid- den attributes include: _Format, _DeflateLevel, _Shuffle, _Storage, _ ChunkSizes, _Endianness, _Fletcher32, and _NOFILL. Previously ncks ig- nored all these attributes in CDL/XML modes. Now it prints these attributes as appropriate in all modes. As of NCO version 4.4.6 (September, 2014), ‘--hdn’ also prints the extended file format (i.e., the format of the file or server sup- plying the data) as _SOURCE_FORMAT. As of NCO version 4.6.1 (August, 2016), ‘--hdn’ also prints the hidden attributes _NCProperties, _IsNetcdf4, and _ SuperblockVersion for netCDF4 files so long as NCO is linked against netCDF library version 4.4.1 or later. Users are referred to the Unidata netCDF Doc- umentation (http://www.unidata.ucar.edu/software/netcdf/docs), or the man pages for ncgen or ncdump, for detailed descriptions of the meanings of these hidden attributes. ‘--cdl’ As of NCO version 4.3.3 (July, 2013), ncks can print extracted data and meta- data to screen (i.e., stdout) as valid CDL (network Common data form De- scription Language). CDL is the human-readable “lingua franca” of netCDF ingested by ncgen and excreted by ncdump. As of NCO version 4.6.9 (Septem- ber, 2017), ncks prints CDL by default, and the “traditional” mode must be explicitly selected with ‘--trd’. Compare ncks “traditional” with CDL print- ing: zender@roulee:~$ ncks --trd -v one ~/nco/data/in.nc one: type NC_FLOAT, 0 dimensions, 1 attribute, chunked? no, compressed? no, packed? no one size (RAM) = 1*sizeof(NC_FLOAT) = 1*4 = 4 bytes one attribute 0: long_name, size = 3 NC_CHAR, value = one Chapter 4: Reference Manual 273 one = 1 zender@roulee:~$ ncks --cdl -v one ~/nco/data/in.nc netcdf in { variables: float one ; one:long_name = "one" ; data: one = 1 ; } // group / Users should note the NCO’s CDL mode outputs successively more verbose additional diagnostic information in CDL comments as the level of debugging increases from zero to two. For example printing the above with ‘-D 2’ yields zender@roulee:~$ ncks -D 2 --cdl -v one ~/nco/data/in.nc netcdf in { // ncgen -k classic -b -o in.nc in.cdl variables: float one ; // RAM size = 1*sizeof(NC_FLOAT) = 1*4 = 4 bytes, ID = 147 one:long_name = "one" ; // char data: one = 1 ; } // group / ncgen converts CDL-mode output into a netCDF file: ncks -v one ~/nco/data/in.nc > ~/in.cdl ncgen -k netCDF-4 -b -o ~/in.nc ~/in.cdl ncks -v one ~/in.nc The HDF4 version of ncgen, often named hncgen, h4_ncgen, or ncgen-hdf, (usually) converts netCDF3 CDL into an HDF file: cd ~/nco/data ncgen -b -o hdf.hdf hdf.cdl # HDF ncgen is sometimes named...ncgen ncgen -b -o in.hdf in.cdl # Fails: Some valid netCDF CDL breaks HDF ncgen hncgen -b -o hdf.hdf hdf.cdl # HDF ncgen is hncgen in some RPM packages h4_ncgen -b -o hdf.hdf hdf.cdl # HDF ncgen is h4_ncgen in Anaconda packages ncgen-hdf -b -o hdf.hdf hdf.cdl # HDF ncgen is ncgen-hdf in some Debian packages hdp dumpsds hdf.hdf # ncdump/h5dump-equivalent for HDF4 h4_ncdump dumpsds hdf.hdf # ncdump/h5dump-equivalent for HDF4 Note that HDF4 does not support netCDF-style groups, so the above com- mands fail when the input file contains groups. Only netCDF4 and HDF5 support groups. In our experience the HDF ncgen command, by whatever 274 NCO 5.0.2-alpha07 User Guide name installed, is not robust and fails on many valid netCDF3 CDL con- structs. The HDF4 version of ncgen will definitely fail on the default NCO input file nco/data/in.cdl. The NCO source code distribution provides nco/data/hdf.cdl that works with the HDF4 version of ncgen, and can be used to test HDF files. ‘--mk_rec_dmn dim’ Change existing dimension dim to a record dimension in the output file. This is the most straightforward way of changing a dimension to a/the record dimen- sion, and works fine in most cases. See Section 4.5 [ncecat netCDF Ensemble Concatenator], page 253, and Section 4.9 [ncpdq netCDF Permute Dimensions Quickly], page 289, for other methods of changing variable dimensionality, in- cluding the record dimension. ‘-H’ Toggle (turn-on or turn-off) default behavior of printing data (not metadata) to screen or copying data to disk. Also activated using ‘--print’ or ‘--prn’. By default ncks prints all metadata but no data to screen when no netCDF output- file is specified. And if output-file is specified, ncks copies all metadata and all data to it. In other words, the printing/copying default is context-sensitive, and ‘-H’ toggles the default behavior. Hence, use ‘-H’ to turn-off copying data (not metadata) to an output file. (It is occasionally useful to write all metadata to a file, so that the file has allocated the required disk space to hold thedata, yet to withold writing the data itself). And use ‘-H’ to turn-on printing data (not metadata) to screen. Unless otherwise specified (with -s), each element of the data hyperslab prints on a separate line containing the names, indices, and, values, if any, of all of the variables dimensions. The dimension and variable indices refer to the location of the corresponding data element with respect to the variable as stored on disk (i.e., not the hyperslab). % ncks --trd -C -v three_dmn_var in.nc lat[0]=-90 lev[0]=100 lon[0]=0 three_dmn_var[0]=0 lat[0]=-90 lev[0]=100 lon[1]=90 three_dmn_var[1]=1 lat[0]=-90 lev[0]=100 lon[2]=180 three_dmn_var[2]=2 ... lat[1]=90 lev[2]=1000 lon[1]=90 three_dmn_var[21]=21 lat[1]=90 lev[2]=1000 lon[2]=180 three_dmn_var[22]=22 lat[1]=90 lev[2]=1000 lon[3]=270 three_dmn_var[23]=23 Printing the same variable with the ‘-F’ option shows the same variable indexed with Fortran conventions % ncks -F -C -v three_dmn_var in.nc lon(1)=0 lev(1)=100 lat(1)=-90 three_dmn_var(1)=0 lon(2)=90 lev(1)=100 lat(1)=-90 three_dmn_var(2)=1 lon(3)=180 lev(1)=100 lat(1)=-90 three_dmn_var(3)=2 ... Printing a hyperslab does not affect the variable or dimension indices since these indices are relative to the full variable (as stored in the input file), and the input file has not changed. However, if the hyperslab is saved to anoutput file and those values are printed, the indices will change: Chapter 4: Reference Manual 275 % ncks --trd -H -d lat,90.0 -d lev,1000.0 -v three_dmn_var in.nc out.nc ... lat[1]=90 lev[2]=1000 lon[0]=0 three_dmn_var[20]=20 lat[1]=90 lev[2]=1000 lon[1]=90 three_dmn_var[21]=21 lat[1]=90 lev[2]=1000 lon[2]=180 three_dmn_var[22]=22 lat[1]=90 lev[2]=1000 lon[3]=270 three_dmn_var[23]=23 % ncks --trd -C -v three_dmn_var out.nc lat[0]=90 lev[0]=1000 lon[0]=0 three_dmn_var[0]=20 lat[0]=90 lev[0]=1000 lon[1]=90 three_dmn_var[1]=21 lat[0]=90 lev[0]=1000 lon[2]=180 three_dmn_var[2]=22 lat[0]=90 lev[0]=1000 lon[3]=270 three_dmn_var[3]=23 ‘--jsn, --json’ As of NCO version 4.6.2 (November, 2016), ncks can print extracted metadata and data to screen (i.e., stdout) as JSON, JavaScript Object Notation, defined here (http://www.json.org). ncks supports JSON output more completely, flexibly, and robustly than any other tool to our knowledge. With ncks one can translate entire netCDF3 and netCDF4 files into JSON, including metadata and data, using all NCO’s subsetting and hyperslabbing capabilities. NCO uses a JSON format we developed ourselves, during a year of discussion among inter- ested researchers. Some refer to this format as NCO-JSON, to disambiguate it from other JSON formats for netCDF data. Other projects have since adopted, and some can generate, NCO-JSON. Projects that support NCO-JSON in- clude ERDDAP (https://coastwatch.pfeg.noaa.gov/erddap/index.html, choose output filetype .ncoJson from this table (https://coastwatch.pfeg. noaa.gov/erddap/griddap/documentation.html#fileType)) and CF-JSON (http://cf-json.org). Behold JSON output in default mode: zender@aerosol:~$ ncks --jsn -v one ~/nco/data/in.nc { "variables": { "one": { "type": "float", "attributes": { "long_name": "one" }, "data": 1.0 } } } NCO converts to (using commonsense rules) and prints all NC TYPEs as one of three atomic types distinguishable as JSON values: float, string, and int2. Floating-point types (NC_FLOAT and NC_DOUBLE) are printed with a decimal point and at least one signficant digit following the decimal point, e.g., 1.0 2 The JSON boolean atomic type is not (yet) supported as there is no obvious netCDF-equivalent to this type. 276 NCO 5.0.2-alpha07 User Guide rather than 1. or 1. Integer types (e.g., NC_INT, NC_UINT64) are printed with no decimal point. String types (NC_CHAR and NC_STRING) are enclosed in double- quotes. The JSON specification allows many possible output formats for netCDF files. NCO developers implemented a working prototype in Octoboer, 2016 and, after discussing options with interested parties here (https://sourceforge.net/p/ nco/discussion/9829/thread/8c4d7e72), finalized the emitted JSON syntax a few weeks later. The resulting JSON backend supports three levels of pedantic- ness, ordered from more concise, flexible, and human-readable to more verbose, restrictive, and 1-to-1 reproducible. JSON-specific switches access these modes and other features. Each JSON configuration option automatically triggers JSON printing, so that specifying ‘--json’ in addition to a JSON configuration option is redundant and unnecessary. Request a specific format level with the pedantic level argument tothe ‘--jsn_fmt lvl’ option. As of NCO version 4.6.3 (December, 2016), the op- tion formerly known as ‘--jsn_att_fmt’ was renamed simply ‘--jsn_fmt’. The more general name reflects the fact that the option controls all JSON formatting, not just attribute formatting. As of version 4.6.3, NCO defaults to demarcate inner dimensions of variable data with (nested) square brackets rather than printing data as an unrolled single dimensional array. An array with C-ordered dimensionality [2,3,4] prints as: % ncks --jsn -v three_dmn_var ~/nco/data/in.nc ... "data": [[[0.0, 1.0, 2.0, 3.0], [4.0, 5.0, 6.0, 7.0], [8.0, 9.0, 10.0,11.0]], [[12.0, 13.0, 14.0, 15.0], [16.0, 17.0, 18.0, 19.0], [20.0,21.0, 22.0, 23.0]]] ... % ncks --jsn_fmt=4 -v three_dmn_var ~/nco/data/in.nc ... "data": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0,22.0, 23.0] ... One can recover the former behavior (and omit the brackets) by adding four to the base pedantic level lvl (as shown above). Besides the potential offset of four, lvl may take one of three values between 0–2: • lvl = 0 is the default mode, and is also explicitly selectable with ‘--jsn_fmt=0’. All values are output without the original NC TYPE token. This allows attributes to print as JSON name-value pairs, rather than as more complex objects: % ncks --jsn_fmt=0 -v att_var ~/nco/data/in_grp.nc ... "att_var": { "shape": ["time"], "type": "float", "attributes": { "byte_att": [0, 1, 2, 127, -128, -127, -2, -1], "char_att": "Sentence one.\nSentence two.\n", "short_att": 37, "int_att": 73, Chapter 4: Reference Manual 277 "long_att": 73, "float_att": [73.0, 72.0, 71.0, 70.010, 69.0010, 68.010, 67.010], "double_att": [73.0, 72.0, 71.0, 70.010, 69.0010, 68.010, 67.0100010] }, "data": [10.0, 10.10, 10.20, 10.30, 10.40101, 10.50, 10.60, 10.70, 10.80, 10.990] ... This least pedantic mode produces the most easily read results, and suf- fices for many (most?) purposes. Any downstream parser is expected to assign an appropriate type as indicated by JSON syntax rules. Because the original attributes’ NC_TYPE are not output, a downstream parser may not exactly reproduce the input file datatypes. For example, whether the orig- inal attribute string was stored as NC_CHAR or NC_STRING will be unknown to a downstream parser. Distinctions between NC_FLOAT and NC_DOUBLE are similarly lost, as are all distinctions among the integer types. In our experience, these distinctions are immaterial for attributes, which are intended for metadata not for large-scale storage. Type-distinctions can, however, significantly impact the size of variable data, responsible for nearly all the storage required by datasets. For instance, storing or transferring an NC_SHORT field as NC_DOUBLE would waste a factor of four in space or bandwidth. This is why NCO always prints the NC_TYPE of variable data. Downstream parsers can (but are not required to) take advantage of the variable’s NC_TYPE to choose the most efficient storage type. The Shape member of the variable object is an ordered array of dimension names such as "shape": ["lat","lon"], similar to its use in NcML. Each name corresponds to a previously defined Dimension object that, taken together, define the rank, shape, and size of the variable. Variables are assumed to be scalar by default. Hence the Shape member is mandatory for arrays, and is always omitted for scalars (by contrast, NcML requires an empty shape string to indicate scalars). • lvl = 1 is a medium-pedantic level that prints all attributes as objects (with explicit types) unless the attribute type match the simplest default JSON value types. In other words, attributes of type NC_FLOAT, NC_CHAR, NC_SHORT, and NC_BYTE are printed as objects with an explicit type so that parsers do not use the default type. Attributes of type NC_DOUBLE, NC_STRING, and NC_INT are printed as JSON arrays, as in the lvl = 0 above: % ncks --jsn_fmt=1 -v att_var ~/nco/data/in.nc ... "att_var": { "shape": ["time"], "type": "float", "attributes": { "byte_att": { "type": "byte", "data": [0, 1, 2, 127, -128, -127, -2, -1]}, "char_att": "Sentence one.\nSentence two.\n", "short_att": { "type": "short", "data": 37}, "int_att": 73, 278 NCO 5.0.2-alpha07 User Guide "long_att": 73, "float_att": [73.0, 72.0, 71.0, 70.010, 69.0010, 68.010, 67.010], "double_att": { "type": "double", "data": [73.0, 72.0, 71.0, 70.010, 69.0010, 68.010, 67.0100010]} }, "data": [10.0, 10.10, 10.20, 10.30, 10.40101, 10.50, 10.60, 10.70, 10.80, 10.990] ... The attributes of type NC_BYTE, NC_SHORT, and NC_DOUBLE are printed as JSON objects that comprise an NC_TYPE and a value list, because their values could conceivably not be representable, or would waste space, if interpreted as NC_INT or NC_FLOAT, respectively. All other attributes may be naturally mapped to the type indicated by the JSON syntax of the value, where numbers are assumed to correspond to NC_FLOAT for floating- point, NC_INT for integers, and NC_CHAR or NC_STRING for strings. This minimal increase in verbosity allows a downstream parser to re-construct the original dataset with nearly identical attributes types to the original. • lvl = 2 is the most pedantic mode, and should be used when preserving all input types (e.g., to ensure exact reproducibility of the input file) is important. This mode always prints attributes as JSON objects with a type value so that any downstream parser can (though it need not) guarantee exact reproduction of the original dataset: % ncks --jsn_fmt=2 -v att_var ~/nco/data/in.nc ... "att_var": { "shape": ["time"], "type": "float", "attributes": { "byte_att": { "type": "byte", "data": [0, 1, 2, 127, -128, -127, -2, -1]}, "char_att": { "type": "char", "data": "Sentence one.\nSentence two.\n"}, "short_att": { "type": "short", "data": 37}, "int_att": { "type": "int", "data": 73}, "long_att": { "type": "int", "data": 73}, "float_att": { "type": "float", "data": [73.0, 72.0, 71.0, 70.010, 69.0010, 68.010, 67.010]}, "double_att": { "type": "double", "data": [73.0, 72.0, 71.0, 70.010, 69.0010, 68.010, 67.0100010]} }, "data": [10.0, 10.10, 10.20, 10.30, 10.40101, 10.50, 10.60, 10.70, 10.80, 10.990] ... That ncks produces correct translations of for all supported datatypes may be verified by a JSON syntax checker command like jsonlint. Please let us know how to improve JSON features for your application. There ‘-M’ Turn-on printing to screen or turn-off copying global and group metadata. This includes file summary information and global and group attributes.--Mtd Also‘ ’ and ‘--Metadata’. By default ncks prints global metadata to screen if no netCDF output file and no variable extraction list is specified-v (with‘ ’). Use ‘-M’ to print global metadata to screen if a netCDF output is specified, or if Chapter 4: Reference Manual 279 a variable extraction list is specified (with-v ‘ ’). Use ‘-M’ to turn-off copying of global and group metadata when copying, subsetting, or appending to an output file. The various combinations of printing switches can be confusing. In an attempt to anticipate what most users want to do, ncks uses context-sensitive defaults for printing. Our goal is to minimize the use of switches required to accomplish the common operations. We assume that users creating a new file or overwriting (e.g., with ‘-O’) an existing file usually wish to copy all global and variable- specific attributes to the new file. In contrast, we assume that users appending (e.g., with ‘-A’ an explicit variable list from one file to another usually wish to copy only the variable-specific attributes to the output file. The switches ‘-H’, ‘-M’, and ‘-m’ switches are implemented as toggles which reverse the default behavior. The most confusing aspect of this is that ‘-M’ inhibits copying global metadata in overwrite mode and causes copying of global metadata in append mode. ncks in.nc # Print VAs and GAs ncks -v one in.nc # Print VAs not GAs ncks -M -v one in.nc # Print GAs only ncks -m -v one in.nc # Print VAs only ncks -M -m -v one in.nc # Print VAs and GAs ncks -O in.nc out.nc # Copy VAs and GAs ncks -O -v one in.nc out.nc # Copy VAs and GAs ncks -O -M -v one in.nc out.nc # Copy VAs not GAs ncks -O -m -v one in.nc out.nc # Copy GAs not VAs ncks -O -M -m -v one in.nc out.nc # Copy only data (no atts) ncks -A in.nc out.nc # Append VAs and GAs ncks -A -v one in.nc out.nc # Append VAs not GAs ncks -A -M -v one in.nc out.nc # Append VAs and GAs ncks -A -m -v one in.nc out.nc # Append only data (no atts) ncks -A -M -m -v one in.nc out.nc # Append GAs not VAs where VAs and GAs denote variable and group/global attributes, respectively. ‘-m’ Turn-on printing to screen or turn-off copying variable metadata. Using-m ‘ ’ will print variable metadata to screen (similar to ncdump -h). This displays all metadata pertaining to each variable, one variable at a time. This includes information on the storage properties of the variable, such as whether it em- ploys chunking, compression, or packing. Also activated using ‘--mtd’ and ‘--metadata’. The ncks default behavior is to print variable metadata to screen if no netCDF output file is specified. Use ‘-m’ to print variable metadata to screen if a netCDF output is specified. Also use ‘-m’ to turn-off copying of variable metadata to an output file. ‘--no_blank’ Print numeric representation of missing values. As of NCO version 4.2.2 (Octo- ber, 2012), NCO prints missing values as blanks (i.e., the underscore character ‘_’) by default. To enable the old behavior of printing the numeric repre- 280 NCO 5.0.2-alpha07 User Guide sentation of missing values (e.g., 1.0e36), use the ‘--no_blank’ switch. Also activated using ‘--noblank’ or ‘--no-blank’. ‘-P’ Print data, metadata, and units to screen. The ‘-P’ switch is a convenience abbreviation for ‘-C -H -M -m -u’. Also activated using ‘--print’ or ‘--prn’. This set of switches is useful for exploring file contents. ‘--prn_fl print-file’ Activate printing formatted output to file print-file. Also ‘--print_file’, ‘--fl_prn’, and ‘--file_print’. One can achieve the same result by redirect- ing stdout to a named file. However, it is slightly faster to print formatted output directly to a file than to stdout: ncks --fl_prn=foo.txt --jsn in.nc ‘-Q’ Print quietly, meaning omit dimension names, indices, and coordinate values when printing arrays. Variable (not dimension) indices are printed. Variable names appear flush left in the output: zender@roulee:~$ ncks --trd -Q -v three_dmn_rec_var -C -H ~/nco/data/in.nc three_dmn_rec_var[0]=1 ... This helps locate specific variables in lists with many variables and different dimensions. See also the ‘-V’ option, which omits all names and indices and prints only variable values. ‘-q’ Quench (turn-off) all printing to screen. This overrides the setting of all print- related switches, equivalent to -H -M -m when in single-file printing mode. When invoked with -R (see Section 3.9 [Retaining Retrieved Files], page 42), ncks automatically sets -q. This allows ncks to retrieve remote files without auto- matically trying to print them. Also ‘--quench’. ‘--rad’ Retain all dimensions. When invoked with --rad (Retain All Dimensions), ncks copies each dimension in the input file to the output file, regardless of whether the dimension is utilized by any variables. Normally ncks discards “orphan dimensions”, i.e., dimensions not referenced by any variables. This switch allows users to keep non-referenced dimensions in the workflow. When invoked in printing mode, causes orphaned dimensions to be printed (they are not printed by default). Also ‘--retain_all_dimensions’, ‘--orphan_dimensions’, and ‘--rph_dmn’. ‘-s format’ String format for text output. Accepts C language escape sequences and printf() formats. Also ‘--string’ and ‘--sng_fmt’. This option is only in- tended for use with traditional (TRD) printing, and thus automatically invokes the ‘--trd’ switch. ‘--fmt_val format’ Supply a printf()-style format for printed output, i.e., in CDL, JSON, TRD, or XML modes. Also ‘--val_fmt’ and ‘--value_format’. One use for this option is to reduce the printed precision of floating point values: # Default printing of original double precision values Chapter 4: Reference Manual 281 # 0.0,0.1,0.12,0.123,0.1234,0.12345,0.123456,0.1234567,0.12345678,0.123456789 % ncks -C -v ppc_dbl ~/nco/data/in.nc ... ppc_dbl = 0, 0.1, 0.12, 0.123, 0.1234, 0.12345, 0.123456, 0.1234567, 0.12345678, 0.123456789 ; ... # Restrict printing to three digits after the decimal % ncks --fmt_val=%.3f -C -v ppc_dbl ~/nco/data/in.nc ... ppc_dbl = 0., 0.1, 0.12, 0.123, 0.123, 0.123, 0.123, 0.123, 0.123, 0.123 ; ... The supplied format only applies to floating point variable valuesNC_FLOAT ( or NC_DOUBLE), and not to other types or to attributes. For reference, the default printf() format for CDL, JSON, TRD, and XML modes is %#.7gf, %#.7g, %g, and %#.7g, respectively, for single-precision data, and, for double-precision data is %#.15g, %#.15g, %.12g, and %#.15g, respectively. NCO introduced this feature in version 4.7.3 (March, 2018). We would appreciate your feedback on whether and how to extend this feature to make it more useful. ‘--secret’ Print summary of ncks hidden features. These hidden or secret features are used mainly by developers. They are not supported for general use and may change at any time. This demonstrates conclusively that I cannot keep a secret. Also ‘--ssh’ and ‘--scr’. ‘--trd, --traditional’ From 1995–2017 ncks dumped the ASCII text representation of netCDF files in what we now call “traditional” mode. Much of this manual contains output printed in traditional mode, which places one value per line, with complete dimensional information. Traditional-mode metadata output includes lower- level information, such as RAM usage and internal variable IDs, than CDL. While this is useful for some developers and user, CDL has, over the years, become more useful than traditional mode for most users. As of NCO version 4.6.9 (September, 2017) CDL became the default printing mode. Traditional printing mode is accessed via the ‘--trd’ option. ‘-u’ Toggle the printing of a variable’s units attribute, if any, with its values. Also ‘--units’. ‘-V’ Print variable values only. Do not print variable and dimension names, indices, and coordinate values when printing arrays. zender@roulee:~$ ncks --trd -V -v three_dmn_rec_var -C -H ~/nco/data/in.nc 1 ... See also the ‘-Q’ option, which prints variable names and indices, but not di- mension names, indices, or coordinate values when printing arrays. Using ‘-V’ is the same as specifying ‘-Q --no_nm_prn’. ‘--xml, --ncml’ As of NCO version 4.3.3 (July, 2013), ncks can print extracted data and meta- data to screen (i.e., stdout) as XML in NcML, the netCDF Markup Language. 282 NCO 5.0.2-alpha07 User Guide ncks supports XML more completely than ‘ncdump -x’. With ncks one can translate entire netCDF3 and netCDF4 files into NcML, including metadata and data, using all NCO’s subsetting and hyperslabbing capabilities. Compare ncks “traditional” with XML printing: zender@roulee:~$ ncks --trd -v one ~/nco/data/in.nc one: type NC_FLOAT, 0 dimensions, 1 attribute, chunked? no, compressed? no, packed? no one size (RAM) = 1*sizeof(NC_FLOAT) = 1*4 = 4 bytes one attribute 0: long_name, size = 3 NC_CHAR, value = one one = 1 zender@roulee:~$ ncks --xml -v one ~/nco/data/in.nc zender@roulee:~$ ncks --xml_spr_nmr=’, ’ -v one_dmn_rec_var in.nc ... 4.8.2 Filters for ncks We encourage the use of standard UNIX pipes and filters to narrow the verbose output of ncks into more precise targets. For example, to obtain an uncluttered listing of the variables in a file try ncks --trd -m in.nc | grep -E ’: type’ | cut -f 1 -d ’ ’ | sed ’s/://’ | sort A Bash user could alias the previous filter to the shell command ncvarlst as shown below. More complex examples could involve command line arguments. For example, a user may frequently be interested in obtaining the value of an attribute, e.g., for textual file examination or for passing to another shell command. Say the attribute is purpose, the variable is z, and the file is in.nc. In this example, ncks --trd -m -v z is too verbose so a robust grep and cut filter is desirable, such as ncks --trd -M -m in.nc | grep -E -i "^z attribute [0-9]+: purpose" | cut -f 11- -d ’ ’ | sort The filters are clearly too complex to remember on-the-fly so the entire procedure could be implemented as a shell command or function called, say, ncattget function ncattget { ncks --trd -M -m ${3} | grep -E -i "^${2} attribute [0-9]+: ${1}" | cut -f 11- -d ’ ’ | sort ; } The shell ncattget is invoked with three arugments that are, in order, the names of the attribute, variable, and file to examine. Global attributes are indicated by using a variable name of global. This definition yields the following results % ncattget purpose z in.nc Height stored with a monotonically increasing coordinate % ncattget Purpose Z in.nc Height stored with a monotonically increasing coordinate % ncattget history z in.nc % ncattget history global in.nc History global attribute. 284 NCO 5.0.2-alpha07 User Guide Note that case sensitivity has been turned off for the variable and attribute names (and could be turned on by removing the ‘-i’ switch to grep). Furthermore, extended regular expressions may be used for both the variable and attribute names. The next two commands illustrate this by searching for the values of attribute purpose in all variables, and then for all attributes of the variable z: % ncattget purpose .+ in.nc 1-D latitude coordinate referred to by geodesic grid variables 1-D longitude coordinate referred to by geodesic grid variables ... % ncattget .+ Z in.nc Height Height stored with a monotonically increasing coordinate meter Extended filters are best stored as shell commands if they are used frequently. Shell commands may be re-used when they are defined in shell configuration files. These filesare usually named .bashrc, .cshrc, and .profile for the Bash, Csh, and Sh shells, respec- tively. # NB: Untested on Csh, Ksh, Sh, Zsh! Send us feedback! # Bash shell (/bin/bash), .bashrc examples # ncattget $att_nm $var_nm $fl_nm : What attributes does variable have? function ncattget { ncks --trd -M -m ${3} | grep -E -i "^${2} attribute [0-9]+: ${1}" | cut -f 11- -d ’ ’ | sort ; } # ncunits $att_val $fl_nm : Which variables have given units? function ncunits { ncks --trd -m ${2} | grep -E -i " attribute [0-9]+: units.+ ${1}" | cut -f 1 -d ’ ’ | sort ; } # ncavg $var_nm $fl_nm : What is mean of variable? function ncavg { ncwa -y avg -O -C -v ${1} ${2} ~/foo.nc ; ncks --trd -H -C -v ${1} ~/foo.nc | cut -f 3- -d ’ ’ ; } # ncavg $var_nm $fl_nm : What is mean of variable? function ncavg { ncap2 -O -C -v -s "foo=${1}.avg();print(foo)" ${2} ~/foo.nc | cut -f 3- -d ’ ’ ; } # ncdmnlst $fl_nm : What dimensions are in file? function ncdmnlst { ncks --cdl -m ${1} | cut -d ’:’ -f 1 | cut -d ’=’ -s -f 1 ; } # ncvardmnlst $var_nm $fl_nm : What dimensions are in a variable? function ncvardmnlst { ncks --trd -m -v ${1} ${2} | grep -E -i "^${1} dimension [0-9]+: " | cut -f 4 -d ’ ’ | sed ’s/,//’ ; } # ncvardmnlatlon $var_nm $fl_nm : Does variable contain both lat and lon dimensions? function ncvardmnlatlon { flg=‘ncks -C -v ${1} -m ${2} | grep -E -i "${1}\(" | grep -E "lat.*lon|lon.*lat"‘ ; [[ ! -z "$flg" ]] && echo "Yes, ${1} has both lat and lon dimensions" || echo "No, ${1} does not have both lat and lon dimensions" } # ncdmnsz $dmn_nm $fl_nm : What is dimension size? function ncdmnsz { ncks --trd -m -M ${2} | grep -E -i ": ${1}, size =" | cut -f 7 -d ’ ’ | uniq ; } # ncgrplst $fl_nm : What groups are in file? function ncgrplst { ncks -m ${1} | grep ’group:’ | cut -d ’:’ -f 2 | cut -d ’ ’ -f 2 | sort ; } # ncvarlst $fl_nm : What variables are in file? function ncvarlst { ncks --trd -m ${1} | grep -E ’: type’ | cut -f 1 -d ’ ’ | sed ’s/://’ | sort ; } # ncmax $var_nm $fl_nm : What is maximum of variable? function ncmax { ncwa -y max -O -C -v ${1} ${2} ~/foo.nc ; ncks --trd -H -C -v ${1} ~/foo.nc | cut -f 3- -d ’ ’ ; } # ncmax $var_nm $fl_nm : What is maximum of variable? function ncmax { ncap2 -O -C -v -s "foo=${1}.max();print(foo)" ${2} ~/foo.nc | cut -f 3- -d ’ ’ ; } # ncmdn $var_nm $fl_nm : What is median of variable? function ncmdn { ncap2 -O -C -v -s "foo=gsl_stats_median_from_sorted_data(${1}.sort());print(foo)" ${2} ~/foo.nc | cut -f 3- -d ’ ’ ; } Chapter 4: Reference Manual 285 # ncmin $var_nm $fl_nm : What is minimum of variable? function ncmin { ncap2 -O -C -v -s "foo=${1}.min();print(foo)" ${2} ~/foo.nc | cut -f 3- -d ’ ’ ; } # ncrng $var_nm $fl_nm : What is range of variable? function ncrng { ncap2 -O -C -v -s "foo_min=${1}.min();foo_max=${1}.max();print(foo_min,\"%f\");print(\" to \");print(foo_max,\"%f\")" ${2} ~/foo.nc ; } # ncmode $var_nm $fl_nm : What is mode of variable? function ncmode { ncap2 -O -C -v -s "foo=gsl_stats_median_from_sorted_data(${1}.sort());print(foo)" ${2} ~/foo.nc | cut -f 3- -d ’ ’ ; } # ncrecsz $fl_nm : What is record dimension size? function ncrecsz { ncks --trd -M ${1} | grep -E -i "^Root record dimension 0:" | cut -f 10- -d ’ ’ ; } # nctypget $var_nm $fl_nm : What type is variable? function nctypget { ncks --trd -m -v ${1} ${2} | grep -E -i "^${1}: type" | cut -f 3 -d ’ ’ | cut -f 1 -d ’,’ ; } # Csh shell (/bin/csh), .cshrc examples (derive others from Bash definitions): ncattget() { ncks --trd -M -m -v ${3} | grep -E -i "^${2} attribute [0-9]+: ${1}" | cut -f 11- -d ’ ’ | sort ; } ncdmnsz() { ncks --trd -m -M ${2} | grep -E -i ": ${1}, size =" | cut -f 7 -d ’ ’ | uniq ; } ncvarlst() { ncks --trd -m ${1} | grep -E ’: type’ | cut -f 1 -d ’ ’ | sed ’s/://’ | sort ; } ncrecsz() { ncks --trd -M ${1} | grep -E -i "^Record dimension:" | cut -f 8- -d ’ ’ ; } # Sh shell (/bin/sh), .profile examples (derive others from Bash definitions): ncattget() { ncks --trd -M -m ${3} | grep -E -i "^${2} attribute [0-9]+: ${1}" | cut -f 11- -d ’ ’ | sort ; } ncdmnsz() { ncks --trd -m -M ${2} | grep -E -i ": ${1}, size =" | cut -f 7 -d ’ ’ | uniq ; } ncvarlst() { ncks --trd -m ${1} | grep -E ’: type’ | cut -f 1 -d ’ ’ | sed ’s/://’ | sort ; } ncrecsz() { ncks --trd -M ${1} | grep -E -i "^Record dimension:" | cut -f 8- -d ’ ’ ; } EXAMPLES View all data in netCDF in.nc, printed with Fortran indexing conventions: ncks -F in.nc Copy the netCDF file in.nc to file out.nc. ncks in.nc out.nc Now the file out.nc contains all the data from in.nc. There are, however, two differences between in.nc and out.nc. First, the history global attribute (see Section 3.43 [History Attribute], page 144) will contain the command used to create out.nc. Second, the variables in out.nc will be defined in alphabetical order. Of course the internal storage of variable in a netCDF file should be transparent to the user, but there are cases when alphabetizing a file is useful (see description of -a switch). Copy all global attributes (and no variables) from in.nc to out.nc: ncks -A -x ~/nco/data/in.nc ~/out.nc The ‘-x’ switch tells NCO to use the complement of the extraction list (see Section 3.12 [Subsetting Files], page 48). Since no extraction list is explicitly specified (with ‘-v’), the default is to extract all variables. The complement of all variables is no variables. Without any variables to extract, the append (‘-A’) command (see Section 2.4 [Appending Variables], page 19) has only to extract and copy (i.e., append) global attributes to the output file. Copy/append metadata (not data) from variables in one file to variables in a second file. When copying/subsetting/appending files (as opposed to printing them), the copying of 286 NCO 5.0.2-alpha07 User Guide data, variable metadata, and global/group metadata are now turned OFF by ‘-H’, ‘-m’, and ‘-M’, respectively. This is the opposite sense in which these switches work when printing a file. One can use these switches to easily replace data or metadata in one file withdataor metadata from another: # Extract naked (data-only) copies of two variables ncks -h -M -m -O -C -v one,three_dmn_rec_var ~/nco/data/in.nc ~/out.nc # Change values to be sure original values are not copied in following step ncap2 -O -v -s ’one*=2;three_dmn_rec_var*=0’ ~/nco/data/in.nc ~/in2.nc # Append in2.nc metadata (not data!) to out.nc ncks -A -C -H -v one,three_dmn_rec_var ~/in2.nc ~/out.nc Variables in out.nc now contain data (not metadata) from in.nc and metadata (not data) from in2.nc. Print variable three_dmn_var from file in.nc with default notations. Next print three_ dmn_var as an un-annotated text column. Then print three_dmn_var signed with very high precision. Finally, print three_dmn_var as a comma-separated list: % ncks --trd -C -v three_dmn_var in.nc lat[0]=-90 lev[0]=100 lon[0]=0 three_dmn_var[0]=0 lat[0]=-90 lev[0]=100 lon[1]=90 three_dmn_var[1]=1 ... lat[1]=90 lev[2]=1000 lon[3]=270 three_dmn_var[23]=23 % ncks --trd -s ’%f\n’ -C -v three_dmn_var in.nc 0.000000 1.000000 ... 23.000000 % ncks --trd -s ’%+16.10f\n’ -C -v three_dmn_var in.nc +0.0000000000 +1.0000000000 ... +23.0000000000 % ncks --trd -s ’%f, ’ -C -v three_dmn_var in.nc 0.000000, 1.000000, ..., 23.000000, Programmers will recognize these as the venerable C language printf() formatting strings. The second and third options are useful when pasting data into text files like reports or papers. See Section 4.2 [ncatted netCDF Attribute Editor], page 218, for more details on string formatting and special characters. As of NCO version 4.2.2 (October, 2012), NCO prints missing values as blanks (i.e., the underscore character ‘_’) by default: % ncks --trd -C -H -v mss_val in.nc lon[0]=0 mss_val[0]=73 lon[1]=90 mss_val[1]=_ lon[2]=180 mss_val[2]=73 lon[3]=270 mss_val[3]=_ Chapter 4: Reference Manual 287 % ncks -s ’%+5.1f, ’ -H -C -v mss_val in.nc +73.0, _, +73.0, _, To print the numeric value of the missing value instead of a blank, use the ‘--no_blank’ option. ncks prints in a verbose fashion by default and supplies a number of switches to pare- down (or even spruce-up) the output. The interplay of the ‘-Q’, ‘-V’, and (otherwise un- documented) ‘--no_nm_prn’ switches yields most desired verbosities: % ncks -v three_dmn_rec_var -C -H ~/nco/data/in.nc time[0]=1 lat[0]=-90 lon[0]=0 three_dmn_rec_var[0]=1 % ncks -Q -v three_dmn_rec_var -C -H ~/nco/data/in.nc three_dmn_rec_var[0]=1 % ncks -V -v three_dmn_rec_var -C -H ~/nco/data/in.nc 1 % ncks -Q --no_nm_prn -v three_dmn_rec_var -C -H ~/nco/data/in.nc 1 % ncks --no_nm_prn -v three_dmn_rec_var -C -H ~/nco/data/in.nc 1 -90 0 1 One dimensional arrays of characters stored as netCDF variables are automatically printed as strings, whether or not they are NUL-terminated, e.g., ncks -v fl_nm in.nc The %c formatting code is useful for printing multidimensional arrays of characters repre- senting fixed length strings ncks -s ’%c’ -v fl_nm_arr in.nc Using the %s format code on strings which are not NUL-terminated (and thus not technically strings) is likely to result in a core dump. Create netCDF out.nc containing all variables, and any associated coordinates, except variable time, from netCDF in.nc: ncks -x -v time in.nc out.nc As a special case of this, consider how to remove a variable such as time_bounds that is identified in a CF Convention (see Section 3.45 [CF Conventions], page 146) compliant ancillary_variables, bounds, climatology, coordinates, or grid_mapping attribute. NCO subsetting assumes the user wants all ancillary variables, axes, bounds and coordinates associated with all extracted variables (see Section 3.13 [Subsetting Coordinate Variables], page 53). Hence to exclude a ancillary_variables, bounds, climatology, coordinates, or grid_mapping variable while retaining the “parent” variable (here time), one must use the ‘-C’ switch: ncks -C -x -v time_bounds in.nc out.nc The ‘-C’ switch tells the operator NOT to necessarily include all the CF ancillary vari- ables, axes, bounds, and coordinates. Hence the output file will contain time and not time_bounds. 288 NCO 5.0.2-alpha07 User Guide Extract variables time and pressure from netCDF in.nc. If out.nc does not exist it will be created. Otherwise the you will be prompted whether to append to or to overwrite out.nc: ncks -v time,pressure in.nc out.nc ncks -C -v time,pressure in.nc out.nc The first version of the command creates an out.nc which contains time, pressure, and any coordinate variables associated with pressure. The out.nc from the second version is guaranteed to contain only two variables time and pressure. Create netCDF out.nc containing all variables from file in.nc. Restrict the dimensions of these variables to a hyperslab. The specified hyperslab is: the fifth value in dimension time; the half-open range lat > 0. in coordinate lat; the half-open range lon < 330. in coordinate lon; the closed interval 0.3 < band < 0.5 in coordinate band; and cross-section closest to 1000. in coordinate lev. Note that limits applied to coordinate values are specified with a decimal point, and limits applied to dimension indices do not have a decimal point See Section 3.16 [Hyperslabs], page 63. ncks -d time,5 -d lat,,0.0 -d lon,330.0, -d band,0.3,0.5 -d lev,1000.0 in.nc out.nc Assume the domain of the monotonically increasing longitude coordinate lon is 0 < lon < 360. Here, lon is an example of a wrapped coordinate. ncks will extract a hyperslab which crosses the Greenwich meridian simply by specifying the westernmost longitude as min and the easternmost longitude as max, as follows: ncks -d lon,260.0,45.0 in.nc out.nc For more details See Section 3.22 [Wrapped Coordinates], page 74. Chapter 4: Reference Manual 289 4.9 ncpdq netCDF Permute Dimensions Quickly SYNTAX ncpdq [-3] [-4] [-5] [-6] [-7] [-A] [-a [-]dim[,...]] [-C] [-c] [--cnk_byt sz_byt] [--cnk_csh sz_byt] [--cnk_dmn nm,sz_lmn] [--cnk_map map] [--cnk_min sz_byt] [--cnk_plc plc] [--cnk_scl sz_lmn] [-D dbg] [-d dim,[min][,[max][,[stride]]] [-F] [--fl_fmt fl_fmt] [-G gpe_dsc] [-g grp[,...]] [--glb ...] [-h] [--hdf] [--hdr_pad nbr] [--hpss] [-L dfl_lvl] [-l path] [-M pck_map] [--mrd] [--no_cll_msr] [--no_frm_trm] [--no_tmp_fl] [-O] [-o output-file] [-P pck_plc] [-p path] [--ppc ...] [-R] [-r] [--ram_all] [-t thr_nbr] [-U] [--unn] [-v var[,...]] [-X ...] [-x] input-file [output-file] DESCRIPTION ncpdq performs one (not both) of two distinct functions per invocation: packing or di- mension permutation. Without any options, ncpdq will pack data with default parameters. The ‘-a’ option tells ncpdq to permute dimensions accordingly, otherwise ncpdq will pack data as instructed/controlled by the ‘-M’ and ‘-P’ options. ncpdq is optimized to perform these actions in a parallel fashion with a minimum of time and memory. The pdq may stand for “Permute Dimensions Quickly”, “Pack Data Quietly”, “Pillory Dan Quayle”, or other silly uses. Packing and Unpacking Functions The ncpdq packing (and unpacking) algorithms are described in Section 4.1.12 [Methods and functions], page 175, and are also implemented in ncap2. ncpdq extends the func- tionality of these algorithms by providing high level control of the packing policy so that users can consistently pack (and unpack) entire files with one command. The user specifies the desired packing policy with the ‘-P’ switch (or its long option equivalents, ‘--pck_plc’ and ‘--pack_policy’) and its pck plc argument. Four packing policies are currently im- plemented: Packing (and Re-Packing) Variables [default] Definition: Pack unpacked variables, re-pack packed variables Alternate invocation: ncpack pck plc key values: ‘all_new’, ‘pck_all_new_att’ Packing (and not Re-Packing) Variables Definition: Pack unpacked variables, copy packed variables Alternate invocation: none pck plc key values: ‘all_xst’, ‘pck_all_xst_att’ 290 NCO 5.0.2-alpha07 User Guide Re-Packing Variables Definition: Re-pack packed variables, copy unpacked variables Alternate invocation: none pck plc key values: ‘xst_new’, ‘pck_xst_new_att’ Unpacking Definition: Unpack packed variables, copy unpacked variables Alternate invocation: ncunpack pck plc key values: ‘upk’, ‘unpack’, ‘pck_upk’ Equivalent key values are fully interchangeable. Multiple equivalent options are provided to satisfy disparate needs and tastes of NCO users working with scripts and from the command line. Regardless of the packing policy selected, ncpdq no longer (as of NCO version 4.0.4 in October, 2010) packs coordinate variables, or the special variables, weights, and other grid properties described in Section 3.45 [CF Conventions], page 146. Prior ncpdq versions treated coordinate variables and grid properties no differently from other variables. How- ever, coordinate variables are one-dimensional, so packing saves little space on large files, and the resulting files are difficult for humans to read. ncpdq will, of course, unpack co- ordinate variables and weights, for example, in case some other, non-NCO software packed them in the first place. Concurrently, Gaussian and area weights and other grid properties are often used to derive fields in re-inflated (unpacked) files, so packing such grid properties causes aconsid- erable loss of precision in downstream data processing. If users express strong wishes to pack grid properties, we will implement new packing policies. An immediate workaround for those needing to pack grid properties now, is to use the ncap2 packing functions or to rename the grid properties prior to calling ncpdq. We welcome your feedback. To reduce required memorization of these complex policy switches, ncpdq may also be invoked via a synonym or with switches that imply a particular policy. ncpack is a synonym for ncpdq and behaves the same in all respects. Both ncpdq and ncpack assume a default packing policy request of ‘all_new’. Hence ncpack may be invoked without any ‘-P’ switch, unlike ncpdq. Similarly, ncunpack is a synonym for ncpdq except that ncpack implicitly assumes a request to unpack, i.e., ‘-P pck_upk’. Finally, the ncpdq ‘-U’ switch (or its long option equivalents ‘--unpack’) requires no argument. It simply requests unpacking. Given the menagerie of synonyms, equivalent options, and implied options, a short list of some equivalent commands is appropriate. The following commands are equivalent for packing: ncpdq -P all_new, ncpdq --pck_plc=all_new, and ncpack. The following com- mands are equivalent for unpacking: ncpdq -P upk, ncpdq -U, ncpdq --pck_plc=unpack, and ncunpack. Equivalent commands for other packing policies, e.g., ‘all_xst’, follow by analogy. Note that ncpdq synonyms are subject to the same constraints and recommen- dations discussed in the secion on ncbo synonyms (see Section 4.3 [ncbo netCDF Binary Operator], page 225). That is, symbolic links must exist from the synonym to ncpdq, or else the user must define an alias. Chapter 4: Reference Manual 291 The ncpdq packing algorithms must know to which type particular types of input vari- ables are to be packed. The correspondence between the input variable type and the output, packed type, is called the packing map. The user specifies the desired packing map with the ‘-M’ switch (or its long option equivalents, ‘--pck_map’ and ‘--map’) and its pck map argument. Six packing maps are currently implemented: Pack Floating Precisions to NC_SHORT [default] Definition: Pack floating precision types to NC_SHORT Map: Pack [NC_DOUBLE,NC_FLOAT] to NC_SHORT Types copied instead of packed: [NC_INT64,NC_UINT64,NC_INT,NC_UINT,NC_ SHORT,NC_USHORT,NC_CHAR,NC_BYTE,NC_UBYTE] pck map key values: ‘flt_sht’, ‘pck_map_flt_sht’ Pack Floating Precisions to NC_BYTE Definition: Pack floating precision types to NC_BYTE Map: Pack [NC_DOUBLE,NC_FLOAT] to NC_BYTE Types copied instead of packed: [NC_INT64,NC_UINT64,NC_INT,NC_UINT,NC_ SHORT,NC_USHORT,NC_CHAR,NC_BYTE,NC_UBYTE] pck map key values: ‘flt_byt’, ‘pck_map_flt_byt’ Pack Higher Precisions to NC_SHORT Definition: Pack higher precision types to NC_SHORT Map: Pack [NC_DOUBLE,NC_FLOAT,NC_INT64,NC_UINT64,NC_INT,NC_UINT] to NC_SHORT Types copied instead of packed: [NC_SHORT,NC_USHORT,NC_CHAR,NC_BYTE,NC_ UBYTE] pck map key values: ‘hgh_sht’, ‘pck_map_hgh_sht’ Pack Higher Precisions to NC_BYTE Definition: Pack higher precision types to NC_BYTE Map: Pack [NC_DOUBLE,NC_FLOAT,NC_INT64,NC_UINT64,NC_INT,NC_UINT,NC_ SHORT,NC_USHORT] to NC_BYTE Types copied instead of packed: [NC_CHAR,NC_BYTE,NC_UBYTE] pck map key values: ‘hgh_byt’, ‘pck_map_hgh_byt’ Pack to Next Lesser Precision Definition: Pack each type to type of next lesser size Map: Pack [NC_DOUBLE,NC_INT64,NC_UINT64] to NC_INT. Pack [NC_FLOAT,NC_ INT,NC_UINT] to NC_SHORT. Pack [NC_SHORT,NC_USHORT] to NC_BYTE. Types copied instead of packed: [NC_CHAR,NC_BYTE,NC_UBYTE] pck map key values: ‘nxt_lsr’, ‘pck_map_nxt_lsr’ 292 NCO 5.0.2-alpha07 User Guide Pack Doubles to Floats Definition: Demote (via type-conversion, not packing) double-precision vari- ables to single-precision Map: Demote NC_DOUBLE to NC_FLOAT. Types copied instead of packed: All except NC_DOUBLE pck map key values: ‘dbl_flt’, ‘pck_map_dbl_flt’, ‘dbl_sgl’, ‘pck_map_dbl_sgl’ The dbl_flt map was introduced in NCO version 4.7.7 (September, 2018). Promote Floats to Doubles Definition: Promote (via type-conversion, not packing) single-precision vari- ables to double-precision Map: Promote NC_FLOAT to NC_DOUBLE. Types copied instead of packed: All except NC_FLOAT pck map key values: ‘flt_dbl’, ‘pck_map_flt_dbl’, ‘sgl_dbl’, ‘pck_map_sgl_dbl’ The flt_dbl map was introduced in NCO version 4.9.1 (December, 2019). The default ‘all_new’ packing policy with the default ‘flt_sht’ packing map reduces the typical NC_FLOAT-dominated file size by about 50%. ‘flt_byt’ packing reduces an NC_ DOUBLE-dominated file by about 87%. The “packing map” ‘pck_map_dbl_flt’ does a pure type-conversion (no packing is in- volved) from NC_DOUBLE to NC_FLOAT. The resulting variables are not packed, they are just single-precision floating point instead of double-precision floating point. This operation is irreversible, and no attributes are created, modified, or deleted for these variables. Note that coordinate and coordinate-like variables will not be demoted as best practices dictate maintaining coordinates in the highest possible precision. The “packing map” ‘pck_map_flt_dbl’ does a pure type-conversion (no packing is in- volved) from NC_FLOAT to NC_DOUBLE. The resulting variables are not packed, they are just double-precision floating point instead of single-precision floating point. This opera- tion is irreversible, and no attributes are created, modified, or deleted for these variables. All single-precision variables, including coordinates, are promoted. Note that this map can double the size of a dataset. The netCDF packing algorithm (see Section 4.1.12 [Methods and functions], page 175) is lossy—once packed, the exact original data cannot be recovered without a full backup. Hence users should be aware of some packing caveats: First, the interaction of packing and data equal to the FillValue is complex. Test the _FillValue behavior by performing a pack/unpack cycle to ensure data that are missing stay missing and data that are not misssing do not join the Air National Guard and go missing. This may lead you to elect a new FillValue. Second, ncpdq actually allows packing into NC_CHAR (with, e.g., ‘flt_chr’). However, the intrinsic conversion of signed char to higher precision types is tricky for values equal to zero, i.e., for NUL. Hence packing to NC_CHAR is not documented or advertised. Pack into NC_BYTE (with, e.g., ‘flt_byt’) instead. Chapter 4: Reference Manual 293 Dimension Permutation ncpdq re-shapes variables in input-file by re-ordering and/or reversing dimensions specified in the dimension list. The dimension list is a whitespace-free, comma separated list of dimension names, optionally prefixed by negative signs, that follows the‘-a’ (or long options ‘--arrange’, ‘--permute’, ‘--re-order’, or ‘--rdr’) switch. To re-order variables by a subset of their dimensions, specify these dimensions in a comma-separated list following ‘-a’, e.g., ‘-a lon,lat’. To reverse a dimension, prefix its name with a negative sign inthe dimension list, e.g., ‘-a -lat’. Re-ordering and reversal may be performed simultaneously, e.g., ‘-a lon,-lat,time,-lev’. Users may specify any permutation of dimensions, including permutations which change the record dimension identity. The record dimension is re-ordered like any other dimension. This unique ncpdq capability makes it possible to concatenate files along any dimension. See Section 2.6.1 [Concatenation], page 20, for a detailed example. The record dimension is always the most slowly varying dimension in a record variable (see Section 3.15 [C and Fortran Index Conventions], page 63). The specified re-ordering fails if it requires creating more than one record dimension amongst all the output variables1. Two special cases of dimension re-ordering and reversal deserve special mention. First, it may be desirable to completely reverse the storage order of a variable. To do this, include all the variable’s dimensions in the dimension re-order list in their original order, and prefix each dimension name with the negative sign. Second, it may useful to transpose a variable’s storage order, e.g., from C to Fortran data storage order (see Section 3.15 [C and Fortran Index Conventions], page 63). To do this, include all the variable’s dimensions in the dimension re-order list in reversed order. Explicit examples of these two techniques appear below. NB: fxm ncpdq documentation will evolve through Fall 2004. I will upload updates to documentation linked to by the NCO homepage. ncpdq is a powerful operator, and I am unfamiliar with the terminology needed to describe what ncpdq does. Sequences, sets, sheesh! I just know that it does “The right thing” according to my gut feelings. Now do you feel more comfortable using it? Let D(x) represent the dimensionality of the variable x. Dimensionality describes the order and sizes of dimensions. If x has rank N, then we may write D(x) as the N-element vector D(x) = [D1,D2,D3,...,Dn−1,Dn,Dn+1,...,DN−2,DN−1,DN ] where Dn is the size of the n’th dimension. The dimension re-order list specified with-a ‘ ’ is the R-element vector R = [R1,R2,R3,...,Rr−1,Rr,Rr+1,...,RR−2,RR−1,RR] There need be no relation between N and R. Let the S-element vector S be the intersection (i.e., the ordered set of unique shared dimensions) of D and R Then S = R ∩ D = [S1,S2,S3,...,Ss−1,Ss,Ss+1,...,SS−2,SS−1,SS] 1 This limitation, imposed by the netCDF storage layer, may be relaxed in the future with netCDF4. 294 NCO 5.0.2-alpha07 User Guide S is empty if R ∈/ D. Re-ordering (or re-shaping) a variable means mapping the input state with dimension- ality D(x) to the output state with dimensionality D0(x0). In practice, mapping occurs in three logically distinct steps. First, we tranlate the user input to a one-to-one mapping M between input and output dimensions, D 7→ D0. This tentative map is final unless external constraints (typically netCDF restrictions) impose themselves. Second, we check and, if necessary, refine the tentative mapping so that the re-shaped variables will co-exist inthe same file without violating netCDF-imposed storage restrictions. This refined map speci- fies the final (output) dimensionality. Third, we translate the output dimensionality into one-dimensional memory offsets for each datum according to the C language convention for multi-dimensional array storage. Dimension reversal changes the ordering of data, though not the rank or dimensionality, and so is part of the third step. Dimensions R disjoint from D play no role in re-ordering. The first step taken to re-order a variable is to determine S. R is constant for all variables, whereas D, and hence S, is variable-specific. S is empty if R ∈/ D. This may be the case for some extracted variables. The user may explicitly specify the one-to-one mapping of input to output dimension order 0 by supplying (with ‘-a’) a re-order list R such that S = N. In this case Dn = Sn. The 0 degenerate case occurs when D = S. This produces the identity mapping Dn = Dn. The mapping of input to output dimension order is more complex when S 6= N. In this 0 0 0 0 case Dn = Dn for the N − S dimensions Dn ∈/ S. For the S dimensions Dn ∈ S, Dn = Ss. EXAMPLES Pack and unpack all variables in file in.nc and store the results in out.nc: ncpdq in.nc out.nc # Same as ncpack in.nc out.nc ncpdq -P all_new -M flt_sht in.nc out.nc # Defaults ncpdq -P all_xst in.nc out.nc ncpdq -P upk in.nc out.nc # Same as ncunpack in.nc out.nc ncpdq -U in.nc out.nc # Same as ncunpack in.nc out.nc The first two commands pack any unpacked variable in the input file. They alsounpack and then re-pack every packed variable. The third command only packs unpacked variables in the input file. If a variable is already packed, the third command copies it unchangedto the output file. The fourth and fifth commands unpack any packed variables. If a variable is not packed, the third command copies it unchanged. The previous examples all utilized the default packing map. Suppose you wish to archive all data that are currently unpacked into a form which only preserves 256 distinct values. Then you could specify the packing map pck map as ‘hgh_byt’ and the packing policy pck plc as ‘all_xst’: ncpdq -P all_xst -M hgh_byt in.nc out.nc Many different packing maps may be used to construct a given file by performing the packing on subsets of variables (e.g., with ‘-v’) and using the append feature with ‘-A’ (see Section 2.4 [Appending Variables], page 19). Chapter 4: Reference Manual 295 Users may wish to unpack data packed with the HDF convention, and then re-pack it with the netCDF convention so that all their datasets use the same packing convention prior to intercomparison. # One-step procedure: For NCO 4.4.0+, netCDF 4.3.1+ # 1. Convert, unpack, and repack HDF file into netCDF file ncpdq --hdf_upk -P xst_new modis.hdf modis.nc # HDF4 files ncpdq --hdf_upk -P xst_new modis.h5 modis.nc # HDF5 files # One-step procedure: For NCO 4.3.7--4.3.9 # 1. Convert, unpack, and repack HDF file into netCDF file ncpdq --hdf4 --hdf_upk -P xst_new modis.hdf modis.nc # HDF4 ncpdq --hdf_upk -P xst_new modis.h5 modis.nc # HDF5 # Two-step procedure: For NCO 4.3.6 and earlier # 1. Convert HDF file to netCDF file ncl_convert2nc modis.hdf # 2. Unpack using HDF convention and repack using netCDF convention ncpdq --hdf_upk -P xst_new modis.nc modis.nc NCO now2 automatically detects HDF4 files. In this case it produces an output file modis.nc which preserves the HDF packing used in the input file. The ncpdq command first unpacks all packed variables using the HDF unpacking algorithm (as specified by ‘--hdf_upk’), and then repacks those same variables using the netCDF algorithm (be- cause that is the only algorithm NCO packs with). As described above the ‘--P xst_new’ packing policy only repacks variables that are already packed. Not-packed variables are copied directly without loss of precision3. Re-order file in.nc so that the dimension lon always precedes the dimension lat and store the results in out.nc: ncpdq -a lon,lat in.nc out.nc ncpdq -v three_dmn_var -a lon,lat in.nc out.nc The first command re-orders every variable in the input file. The second command extracts and re-orders only the variable three_dmn_var. Suppose the dimension lat represents latitude and monotonically increases increases from south to north. Reversing the lat dimension means re-ordering the data so that latitude values decrease monotonically from north to south. Accomplish this with % ncpdq -a -lat in.nc out.nc 2 Prior to NCO 4.4.0 and netCDF 4.3.1 (January, 2014), NCO requires the ‘--hdf4’ switch to correctly read HDF4 input files. For example,ncpdq ‘ --hdf4 --hdf_upk -P xst_new modis.hdf modis.nc’. That switch is now obsolete, though harmless for backwards compatibility. Prior to version 4.3.7 (October, 2013), NCO lacked the software necessary to circumvent netCDF library flaws handling HDF4 files, and thus NCO failed to convert HDF4 files to netCDF files. In those cases, usethe ncl_convert2nc command distributed with NCL to convert HDF4 files to netCDF. 3 ncpdq does not support packing data using the HDF convention. Although it is now straightforward to support this, we think it might sow more confusion than it reaps. Let us know if you disagree and would like NCO to support packing data with HDF algorithm. 296 NCO 5.0.2-alpha07 User Guide % ncks --trd -C -v lat in.nc lat[0]=-90 lat[1]=90 % ncks --trd -C -v lat out.nc lat[0]=90 lat[1]=-90 This operation reversed the latitude dimension of all variables. Whitespace immediately preceding the negative sign that specifies dimension reversal may be dangerous. Quotes and long options can help protect negative signs that should indicate dimension reversal from being interpreted by the shell as dashes that indicate new command line switches. ncpdq -a -lat in.nc out.nc # Dangerous? Whitespace before "-lat" ncpdq -a ’-lat’ in.nc out.nc # OK. Quotes protect "-" in "-lat" ncpdq -a lon,-lat in.nc out.nc # OK. No whitespace before "-" ncpdq --rdr=-lat in.nc out.nc # Preferred. Uses "=" not whitespace To create the mathematical transpose of a variable, place all its dimensions in the dimen- sion re-order list in reversed order. This example creates the transpose of three_dmn_var: % ncpdq -a lon,lev,lat -v three_dmn_var in.nc out.nc % ncks --trd -C -v three_dmn_var in.nc lat[0]=-90 lev[0]=100 lon[0]=0 three_dmn_var[0]=0 lat[0]=-90 lev[0]=100 lon[1]=90 three_dmn_var[1]=1 lat[0]=-90 lev[0]=100 lon[2]=180 three_dmn_var[2]=2 ... lat[1]=90 lev[2]=1000 lon[1]=90 three_dmn_var[21]=21 lat[1]=90 lev[2]=1000 lon[2]=180 three_dmn_var[22]=22 lat[1]=90 lev[2]=1000 lon[3]=270 three_dmn_var[23]=23 % ncks --trd -C -v three_dmn_var out.nc lon[0]=0 lev[0]=100 lat[0]=-90 three_dmn_var[0]=0 lon[0]=0 lev[0]=100 lat[1]=90 three_dmn_var[1]=12 lon[0]=0 lev[1]=500 lat[0]=-90 three_dmn_var[2]=4 ... lon[3]=270 lev[1]=500 lat[1]=90 three_dmn_var[21]=19 lon[3]=270 lev[2]=1000 lat[0]=-90 three_dmn_var[22]=11 lon[3]=270 lev[2]=1000 lat[1]=90 three_dmn_var[23]=23 To completely reverse the storage order of a variable, include all its dimensions in the re-order list, each prefixed by a negative sign. This example reverses the storage orderof three_dmn_var: % ncpdq -a -lat,-lev,-lon -v three_dmn_var in.nc out.nc % ncks --trd -C -v three_dmn_var in.nc lat[0]=-90 lev[0]=100 lon[0]=0 three_dmn_var[0]=0 lat[0]=-90 lev[0]=100 lon[1]=90 three_dmn_var[1]=1 lat[0]=-90 lev[0]=100 lon[2]=180 three_dmn_var[2]=2 ... lat[1]=90 lev[2]=1000 lon[1]=90 three_dmn_var[21]=21 lat[1]=90 lev[2]=1000 lon[2]=180 three_dmn_var[22]=22 Chapter 4: Reference Manual 297 lat[1]=90 lev[2]=1000 lon[3]=270 three_dmn_var[23]=23 % ncks --trd -C -v three_dmn_var out.nc lat[0]=90 lev[0]=1000 lon[0]=270 three_dmn_var[0]=23 lat[0]=90 lev[0]=1000 lon[1]=180 three_dmn_var[1]=22 lat[0]=90 lev[0]=1000 lon[2]=90 three_dmn_var[2]=21 ... lat[1]=-90 lev[2]=100 lon[1]=180 three_dmn_var[21]=2 lat[1]=-90 lev[2]=100 lon[2]=90 three_dmn_var[22]=1 lat[1]=-90 lev[2]=100 lon[3]=0 three_dmn_var[23]=0 Creating a record dimension named, e.g., time, in a file which has no existing record dimension is simple with ncecat: ncecat -O -u time in.nc out.nc # Create degenerate record dimension named "time" Now consider a file with all dimensions, including time, fixed (non-record). Suppose the user wishes to convert time from a fixed dimension to a record dimension. This maybe useful, for example, when the user wishes to append additional time slices to the data. As of NCO version 4.0.1 (April, 2010) the preferred method for doing this is with ncks: ncks -O --mk_rec_dmn time in.nc out.nc # Change "time" to record dimension Prior to 4.0.1, the procedure to change an existing fixed dimension into a record di- mension required three separate commands, ncecat followed by ncpdq, and then ncwa. The recommended method is now to use ‘ncks --fix_rec_dmn’, yet it is still instructive to present the original procedure, as it shows how multiple operators can achieve the same ends by different means: ncecat -O in.nc out.nc # Add degenerate record dimension named "record" ncpdq -O -a time,record out.nc out.nc # Switch "record" and "time" ncwa -O -a record out.nc out.nc # Remove (degenerate) "record" The first step creates a degenerate (size equals one) record dimension named (by default) record. The second step swaps the ordering of the dimensions named time and record. Since time now occupies the position of the first (least rapidly varying) dimension, itbe- comes the record dimension. The dimension named record is no longer a record dimension. The third step averages over this degenerate record dimension. Averaging over a degener- ate dimension does not alter the data. The ordering of other dimensions in the file (lat, lon, etc.) is immaterial to this procedure. See Section 4.5 [ncecat netCDF Ensemble Concate- nator], page 253, and Section 4.8 [ncks netCDF Kitchen Sink], page 263, for other methods of changing variable dimensionality, including the record dimension. 298 NCO 5.0.2-alpha07 User Guide 4.10 ncra netCDF Record Averager SYNTAX ncra [-3] [-4] [-5] [-6] [-7] [-A] [-C] [-c] [--cb y1,y2,m1,m2,tpd] [--cnk_byt sz_byt] [--cnk_csh sz_byt] [--cnk_dmn nm,sz_lmn] [--cnk_map map] [--cnk_min sz_byt] [--cnk_plc plc] [--cnk_scl sz_lmn] [-D dbg] [-d dim,[min][,[max][,[stride][,[subcycle][,[interleave]]]]] [-F] [--fl_fmt fl_fmt] [-G gpe_dsc] [-g grp[,...]] [--glb ...] [-h] [--hdf] [--hdr_pad nbr] [--hpss] [-L dfl_lvl] [-l path] [--mro] [-N] [-n loop] [--no_cll_msr] [--no_cll_mth] [--no_frm_trm] [--no_tmp_fl] [-O] [-o output- file] [-p path] [--ppc ...] [--prm_int] [--prw wgt_arr] [-R] [-r] [--ram_all] [--rec_apn] [--rth_dbl|flt] [-t thr_nbr] [--unn] [-v var[,...]] [-w wgt] [-X ...] [-x] [-y op_typ] [input-files][output-file] DESCRIPTION ncra computes statistics (including, though not limited to, averages) of record variables across an arbitrary number of input-files. The record dimension is, by default, retained as a degenerate (size 1) dimension in the output variables. See Section 2.6 [Statistics vs. Concatenation], page 20, for a description of the distinctions between the various statistics tools and concatenators. As a multi-file operator, ncra will read the list of input-files from stdin if they are not specified as positional arguments on the command line (see Section 2.7 [Large Numbers of Files], page 21). Input files may vary in size, but each must have a record dimension. The record coordi- nate, if any, should be monotonic (or else non-fatal warnings may be generated). Hyperslabs of the record dimension which include more than one file work correctly. ncra supports the stride argument to the ‘-d’ hyperslab option (see Section 3.16 [Hyperslabs], page 63) for the record dimension only, stride is not supported for non-record dimensions. ncra always aver- ages coordinate variables (e.g., time) regardless of the arithmetic operation type performed on non-coordinate variables (see Section 3.39 [Operation Types], page 129). As of NCO version 4.4.9, released in May, 2015, ncra accepts user-specified weights with the ‘-w’ (or long-option equivalent ‘--wgt’, ‘--wgt_var’, or ‘--weight’) switch. When no weight is specified, ncra weights each record (e.g., time slice) in the input-files equally. ncra does not attempt to see if, say, the time coordinate is irregularly spaced and thus would require a weighted average in order to be a true time-average. Specifying unequal weights is entirely the user’s responsibility. Weights specified with ‘-w wgt’ may take one of two forms. In the first form, thewgt ‘ ’ argument is a comma-separated list of values by which to weight each file (recall that files may have multiple timesteps). In this form the number of weights specified must equal the number of files specified in the input file list, or else the program will exit. Inthesecond form, the ‘wgt’ argument is the name of a weighting variable present in every input file. The variable may be a scalar or a one-dimensional record variable. Scalar weights are applied Chapter 4: Reference Manual 299 uniformly to the entire file (i.e., this produces the same arithmetic result as supplying the same value as a per-file weight option on the command-line). One-dimensional weights apply to each corresponding record (i.e., per-record weights), and are suitable for dynamically changing timesteps. By default, any weights specified (whether by value or by variable name) are nor- malized to unity by dividing each specified weight by the sum of all the weights. This means, for example, that, ‘-w 0.25,0.75’ is equivalent to ‘-w 2.0,6.0’ since both are equal when normalized. This behavior simplifies specifying weights based on count- able items. For example, time-weighting monthly averages for March, April, and May to obtain a spring seasonal average can be done with ‘-w 31,30,31’ instead of ‘-w 0.33695652173913043478,0.32608695652173913043,0.33695652173913043478’. However, sometimes one wishes to use weights in “dot-product mode”, i.e., multiply by the (non-normalized) weights. As of NCO version 4.5.2, released in July, 2015, ncra accepts the ‘-N’ (or long-option equivalent ‘--no_nrm_by_wgt’) switch that prevents auto- matic weight normalization. When this switch is used, the weights will not be normalized (unless the user provides them as normalized), and the numerator of the weighted average will not be divided by the sum of the weights (which is one for normalized weights). As of NCO version 4.9.4, released in September, 2020, ncra supports the ‘--per_record_weights’ (or ‘--prw’) flag to utilize the command-line weights separately specified by ‘-w wgt_arr’ (or ‘--wgt wgt_arr’) for per-record weights instead of per-file- weights, where wgt arr is a 1-D array of weights. This is useful when computing weighted averages with cyclically varying weights, since the weights given on the command line will be repeated for the length of the timeseries. Consider, for example, a CMIP6 timeseries of historical monthly mean emissions that one wishes to convert to a timeseries of annual-mean emissions. One can now weight each month by its number of days via: ncra --per_record_weights --mro -d time,,,12,12 --wgt \ 31,28,31,30,31,30,31,31,30,31,30,31 ~/monthly.nc ~/annual.nc Note that the twelve weights will be implicitly repeated throughtout the duration of the input file(s), which in this case may therefore specify an interannual monthly timeseries that is reduced to a timeseries of annual-means in the output. Bear these exceptions in mind when weighting input: First, ncra only applies weights if the arithmetic operation type is averaging (see Section 3.39 [Operation Types], page 129), i.e., for timeseries mean and for timeseries mean absolute value. Weights are never applied for minimization, square-roots, etc. Second, ncra never weights coordinate variables (e.g., time) regardless of the weighting performed on non-coordinate variables. As of NCO version 4.9.4, released in September, 2020, ncra supports the ‘--promote_ints’ (or ‘prm_ints’) flags to output statistics of integer-valued input vari- ables in floating-point precision in the output file. By default, arithmetic operators suchas ncra auto-promote integers to double-precision prior to arithmetic, then conduct the arith- metic, then demote the values back to integers for final output. The final stage (demotion) of this default behavior quantizes the mantissa of the values and prevents, e.g., retaining the statisitical means of Boolean (0 or 1-valued) input data as floating point data. The ‘--promote_ints’ flag eliminates the demotion and causes the statistical means ofinteger 300 NCO 5.0.2-alpha07 User Guide (NC_BYTE, NC_SHORT, NC_INT, NC_INT64) inputs to be output as single-precision floating point (NC_FLOAT) variables. This allows useful arithmetic to be performed on Boolean values stored in the space-conserving NC_BYTE (single-byte) format. ncra --prm_ints in*.nc out.nc EXAMPLES Average files 85.nc, 86.nc,... 89.nc along the record dimension, and store the results in 8589.nc: ncra 85.nc 86.nc 87.nc 88.nc 89.nc 8589.nc ncra 8[56789].nc 8589.nc ncra -n 5,2,1 85.nc 8589.nc These three methods produce identical answers. See Section 3.6 [Specifying Input Files], page 34, for an explanation of the distinctions between these methods. Assume the files 85.nc, 86.nc,... 89.nc each contain a record coordinate time of length 12 defined such that the third record in 86.nc contains data from March 1986, etc. NCO knows how to hyperslab the record dimension across files. Thus, to average data from December, 1985 through February, 1986: ncra -d time,11,13 85.nc 86.nc 87.nc 8512_8602.nc ncra -F -d time,12,14 85.nc 86.nc 87.nc 8512_8602.nc The file 87.nc is superfluous, but does not cause an error.-F The‘ ’ turns on the Fortran (1-based) indexing convention. The following uses the stride option to average all the March temperature data from multiple input files into a single output file ncra -F -d time,3,,12 -v temperature 85.nc 86.nc 87.nc 858687_03.nc See Section 3.17 [Stride], page 66, for a description of the stride argument. Assume the time coordinate is incrementally numbered such that January, 1985 = 1 and December, 1989 = 60. Assuming ‘??’ only expands to the five desired files, the following averages June, 1985–June, 1989: ncra -d time,6.,54. ??.nc 8506_8906.nc ncra -y max -d time,6.,54. ??.nc 8506_8906.nc The second example identifies the maximum instead of averaging. See Section 3.39 [Operation Types], page 129, for a description of all available statistical operations. ncra includes the powerful subcycle and multi-record output features (see Section 3.19 [Subcycle], page 68). This example uses these features to compute and output winter (DJF) averages for all winter seasons beginning with year 1990 and continuing to the end of the input file: ncra -O --mro -d time,"1990-12-01",,12,3 in.nc out.nc The ‘-w wgt’ option weights input data per-file when explicit numeric weights are given on the command-line, or per-timestep when the argument is a record variable that resides in the file: ncra -w 31,31,28 dec.nc jan.nc feb.nc out.nc # Per-file weights Chapter 4: Reference Manual 301 ncra -w delta_t in1.nc in2.nc in3.nc out.nc # Per-timestep weights The first example weights the input differently per-file to produce correctly weighted winter seasonal mean statistics. The second example weights the input per-timestep to produce correctly weighted mean statistics. 302 NCO 5.0.2-alpha07 User Guide 4.11 ncrcat netCDF Record Concatenator SYNTAX ncrcat [-3] [-4] [-5] [-6] [-7] [-A] [-C] [-c] [--cnk_byt sz_byt] [--cnk_csh sz_byt] [--cnk_dmn nm,sz_lmn] [--cnk_map map] [--cnk_min sz_byt] [--cnk_plc plc] [--cnk_scl sz_lmn] [-D dbg] [-d dim,[min][,[max][,[stride][,[subcycle][,[interleave]]]]] [-F] [--fl_fmt fl_fmt] [-G gpe_dsc] [-g grp[,...]] [--glb ...] [-h] [--hdr_pad nbr] [--hpss] [-L dfl_lvl] [-l path] [--md5_digest] [-n loop] [--no_tmp_fl] [--no_cll_msr] [--no_frm_trm] [--no_tmp_fl] [-O] [-o output- file] [-p path] [--ppc ...] [-R] [-r] [--ram_all] [--rec_apn] [-t thr_nbr] [--unn] [-v var[,...]] [-X ...] [-x] [input-files][output-file] DESCRIPTION ncrcat concatenates record variables across an arbitrary number of input-files. The final record dimension is by default the sum of the lengths of the record dimensions inthe input files. See Section 2.6 [Statistics vs. Concatenation], page 20, for a description of the distinctions between the various statistics tools and concatenators. As a multi-file operator, ncrcat will read the list of input-files from stdin if they are not specified as positional arguments on the command line (see Section 2.7 [Large Numbers of Files], page 21). Input files may vary in size, but each must have a record dimension. The record coordi- nate, if any, should be monotonic (or else non-fatal warnings may be generated). Hyperslabs along the record dimension that span more than one file are handled correctly. ncra sup- ports the stride argument to the ‘-d’ hyperslab option for the record dimension only, stride is not supported for non-record dimensions. Concatenating a variable packed with different scales multiple datasets is beyond the capabilities of ncrcat (and ncecat, the other concatenator (Section 2.6.1 [Concatenation], page 20). ncrcat does not unpack data, it simply copies the data from the input-files, and the metadata from the first input-file, to the output-file. This means that data compressed with a packing convention must use the identical packing parameters (e.g., scale_factor and add_offset) for a given variable across all input files. Otherwise the concatenated dataset will not unpack correctly. The workaround for cases where the packing parameters differ across input-files requires three steps: First, unpack the data using ncpdq. Second, concatenate the unpacked data using ncrcat, Third, re-pack the result with ncpdq. ncrcat applies special rules to ARM convention time fields (e.g., time_offset). See Section 3.46 [ARM Conventions], page 150, for a complete description. EXAMPLES Concatenate files 85.nc, 86.nc,... 89.nc along the record dimension, and store the results in 8589.nc: ncrcat 85.nc 86.nc 87.nc 88.nc 89.nc 8589.nc Chapter 4: Reference Manual 303 ncrcat 8[56789].nc 8589.nc ncrcat -n 5,2,1 85.nc 8589.nc These three methods produce identical answers. See Section 3.6 [Specifying Input Files], page 34, for an explanation of the distinctions between these methods. Assume the files 85.nc, 86.nc,... 89.nc each contain a record coordinate time of length 12 defined such that the third record in 86.nc contains data from March 1986, etc. NCO knows how to hyperslab the record dimension across files. Thus, to concatenate data from December, 1985–February, 1986: ncrcat -d time,11,13 85.nc 86.nc 87.nc 8512_8602.nc ncrcat -F -d time,12,14 85.nc 86.nc 87.nc 8512_8602.nc The file 87.nc is superfluous, but does not cause an error. When ncra and ncrcat en- counter a file which does contain any records that meet the specified hyperslab criteria, they disregard the file and proceed to the next file without failing. The‘-F’ turns on the Fortran (1-based) indexing convention. The following uses the stride option to concatenate all the March temperature data from multiple input files into a single output file ncrcat -F -d time,3,,12 -v temperature 85.nc 86.nc 87.nc 858687_03.nc See Section 3.17 [Stride], page 66, for a description of the stride argument. Assume the time coordinate is incrementally numbered such that January, 1985 = 1 and December, 1989 = 60. Assuming ?? only expands to the five desired files, the following concatenates June, 1985–June, 1989: ncrcat -d time,6.,54. ??.nc 8506_8906.nc 304 NCO 5.0.2-alpha07 User Guide 4.12 ncremap netCDF Remapper SYNTAX ncremap [-3] [-4] [-5] [-6] [-7] [-a alg_typ] [--a2o] [--add_fll] [-D dbg_lvl] [-d dst_fl] [--d2f] [--dpt] [--dpt_fl=dpt_fl] [--dt_sng=dt_sng] [--esmf_typ=esmf_typ] [--fl_fmt=fl_fmt] [-G grd_sng] [-g grd_dst] [-I drc_in] [-i input-file] [-j job_nbr] [-L dfl_lvl] [-M] [-m map_fl] [--mpi_nbr=mpi_nbr] [--mpi_pfx=mpi_pfx] [--msh_fl=msh_fl] [--msk_apl] [--msk_dst=msk_dst] [--msk_out=msk_out] [--msk_src=msk_src] [--mss_val=mss_val] [-n nco_opt] [--nm_dst=nm_dst] [--nm_src=nm_src] [--no_cll_msr] [--no_frm_trm] [--no_permute] [--no_stdin] [--no_stg_grd] [-O drc_out] [-o output-file] [-P prc_typ] [-p par_typ] [--pdq=pdq_opt] [--ppc=ppc_opt] [--preserve=prs_stt] [-R rgr_opt] [--rgn_dst] [--rgn_src] [--rnr_thr=rnr_thr] [--rrg_bb_wesn=bb_wesn] [--rrg_dat_glb=dat_glb] [--rrg_grd_glb=grd_glb] [--rrg_grd_rgn=grd_rgn] [--rrg_rnm_sng=rnm_sng] [-s grd_src] [--sgs_frc=sgs_frc] [--sgs_msk=sgs_msk] [--sgs_nrm=sgs_nrm] [--skl=skl-file] [--stdin] [-T drc_tmp] [-t thr_nbr] [-U] [-u unq_sfx] [--ugrid=ugrid-file] [--uio] [-V rgr_var] [-v var_lst[,...]] [--version] [--vrb=vrb_lvl] [--vrt_fl=vrt_fl] [--vrt_ntp=vrt_ntp] [--vrt_xtr=vrt_xtr] [-W wgt_opt] [-w wgt_cmd] [-x xtn_lst[,...]] [--xcl_var] [--xtr_nsp=xtr_nsp] [--xtr_xpn=xtr_xpn] [input-files][output-file] DESCRIPTION ncremap remaps the data file(s) in input-file, in drc in, or piped through standard input, to the horizontal grid specified by (in descending order of precedence) map fl, grd dst, or dst fl and stores the result in output-file(s). If a vertical grid vrt fl is provided, ncremap will (also) vertically interpolate the input file(s) to that grid. When no input-file is provided, ncremap operates in “map-only” mode where it exits after producing an annotated map-file. ncremap was introduced to NCO in version 4.5.4 (December, 2015). ncremap is a “super-operator” that orchestrates the regridding features of several differ- ent programs including other NCO operators. Under the hood NCO applies pre-computed remapping weights or, when necessary, generates and infers grids, generates remapping weights itself or calls external programs to generate the weights, and then applies the weights (i.e., regrids). Unlike the rest of NCO, ncremap and ncclimo are shell scripts, not compiled binaries1. As of NCO 4.9.2 (February, 2020), the ncclimo and ncremap scripts export the environment variable HDF5_USE_FILE_LOCKING with a value of FALSE. This prevents failures of these op- erators that can occur with some versions of the underlying HDF library that attempt to lock 1 This means that newer (including user-modified) versions of ncremap work fine without re-compiling NCO. Re-compiling is only necessary to take advantage of new features or fixes in the NCO binaries, not to improve ncremap. One may download and give executable permissions to the latest source at https://github.com/nco/nco/tree/master/data/ncremap without re-installing the rest of NCO. Chapter 4: Reference Manual 305 files on file systems that cannot or do not support it. ncremap wraps the underlying regrid- der (ncks) and external executables to produce a friendly interface to regridding. Without any external dependencies, ncremap applies weights from a pre-exisiting map-file to a source data file to produce a regridded dataset. Source and destination datasets may beonany Swath, Curvilinear, Rectangular, or Unstructured Data (SCRUD) grid. ncremap will also use its own algorithms or, when necessary, external programs ESMF’s ESMF_RegridWeightGen (ERWG) or TempestRemap’s GenerateOverlapMesh/GenerateOfflineMap) to generate weights and mapfiles. In order to use the weight-generation options, either invoke anin- ternal NCO weight-generation algorithm (e.g., ‘--alg_typ=nco’), or ensure that one or both of the external weight-generation packages is installed and on your $PATH. The recommended way to obtain ERWG is as distributed in binary format. Many (most?) NCO users already have NCL on their system(s), and NCL usually comes with ERWG. Since about June, 2016, the Conda NCO package will also install ERWG2. Then be sure the directory containing the ERWG executable is on your $PATH before using ncremap. As a fallback, ERWG may also be installed from source: https://earthsystemcog.org/ projects/esmf/download_last_public. ncremap can also generate and utilize mapfiles created by TempestRemap, https://github.com/ClimateGlobalChange/tempestremap. Until about April, 2019, TempestRemap had to be built from source because there were no binary distributions of it. As of NCO version 4.8.0, released in May, 2019, the Conda NCO package automatically installs the new TempestRemap Conda package so building from source is not necessary. Please contact those projects for support on building and installing their software, which makes ncremap more functional and user-friendly. Please ensure you have the latest version of ERWG or TempestRemap before reporting any related problems to NCO. As mentioned above, ncremap orchestrates the regridding features of several different programs. ncremap runs most quickly when it is supplied with a pre-computed mapfile. However, ncremap will also (call other programs to) compute mapfiles when necessary and when given sufficient grid information. Thus it is helpful to understand when ncremap will and will not internally generate a mapfile. Supplying input data files and a pre-computed mapfile without any other grid information causes ncremap to regrid the data files without first pausing to internally generate a mapfile. On the other hand, supplying anygrid information (i.e., using any of the ‘-d’, ‘-G’, ‘-g’, or ‘-s’ switches described below), causes ncremap to internally (re-)generate the mapfile by combining the supplied and inferred grid information. A generated mapfile is given a default name unless a user-specified nameis supplied with ‘-m map_fl’. Fields not regridded by ncremap Most people ultimately use ncremap to regrid data, yet not all data can or should be regridded in the sense of applying a sparse-matrix of weights to an input field to produce and output field. Certain fields (e.g., the longitude coordinate) specify the grid.These fields must be provided in order to compute the weights that are used to regrid. The regridded usually copies these fields “as is” directly into regridded files, where they describe the destination grid, and replace or supercede the source grid information. Other fields are extensive grid properties (e.g., the number of cells adjacent to a given cell) that may 2 Install the Conda NCO package with ‘conda install -c conda-forge nco’. 306 NCO 5.0.2-alpha07 User Guide apply only to the source (not the destination) grid, or be too difficult to re-compute for the destination grid. ncremap contains an internal database of fields that it will not propagate or regrid. First are variables with names identical to the coordinate names found in an ever-growing collection of publicly available geoscience datasets (CMIP, NASA, etc.): area, gridcell_area, gw, LAT, lat, Latitude, latitude, nav_lat, global_latitude0, latitude0, slat, TLAT, ULAT, XLAT, XLAT_M, CO_Latitude, S1_Latitude, lat_bnds, lat_ vertices, latt_bounds, latu_bounds, latitude_bnds, LatitudeCornerpoints, bounds_ lat, LON, lon, Longitude, longitude, nav_lon, global_longitude0, longitude0, slon, TLON, TLONG, ULON, ULONG, XLONG, XLONG_M, CO_Longitude, S1_Longitude, lon_bnds, lon_vertices, lont_bounds, lonu_bounds, longitude_bnds, LongitudeCornerpoints, bounds_lon, and w_stag. Files produced by MPAS models may contain these variables that will not be regridded: angleEdge, areaTriangle, cellsOnCell, cellsOnEdge, cellsOnVertex, dcEdge, dvEdge, edgeMask, edgesOnCell, edgesOnEdge, edgesOnVertex, indexToCellID, indexToEdgeID, indexToVertexID, kiteAreasOnVertex, latCell, latEdge, latVertex, lonCell, lonEdge, lonVertex, maxLevelEdgeTop, meshDensity, nEdgesOnCell, nEdgesOnEdge, vertexMask, verticesOnCell, verticesOnEdge, weightsOnEdge, xEdge, yEdge, zEdge, xVertex, yVertex, and zVertex. Most of these fields that ncremap will not regrid are also fields that NCO size-and- rank-preserving operators will not modify, as described in Section 3.45 [CF Conventions], page 146. Options specific to ncremap The following summarizes features unique to ncremap. Features common to many operators are described in Chapter 3 [Shared features], page 29. ‘-a alg_typ (--alg_typ, --algorithm, --regrid_algorithm)’ Specifies the interpolation algorithm for weight-generation for useby ESMF_ RegridWeightGen (ERWG), NCO, and/or TempestRemap. ncremap unbundles this algorithm choice from the rest of the weight-generator invocation syntax be- cause users more frequently change interpolation algorithms than other options (that can be changed with ‘-W wgt_opt’). ncremap can invoke all seven ERWG weight generation algorithms, one NCO algorithm, and eight TempestRemap algorithms. The seven ERWG weight generation algorithms are: bilinear (default, accept- able abbreviations are bilin, blin, bln), conserve (or conservative, cns, c1, or aave), conserve2nd (or conservative2nd, c2, or c2nd)(NCO supports conserve2nd as of version 4.7.4 (April, 2018)), nearestdtos (or nds or dtos or ndtos), neareststod (or nsd or stod or nstod), and patch (or pch or patc). See ERWG documentation here (http://www.earthsystemmodeling. org/esmf_releases/public/ESMF_6_3_0rp1/ESMF_refdoc/node3.html# SECTION03020000000000000000) for detailed descriptions of ERWG algorithms. Chapter 4: Reference Manual 307 ncremap implements its own internal weight-generation algorithm as of NCO version 4.8.0 (May, 2019). The first NCO-native algorithm is a first-order con- servative algorithm that competes well in accuracy with similar algorithms (e.g., ERWG’s conservative algorithm). This algorithm is built-in to NCO and requires no external software so it is the default weight generation algorithm and is rec- ommended for everyday use. The algorithm may also be explicitly invoked with nco_con (or nco_cns, nco_conservative, or simply nco). As of NCO version 4.9.4 (September, 2019) ncremap supports a second internal weight-generation algorithm based on distance-weighted extrapolation (DWE). DWE is similar to the ERWG nearestidavg extrapolation alorithm, and accepts the same two parameters as input: ‘--xtr_xpn xtr_xpn’ sets the (absolute value of) the exponent used in inverse distance weighting (default is 2.0), and ‘--xtr_nsp xtr_nsp’ sets the number of source points used in the extrapolation (default is 8). Currently, ncremap applies NCO’s DWE to the entire destination grid, not just to points with missing/masked values, whereas ERWG uses DWE solely for extrapolation to missing data points. We intend NCO’s DWE to offer both types of functionality in a future release. ncremap --alg_typ=nco_dwe -s src.nc -d dst.nc -m map.nc ncremap -a nco_dwe --xtr_xpn=1.0 -s src.nc -d dst.nc -m map.nc ncremap -a nco_dwe --xtr_nsp=1 -s src.nc -d dst.nc -m map.nc ncremap can invoke eight preconfigured TempestRemap weight-generation al- gorithms, and one generic algorithm (tempest) for which users should provide their own options. As of NCO version 4.7.2 (January, 2018), ncremap imple- mented the six E3SM-recommended TempestRemap mapping algorithms be- tween FV and SE flux, state, and other variables. ncremap originated some (we hope) common-sense names for these algorithms (se2fv_flx, se2fv_stt, se2fv_alt, fv2se_flx, fv2se_stt, and fv2se_alt), and also allows more mathematically precise synonyms (shown below). As of NCO version 4.9.0 (De- cember, 2019), ncremap added two further boutique mappings (fv2fv_flx and fv2fv_stt). Finally, the ‘-a tempest’ algorithm can be specified with the pre- cise TempestRemap options as arguments to the ‘-W’ (or ‘--wgt_opt’) option. Note that support for the named algorithms requires TempestRemap version 2.0.0 or later (some option combinations fail with earlier versions). Generate and use the recommended weights to remap fluxes from SE to FV grids, for example, with ncremap -a se2fv_flx --src_grd=se.g --dst_grd=fv.nc -m map.nc ncremap -m map.nc in.nc out.nc This causes ncremap to automatically invoke TempestRemap with the boutique options ‘--in_type cgll --in_np 4 --out_type fv --mono’ that are recom- mended by E3SM for conservative and monotone remapping of fluxes. Tem- pestRemap options have the following meanings: mono specifies a monotone remapping, i.e., one that does not generate any new extrema in the field vari- ables.a cgll indicates the input or output are represented by a continuous Galerkin method on Gauss-Lobatto-Legendre nodes. This is appropriate for spectral element datasets. (TempestRemap also supports, although NCO does not invoke, the dgll option for a discontinuous Galerkin method on Gauss- 308 NCO 5.0.2-alpha07 User Guide Lobatto-Legendre nodes.) It is equivalent to, yet simpler to remember and to invoke than ncremap -a tempest --src_grd=se.g --dst_grd=fv.nc -m map.nc \ -W ’--in_type cgll --in_np 4 --out_type fv --mono’ Specifying ‘-a tempest’ without additional options in the ‘-W’ clause causes TempestRemap to employ defaults. The default configuration requires both input and output grids to be FV, and produces a conservative, non-monotonic mapping. The ‘-a fv2fv’ option described below may produce more desirable results than this default for many users. Using ‘-a tempest’ alone without other options for spectral element grids will lead to undefined and likely unintentional results. In other words, ‘-a tempest’ is intended to be used in conjunction with a ‘-W’ option clause to supply your own combination of TempestRemap options that does not duplicate one of the boutique option collections that already has its own name. The full list of supported canonical algorithm names, their synonyms, and bou- tique options passed to GenerateOfflineMap are: se2fv_flx (synonyms mono_se2fv, conservative_monotone_se2fv) Options: ‘--in_type cgll --in_np 4 --out_type fv --mono --correct_areas’ fv2se_flx (synonyms monotr_fv2se, conservative_monotone_fv2se), Options: ‘--in_type cgll --in_np 4 --out_type fv --mono --correct_areas’. For fv2se_flx the weights are gen- erated with options identical to se2fv_flx, and then the transpose of the resulting weight matrix is employed. se2fv_stt (synonyms highorder_se2fv, accurate_conservative_nonmonotone_se2fv), Options: ‘--in_type cgll --in_np 4 --out_type fv --correct_areas’ fv2se_stt (synonyms highorder_fv2se, accurate_conservative_nonmonotone_fv2se), Options: ‘--in_type fv --in_np 2 --out_type cgll --out_np 4’ se2fv_alt (synonyms intbilin_se2fv, accurate_monotone_nonconservative_se2fv), Options: ‘--in_type cgll --in_np 4 --out_type fv --mono3 --noconserve --correct_areas’ fv2se_alt (synonyms mono_fv2se, conservative_monotone_fv2se_alt), Options: ‘--in_type fv --in_np 1 --out_type cgll --out_np 4 --mono’ se2se (synonyms cs2cs, conservative_monotone_se2se), Options: ‘--in_type cgll --in_np 4 --out_type cgll --out_np 4 --mono’ Chapter 4: Reference Manual 309 fv2fv (synonyms rll2rll), Options: ‘--in_type fv --in_np 2 --out_type fv --correct_areas’ fv2fv_flx (synonyms fv2fv_mono, conservative_monotone_fv2fv), Options: ‘--in_type fv --in_np 1 --out_type fv --out_np 1 --correct_areas’ fv2fv_stt (synonyms fv2fv_highorder, accurate_conservative_nonmonotone_fv2fv), Options: ‘--in_type fv --in_np 2 --out_type fv --correct_areas’ Thus these boutique options are specialized for SE grids with fourth or- der resolution (np = 4). Full documentation of the E3SM-recommended boutique options for TempestRemap is here (https:// acme-climate.atlassian.net/wiki/spaces/Docs/pages/178848194/ Transition+to+TempestRemap+for+Atmosphere+grids) (may require E3SM- authorization to view). Let us know if you would like other boutique Tem- pestRemap switch sets added as canonical options for ncremap. ‘--a2o (--a2o, --atm2ocn, --b2l, --big2ltl, --l2s, --lrg2sml)’ Use one of these flags (that take no arguments) to cause TempestRemap to generate mapping weights from a source grid that has more coverage than the destination grid, i.e., the destination grid is a subset of the source. When com- puting the intersection of two meshes, TempestRemap uses an algorithm (in an executable named GenerateOverlapMesh) that expects the mesh with less cov- erage to be the first grid, and the grid with greater coverage to be the second, re- gardless of the mapping direction. By default, ncremap supplies the source grid first and the destination second, but this order causes GenerateOverlapMesh (which is agnostic about ordering for grids of equal coverage) to fail when the source grid covers regions not in the destination grid. For example, a global atmosphere grid has more coverage than a global ocean grid, so that remapping from atmosphere-to-ocean would require invoking the ‘--atm2ocn’ switch: # Use --a2o to generate weights for "big" to "little" remaps: ncremap --a2o -a se2fv_flx --src_grd=atm_se_grd.nc \ --dst_grd=ocn_fv_grd.nc -m map.nc # Otherwise, omit it: ncremap -a fv2se_flx --src_grd=ocn_fv_grd.nc \ --dst_grd=atm_se_grd.nc -m map.nc ncremap -a se2fv_flx --src_grd=atm_se_grd.nc \ --dst_grd=atm_fv_grd.nc -m map.nc # Only necessary when generating, not applying, weights: ncremap -m atm2ocn.nc in.nc out.nc As shown in the second example above, remapping from global ocean-to- atmosphere grids does not require (and should not invoke) this switch. The third example shows that the switch is only needed when generating weights, not when applying them. The switch is never needed (and is ignored) when generat- ing weights with ERWG (which constructs the intersection mesh with a different 310 NCO 5.0.2-alpha07 User Guide algorithm than TempestRemap). Attempting to remap a larger source grid to a subset destination grid without using ‘--a2o’ causes GenerateOverlapMesh to emit an error (and a potential workaround) like this: ....Nearest target face 130767 ....ERROR: No overlapping face found ....This may be caused by mesh B being a subset of mesh A ....Try swapping order of mesh A and B, or override with \ --allow_no_overlap ....EXCEPTION (../src/OverlapMesh.cpp, Line 1738) Exiting The ‘--a2o’ switch and its synonyms are available in version 4.7.3 (March, 2018) and later. As of NCO version 4.9.9 (May, 2021), ncremap automatically transmits the option ‘--allow_no_overlap’ to GenerateOverlapMesh so that regional meshes that do not completely overlap may be intersected. This is thought to have no effect on global mappings. Please let us know if these capabilities do not work for you. ‘--add_fll (--add_fll, --add_fill_value, --fll_mpt, --fill_empty)’ Introduced in NCO version 5.0.0 (released June, 2021), this switch (which takes no argument) causes the regridder to add a _FillValue attribute to fields with empty destination cells. Empty destination cells are those that have no non-zero weights from the source grids. When a contiguous geophysical field (e.g., air temperature) without a _FillValue is mapped to such a destination grid, the empty destination values are normally set to zero (because no source grid cells contribute). However, zero is a valid value for many geophysical fields. Use this switch to ensure that empty destination gridcells are always set to _FillValue. The default _FillValue will be used in the output file for input fields that lack a _FillValue. This flag has no effect on input fields that already havea _FillValue. ncremap --add_fll -v FLNS -m map.nc in.nc out.nc Note that --add_fll is automatically triggered by --msk_apl to ensure that masked fields regridded with TempestRemap-generated map-files have _ FillValues consistent with map-files generated by ESMF and NCO. ‘--version (--version, --vrs, --config, --configuration, --cnf)’ This switch (which takes no argument) causes the operator to print its version and configuration. This includes the copyright notice, URLs to the BSD and NCO license, directories from which the NCO scripts and binaries are running, and the locations of any separate executables that may be used by the script. ‘--d2f (--d2f, --d2s, --dbl_flt, --dbl_sgl, --double_float)’ This switch (which takes no argument) demotes all double precision non- coordinate variables to single precision. Internally ncremap invokes ncpdq to apply the dbl_flt packing map to an intermediate version of the input file be- fore regridding it. This switch has no effect on files that are not regridded. To demote the precision in such files, use ncpdq to apply the dbl_flt packing map to the file directly. Files without any double precision fields will be unaltered. Chapter 4: Reference Manual 311 ‘-D dbg_lvl (--dbg_lvl, --dbg, --debug, --debug_level)’ Specifies a debugging level similar to the restof NCO. If dbg lvl = 1, ncremap prints more extensive diagnostics of its behavior. If dbg lvl = 2, ncremap prints the commands it would execute at any higher or lower debugging level, but does not execute these commands. If dbg lvl > 2, ncremap prints the diagnostic information, executes all commands, and passes-through the debugging level to the regridder (ncks) for additional diagnostics. ‘--devnull=dvn_flg (--devnull, --dev_nll, --dvn_flg)’ The dvn flg controls whether ncremap suppresses regridder output or sends it to /dev/null. The default value of dvn flg is “Yes”, so that ncremap prints little output to the terminal. Set dvn flg to “No” to allow the internal regridder executables (mainly ncks) to send their output to the terminal. ‘--dpt (--dpt, --add_dpt, --depth, --add_depth)’ ‘--dpt_fl=dpt_fl (--dpt_fl, --depth_file, --mpas_fl, --mpas_depth)’ The ‘--dpt’ switch (which takes no argument) and the ‘--dpt_fl=dpt_fl’ option which automatically sets the switch and also takes a filename argument, both control the addition of a depth coordinate to MPAS ocean datasets. Depth is the vertical distance below sea surface and, like pressure in the atmosphere, is an important vertical coordinate whose explicit values are often omitted from datasets yet may be computed from other variables (gridbox thickness, pressure difference) and grid information. Moreover, users are often more interested in the approximate depth, aka reference depth, of a given ocean layer independent of its horizontal position. To invoke either of these options first obtain and place the add_depth.py command on the executable path (i.e., $PATH), and use ncremap --config to verify that it is found. These options tell ncremap to invoke add_depth.py which uses the refBottomDepth variable in the current data file or, if specified, the dpt fl, to create and add a depth coordinate to the current file (before regridding). As of NCO version 4.7.9 (February, 2019), the depth coordinate is an approx- imate, one-dimensional, globally uniform coordinate that neglects horizontal variations in depth that can occur near strong bathymetry or under ice shelves. Like its atmospheric counterpart in many models, the lev pressure-coordinate, depth is useful for plotting purposes and global studies. It would not be diffi- cult to modify these options to add other depth information based on the 3D cell-thickness field to ocean files (please ask Charlie if interested inthis). ‘-d dst_fl (--dst_fl, --destination_file, --tpl, tpl_fl, --template_file, --template)’ Specifies a data file to serve as a template for inferring the destination grid. Currently dst fl must be a data file (not a gridfile, SCRIP or otherwise) from which NCO can infer the destination grid. The more coordinate and boundary information and metadata the better NCO will do at inferring the grid. If dst fl has cell boundaries then NCO will use those. If dst fl has only cell-center coordinates (and no edges), then NCO will guess-at (for rectangular grids) or interpolate (for curvilinear grids) the edges. Unstructured grids must supply cell boundary information, as it cannot be interpolated or guessed-at. NCO 312 NCO 5.0.2-alpha07 User Guide only reads coordinate and grid data and metadata from dst fl. dst fl is not modified, and may have read-only permissions. ‘--dt_sng=dt_sng (--dt_sng, --date_string)’ Specifies the date-string use in the full name of map-files created in MWF mode. Map-file names include, by convention, a string to indicate the approximate date (and thus algorithm versions employed) of weight generation. ncremap uses the dt sng argument to encode the date into output map-file names of this format: map_nm_src_to_nm_dst_alg_typ.dt_sng.nc. MWF mode defaults dt sng to the current date in YYYYMMDD-format. ‘--esmf_typ=esmf_typ (--esmf_typ, --esmf_mth, --esmf_extrap_type, --esmf_extrap_method)’ Specifies the extrapolation method used to compute unmapped destination point values with the ERWG weight generator. Valid values, their synonyms, and their meanings are neareststod (synonyms stod and nsd) which uses the nearest valid source value, nearestidavg (synonyms idavg and id) which uses an inverse distance-weighted (with an exponent of xtr xpn) average of the near- est xtr nsp valid source values, and none (synonyms nil and nowaydude) which forbids extrapolation. Default is esmf typ = none. The arguments to options ‘--xtr_xpn=xtr_xpn’ (which defaults to 2.0) and ‘--xtr_nsp=xtr_nsp’ (which defaults to 8) set the parameters that control the extrapolation nearestidavg algorithm. For more information on ERWG extrapolation, see documentation here (http://www.earthsystemmodeling.org/esmf_releases/last_built/ ESMF_refdoc/node3.html#SECTION03022300000000000000). NCO supports this feature as of version 4.7.4 (April, 2018). ‘--xtr_nsp=xtr_nsp (--xtr_nsp, --esmf_pnt_src_nbr, --esmf_extrap_num_src_pnts)’ Specifies the number of source points to use in extrapolating unmapped des- tination point values with the ERWG weight generator. This option is only useful in conjunction with explicitly requested extrapolation types esmf typ = neareststod and esmf typ = nearestidavg. Default is xtr nsp = 8. For more information on ERWG extrapolation, see documentation here (http:// www.earthsystemmodeling.org/esmf_releases/last_built/ESMF_refdoc/ node3.html#SECTION03022300000000000000). NCO supports this feature as of version 4.7.4 (April, 2018). ‘--xtr_xpn=xtr_xpn (--xtr_xpn, --esmf_pnt_src_nbr, --esmf_extrap_num_src_pnts)’ Specifies the number of source points to use in extrapolating unmapped des- tination point values with the ERWG weight generator. This option is only useful in conjunction with explicitly requested extrapolation types esmf typ = neareststod and esmf typ = nearestidavg. Default is xtr xpn = 2.0. For more information on ERWG extrapolation, see documentation here (http:// www.earthsystemmodeling.org/esmf_releases/last_built/ESMF_refdoc/ node3.html#SECTION03022300000000000000). NCO supports this feature as of version 4.7.4 (April, 2018). Chapter 4: Reference Manual 313 ‘-g grd_dst (--grd_dst, --grid_dest, --dest_grid, --destination_grid)’ Specifies the destination gridfile. An existing gridfile may be in any formatac- cepted by the weight generator. NCO will use ERWG or TempestRemap to com- bine grd dst with a source gridfile (either inferred from input-file, supplied with ‘-s grd_src’, or generated from ‘-G grd_sng’) to produce remapping weights. When grd dst is used as input, it is not modified, and may have read-only per- missions. When grd dst is inferred from input-file or created from grd sng, it will be generated in SCRIP format. As of NCO version 4.6.8 (August, 2017), ncremap supports most of the file for- mat options that the rest of NCO has long supported (see Section 3.10 [File Formats and Conversion], page 42). This includes short flags (e.g., ‘-4’) and key-value options (e.g., ‘--fl_fmt=netcdf4’) though not long-flags without val- ues (e.g., ‘--netcdf4’). However, ncremap can only apply the full suite of file format options to files that it creates, i.e., regridded files. The weight generators (ERWG and TempestRemap) are limited in the file formats that they read and write. Currently (August, 2017), ERWG supports CLASSIC, 64BIT_OFFSET, and NETCDF4, while TempestRemap supports only CLASSIC. These can of course be converted to other formats using ncks (see Section 3.10 [File Formats and Con- version], page 42). However, map-files produced in other non-CLASSIC formats can remap significantly larger grids than CLASSIC-format map-files. ‘-G grd_sng (--grd_sng, --grid_generation, --grid_gen, --grid_string)’ Specifies, with together with other options, a source gridfile tocreate3. ncremap creates the gridfile in SCRIP format by default, and then, should the requisite options for regridding be present, combines that with the destination grid (ei- ther inferred from input-file or supplied with ‘-g grd_dst’ and generates map- ping weights. Manual grid-file generation is not frequently used since ncremap can infer many grids directly from the input-file, and few users wish to keep track of SCRIP grids when they can be easily regenerated as intermediate files. This option also allows one to visually tune a grid by rapidly generating candi- dates and inspecting the results. If a desired grid-file is unavailable, and no dataset on that grid is available (so inferral cannot be used), then one must manually create a new grid. Users create new grids for many reasons including dataset intercomparisons, regional studies, and fine-tuned graphics. NCO and ncremap support manual generation of the most common rectangular grids as SCRIP-format grid-files. Create a grid by supplying ncremap with a grid-file name and “grid-formula” (grd sng) that contains, at a minimum, the grid-resolution. The grid-formula is a hash- separated string of name-value pairs each representing a grid parameter. All parameters except grid resolution have reasonable defaults, so a grid-formula can be as simple as ‘latlon=180,360’: ncremap -g grd.nc -G latlon=180,360 3 As of version 4.7.6 (August, 2018)), NCO’s syntax for gridfile generation is much improved and stream- lined, and is the syntax described here. This is also called “Manual Grid-file Generation”. An earlier syntax (described at see Section 3.24 [Grid Generation], page 77) accessed through ncks options still underlies the new syntax, though it is less user-friendly. Both old and new syntax work well and produce finer rectangular grids than any other software we know of. 314 NCO 5.0.2-alpha07 User Guide The SCRIP-format grid-file grd.nc is a valid source or destination grid for ncremap and other regridders. Grid-file generation documentation in the NCO Users Guide at http://nco. sf.net/nco.html#grid describes all the grid parameters and contains many examples. Note that the examples in this section use grid generation API for ncremap version 4.7.6 (August, 2018) and later. Earlier versions can use the ncks API explained at Section 3.24 [Grid Generation], page 77, in the Users Guide. The most useful grid parameters (besides resolution) are latitude type (lat typ), longitude type (lon typ), title (ttl), and, for regional grids, the SNWE bound- ing box (snwe). The three supported varieties of global rectangular grids are Uniform/equiangular (lat typ = uni), Cap/FV (lat typ = cap), and Gaus- sian (lat typ = gss). The four supported varieties of longitude types are the first (westernmost) gridcell centered at Greenwichlon ( typ = grn_ctr), west- ern edge at Greenwish (grn_wst), or at the Dateline (lon typ = 180_ctr and lon typ = 180_wst, respectively). Grids are global, uniform, and have their first longitude centered at Greenwich by default. The grid-formula forthis is ‘lat_typ=uni#lon_typ=grn_ctr’. Some examples (remember, this API re- quires NCO 4.7.6+): ncremap -g grd.nc -G latlon=180,360 # 1x1 Uniform grid ncremap -g grd.nc -G latlon=180,360#lat_drc=n2s # 1x1 Uniform grid, N->S not S->N ncremap -g grd.nc -G latlon=180,360#lon_typ=grn_wst # 1x1 Uniform grid, Greenwich-west edge ncremap -g grd.nc -G latlon=129,256#lat_typ=cap # 1.4x1.4 FV grid ncremap -g grd.nc -G latlon=94,192#lat_typ=gss # T62 Gaussian grid ncremap -g grd.nc -G latlon=361,576#lat_typ=cap#lon_typ=180_ctr # MERRA2 FV grid ncremap -g grd.nc -G latlon=94,192#lat_typ=gss#lat_drc=n2s # NCEP2 T62 Gaussian grid Regional grids are a powerful tool in regional process analyses, and can be much smaller in size than global datasets. Regional grids are always uniform. Specify the rectangular bounding box, i.e., the outside edges of the region, in SNWE order: ncremap -g grd.nc -G ttl="Equi-Angular 1x1 Greenland grid"#latlon=30,90#snwe=55.0,85.0,-90.0,0.0 ‘-I in_drc (--in_drc, --drc_in, --dir_in, --in_dir, input)’ Specifies the input directory, i.e., the directory which contains the input file(s). If in fl is also specified, then the input filepath is constructed by appending a slash and the filename to the directory:in_drc ‘ /in_fl’. Specifying in drc without in fl causes ncremap to attempt to remap every file in in drc that ends with one of these suffixes: .nc, .nc3, .nc4, .nc5, .nc6, .nc7, .cdf, .hdf, .he5, or .h5. When multiple files are regridded, each output file takes the name ofthe corresponding input file. There is no namespace conflict because the input and output files are in separate directories. Note that ncremap can instead accept a list of input files through standard input (e.g.,ls ‘ *.nc | ncremap ...’) or as positional command-line arguments (e.g., ‘ncremap in1.nc in2.nc ...’). ‘-i in_fl (--in_fl, --in_file, --input_file)’ Specifies the file containing data on the source grid to be remapped tothedes- tination grid. When provided with the optional map fl, ncremap only reads Chapter 4: Reference Manual 315 data from in fl in order to regrid it. Without the optional map fl or src grd, ncremap will try to infer the source grid from in fl, and so must read coordi- nate and metatdata information from in fl. In this case the more coordinate and boundary information and metadata, the better NCO will do at inferring the source grid. If in fl has cell boundaries then NCO will use those. If in fl has only cell-center coordinates (and no edges), then NCO will guess (for rectangular grids) or interpolate (for curvilinear grids) the edges. Unstructured grids must supply cell boundary information, as it cannot be interpolated or guessed-at. in fl is not modified, and may have read-only permissions. Note that ncremap can instead accept input file name(s) through standard input (e.g.,ls ‘ *.nc | ncremap ...’) or as positional command-line arguments (e.g., ‘ncremap in1.nc in2.nc ...’). When one or three-or-more positional arguments are given, they are all interpreted as input filename(s). Two positional arguments are inter- preted as a single input-file and its corresponding output-file. ‘-j job_nbr (--job_nbr, --job_number, --jobs)’ Specifies the number of simultaneous regridding processes to spawn during par- allel execution for both Background and MPI modes. In both parallel modes ncremap spawns processes in batches of job nbr jobs, then waits for those pro- cesses to complete. Once a batch finishes, ncremap spawns the next batch. In Background mode, all jobs are spawned to the local node. In MPI mode, all jobs are spawned in round-robin fashion to all available nodes until job nbr jobs are running. If regridding consumes so much RAM (e.g., because variables are large and/or the number of threads is large) that a single node can perform only one re- gridding job at a time, then a reasonable value for job nbr is the number of nodes, node nbr. Often, however, nodes can regrid multiple files simultane- ously. It can be more efficient to spawn multiple jobs per node than to increase the threading per job because I/O contention for write access to a single file prevents threading from scaling indefinitely. By default job nbr = 2 in Background mode, and job nbr = node nbr in MPI mode. This helps prevent users from overloading nodes with too many jobs. Subject to the availability of adequate RAM, expand the number of jobs per node by increasing job nbr until, ideally, each core on the node is used. Remember that processes and threading are multiplicative in core use. Four jobs each with four threads each consumes sixteen cores. As an example, consider regridding 100 files with a single map. Say you havea five-node cluster, and each node has 16 cores and can simultaneously regridtwo files using eight threads each. (One needs to test a bit to optimize theseparam- eters.) Then an optimal (in terms of wallclock time) invocation would request five nodes with 10 simultaneous jobs of eight threads. On PBS or SLURM batch systems this would involve a scheduler command like ‘qsub -l nodes=5 ...’ or ‘sbatch --nodes=5 ...’, respectively, followed by ‘ncremap --par_typ=mpi --job_nbr=10 --thr_nbr=8 ...’. This job will likely complete between five and ten-times faster than a serial-mode invocation of ncremap to regrid the same files. The uncertainty range is due to unforeseeable, system-dependent load and I/O charateristics. Nodes that can simultaneously write to more than 316 NCO 5.0.2-alpha07 User Guide one file fare better with multiple jobs per node. Nodes with only oneI/O channel to disk may be better exploited by utilizing more threads per process. ‘-M (--mlt_map, --multimap, --no_multimap, --nomultimap)’ ncremap assumes that every input file is on a unique grid unless a source grid- file is specified (with ‘-s grd_src’) or multiple-mapfile generation is explicitly turned-off (with ‘-M’). The ‘-M’ switch is a toggle, it requires and accepts no argument. Toggling ‘-M’ tells ncremap to generate at most one mapfile regard- less of the number of input files. If-M ‘ ’ is not toggled (and neither ‘-m map_fl’ nor ‘-s grd_src’ is invoked) then ncremap will generate a new mapfile for each input file. Generating new mapfiles for each input file is necessary forprocess- ing batches of data on different grids (e.g., swath-like data), and slow, tedious, and unnecessary when batch processing data on the same grids. ‘-m map_fl (--map_fl, --map, --map_file, --rgr_map, --regrid_map)’ Specifies a mapfile (i.e., weight-file) to remap the source to destination grid.If map fl is specified in conjunction with any of the‘-d’, ‘-G’, ‘-g’, or ‘-s’ switches, then ncremap will name the internally generated mapfile map fl. Otherwise (i.e., if none of the source-grid switches are used), ncremap assumes that map fl is a pre-computed mapfile. In that case, the map fl must be in SCRIP format, although it may have been produced by any application (usually ERWG or TempestRemap). If map fl has only cell-center coordinates (and no edges), then NCO will guess-at or interpolate the edges. If map fl has cell boundaries then NCO will use those. A pre-computed map fl is not modified, and may have read-only permissions. The user will be prompted to confirm if a newly generated map-file named map fl would overwrite an existing file. ncremap adds provenance information to any newly generated map-file whose name was specified with ‘-m map_fl’. This provenance includes a history attribute that contains the command invoking ncremap, and the map-generating command invoked by ncremap. ‘--mpi_pfx=mpi_pfx (--mpi_pfx, --mpi_prefix, --srun_cmd, --srun_command)’ ‘--mpi_nbr=mpi_nbr (--mpi_nbr, --mpi_number, --tsk_nbr, --task_number)’ The ‘--mpi_pfx=mpi_pfx’ option specifies an appropriate job scheduler pre- fix for MPI-enabled weight-generation executables such as ESMF’s ESMF_ RegridWeightGen and MoabTempest’s mbtempest. Other weight generators (ncks, GenerateOfflineMap) are unaffected by this option since they are not MPI-enabled. mpi pfx defaults to mpirun -n ${mpi_nbr} on all machines ex- cept those whose $HOSTNAME matches an internal database of DOE-operated supercomputers where mpi pfx defaults to srun -n ${mpi_nbr}. When invok- ing ‘--mpi_pfx’, be sure to explicitly define the number of MPI tasks-per-node, e.g., ncremap --mpi_pfx=’srun -n 16’ ... ncremap --mpi_pfx=’srun --mpi=pmi2 -n 4’ ... The separate ‘--mpi_nbr=mpi_nbr’ option specifies the number of tasks-per- node that MPI-enabled weight generators will request. It preserves the default job scheduler prefix (srun or mpirun): ncremap --mpi_nbr=4 ... # 16 MPI tasks-per-node for ERWG/mbtempest Chapter 4: Reference Manual 317 ncremap --mpi_nbr=16 ... # 4 MPI tasks-per-node for ERWG/mbtempest Thus ‘--mpi_nbr=mpi_nbr’ can be used to create host-independent ncremap commands to facilitate benchmarking the scaling of weight-generators across hosts that work with the default value of mpi pfx. The ‘--mpi_pfx’ option will prevail and ‘--mpi_nbr’ will be ignored if both are used in the same ncremap invocation. Note that ‘mpi_pfx’ is only used internally by ncremap to exploit the MPI capabilities of select weight-generators. It is not used to control and does not affect the distribution of multiple ncremap commands among a cluster of nodes. ‘--msh_fl=msh_fl (--msh_fl, --msh, --mesh, --mesh_file)’ Specifies a meshfile (aka intersection mesh, aka overlap mesh) that stores the grid formed by the intersection of the source and destination grids. If not specified then ncremap will name any internally generated meshfile with a tem- porary name and delete the file prior to exiting. NCO and TempestRemap support archiving the meshfile, and ERWG does not. NCO stores the meshfile in SCRIP format, while TempestRemap stores it in Exodus format (with a ‘.g’ suffix). ncremap adds provenance information to any newly generated mesh-file whose name was specified with ‘--msh_fl=msh_fl’. This provenance includes a history attribute that contains the command invoking ncremap, and the map-generating command invoked by ncremap. ‘--msk_apl (--msk_apl, --mask_apply, --msk_app)’ Introduced in NCO version 5.0.0 (released June, 2021), this switch (which takes no argument) causes the regridder to apply msk out (i.e., mask_b) to vari- ables after regridding. Some weight generators (e.g., Tempest) ignore masks and thus produce non-zero weights for masked destination cells, and/or from masked source cells. This flag causes regridded files produced with such map- files to adhere to the destination mask rules (though source mask rulesmay still be violated). This feature is especially useful in placing missing values (aka, _FillValue) in destination cells that should be empty, so that regridded files have _FillValue distributions identical with output from other weight- generators such as ESMF and NCO. ncremap --msk_apl -v FLNS -m map.nc in.nc out.nc ncremap --msk_apl --add_fll -v FLNS -m map.nc in.nc out.nc # Equivalent Note that --msk_apl automatically triggers --add_fll to ensure that masked fields regridded with TempestRemap-generated map-files have _FillValues consistent with map-files generated by ESMF and NCO. ‘--msk_dst=msk_dst (--msk_dst, --dst_msk, --mask_destination, --mask_dst)’ Specifies a template variable to use for the integer mask of the destination grid when inferring grid files and/or creating map-files (i.e., generating weights). Any variable on the same horizontal grid as a data file can serve as a mask template for that grid. The mask will be one (i.e., gridcells will participate in regridding) where msk dst has valid, non-zero values in the data file from which NCO infers the destination grid. The mask will be zero (i.e., gridcells will not participate in regridding) where msk nm has a missing value or is zero. A typical example of this option would be to use Sea-surface Temperature 318 NCO 5.0.2-alpha07 User Guide (SST) as a template variable for an ocean mask because SST is often defined only over ocean, and missing values might denote locations to which regridded quantities should never be placed. The special value msk dst = none prevents the regridder from inferring and treating any variable (even one named, e.g., mask) in a source file as a mask variable. This guarantees that all points inthe inferred destination grid will be unmasked. msk dst, msk out, and msk src are related yet distinct: msk dst is the mask template variable in the destination file (whose grid will be inferred), msk out is the name to give the destination mask in regridded data files, and msk src is the mask template variable in the source file (whose grid will be inferred). msk src and msk dst only affect inferred grid files for the source and destination grids, respectively, whereas msk out only affects regridded files. ‘--msk_out=msk_out (--msk_out, --out_msk, --mask_destination, --mask_out)’ Use of this option tells ncremap to include a variable named msk out in any regridded file. The variable msk out will contain the integer-valued regridding mask on the destination grid. The mask will be one (i.e., fields may have valid values in this gridcell) or zero (i.e., fields will have missing values in this grid- cell). By default, ncremap does not output the destination mask to the regrid- ded file. This option changes that default behavior and causes ncremap to ingest the default destination mask variable contained in the map-file. ERWG gener- ates SCRIP-format map-files that contain the destination mask in the variable named mask_b. SCRIP generates map-files that contain the destination mask in the variable named dst_grid_imask. The msk_out option works with map-files that adhere to either of these conventions. Tempest generates map-files that do not typically contain the destination mask, and so the msk_out option has no effect on files that Tempest regrids. msk dst, msk out, and msk src are re- lated yet distinct: msk dst is the mask template variable in the destination file (whose grid will be inferred), msk out is the name to give the destination mask in regridded data files, and msk src is the mask template variable in the source file (whose grid will be inferred). msk src and msk dst only affect inferred grid files for the source and destination grids, respectively, whereas msk out only affects regridded files. ‘--msk_src=msk_src (--msk_src, --src_msk, --mask_source, --mask_src)’ Specifies a template variable to use for the integer mask of the source grid when inferring grid files and/or creating map-files (i.e., generating weights). Any variable on the same horizontal grid as a data file can serve as a mask template for that grid. The mask will be one (i.e., gridcells will participate in regridding) where msk src has valid, non-zero values in the data file from which NCO infers the source grid. The mask will be zero (i.e., gridcells will not participate in regridding) where msk nm has a missing value or is zero. A typical example of this option would be to use Sea-surface Temperature (SST) as a template variable for an ocean mask because SST is often defined only over ocean, and missing values might denote locations from which regridded quantities should emanate. The special value msk src = none prevents the regridder from inferring and treating any variable (even one named, e.g., mask) in a source file as a mask variable. This guarantees that all points in the inferred Chapter 4: Reference Manual 319 source grid will be unmasked. msk dst, msk out, and msk src are related yet distinct: msk dst is the mask template variable in the destination file (whose grid will be inferred), msk out is the name to give the destination mask in regridded data files, and msk src is the mask template variable in the source file (whose grid will be inferred). msk src and msk dst only affect inferred grid files for the source and destination grids, respectively, whereas msk out only affects regridded files. ‘--mss_val=mss_val (--mss_val, --fll_val, --missing_value, --fill_value)’ Specifies the numeric value that indicates missing data when processing MPAS datasets, i.e., when ‘-P mpas’ is invoked. The default missing value is -9.99999979021476795361e+33 which is correct for the MPAS ocean and sea- ice models. Currently (January, 2018) the MPAS land-ice model uses -1.0e36 for missing values. Hence this option is usually invoked as ‘--mss_val=-1.0e36’ to facilitate processing of MPAS land-ice datasets. ‘-n nco_opt (--nco_opt, --nco_options, --nco)’ Specifies a string of options to pass-through unaltered to ncks. nco opt defaults to ‘-O --no_tmp_fl’. ‘--nm_dst=nm_dst (--nm_dst, --name_dst, --name_short_destination, --nm_sht_dst)’ Specifies the short name for the destination grid to use in the full name ofmap- files created in MWF mode. Map-file names include, by convention, shortened versions of both the source and destination grids. ncremap uses the nm dst argument to encode the destination grid name into the output map-file name of this format: map_nm_src_to_nm_dst_alg_typ.dt_sng.nc. MWF mode re- quires this argument, there is no default. ‘--nm_src=nm_src (--nm_src, --name_src, --name_short_source, --nm_sht_src)’ Specifies the short name for the source grid to use in the full nameofmap- files created in MWF mode. Map-file names include, by convention, shortened versions of both the source and destination grids. ncremap uses the nm dst ar- gument to encode the source grid name into the output map-file name of this for- mat: map_nm_src_to_nm_dst_alg_typ.dt_sng.nc. MWF mode requires this argument, there is no default. ‘--no_cll_msr (--no_cll_msr, --no_cll, --no_cell_measures, --no_area)’ This switch (which takes no argument) controls whether ncclimo and ncremap add measures variables to the extraction list along with the primary variable and other associated variables. See Section 3.45 [CF Conventions], page 146, for a detailed description. ‘--no_frm_trm (--no_frm_trm, --no_frm, --no_formula_terms)’ This switch (which takes no argument) controls whether ncclimo and ncremap add formula variables to the extraction list along with the primary variable and other associated variables. See Section 3.45 [CF Conventions], page 146, for a detailed description. 320 NCO 5.0.2-alpha07 User Guide ‘--no_stg_grd (--no_stg_grd, --no_stg, --no_stagger, --no_staggered_grid)’ This switch (which takes no argument) controls whether regridded output will contain the staggered grid coordinates slat, slon, and w_stag (see Section 3.25 [Regridding], page 86). By default the staggered grid is output for all files regridded from a Cap (aka FV) grid, except when the regridding is performed as part of splitting (reshaping) into timeseries. ‘-O out_drc (--out_drc, --drc_out, --dir_out, --out_dir, --output)’ Specifies the output directory, i.e., the directory name to contain theoutput file(s). If out fl is also specified, then the output filepath is constructed by appending a slash and the filename to the directory: ‘out_drc/out_fl’. Speci- fying out drc without out fl causes ncremap to name each output file the same as the corresponding input file. There is no namespace conflict because the input and output files will be in separate directories. ‘-o out_fl (--out_fl, --output_file, --out_file)’ Specifies the output filename, i.e., the name of the file to contain thedata from in fl remapped to the destination grid. If out fl already exists it will be overwritten. Specifying out fl when there are multiple input files (i.e., from using ‘-I in_drc’ or standard input) generates an error (output files will be named the same as input files). Two positional arguments are interpreted asa single input-file and its corresponding output-file. ‘-P prc_typ (--prc_typ, --pdq_typ, --prm_typ, --procedure)’ Specifies the permutation mode desired. Asof NCO version 4.5.5 (February, 2016), one can tell ncremap to invoke special processing procedures for different types of input data. For instance, to automatically permute the dimensions in the data file prior to regridding for a limited (though growing) number of data-file types that encounter the ncremap limitation concerning dimension ordering. Valid procedure types include ‘airs’ for NASA AIRS satellite data, ‘eam’ or ‘cam’ for DOE EAM and NCAR CAM model data, ‘elm’ or ‘clm’ for DOE ELM and NCAR CLM model data, ‘cice’ for CICE ice model data (must be on 2D grids), ‘cism’ for NCAR CISM land ice model data, ‘mpascice’, ‘mpasseaice’, ‘mpas-seaice’, or ‘mpassi’ for MPAS sea-ice model data, ‘mpaso’ or ‘mpas-ocean’ for MPAS ocean model data, ‘mod04’ for Level 2 MODIS MOD04 product, ‘mwf’ for making all weight-files for a pair of grids, ‘sgs’ for datasets containing sub-gridscale (SGS) data (such as CLM/CTSM/ELM land model data and CICE/MPAS-Seaice sea-ice model data), and ‘nil’ (for none). The default prc typ is ‘nil’, which means ncremap does not perform any special procedures prior to regridding. The AIRS procedure calls ncpdq to permute dimensions from their order in the input file to this order: StdPressureLev,GeoTrack,GeoXTrack. The ELM, CLM, and CICE procedures set idiosyncratic model values and then invoke the Sub-gridscale (SGS) procedure (see below). The MOD04 procedure unpacks in- put data. The MPAS procedures permute input data dimensions into this order: Time,depth,nVertInterfaces,nVertLevels,nVertLevelsP1,nZBGCTracers,nBioLayersP1,nAlgaeIceLayers,nDisIronIceLayers,nIceLayers,maxEdges,MaxEdges2,nCategories,R3,ONE,TWO,FOUR,nEdges,nCells, and invokes renormalization. An MPAS dataset that contains any other dimensions will fail to regrid until/unless those dimensions are added to the ncremap dimension permutation option. Chapter 4: Reference Manual 321 MWF-mode: As mentioned above in other options, ncremap includes an MWF-mode (for “Make All Weight Files”) that generates and names, with one command and in a self-consistent manner, all combinations of (for instance, E3SM or CESM) global atmosphere<->ocean maps with both ERWG and Tempest. MWF-mode automates the laborious and error-prone process of generating numerous map-files with various switches. Its chief use occurs when developing and testing new global grid-pairs for the E3SM atmosphere and ocean components. Invoke MWF-mode with a number of specialized options to control the naming of the output map-files: ncremap -P mwf -s grd_ocn -g grd_atm --nm_src=ocn_nm \ --nm_dst=atm_nm --dt_sng=date where grd ocn is the "global" ocean grid, grd atm, is the global atmosphere grid, nm src sets the shortened name for the source (ocean) grid as it will appear in the output map-files, nm dst sets, similarly, the shortend named for the des- tination (atmosphere) grid, and dt sng sets the date-stamp in the output map- file name map_${nm_src}_to_${nm_dst}_${alg_typ}.${dt_sng}.nc. Setting nm src, nm dst, and dt sng, is optional though highly recommended. For ex- ample, ncremap -P mwf -s ocean.RRS.30-10km_scrip_150722.nc \ -g t62_SCRIP.20150901.nc --nm_src=oRRS30to10 --nm_dst=T62 \ --dt_sng=20180901 produces the 10 ERWG map-files: 1. map_oRRS30to10_to_T62_aave.20180901.nc 2. map_oRRS30to10_to_T62_blin.20180901.nc 3. map_oRRS30to10_to_T62_ndtos.20180901.nc 4. map_oRRS30to10_to_T62_nstod.20180901.nc 5. map_oRRS30to10_to_T62_patc.20180901.nc 6. map_T62_to_oRRS30to10_aave.20180901.nc 7. map_T62_to_oRRS30to10_blin.20180901.nc 8. map_T62_to_oRRS30to10_ndtos.20180901.nc 9. map_T62_to_oRRS30to10_nstod.20180901.nc 10. map_T62_to_oRRS30to10_patc.20180901.nc The ordering of source and destination grids is immaterial for ERWG maps since MWF-mode produces all map combinations. However, as described above in the TempestRemap section, the Tempest overlap-mesh generator must be called with the smaller grid preceding the larger grid. For this reason, always invoke MWF-mode with the smaller grid (i.e., the ocean) as the source, otherwise some Tempest map-file will fail to generate. The six optimized SE<->FV Tempest maps described above in the TempestRemap section will be generated when the destination grid has a ‘.g’ suffix which ncremap interprets as indicating an Exodus-format SE grid (NB: this assumption is an implementation convenience that can be modified if necessary). For example, 322 NCO 5.0.2-alpha07 User Guide ncremap -P mwf -s ocean.RRS.30-10km_scrip_150722.nc -g ne30.g \ --nm_src=oRRS30to10 --nm_dst=ne30np4 --dt_sng=20180901 produces the 6 TempestRemap map-files: 1. map_oRRS30to10_to_ne30np4_monotr.20180901.nc 2. map_oRRS30to10_to_ne30np4_highorder.20180901.nc 3. map_oRRS30to10_to_ne30np4_mono.20180901.nc 4. map_ne30np4_to_oRRS30to10_mono.20180901.nc 5. map_ne30np4_to_oRRS30to10_highorder.20180901.nc 6. map_ne30np4_to_oRRS30to10_intbilin.20180901.nc MWF-mode takes significant time to complete~ ( 20 minutes on my Mac- BookPro) for the above grids. To accelerate this, consider installing the MPI- enabled instead of the serial version of ERWG. Then use the ‘--wgt_cmd’ option to tell ncremap the MPI configuration to invoke ERWG with, for example: ncremap -P mwf --wgt_cmd=’mpirun -np 12 ESMF_RegridWeightGen’ \ -s ocean.RRS.30-10km_scrip_150722.nc -g t62_SCRIP.20150901.nc \ --nm_src=oRRS30to10 --nm_dst=T62 --dt_sng=20180901 Background and distributed node parallelism (as described above in the the Parallelism section) of MWF-mode are possible though not yet implemented. Please let us know if this feature is desired. RRG-mode: EAM and CAM-SE will produce regional output if requested to with the finclNlonlat namelist parameter. Output for a single region can be higher temporal resolution than the host global simulation. This facilitates detailed yet economical regional process studies. Regional output files are in a special format that we call RRG (for “regional regridding”). An RRG file may con- tain any number of rectangular regions. However, ncremap can process only one region per invocation (change the argument to the ‘--rnm_sng’ option, de- scribed below, in each invocation). The coordinates and variables for one region do not interfere with other (possibly overlapping) regions because all variables and dimensions are named with a per-region suffix string, e.g., lat_128e_to_ 134e_9s_to_16s. ncremap can easily regrid RRG output from an atmospheric FV-dycore because ncremap can infer (as discussed above) the regional grid from any rectangular FV data file. Regridding regional SE data, however, is more complex because SE gridcells are essentially weights without vertices and SE weight-generators are not yet flexible enough to output regional weights. To summarize, regridding RRG data leads to three SE-specific difficulties (#1–3 below) and two difficulties (#4–5) shared with FV RRG files: 1. RRG files contain only regional gridcell center locations, not weights 2. Global SE grids have well-defined weights not vertices for each gridpoint 3. Grid generation software (ESMF and TempestRemap) only create global not regional SE grid files 4. Non-standard variable names and dimension names 5. Regional files can contain multiple regions Chapter 4: Reference Manual 323 ncremap’s RRG mode resolves these issues to allow trouble-free regridding of SE RRG files. The user must provide two additional input arguments, ‘--dat_glb=dat_glb’ (or synonynms ‘--rrg_dat_glb’, ‘--data_global’, or ‘--global_data’) and ‘--grd_glb=grd_glb’ (or synonyms ‘--rrg_grd_glb’, ‘--grid_global’, or ‘global_grid’) that point to a global SE dataset and grid, respectively, of the same resolution as the model that generated the RRG datasets. Hence a typical RRG regridding invocation is: ncremap --dat_glb=dat_glb.nc --grd_glb=grd_glb.nc -g grd_rgn.nc \ dat_rgn.nc dat_rgr.nc Here grd_rgn.nc is a regional destination grid-file, dat_rgn.nc is the RRG file to regrid, and dat_rgr.nc is the regridded output. Typically grd_rgn.nc is a uniform rectangular grid covering the same region as the RRG file. Generate this as described in the last example in the section that describes Manual Grid-file Generation with the ‘-G’ option. grd_glb.nc is the standard dual-grid grid-file for the SE resolution, e.g., ne30np4_pentagons.091226.nc. ncremap regrids the global data file dat_glb.nc to the global dual-grid in order to produce a intermediate global file annotated with gridcell vertices. Then it hyperslabs the lat/lon coordinates (and vertices) from the regional domain to use with regridding the RRG file. A grd_glb.nc file with only one 2D field suffices (and is fastest) for producing the information needed by the RRG procedure. One can prepare an optimal dat_glb.nc file by subsetting any 2D variable from any full global SE output dataset with, e.g., ‘ncks -v FSNT in.nc dat_glb.nc’. ncremap RRG mode supports two additional options to override internal param- eters. First, the per-region suffix string may be set with‘--rnm_sng=rnm_sng’ (or synonyms ‘--rrg_rnm_sng’ or ‘--rename_string’). RRG mode will, by default, regrid the first region it finds inan RRG file. Explicitly set the desired region with rnm sng for files with multiple regions, e.g., ‘--rnm_sng=_128e_to_134e_9s_to_16s’. Second, the regional bounding-box may be explicitly set with ‘--bb_wesn=lon_wst,lon_est,lat_sth,lat_nrt’. The normal parsing of the bounding-box string from the suffix string may fail in (as yet undiscovered) corner cases, and the ‘--bb_wesn’ option provides a workaround should that occur. The bounding-box string must include the en- tire RRG region (not a subset thereof), specified in WESN order. The two override options may be used independently or together, as in: ncremap --rnm_sng=’_128e_to_134e_9s_to_16s’ --bb_wesn=’128,134,-16,-9’ \ --dat_glb=dat_glb.nc --grd_glb=grd_glb.nc -g grd_rgn.nc \ dat_rgn.nc dat_rgr.nc RRG-mode supports most normal ncremap options, including input and output methods and regridding algorithms. SGS-mode: ncremap has a sub-gridscale (SGS) mode that performs the special pre- processing and weighting necessary to to conserve fields that represent frac- tional spatial portions of a gridcell, and/or fractional temporal periods of the analysis. Spatial fields output by most geophysical models are intensive, and so by default the regridder attempts to conserve the integral of the area times 324 NCO 5.0.2-alpha07 User Guide the field value such that the integral is equal on source and destination grids. However some models (like ELM, CLM, CICE, and MPAS-Seaice) output gridcell values intended to apply to only a fraction sgs frc (for “sub-gridscale fraction”) of the gridcell. The sub-gridscale (SGS) fraction usually changes spatially with the distribution of land and ocean, and spatiotemporally with the distribution of sea ice and possibly vegetation. For concreteness consider a sub-grid field that represents the land fraction. Land fraction is less than one in gridcells that resolve coastlines or islands. ELM and CLM happily output temperature values valid only for a small (i.e., sgs frc << 1) island within the larger grid- cell. Model architecture dictates this behavior and savvy researchers expect it. The goal of the NCO weight-application algorithm is to treat SGS fields as seamlessly as possible so that those less familiar with sub-gridscale models can easily regrid them correctly. Fortunately, models like ELM and CLM that run on the same horizontal grid as the overlying atmosphere can use the same mapping-file as the atmosphere, so long as the SGS weight-application procedure is invoked. Not invoking an SGS-aware weight application algorithm is equivalent to assuming sgs frc = 1 everywhere. Regridding sub-grid values correctly versus incorrectly (e.g., with and without SGS-mode) alters global-mean answers for land-based quantities by about 1% for horizontal grid resolutions of about one degree. The resulting biases are in intricately shaped regions (coasts, lakes, sea-ice floes) and so are easy to overlook. To invoke SGS mode and correctly regrid sub-gridscale data, specify the names of the fractional area sgs frc and, if applicable, the mask variable sgs msk (strictly, this is only necessary if these names differ from their respective defaults landfrac and landmask). Trouble will ensue if sgs frc is a percentage or an ab- solute area rather than a fractional area (between zero and one). ncremap must know the normalization factor sgs nrm by which sgs frc must be divided (not multiplied) to obtain a true, normalized fraction. Datasets (such as those from CICE) that store sgs frc in percent should specify the option ‘--sgs_nrm=100’ to instruct ncremap to normalize the sub-grid area appropriately before regrid- ding. ncremap will re-derive sgs msk based on the regridded values of sgs frc: sgs msk = 1 is assigned to destination gridcells with sgs frc > 0.0, and all others sgs msk = 0. As of NCO version 4.6.8 (released June, 2017), invoking any of the options ‘--sgs_frc’, ‘--sgs_msk’, or ‘--sgs_nrm’, automatically triggers SGS-mode, so that also invoking ‘-P sgs’ is redundant though legal. As of NCO version 4.9.0 (released December, 2019), the values of the sgs frc and sgs msk variables should be explicitly specified. In previous versions they defaulted to landfrac and landmask, respectively, when ‘-P sgs’ was selected. This behavior still exists but will likely be deprecated in a future version. The area and sgs frc fields in the regridded file will be in units of sterradians and fraction, respectively. However, ncremap offers custom options to reproduce the idiosyncratic data and metadata format of two particular models, ELM and CICE. When invoked with ‘-P elm’ (or ‘-P clm’), a final step converts the output area from sterradians to square kilometers. When invoked with Chapter 4: Reference Manual 325 ‘-P cice’, the final step converts the output area from sterradians to square meters, and the output sgs_frc from a fraction to a percent. # ELM/CLM: output "area" in [sr] ncremap --sgs_frc=landfrac --sgs_msk=landmask in.nc out.nc ncremap -P sgs in.nc out.nc # Deprecated in 4.9.0 # ELM/CLM pedantic format: output "area" in [km2] ncremap -P elm in.nc out.nc # Same as -P clm, alm, ctsm # CICE: output "area" in [sr] ncremap --sgs_frc=aice --sgs_msk=tmask --sgs_nrm=100 in.nc out.nc # CICE pedantic format: output "area" in [m2], "aice" in [%] ncremap -P cice in.nc out.nc # MPAS-Seaice: both commands are equivalent ncremap -P mpasseaice in.nc out.nc ncremap --sgs_frc=timeMonthly_avg_iceAreaCell in.nc out.nc It is sometimes convenient to store the sgs frc field in an external file from the field(s) to be regridded. For example, CMIP-style timeseries are often written with only one variable per file. NCO supports this organization by accepting sgs frc arguments in the form of a filename followed by a slash and thena variable name: ncremap --sgs_frc=sgs_landfrac_ne30.nc/landfrac -m map.nc in.nc out.nc Files regridded using explicitly specified SGS options will differ slightly from those regridded using the ‘-P elm’ or ‘-P cice’ options. The former will have an area field in sterradians, the generic units used internally by the regridder. The latter produces model-specific area fields in square kilometers (for ELM) or square meters (for CICE), as expected in the raw output from these two models. To convert from angular to areal values, NCO assumes a spherical Earth with radius 6,371,220 m or 6,371,229 m, for ELM and CICE, respectively. The ouput sgs frc field is expressed as a decimal fraction in all cases except-P for‘ cice’ which stores the fraction in percent. Thus the generic SGS and model-specific convenience options produce equivalent results, and the latter is intended to be indistinguishable (in terms of metadata and units) to raw model output. This makes it more interoperable with many existing analysis scripts. ‘-p par_typ (--par_typ, --par_md, --parallel_type, --parallel_mode, --parallel)’ Specifies the desired file-level parallelism mode, either Background, MPI, or Serial. File-level parallelism accelerates throughput when regridding multiple files in one ncremap invocation, and has no effect when only one file is tobe regridded. Note that the ncclimo and ncremap semantics for selecting file- level parallelism are identical, though their defaults differ (Background mode for ncclimo and Serial mode for ncremap). Select the desired mode with the argument to ‘--par_typ=par_typ’. Explicitly select Background mode with par typ values of bck, background, or Background. The values mpi or MPI select MPI mode, and the srl, serial, Serial, nil, or none will select Serial 326 NCO 5.0.2-alpha07 User Guide mode (which disables file-level parallelism, though still allows intra-file OpenMP parallelism). The default file-level parallelism for ncremap is Serial mode (i.e., no file-level parallelism), in which ncremap processes one input file at a time. Background and MPI modes implement true file-level parallelism. Typically both these par- allel modes scale well with sufficent memory unless and until I/O contention becomes the bottleneck. In Background mode ncremap issues all commands to regrid the input file list as UNIX background processes on the local node. Nodes with mutiple cores and sufficient RAM take advantage of this to simultaneously regrid multiple files. In MPI mode ncremap issues commands to regrid the input file list in round-robin fashion to all available compute nodes. Priorto NCO ver- sion 4.9.0 (released December, 2019), Background and MPI parallelism modes both regridded all the input files at one time and there was no way to limit the number of files being simultaneously regridded. Subsequent versions allow finer grained parallelism by introducing the ability to limit the number of discrete workflow elements or “jobs” (i.e., file regriddings) to perform simultaneously within an ncremap invocation or “workflow”. As of NCO version 4.9.0 (released December, 2019), the ‘--job_nbr=job_nbr’ option specifies the maximum number of files to regrid simultaneously onall nodes being harnessed by the workflow. Thus job nbr is an additional param- eter to fine-tune file level parallelism (it has no effect in Serial mode). Please see the ncremap job nbr documentation for more details. ‘--pdq_opt pdq_opt (--pdq, --prm_opt, --prm, --permute)’ Specifies the dimension permutation option used by ncpdq prior to regrid- ding. Synonyms include ‘--pdq’, ‘--prm’, ‘--prm_opt’, and ‘--permute’. Files to be regridded must have their horizontal spatial dimension(s) in the last (most-rapidly-varying) position. Most data files store variables with dimen- sions arranged in this order, and ncremap internally sets the permutation op- tion for datasets known (via the --prc_typ option) to require permutation. Use ‘--pdq=pdq_opt’ to override the internally preset defaults. This is useful when regridding files that contain new dimensions that ncremap has not en- countered before. For example, if a development version of an MPAS model inserts a new dimension new_dim after the horizontal spatial dimension nCells in some variables, that would prevent the regridder from working because the horizontal dimension(s) must be the last dimension(s). The workaround is to instruct ncremap what the permutation option to ncpdq should be in order to place the horizontal spatial dimension(s) at the end of all variables: ncremap --pdq=’-a Time,new_dim,nCells’ --map=map.nc in.nc out.nc ncremap --pdq=’-a time,new_dim,lat,lon’ --map=map.nc in.nc out.nc ‘--no_permute (--no_permute, --no_prm, --no_pdq, --no_ncpdq)’ Introduced in NCO version 5.0.0 (released June, 2021), this switch (which takes no argument) causes the regridder to skip the default permutation of dimen- sions before regridding (notably MPAS) datasets known to store data with non- horizontal most-rapidly varying dimensions. ncremap normally ensures that input fields are stored in the shape expected by regridder weights (horizontal Chapter 4: Reference Manual 327 dimensions last) by permuting the dimensions with ncpdq. However, permuta- tion consumes time and generates an extra intermediate file. Avoid this time penalty by using the ‘--no_permute’ flag if the input fields are known to already have trailing horizontal dimensions. ‘--preserve=prs_stt (--preserve, --prs_stt, --preserve_statistic)’ This is a simple, intuitive option to specify how weight application should treat destination gridcells that are not completely overlapped by source gridcells with valid values. Destination gridcells that are completely overlapped by valid source values are unaffected. The two statistics that can be preserved forin- completely overlapped gridcells are the local mean and/or the global integral of the source values. Hence the valid values for this option are ‘integral’ (or, as of NCO version 5.0.2, released in September, 2021, its synonyms ‘global’ and ‘global_integral’) and ‘mean’ (or its synonyms ‘local’ and ‘local_mean’). Specifying --preserve=integral sets the destination gridcell equal to the sum of the valid source values times their source weights. This sum is not renormal- ized by the (valid) fractional area covered. This is exactly equivalent to setting --rnr=off, i.e., no renormalization (see Section 3.25 [Regridding], page 86). If the weights were generated by a conservative algorithm then the output will be conservative, and will conserve the global integral of the input field in all cases. This is often desired for regridding quantities that should be conserved, e.g., fluxes, and is the default weight application method in ncremap (except in MPAS-mode). Specifying --preserve=mean sets the destination gridcell equal to the mean of the valid source values times their source weights. This is exactly equivalent to setting --rnr=0.0, i.e., renormalizing the integral value by the (valid) fractional area covered (see Section 3.25 [Regridding], page 86). This is often desired for regridding state variables, e.g., temperature, though it is not the default behavior and must be explicitly requested (except in MPAS-mode). These two types of preserved statistics, integral and mean, produce identical output in all gridcells where there are no missing data, i.e., where valid data completely tile the gridcell. By extension, these two statistics produce identical global means if valid data completely tile the sphere. ‘-R rgr_opt (--rgr_opt, --regrid_options)’ ncremap passes rgr opt directly through to the regridder. This is useful to cus- tomize output grids and metadata. One use is to rename output variables and dimensions from the defaults provided by or derived from input data. The de- fault value is ‘--rgr lat_nm_out=lat --rgr lon_nm_out=lon’, i.e., by default ncremap always names latitude and longitude “lat” and “lon”, respectively, regardless of their input names. Users might use this option to set different canonical axes names, e.g., ‘--rgr lat_nm_out=y --rgr lon_nm_out=x’. ‘-r rnr_thr (--rnr_thr, --thr_rnr, --rnr, --renormalize, --renormalization_threshold)’ Use this option to request renormalized (see Section 3.25 [Regridding], page 86) weight-application and to specify the weight threshold, if any. For example, ‘-r 0.9’ tells the regridder to renormalize with a weight threshold of 90%, so that all destination gridcells with at least 90% of their area contributed by valid source gridcells will be contain valid (not missing) values that are the area-weighted 328 NCO 5.0.2-alpha07 User Guide mean of the valid source values. If the weights are conservative, then the output gridcells on the destination grid will preserve the mean of the input gridcells. Specifying ‘-r 0.9’ and ‘--rnr_thr=0.9’ are equivalent. Renormalization can be explicitly turned-off by setting rnr thr to either of the values ‘off’, or ‘none’. The ‘--preserve=prs_stt’ option performs the same task as this option except it does not allow setting an arbitrary threshold fraction. ‘--rgn_dst (--rgn_dst, --dst_rgn, --regional_destination)’ ‘--rgn_src (--rgn_src, --src_rgn, --regional_source)’ Use these flags which take no argument to indicate that a user-supplied (i.e., with ‘-s grd_src’ or ‘-g grd_dst’) grid is regional. The ERWG weight- generator (at least all versions before 8.0) needs to be told whether the source, destination, or both grids are regional or global in order to optimize weight production. ncremap supplies this information to the regridder for grids it au- tomatically infers from data files. However, the regridder needs to be explicitly told if user-supplied (i.e., with either ‘-s grd_src’ or ‘-g grd_dst’) grids are regional because the regridder does not examine supplied grids before calling ERWG which assumes, unless told otherwise, that grids are global in extent. The sole effect of these flags is to add the arguments--src_regional ‘ ’ and/or ‘--dst_regional’ to ERWG calls. Supplying regional grids without invoking these flags may dramatically increase the map-file size and time to compute. According to E3SM MPAS documentation, ERWG “considers a mesh to be re- gional when the mesh is not a full sphere (including if it is planar and does not cover the full sphere). In other words, all MPAS-O and MPAS-LI grids are regional” to ERWG. ‘-s grd_src (--grd_src, --grid_source, --source_grid, --src_grd)’ Specifies the source gridfile. NCO will use ERWG or TempestRemap weight- generator to combine this with a destination gridfile (either inferred from dst fl, or generated by supplying a ‘-G grd_sng’ option) to generate remap- ping weights. grd src is not modified, and may have read-only permissions. One appropriate circumstance to specify grd src is when the input-file(s) do not contain sufficient information for NCO to infer an accurate or complete source grid. (Unfortunately many dataset producers do not record information like cell edges/vertices in their datasets. This is problematic for non-rectangular grids.) NCO assumes that grd src, when supplied, applies to every input-file. Thus NCO will call the weight generator only once, and will use that map fl to regrid every input-file. Although ncremap usually uses the contents of a pre-existing grd src to cre- ate mapping weights, there are some situations where ncremap creates the file specified by grd src (i.e., treats it as a location for storing output). When a source grid is inferred or created from other user-specified input, ncremap will store it in the location specified by grd src. This allows users to, for example, name the grid on which an input dataset is stored when that grid is not known a priori. This functionality is only available for SCRIP-format grids. Chapter 4: Reference Manual 329 ‘--skl_fl=skl_fl (--skl_fl, --skl, --skl_fl)’ Normally ncremap only creates a SCRIP-format gridfile named grd dst when it receives the grd_sng option. The ‘--skl’ option instructs ncremap to also produce a “skeleton” file based on the grd_sng argument. A skeleton file is a bare-bones datafile on the specified grid. It contains the complete lati- tude/longitude grid and an area field. Skeleton files are useful for validating that the grid-creation instructions in grd sng perform as expected. ‘--no_stdin (--no_stdin, --no_inp_std, --no_redirect, --no_standard_input)’ First introduced in NCO version 4.8.0 (released May, 2019), this switch (which takes no argument) disables checking standard input (aka stdin) for input files. This is useful because ncclimo and ncremap may mistakenly expect input to be provided on stdin in environments that use stdin for other purposes. Some non-interactive environments (e.g., crontab, nohup, Azure CI, CWL), may use standard input for their own purposes, and thus confuse NCO into thinking that you provided the input files names via the stdin mechanism. In such cases users may disable the automatic checks for standard input by explicitly invoking the ‘--no_stdin’ flag. This switch is usually not required for jobsin an interactive shell. Interactive SLURM shells can also commandeer stdin, as is the case on the DOE machine named Chrysalis. This behavior appears to vary depending on the SLURM implementation. srun -N 1 -n 1 ncremap --no_stdin -m map.nc in.nc out.nc ‘-T tmp_drc (--tmp_drc, --drc_tmp, --tmp_dir, --dir_tmp, --tmp_drc)’ Specifies the directory in which to place intermediate output files. Depending on how it is invoked, ncremap may generate a few or many intermediate files (grids and maps) that it will, by default, remove upon successful completion. These files can be large, so the option toset tmp drc is offered to ensure their location is convenient to the system. If the user does not specify tmp drc, then ncremap uses the value of $TMPDIR, if any, or else /tmp if it exists, or else it uses the current working director ($PWD). ‘-t thr_nbr (--thr_nbr, --thr, --thread_number, --threads)’ Specifies the number of threads used per regridding process (see Section 3.3 [OpenMP Threading], page 30). ncremap can use OpenMP shared-memory techniques to simultaneosly regrid multiple variables within a single file. This shared memory parallelism is quite efficient because it uses a single copyof the regridding weights in physical memory to regrid multiple variable simul- taneously. Even so, simultaneously regridding multiple variables, especially at high resolution, may be memory-limited, meaning that the insufficient RAM can often limit the number of variables that the system can simultaneously regrid. By convention all variables to be regridded share the same regridding weights stored in a map-file, so that only one copy of the weights needs tobe in memory, just as in Serial mode. However, the per-thread (i.e., per-variable) OpenMP memory demands are considerable, with the memory required to re- grid variables amounting to no less than about 5–7 times (for type NC_FLOAT) and 2.5–3.5 times (for type NC_DOUBLE) the size of the uncompressed variable, respectively. Memory requirements are so high because the regridder performs 330 NCO 5.0.2-alpha07 User Guide all arithmetic in double precision to retain the highest accuracy, and must al- locate separate buffers to hold the input and output (regridded) variable, a tally array to count the number of missing values and an array to sum the of the weights contributing to each output gridcell (the last two arrays are only necessary for variables with a _FillValue attribute). The input, output, and weight-sum arrays are always double precision, and the tally array is composed of four-byte integers. Given the high memory demands, one strategy to opti- mize thr nbr for repetitious workflows is to increase it to keep doubling it(1, 2, 4, . . .) until throughput stops improving. With sufficient RAM, the NCO regridder scales well up to 8–16 threads. ‘-U (--unpack, --upk, --upk_inp)’ This switch (which takes no argument) causes ncremap to unpack (see Section 3.38 [Packed data], page 126) input data before regridding it. This switch causes unpacking at the regridding stage that occurs after map genera- tion. Hence this switch does not benefit grid inferral. Grid inferral examines only the coordinate variables in a dataset. If coordinates are packed (a terrible practice) in a file from which a grid will be inferred, users should first manually unpack the file (this option will not help). Fortunately, coordinate variables are usually not packed, even in files with other packed data. Many institutions (like NASA) pack datasets to conserve space before distribut- ing them. This option allows one to regrid input data without having to man- ually unpack it first. Beware that NASA uses at least three different and in- compatible versions of packing in its L2 datasets. The unpacking algorithm employed by this option is the default netCDF algorithm, which is appropri- ate for MOD04 and is inappropriate for MOD08 and MOD13. See Section 3.38 [Packed data], page 126, for more details and workarounds. ‘--ugrid_fl=ugrid_fl (--ugrid_fl, --ugrid, --ugrid_fl)’ Normally ncremap only infers a gridfile named grd dst in SCRIP-format. The ‘ugrid_fl’ option instructs ncremap to infer both a SCRIP-format gridfile named grd dst and a UGRID-format gridfile named ugrid fl. This is an ex- perimental feature and the UGRID file is only expected to be valid for global rectangular grids. ‘-u unq_sfx (--unq_sfx, --unique_suffix, --suffix)’ Specifies the suffix used to label intermediate (internal) files generated bythe regridding workflow. Unique names are required to avoid interference among parallel invocations of ncremap. The default unq sfx generated internally by ncremap is ‘.pidPID’ where PID is the process ID. Applications can provide their own more or less informative suffixes using the ‘--unq_sfx=unq_sfx’ option. The suffix should be unique so that no two simultaneously execut- ing instances of ncremap can generate the same file. For example, when the ncclimo climatology script issues a dozen ncremap commands to regrid all twelve months simultaneously, it uses ‘--unq_sfx=mth_idx’ to encode the cli- matological month index in the unique suffix. Note that the controlling pro- cess PID is insufficient to disambiguate all the similar temporary files when the input file list is divided into multiple concurrent jobs (controlled bythe Chapter 4: Reference Manual 331 ‘--job_nbr=job_nbr’ option). Those files have their user-provided or inter- nally generated unq sfx extended by fl idx, their position in the input file list, so that their full suffix is ‘.pidPID.fl_idx’. Finally, a special value of unq sfx is available to aid developers: if unq sfx is ‘noclean’ then ncremap retains (not removes) all intermediate files after completion. ‘-v var_lst (--var_lst, --var, --vars, --variables, --variable_list)’ The ‘-v’ option causes ncremap to regrid only the variables in var lst. It behaves like subsetting (see Section 3.12 [Subsetting Files], page 48) in the rest of NCO. ‘-V var_rgr (--var_rgr, --rgr_var, --var_cf, --cf_var, cf_variable)’ The ‘-V’ option tells ncremap to use the same grid as var rgr in the in- put file. If var rgr adheres to the CF coordinates convention described here (http://cfconventions.org/cf-conventions/cf-conventions.html# coordinate-system), then ncclimo will infer the grid as represented by those coordinate variables. This option simplifies inferring grids when the grid co- ordinate names are unknown, since ncclimo will follow the CF convention to learn the identity of the grid coordinates. Until NCO version 4.6.0 (May, 2016), ncremap would not follow CF conventions to identify coordinate variables. Instead, ncremap used an internal database of “usual suspects” to identify latitude and longitude coordinate variables. Now, if var rgr is CF-compliant, then ncremap will automatically identify the horizontal spatial dimensions. If var rgr is supplied but is not CF-compliant, then ncremap will still attempt to identify horizontal spatial dimensions using its internal database of “likely names”. If both these automated methods fail, manually supply ncremap with the names of the horizontal spatial dimensions # Method used to obtain horizontal spatial coordinates: ncremap -V var_rgr -d dst.nc -O ~/rgr in.nc # CF coordinates convention ncremap -d dst.nc -O ~/rgr in.nc # Internal database ncremap -R "--rgr lat_nm=xq --rgr lon_nm=zj" -d dst.nc -O ~/rgr in.nc # Manual ‘--vrb=vrb_lvl (--vrb_lvl, --vrb, --verbosity, --verbosity_level)’ Specifies a verbosity level similar to the rest of NCO. If vrb lvl = 0, ncremap prints nothing except potentially serious warnings. If vrb lvl = 1, ncremap prints the basic filenames involved in the remapping. If vrb lvl = 2, ncremap prints helpful comments about the code path taken. If vrb lvl > 2, ncremap prints even more detailed information. Note that vrb lvl is distinct from dbg lvl which is passed to the regridder (ncks) for additional diagnostics. ‘--vrt_fl=vrt_fl (--vrt_fl, --vrt, --vrt_crd, --vertical_file)’ The ‘--vrt_fl=vrt_fl’ option instructs ncremap to vertically interpolate the input file to the vertical coordinate grid contained in thefile vrt fl. This option first appeared in NCO version 4.8.0, released in May, 2019. The vertical gridfile vrt fl must specify a vertical gridtype that ncremap understands, currently either pure-pressure or hybrid-coordinate pressure. We plan to add pure-sigma coordinates in the future. Besides the vertical grid-type, the main assumptions, constraints, and priorities for future development of vertical regridding are: 332 NCO 5.0.2-alpha07 User Guide 1. Input datasets must have netCDF (and thus C-based) dimension-ordering all other dimensions, a single vertical dimension, then one or two horizon- tal dimensions so that the horizontal dimension(s) vary more rapidly than the vertical. Eliminating this constraint will remain low priority until we are lobbied with compelling use-cases. 2. The vertical interpolation algorithm defaults to linear in log(pressure). This assumption is more natural for gases (like the atmosphere) than for condensed media (like oceans or Earth’s interior). To instead interpolate linearly in the vertical coordinate, use the ‘ntp_mth=lin’ options (as of NCO 4.9.0). 3. Vertical interpolation and horizontal regridding may be invoked simulta- neously (as of NCO 4.9.0) by the user simply by supplying both a map-file and a vertical grid-file to ncremap. When this occurs, ncremap internally performs the vertical interpolation prior to the horizontal regridding. 4. The default extrapolation method uses nearest neighbor except for tem- perature and geopotential (those extrapolation methods are described be- low). These defaults are well-suited to extrapolate valid initial conditions from data on older vertical grids. Note that the default approximation used for geopotential is inaccurate in cold regions. As of July 2019 and NCO version 4.8.1, one may instead set points outside the input domain to missing-values with the ‘--xtr_opt=mss_val’ option. Other extrapolation options, not yet exposed to user-access, include: dying, setting to 0.0, and linear extrapolation. Supporting these other methods, or improving the existing special-case approximations for temperature or geopotential, will remain low priority until we are lobbied with compelling use-cases for other algorithms. 5. Missing values are not (yet) treated specially Eliminating this constraint is not a priority because atmospheric datasets often contain no missing data. This could become a high priority issue if ocean modelers show interest in employing this tool to regrid to/from depth coordinates where missing values indicate bathymetry. 6. Time-varying vertical grids are only allowed for hybrid grids (not pure pres- sure grids), and these must store the time dimension as a record dimension. This constraint applies to the vertical grid only, not to the other fields in the dataset. Hence this does not preclude interpolating timeseries to/from time-invariant vertical grids. For example, time-varying hybrid grid data such as temperature may be interpolated to timeseries on a time-invariant pressure grid. Eliminating this constraint will not be a priority unless/until an important use-case is identified. 7. Variable names for input and output vertical grids must match E3SM/CESM, ECMWF, and NCEP implementations. These names include hyai, hyam, hybi, hybm, ilev, lev, P0, and PS (for E3SM/CESM hybrid grids), lev, lev_2, and lnsp (for ECMWF hybrid grids only), and plev (for pure-pressure grids). The infrastructure to provide alternate names for any of these input/output variables names is straightforward, and is Chapter 4: Reference Manual 333 heavily used for horizontal spatial regridding. Allowing this functionality will not be a priority until we are presented with a compelling use-case. The simplest vertical grid-type, a pure-pressure grid, contains the horizontally uniform vertical pressure levels in a one-dimensional coordinate array named plev. The plev dimension may have any number of levels and the values must monotonically increase or decrease. A 17-level NCEP pressure grid, for example, is easy to create: # Construct monotonically decreasing 17-level NCEP pressure grid ncap2 -O -v -s ’defdim("plev",17);plev[$plev]={100000,92500,85000, \ 70000,60000,50000,40000,30000,25000,20000,15000,10000,7000,5000, \ 3000,2000,1000};’ vrt_prs_ncep_L17.nc Hybrid-coordinate grids are a hybrid between a sigma-coordinate grid (where each pressure level is a fixed fraction of a spatiotemporally varying surface pres- sure) and a pure-pressure grid that is spatially invariant (as described above). The so-called hybrid A and B coefficients specify the fractional weight ofthe pure-pressure and sigma-grids, respectively, at each level. The hybrid grid- file must specify A and B coefficients for both layer midpoints and interfaces with these standard (as employed by CESM and E3SM) names and dimensions: hyai(ilev), hybi(ilev), hyam(lev), and hybm(lev). The reference pressure and surface pressure must be named P0 and PS, respectively. The pressures at all midpoints and interfaces are then defined as prs_mdp[time,lev, lat,lon]=hyam*P0+hybm*PS # Midlayer prs_ntf[time,ilev,lat,lon]=hyai*P0+hybi*PS # Interface The scalar reference pressure P0 is typically 100000 Pa (or 1000 mb) while the surface pressure PS is a (possibly time-varying) array with one or two spatial dimensions, and its values are in the same dimensional units (e.g., Pa or hPa) as P0. It is often useful to create a vertical grid file from existing model or reanalysis output. We call vertical grid files “skinny” if they contain only the vertical information. Skinny grid-files are easy to create with ncks, e.g., ncks -C -v hyai,hyam,hybi,hybm,P0 in_L128.nc vrt_hyb_L128.nc Such files are extremely small and portable, and represent all the hybrid files created by the model because the vertical grid parameters are time-invariant. A “fat” vertical grid file would also include the time-varying grid information, i.e., the surface pressure field. Fat grid-files are also easy to create with ncks, e.g., ncks -C -v hyai,hyam,hybi,hybm,P0,PS in_L128.nc vrt_hyb_L128.nc The full (layer-midpoint) and half (layer-interface) pressure fields prs_mdp and prs_ntf, respectively, can be reconstructed from any fat grid-file with an ncap2 command: ncap2 -s ’prs_mdp[time,lat,lon,lev]=P0*hyam+PS*hybm’ \ -s ’prs_ntf[time,lat,lon,ilev]=P0*hyai+PS*hybi’ in.nc out.nc Hybrid-coordinate grids define a pure-sigma or pure-pressure grid when either their A or B coefficients are zero, respectively. For example, the following cre- 334 NCO 5.0.2-alpha07 User Guide ates the hybrid-coordinate representation of a pure-pressure grid with midpoints every 100 mb from 100 mb to 1000 mb: ncap2 -O -v -s ’defdim("ilev",11);defdim("lev",10);P0=100000.0; \ hyai=array(0.05,0.1,$ilev);hyam=array(0.1,0.1,$lev); \ hybi=0.0*hyai;hybm=0.0*hyam;’ vrt_hyb_prs_L10.nc NCO currently has no other means of representing pure sigma vertical grids (as opposed to pure pressure grids). As of July 2019 and NCO version 4.8.1, NCO supports regridding ECMWF datasets in IFS hybrid vertical coordinate format to CESM/E3SM-format hy- brid vertical grids. The native IFS hybrid datasets that we have seen store pressure coordinates in terms of a slightly different formula that employs the log of surface pressure (lnsp) instead of surface pressure PS, that redefines hyai and hyam to be pure-pressure offsets (rather than coefficients), and that omits P0: prs_mdp[time,lev, lat,lon]=hyam+hybm*exp(lnsp) # Midlayer prs_ntf[time,lev_2,lat,lon]=hyai+hybi*exp(lnsp) # Interface Note that ECMWF also alters the names of the vertical half-layer coordinate and employs distinct dimensions (nhym and nhyi) for the hybrid variables hyai(nhyi), hybi(nhyi), hyam(nhym), and hybm(nhym). ECMWF uses the vertical coordinates lev and lev_2 for full-layer (i.e., midlayer) and half-layer (i.e., interface) for all other variables. The lev and ilev coordinates of a hybrid grid are defined by the hybrid coef- ficients and reference pressure, and are by convention stored in millibars (not Pascals) as follows: ilev[ilev]=P0*(hyai+hybi)/100.0; lev[lev]=P0*(hyam+hybm)/100.0; A vertical hybrid grid file vrt fl must contain at least hyai, hybi, hyam, hybm(lev) and P0; PS, lev, and ilev are optional. (Exceptions for ECMWF grids are noted above). All hybrid-coordinate data files must contain PS. In- terpolating a pure-pressure coordinate data file to hybrid coordinates requires, therefore, that the hybrid-coordinate vrt fl must contain PS and/or the input data file must contain PS. If both contain PS then the PS from the vrt fl takes precedence and will be used to construct the hybrid grid and then copied with- out to the output file. In all cases lev and ilev are optional in input hybrid-coordinate data files and vertical grid-files. They are diagnosed from the other parameters using the above definitions. The minimal requirements—a plev coordinate for a pure- pressure grid or five parameters for a hybrid grid—allow vertical gridfiles to be much smaller than horizontal gridfiles such as SCRIP files. Moreover, data files from ESMs or analyses (NCEP, MERRA2, ERA5) are also valid gridfiles. The flexibility in gridfile structure makes it easy to intercompare data fromthe same or different sources. ncremap supports vertical interpolation between all combinations of pure- pressure and hybrid-pressure grids. The input and output (aka source and Chapter 4: Reference Manual 335 destination) pressure grids may monotonically increase or decrease indepen- dently of eachother (i.e., one may increase and the other may decrease). When an output pressure level is outside the input pressure range for that column, then all variables must be extrapolated (not interpolated) to that/those level(s). By default ncremap sets all extrapolated values to the nearest valid value. Temperature and geopotential height are exceptions to this rule. Temperature variables (those named T or ta, anyway) are extrapolated upwards towards space using the nearest neighbor assumption, and downwards beneath the sur- face assuming a moist adiabatic lapse rate of 6.5 degrees centigrade per 100 millibars. Geopotential variables (those named Z3 or zg, anyway) are extrapo- lated upwards and downwards using the hypsometric equation4 with constant global mean virtual temperature T = 288K. This assumption leads to unre- alistic values where T differs significantly from the global mean surface tem- perature. Using the local T itself would be a much better approximation, yet would require a time-consuming implementation. Please let us know if accurate surface geopotential extrapolation in cold regions is important to you. Interpolation to and from hybrid coordinate grids works on both midpoint and interface fields (i.e., on variables with lev or ilev dimensions), while interpo- lation to and from pure-pressure grids applies to fields with, or places output of fields on, a plev dimension. All other fields pass through the interpola- tion procedure unscathed. Input can be rectangular (aka RLL), curvilinear, or unstructured. ‘--vrt_ntp=vrt_ntp (--vrt_ntp, --ntp_mth, --interpolation_type, --interpolation_method)’ Specifies the interpolation method for destination points within the vertical range of the input data during vertical interpolation. Valid values and their syn- onyms are lin (synonyms linear and lnr), and log (synonyms logarithmic and lgr). Default is vrt ntp = log. The vertical interpolation algorithm de- faults to linear in log(pressure). Logarithmic interpolation is more natural for gases like the atmosphere, because it is compressible, than for condensed media like oceans or Earth’s interior, which are incompressible. To instead interpolate linearly in the vertical coordinate, use the ‘ntp_mth=lin’ option. NCO supports this feature as of version 4.9.0 (December, 2019). ‘--vrt_xtr=vrt_xtr (--vrt_xtr, --xtr_mth, --extrapolation_type, --extrapolation_method)’ Specifies the extrapolation method for destination points outside the vertical range of the input data during vertical interpolation. Valid values and their synonyms are mss_val (synonyms msv and missing_value), and nrs_ngh (syn- onyms nn and nearest_neighbor). Default is vrt xtr = nrs_ngh. NCO sup- ports this feature as of version 4.8.1 (July, 2019). ‘-W wgt_opt (--wgt_opt, --weight_options, --esmf_opt, --esmf_options, --tps_opt, --tempest_options)’ ncremap passes wgt opt directly through to the weight-generator (cur- rently ERWG or TempestRemap’s GenerateOfflineMap) (and not to 4 Z2 − Z1 = (Rd ∗ Tv/g0) ∗ ln(p1/p2) = (Rd ∗ Tv/g0) ∗ (ln(p1) − ln(p2)) 336 NCO 5.0.2-alpha07 User Guide GenerateOverlapMesh). The user-specified contents of wgt opt, if any, su- percede the default contents for the weight-generator. The default option for ERWG is ‘--ignore_unmapped’). ncremap 4.7.7 and later additionally set the ERWG ‘--ignore_degenerate’ option, though if the run-time ERWG reports its version is 7.0 (March, 2018) or later. This is done to preserve backwards compatibility since, ERWG 7.1.0r and later require ‘--ignore_degenerate’ to successfully regrid some datasets (e.g., CICE) that previous ERWG versions han- dle fine. Users of earlier versions of ncremap that call ESMF 7.1.0r and later can explicitly pass the base ERWG options with ncremap’s ‘--esmf_opt’ option: # Use when NCO <= 4.7.6 and ERWG >= 7.1.0r ncremap --esmf_opt=’--ignore_unmapped --ignore_degenerate’ ... The ERWG and TempestRemap documentation shows all available options. For example, to cause ERWG to output to a netCDF4 file, pass-W ‘ "--netcdf4"’ to ncremap. By default, ncremap runs GenerateOfflineMap without any options. To cause GenerateOfflineMap to use a _FillValue of −1, pass ‘-W ’--fillvalue -1.0’’ to ncremap. Other common options include enforcing monotonicity (which is not the default in TempestRemap) constraints. To guarantee mono- tonicity in regridding from Finite Volume FV to FV maps (e.g., MPAS-to- rectangular), pass ‘-W ’-in_np 1’’ to ncremap. To guarantee monotonicity in regridding from Finite Element FE to FV maps, pass ‘-W ’--mono’’. Common sets of specialized options recommended for TempestRemap are collected into six boutique algorithms invokable with ‘--alg_typ’ as described above. ‘-w wgt_cmd (--wgt_cmd, --weight_command, --wgt_gnr, --weight_generator)’ Specifies a (possibly extended) command to use to run the weight-generator when a map-file is not provided. This command overrides the default exe- cutable executable for the weight generator, which is ESMF_RegridWeightGen for ESMF and GenerateOfflineMap for TempestRemap. (There is cur- rently no way to override GenerateOverlapMesh for TempestRemap). The wgt cmd must accept the same arguments as the default command. Examples include ‘mpirun -np 24 ESMF_RegridWeightGen’, ‘mpirun-openmpi-mp -np 16 ESMF_RegridWeightGen’, and other ways of exploiting parallelism that are system-dependent. Specifying wgt cmd and supplying (with ‘-m’) a map-file is not permitted (since the weight-generator would not be used). ‘--xcl_var (--xcl_var, --xcl, --exclude, --exclude_variables)’ This flag (which takes no argument) changes var lst, as set by the --var_lst option, from an extraction list to an exclusion list so that variables in var lst will not be processed, and variables not in var lst will be processed. Thus the option ‘-v var_lst’ must also be present for this flag to take effect. Variables explicitly specified for exclusion--xcl by‘ --vars=var_lst[,...]’ need not be present in the input file. ‘-x xtn_lst (--xtn_lst, --xtn_var, --var_xtn, --extensive, --extensive_variables)’ The ‘-x’ option causes ncremap to treat the variables in xtn lst as extensive, meaning that their value depends on the gridcell boundaries. Support for ex- Chapter 4: Reference Manual 337 tensive variables during regridding is nascent. Currently variables marked as extensive are summed, not regridded. We are interested in “real-world” situa- tions that require regridding extensive variables, please contact us if you have one. Limitations to ncremap ncremap has two significant limitations to be aware of. First, for two-dimensional input grids the fields to be regridded must have latitude and longitude, or, in the case ofcurvi- linear data, the two equivalent horizontal dimensions, as the final two dimensions in in fl. Fields with other dimension orders (e.g., ‘lat,lev,lon’) will not regrid properly. To cir- cumvent this limitation one can employ ncpdq (see Section 4.9 [ncpdq netCDF Permute Dimensions Quickly], page 289) to permute the dimensions before (and un-permute them after) regridding. ncremap utilizes this method internally for some common input grids. For example, # AIRS Level2 vertical profiles ncpdq -a StdPressureLev,GeoTrack,GeoXTrack AIRS_L2.hdf AIRS_L2_ncpdq.nc ncremap -i AIRS_L2_ncpdq.nc -d dst_1x1.nc -O ~/rgr # MPAS-O fields ncpdq -a Time,nVertLevels,maxEdges,MaxEdges2,nEdges,nCells mpas.nc mpas_ncpdq.nc ncremap -R "--rgr col_nm=nCells" -i mpas_ncpdq.nc -m mpas120_to_t62.nc -O ~/rgr The previous two examples occur so frequently that ncremap has been specially equipped to handle AIRS and MPAS files. As of NCO version 4.5.5 (February, 2016), the follow- ing ncremap commands with the ‘-P prc_typ’ option automagically perform all required permutation and renaming necessary: # AIRS Level2 vertical profiles ncremap -P airs -i AIRS_L2.nc -d dst_1x1.nc -O ~/rgr # MPAS-O/I fields ncremap -P mpas -i mpas.nc -m mpas120_to_t62.nc -O ~/rgr The machinery to handle permutations and special options for other datafiles is relatively easy to extend with new prc typ options. If you work with common datasets that could benefit from their own pre-processing options, contact us and we will try to implement them. The second limitation is that to perform regridding, ncremap must read weights from an on-disk mapfile, and cannot yet compute weights itself and use them directly from RAM. This makes ncremap an “offline regridder” and unnecessarily slow compared to an “integrated regridder” that computes weights and immediately applies them in RAM without any disk-access. In practice, the difference is most noticeable when the weights are easily computable “on the fly”, e.g., rectangular-to-rectangular mappings. Otherwise the weight-generation takes much more time than the weight-application, at which ncremap is quite fast. As of NCO version 4.9.0, released in December, 2019, regridder supports generation of intersection grids and overlap weights for all finite volume grid combinations. However these weights are first stored in an offline mapfile, are not usable otherwise. One side-effect of ncremap being an offline regridder is that, when necessary, itcan generate files to store intermediate versions of grids, maps, and data. These filesare 338 NCO 5.0.2-alpha07 User Guide named, by default, ncremap_tmp_att.nc${unq_sfx}, ncremap_tmp_d2f.nc${unq_sfx}, ncremap_tmp_grd_dst.nc${unq_sfx}, ncremap_tmp_grd_src.nc${unq_sfx}, ncremap_ tmp_gnr_out.nc${unq_sfx}, ncremap_tmp_map_*.nc${unq_sfx}, ncremap_tmp_msh_ ovr_*.nc${unq_sfx}, and ncremap_tmp_pdq.nc${unq_sfx}. They are placed in drc out with the output file(s). In general, no intermediate grid or map files are generated whenthe map-file is provided. Intermediate files are always generated when the‘-P prm_typ’ option is invoked. By default these files are automatically removed upon successful completion of the script, unless ncremap was invoked by ‘--unq_sfx=noclean’ to explitly override this “self-cleaning” behavior. Nevertheless, early or unexpected termination of ncremap will almost always leave behind a collection of these intermediate files. Should intermediate files proliferate and/or annoy you, locate and/or remove all such files under the current directory with find . -name ’ncremap_tmp*’ rm ‘find . -name ’ncremap_tmp*’‘ EXAMPLES Regrid input file in.nc to the spatial grid in file dst.nc and write the output to out.nc: ncremap -d dst.nc in.nc out.nc ncremap -d dst.nc -i in.nc -o out.nc ncremap -d dst.nc -O regrid in.nc out.nc ncremap -d dst.nc in.nc regrid/out.nc ncremap -d dst.nc -O regrid in.nc # output named in.nc NCO infers the destination spatial grid from dst.nc by reading its coordinate variables and CF attributes. In the first example, ncremap places the output in out.nc. In the second and third examples, the output file is regrid/out.nc. In the fourth example, ncremap places the output in the specified output directory. Since no output filename is provided, the output file will be named regrid/in.nc. Generate a mapfile with ncremap and store it for later re-use. A pre-computed mapfile (supplied with ‘-m map_fl’) eliminates time-consuming weight-generation, and thus consid- erably reduces wallclock time: ncremap -m map.nc in.nc out.nc ncremap -m map.nc -I drc_in -O regrid As of NCO version 4.7.2 (January, 2018), ncremap supports “canonical” argument or- dering of command line arguments most frequently desired for one-off regridding, where a single input and output filename are supplied as command-line positional arguments with- out switches, pipes, or redirection: ncremap -m map.nc in.nc out.nc # Requires 4.7.2+ ncremap -m map.nc -i in.nc -o out.nc ncremap -m map.nc -o out.nc in.nc ncremap -m map.nc -O out_dir in1.nc in2.nc ncremap -m map.nc -o out.nc < in.nc ls in.nc | ncremap -m map.nc -o out.nc Chapter 4: Reference Manual 339 These are all equivalent methods, but the canonical ordering shown in the first example only works in NCO version 4.7.2 and later. ncremap annotates the gridfiles and mapfiles that it creates with helpful metadata con- taining the full provenance of the command. Consequently, ncremap is a sensible tool for generating mapfiles for later use. To generate a mapfile with the specified (non-default) name map.nc, and then regrid a single file, ncremap -d dst.nc -m map.nc in.nc out.nc To test the remapping workflow, regrid only one or a few variables instead of the entire file: ncremap -v T,Q,FSNT -m map.nc in.nc out.nc Regridding generally scales linearly with the size of data to be regridded, so eliminating unnecessary variables produces a snappier response. Regrid multiple input files with a single mapfile map.nc and write the output to the regrid directory: ncremap -m map.nc -I drc_in -O regrid ls drc_in/*.nc | ncremap -m map.nc -O regrid The three ways NCO obtains the destination spatial grid are, in decreasing order of prece- dence, from map fl (specified with-m ‘ ’), from grd dst (specified with-g ‘ ’), and (inferred) from dst fl (specified with-d ‘ ’). In the first example all likely data files from drc in are regridded using the same specified mapfile, map fl = map.nc. Each output file is written to drc out = regrid with the same name as the corresponding input file. The second example obtains the input file list from standard input, and uses the mapfile and output directory as before. If multiple input files are on the same grid, yet the mapfile does not exist inadvance, one can still regrid all input files without incurring the time-penalty of generating multiple mapfiles. To do so, provide the (known-in-advance) source gridfile or toggle the‘-M’ switch: ncremap -M -I drc_in -d dst.nc -O regrid ls drc_in/*.nc | ncremap -M -d dst.nc -O regrid ncremap -I drc_in -s grd_src.nc -d dst.nc -O regrid ls drc_in/*.nc | ncremap -s grd_src.nc -d dst.nc -O regrid ncremap -I drc_in -s grd_src.nc -g grd_dst.nc -O regrid ls drc_in/*.nc | ncremap -s grd_src.nc -g grd_dst.nc -O regrid The first two examples explicitly toggle the multi-map-generation switch (with-M ‘ ’), so that ncremap refrains from generating multiple mapfiles. In this case the source gridis inferred from the first input file, the destination grid is inferred from dst.nc, and ncremap uses ERWG to generate a single mapfile and uses that to regrid every input file.The next four examples are variants on this theme. In these cases, the user provides (with ‘-s grd_src.nc’) the source gridfile, which will be used directly instead of being inferred. Any of these styles works well when each input file is known in advance to be on the same grid, e.g., model data for successive time periods in a simulation. 340 NCO 5.0.2-alpha07 User Guide The most powerful, time-consuming (yet simultaneously time-saving!) feature of ncremap is its ability to regrid multiple input files on unique grids. Both input and output can be on any CRUD grid. ncremap -I drc_in -d dst.nc -O regrid ls drc_in/*.nc | ncremap -d dst.nc -O regrid ncremap -I drc_in -g grd_dst.nc -O regrid ls drc_in/*.nc | ncremap -g grd_dst.nc -O regrid There is no pre-supplied map fl or grd src in these examples, so ncremap first infers the output grid from dst.nc (first two examples), or directly uses the supplied gridfile grd_dst (second two examples), and calls ERWG to generate a new mapfile for each input file, whose grid it infers. This is necessary when each input file is on a unique grid, e.g., swath-like data from satellite observations or models with time-varying grids. These examples require remarkably little input, since ncremap automates most of the work. Finally, ncremap uses the parallelization options ‘-p par_typ’ and ‘-j job_nbr’ to help manage high-volume workflow. On a single node such as a local workstation, use Back- ground mode to regrid multiple files in parallel ls drc_in/*.nc | ncremap -p bck -d dst.nc -O regrid ls drc_in/*.nc | ncremap -p bck -j 4 -d dst.nc -O regrid Both examples will eventually regrid all input files. The first example regrids two at atime because two is the default batch size ncremap employs. The second example regrids files in batches of four at a time. Increasing job nbr will increase throughput so long as the node is not I/O-limited. Multi-node clusters can exploit inter-node parallelism in MPI-mode: qsub -I -A CLI115 -V -l nodes=4 -l walltime=03:00:00 -N ncremap ls drc_in/*.nc | ncremap -p mpi -j 4 -d dst.nc -O regrid This example shows a typical request for four compute nodes. After receiving the login prompt from the interactive master node, execute the ncremap command with ‘-p mpi’. ncremap will send regridding jobs in round-robin fashion to all available compute nodes until all jobs finish. It does this by internally prepending an MPI execution command, like ‘mpirun -H node_name -npernode 1 -n 1’, to the usual regridding command. MPI- mode typically has excellent scaling because most nodes have independent access to hard storage. This is the easiest way to speed your cumbersome job by factors of ten or more. As mentioned above under Limitations, parallelism is currently only supported when all regridding uses the same map-file. Chapter 4: Reference Manual 341 4.13 ncrename netCDF Renamer SYNTAX ncrename [-a old_name,new_name] [-a ...] [-D dbg] [-d old_name,new_name] [-d ...] [-g old_name,new_name] [-g ...] [--glb ...] [-h] [--hdf] [--hdr_pad nbr] [--hpss] [-l path] [-O] [-o output-file] [-p path] [-R] [-r] [-v old_name,new_name] [-v ...] input-file [[output-file]] DESCRIPTION ncrename renames netCDF dimensions, variables, attributes, and groups. Each object that has a name in the list of old names is renamed using the corresponding name in the list of new names. All the new names must be unique. Every old name must exist in the input file, unless the old name is preceded by the period (or “dot”) character ‘.’. The validity of old name is not checked prior to the renaming. Thus, if old name is specified without the ‘.’ prefix that indicates the presence of old name is optional, and old name is not present in input-file, then ncrename will abort. The new name should never be prefixed by a ‘.’ (or else the period will be included as part of the new name). As of NCO version 4.4.6 (released October, 2014), the old name and new name arguments may include (or be, for groups) partial or full group paths. The OPTIONS and EXAMPLES show how to select specific variables whose attributes are to be renamed. Caveat lector: Unforunately from 2007–present (March, 2021) the netCDF library (ver- ¨ sions 4.0.0–4.7.4) contains bugs or limitations that sometimes prevent NCO from correctly renaming coordinate variables, dimensions, and groups in netCDF4 files. (To our knowledge the netCDF library calls for renaming always work well on netCDF3 files so one workaround to many netCDF4 issues is convert to netCDF3, rename, then convert back). To understand the renaming limitations associated with particular netCDF versions, read the ncrename documentation below in its entirety. © Although ncrename supports full pathnames for both old name and new name, this is really “window dressing”. The full-path to new name must be identical to the full-path to old name in all classes of objects (attributes, variables, dimensions, or groups). In other words, ncrename can change only the local names of objects, it cannot change the location of the object in the group hierarchy within the file. Hence using a full-path in new name is redundant. The object name is the terminal path component of new name and this object must already exist in the group specified by the old name path. ncrename is an exception to the normal NCO rule that the user will be interactively prompted before an existing file is changed, and that a temporary copy of an output file is constructed during the operation. If only input-file is specified, then ncrename changes object names in the input-file in place without prompting and without creating a temporary copy of input-file. This is because the renaming operation is considered reversible if the user makes a mistake. The new name can easily be changed back to old name by using ncrename one more time. 342 NCO 5.0.2-alpha07 User Guide Note that renaming a dimension to the name of a dependent variable can be used to invert the relationship between an independent coordinate variable and a dependent variable. In this case, the named dependent variable must be one-dimensional and should have no missing values. Such a variable will become a coordinate variable. According to the netCDF User Guide, renaming objects in netCDF files does not incur the penalty of recopying the entire file when the new name is shorter than the old name. Thus ncrename may run much faster (at least on netCDF3 files) if judicious use of header padding (see Section 3.2 [Metadata Optimization], page 29) was made when producing the input-file. Similarly, using the ‘--hdr_pad’ option with ncrename helps ensure that future metadata changes to output-file occur as swifly as possible. OPTIONS ‘-a old_name,new_name’ Attribute renaming. The old and new names of the attribute are specified with ‘-a’ (or ‘--attribute’) by the associated old name and new name values. Global attributes are treated no differently than variable attributes. This option may be specified more than once. As mentioned above, all occurrences ofthe attribute of a given name will be renamed unless the ‘.’ form is used, with one exception. To change the attribute name for a particular variable, specify the old name in the format old var name@old att name. The ‘@’ symbol delimits the variable from the attribute name. If the attribute is uniquely named (no other variables contain the attribute) then the old var name@old att name syntax is redundant. The old var name variable names global and group have special significance. They indicate that old att nm should only be renamed where it occurs as a global (i.e., root group) metadata attribute (for global), or (for group) as any group attribute, and not where it occurs as a variable attribute. The var name@att name syntax is accepted, though not required, for the new name. ‘-d old_name,new_name’ Dimension renaming. The old and new names of the dimension are speci- fied with ‘-d’ (or ‘--dmn’, ‘--dimension’) by the associated old name and new name values. This option may be specified more than once. ‘-g old_name,new_name’ Group renaming. The old and new names of the group are specified with-g ‘ ’ (or ‘--grp’, ‘--group’) by the associated old name and new name values. This option may be specified more than once. This functionality is only available in NCO version 4.3.7 (October, 2013) or later, and only when built on netCDF library version 4.3.1-rc1 (August, 2013) or later. ‘-v old_name,new_name’ Variable renaming. The old and new names of the variable are specified with ‘-v’ (or ‘--variable’) by the associated old name and new name values. This option may be specified more than once. EXAMPLES Chapter 4: Reference Manual 343 Rename the variable p to pressure and t to temperature in netCDF in.nc. In this case p must exist in the input file (or ncrename will abort), but the presence of t is optional: ncrename -v p,pressure -v .t,temperature in.nc Rename the attribute long_name to largo_nombre in the variable u, and no other vari- ables in netCDF in.nc. ncrename -a u@long_name,largo_nombre in.nc Rename the group g8 to g20 in netCDF4 file in_grp.nc: ncrename -g g8,g20 in_grp.nc Rename the variable /g1/lon to longitude in netCDF4 in_grp.nc: ncrename -v /g1/lon,longitude in_grp.nc ncrename -v /g1/lon,/g1/longitude in_grp.nc # Alternate ncrename does not automatically attach dimensions to variables of the same name. This is done to make renaming an easy way to change whether a variable is a coordinate. If you want to rename a coordinate variable so that it remains a coordinate variable, you must separately rename both the dimension and the variable: ncrename -d lon,longitude -v lon,longitude in.nc Unfortunately, the netCDF4 library had a longstanding bug (all versions until 4.3.1-rc5 released in December, 2013) that crashed NCO when performing this operation. Simultane- ously renaming variables and dimensions in netCDF4 files with earlier versions of netCDF is impossible; it must instead be done in two separate ncrename invocations (e.g., first rename the variable, then rename the dimension) to avoid triggering the libary bug. A related bug causes unintended side-effects with ncrename also built with all versions of the netCDF4 library until 4.3.1-rc5 released in December, 2013): This bug caused renaming either a dimension or its associated coordinate variable (not both, which would fail as above) in a netCDF4 file to inadvertently rename both: # Demonstrate bug in netCDF4/HDF5 library prior to netCDF-4.3.1-rc5 ncks -O -h -m -M -4 -v lat_T42 ~/nco/data/in.nc ~/foo.nc ncrename -O -v lat_T42,lat ~/foo.nc ~/foo2.nc # Also renames dimension ncrename -O -d lat_T42,lat ~/foo.nc ~/foo2.nc # Also renames variable To avoid this faulty behavior, either build NCO with netCDF version 4.3.1-rc5 or later, or convert the file to netCDF3 first, then rename as intended, then convert back. Unforunately while this bug and the related coordinate renaming bug were fixed in 4.3.1-rc5 (released in December, 2013), a new and related bug was discovered in October 2014. Another netCDF4 bug that causes unintended side-effects with ncrename affects (at least) versions 4.3.1–4.3.2 and all snapshots of the netCDF4 library until January, 2015. This bug (fixed in 4.3.3 in February, 2015) corrupts values or renamed netCDF4 coordinate variables (i.e., variables with underlying dimensions of the same name) and other (non- coordinate) variables that include an underlying dimension that was renamed. In other words, renaming coordinate variables and dimensions succeeds yet it corrupts the values 344 NCO 5.0.2-alpha07 User Guide contained by the affected array variables. This bug corrupts affected variables by replacing their values with the default _FillValue for that variable’s type: # Demonstrate bug in netCDF4 libraries prior to version 4.3.3 ncks -O -4 -C -M -v lat ~/nco/data/in.nc ~/bug.nc ncrename -O -v lat,tal ~/bug.nc ~/foo.nc # Broken until netCDF-4.3.3 ncrename -O -d lat,tal ~/bug.nc ~/foo.nc # Broken until netCDF-4.3.3 ncrename -O -d lat,tal -v lat,tal ~/bug.nc ~/foo.nc # Broken too ncks ~/foo.nc To avoid this faulty behavior, either build NCO with netCDF version 4.3.3 or later, or convert the file to netCDF3 first, then rename as intended, then convert back. Thisbug does not affect renaming of groups or of attributes. Yet another netCDF4 bug that causes unintended side-effects with ncrename affects only snapshots from January–February, 2015, and released version 4.3.3 (February, 2015). It was fixed in (and was the reason for releasing) netCDF version 4.3.3.1 (March, 2015). Thisbug causes renamed attributes of coordinate variables in netCDF4 to files to disappear: # Demonstrate bug in netCDF4 library version 4.3.3 ncrename -O -h -a /g1/lon@units,new_units ~/nco/data/in_grp.nc ~/foo.nc ncks -v /g1/lon ~/foo.nc # Shows units and new_units are both gone Clearly, renaming dimensions in netCDF4 files is non-trivial. The penultimate chapter in this saga is a netCDF4 bug discovered in September, 2015, and present in versions 4.3.3.1 (and possibly earlier versions too) and later. As of this writing (February, 2018), this bug is still present in netCDF4 version 4.6.0.1-development. This bug causes ncrename to create corrupted output files when attempting to rename two or more dimensions simultaneously. The workaround is to rename the dimensions sequentially, in two separate ncrename calls. # Demonstrate bug in netCDF4 library versions 4.3.3.1--4.6.1+ ncrename -O -d lev,z -d lat,y -d lon,x ~/nco/data/in_grp.nc ~/foo.nc # Completes but file is unreadable ncks -v one ~/foo.nc # File is unreadable (multiple dimensions with same ID?) A new netCDF4 renaming bug was discovered in March, 2017. It is present in versions 4.4.1–4.6.0 (and possibly earlier versions). This bug was fixed in netCDF4 version 4.6.1 (Yay Ed!). This bug caused ncrename to fail to rename a variable when the result would become a coordinate. # Demonstrate bug in netCDF4 library versions 4.4.1--4.6.0 ncrename -O -v non_coord,coord ~/nco/data/in_grp.nc ~/foo.nc # Fails (HDF error) The fix is to upgrade to netCDF version 4.6.1. The workaround is to convert to netCDF3, then rename, then convert back to netCDF4. A potentially new netCDF4 bug was discovered in November, 2017 and is now fixed. It is present in versions 4.4.1.1–4.6.0 (and possibly earlier versions too). This bug causes ncrename to fail to rename a variable when the result would become a coordinate. Oddly this issue shows that simultaneously renaming a dimension and coordinate can succeed (in contrast to a bug described above), and that separating that into two steps can fail. # Demonstrate bug in netCDF4 library versions 4.4.1--4.6.0 # 20171107: https://github.com/Unidata/netcdf-c/issues/597 Chapter 4: Reference Manual 345 # Create test dataset ncks -O -C -v lon ~/nco/data/in_grp.nc ~/in_grp.nc ncks -O -x -g g1,g2 ~/in_grp.nc ~/in_grp.nc # Rename dimension then variable ncrename -d lon,longitude ~/in_grp.nc # works ncrename -v lon,longitude ~/in_grp.nc # borken "HDF error" # Rename variable then dimension ncrename -v lon,longitude ~/in_grp.nc # works ncrename -d lon,longitude ~/in_grp.nc # borken "nc4_reform_coord_var: Assertion ‘dim_datasetid > 0’ failed." # Oddly renaming both simultaneously works: ncrename -d lon,longitude -v lon,longitude ~/in_grp.nc # works The fix is to upgrade to netCDF version 4.6.1. The workaround is to convert to netCDF3, then rename, then convert back to netCDF4. A new netCDF3 bug was discovered in April, 2018 and is now fixed. It is present in netCDF versions 4.4.1–4.6.0 (and possibly earlier versions too). This bug caused ncrename to fail to rename many coordinates and dimensions simultaneously. This bug affects netCDF3 64BIT_OFFSET files and possibly other formats as well. As such it isthefirst and so far only bug we have identified that affects netCDF3 files. cp /glade/scratch/gus/GFDL/exp/CM3_test/pp/0001/0001.land_month_crop.AllD.nc ~/correa_in.nc ncrename -O -d grid_xt,lon -d grid_yt,lat -v grid_xt,lon -v grid_yt,lat \ -v grid_xt_bnds,lon_bnds -v grid_yt_bnds,lat_bnds ~/correa_in.nc ~/correa_out.nc The fix is to upgrade to netCDF version 4.6.1. Create netCDF out.nc identical to in.nc except the attribute _FillValue is changed to missing_value, the attribute units is changed to CGS_units (but only in those variables which possess it), the attribute hieght is changed to height in the variable tpt, and in the variable prs_sfc, if it exists. ncrename -a _FillValue,missing_value -a .units,CGS_units \ -a tpt@hieght,height -a [email protected],height in.nc out.nc The presence and absence of the ‘.’ and ‘@’ features cause this command to execute successfully only if a number of conditions are met. All variables must have a _FillValue attribute and _FillValue must also be a global attribute. The units attribute, on the other hand, will be renamed to CGS_units wherever it is found but need not be present in the file at all (either as a global or a variable attribute). The variable tpt must contain the hieght attribute. The variable prs_sfc need not exist, and need not contain the hieght attribute. Rename the global or group attribute Convention to Conventions ncrename -a Convention,Conventions in.nc # Variable and group atts. ncrename -a .Convention,Conventions in.nc # Variable and group atts. ncrename -a @Convention,Conventions in.nc # Group atts. only ncrename -a @.Convention,Conventions in.nc # Group atts. only ncrename -a global@Convention,Conventions in.nc # Group atts. only ncrename -a [email protected],Conventions in.nc # Group atts. only 346 NCO 5.0.2-alpha07 User Guide ncrename -a global@Convention,Conventions in.nc # Global atts. only ncrename -a [email protected],Conventions in.nc # Global atts. only The examples without the @ character attempt to change the attribute name in both Global or Group and variable attributes. The examples with the @ character attempt to change only global and group Convention attributes, and leave unchanged any Convention attributes attached directly to variables. Attributes prefixed with a period.Convention ( ) need not be present. Attributes not prefixed with a period (Convention) must be present. Variables prefixed with a period. ( or .global) need not be present. Variables not prefixed with a period (global) must be present. Chapter 4: Reference Manual 347 4.14 ncwa netCDF Weighted Averager SYNTAX ncwa [-3] [-4] [-5] [-6] [-7] [-A] [-a dim[,...]] [-B mask_cond] [-b] [-C] [-c] [--cnk_byt sz_byt] [--cnk_csh sz_byt] [--cnk_dmn nm,sz_lmn] [--cnk_map map] [--cnk_min sz_byt] [--cnk_plc plc] [--cnk_scl sz_lmn] [-D dbg] [-d dim,[min][,[max][,[stride]]] [-F] [--fl_fmt fl_fmt] [-G gpe_dsc] [-g grp[,...]] [--glb ...] [-h] [--hdr_pad nbr] [--hpss] [-I] [-L dfl_lvl] [-l path] [-M mask_val] [-m mask_var] [-N] [--no_cll_msr] [--no_cll_mth] [--no_frm_trm] [--no_tmp_fl] [-O] [-o output- file] [-p path] [--ppc ...] [-R] [-r] [--ram_all] [--rth_dbl|flt] [-T mask_comp] [-t thr_nbr] [--unn] [-v var[,...]] [-w weight] [-X ...] [-x] [-y op_typ] input-file [output-file] DESCRIPTION ncwa performs statistics (including, but not limited to, averages) on variables in a single file over arbitrary dimensions, with options to specify weights, masks, and normalization. See Section 2.6 [Statistics vs. Concatenation], page 20, for a description of the distinctions between the various statistics tools and concatenators. The default behavior of ncwa is to arithmetically average every numerical variable over all dimensions and to produce a scalar result for each. Averaged dimensions are, by default, eliminated as dimensions. Their corresponding coordinates, if any, are output as scalar variables. The ‘-b’ switch (and its long option equivalents ‘--rdd’ and ‘--retain-degenerate-dimensions’) causes ncwa to retain aver- aged dimensions as degenerate (size 1) dimensions. This maintains the association between a dimension (or coordinate) and variables after averaging and simplifies, for instance, later concatenation along the degenerate dimension. To average variables over only a subset of their dimensions, specify these dimensions in a comma-separated list following ‘-a’, e.g., ‘-a time,lat,lon’. As with all arithmetic operators, the operation may be restricted to an arbitrary hyperslab by employing the ‘-d’ option (see Section 3.16 [Hyperslabs], page 63). ncwa also handles values matching the variable’s _FillValue attribute correctly. Moreover, ncwa understands how to manipulate user-specified weights, masks, and normalization options. With these options, ncwa can compute sophisticated averages (and integrals) from the command line. mask var and weight, if specified, are broadcast to conform to the variables being aver- aged. The rank of variables is reduced by the number of dimensions which they are averaged over. Thus arrays which are one dimensional in the input-file and are averaged by ncwa appear in the output-file as scalars. This allows the user to infer which dimensions may have been averaged. Note that that it is impossible for ncwa to make make a weight or mask var of rank W conform to a var of rank V if W > V. This situation often arises when coordinate variables (which, by definition, are one dimensional) are weighted and averaged. ncwa assumes you know this is impossible and so ncwa does not attempt to broadcast weight or mask var to conform to var in this case, nor does ncwa print a warning message telling 348 NCO 5.0.2-alpha07 User Guide you this, because it is so common. Specifying dbg > 2 does cause ncwa to emit warnings in these situations, however. Non-coordinate variables are always masked and weighted if specified. Coordinate vari- ables, however, may be treated specially. By default, an averaged coordinate variable, e.g., latitude, appears in output-file averaged the same way as any other variable containing an averaged dimension. In other words, by default ncwa weights and masks coordinate variables like all other variables. This design decision was intended to be helpful but for some applications it may be preferable not to weight or mask coordinate variables just like all other variables. Consider the following arguments to ncwa: -a latitude -w lat_wgt -d latitude,0.,90. where lat_wgt is a weight in the latitude dimension. Since, by default ncwa weights coordinate variables, the value of latitude in the output-file depends on the weights in lat wgt and is not likely to be 45.0, the midpoint latitude of the hyperslab. Op- tion ‘-I’ overrides this default behavior and causes ncwa not to weight or mask coordinate variables1. In the above case, this causes the value of latitude in the output-file to be 45.0, an appealing result. Thus, ‘-I’ specifies simple arithmetic averages for the coordinate vari- ables. In the case of latitude, ‘-I’ specifies that you prefer to archive the arithmetic mean latitude of the averaged hyperslabs rather than the area-weighted mean latitude.2. As explained in See Section 3.39 [Operation Types], page 129, ncwa always averages coordinate variables regardless of the arithmetic operation type performed on the non- coordinate variables. This is independent of the setting of the ‘-I’ option. The mathematical definition of operations involving rank reduction is given above (see Section 3.39 [Operation Types], page 129). 4.14.1 Mask condition Each xi also has an associated masking weight mi whose value is 0 or 1 (false or true). The value of mi is always 1 unless a mask var is specified (with ‘-m’). As noted above, mask var is broadcast, if possible, to conform to the variable being averaged. In this case, the value of mi depends on the mask condition also known as the truth condition. As expected, mi = 1 when the mask condition is true and mi = 0 otherwise. The mask condition has the syntax mask var mask comp mask val. The preferred method to specify the mask condition is in one string with the ‘-B’ or ‘--mask_condition’ switches. The older method is to use the three switches ‘-m’, ‘-T’, and ‘-M’ to specify the mask var, mask comp, and mask val, respectively.3. The mask condition string is auto- matically parsed into its three constituents mask var, mask comp, and mask val. Here mask var is the name of the masking variable (specified with-m ‘ ’, ‘--mask-variable’, ‘--mask_variable’, ‘--msk_nm’, or ‘--msk_var’). The truth mask comp argument (specified with-T ‘ ’, ‘--mask_comparator’, ‘--msk_cmp_typ’, or ‘--op_rlt’ may be any one of the six arithmetic comparators: eq, ne, gt, lt, ge, le. These are the Fortran-style character abbreviations for the logical comparisons =, 6=, >, <, 1 The default behavior of (‘-I’) changed on 19981201—before this date the default was not to weight or mask coordinate variables. 2 If lat_wgt contains Gaussian weights then the value of latitude in the output-file will be the area- weighted centroid of the hyperslab. For the example given, this is about 30 degrees. 3 The three switches ‘-m’, ‘-T’, and ‘-M’ are maintained for backward compatibility and may be deprecated in the future. It is safest to write scripts using ‘--mask_condition’. Chapter 4: Reference Manual 349 ≥, ≤. The mask comparator defaults to eq (equality). The mask val argument to ‘-M’ (or ‘--mask-value’, or ‘--msk_val’) is the right hand side of the mask condition. Thus for the i’th element of the hyperslab to be averaged, the mask condition is maski mask comp mask val. Each xi is also associated with an additional weight wi whose value may be user-specified. The value of wi is identically 1 unless the user specifies a weighting variable weight (with ‘-w’, ‘--weight’, or ‘--wgt var’). In this case, the value of wi is determined by the weight variable in the input-file. As noted above, weight is broadcast, if possible, to conform to the variable being averaged. M is the number of input elements xi which actually contribute to output element xj. M is also known as the tally and is defined as i=N X M = µimi i=1 M is identical to the denominator of the generic averaging expression except for the omission of the weight wi. Thus M = N whenever no input points are missing values or are masked. Whether an element contributes to the output, and thus increments M by one, has more to do with the above two criteria (missing value and masking) than with the numeric value of the element per se. For example, xi = 0.0 does contribute to xj (assuming the _FillValue attribute is not 0.0 and location i is not masked). The value xi = 0.0 will not change the numerator of the generic averaging expression, but it will change the denominator (unless its weight wi = 0.0 as well). 4.14.2 Normalization and Integration ncwa has one switch which controls the normalization of the averages appearing in the output-file. Short option ‘-N’ (or long options ‘--nmr’ or ‘--numerator’) prevents ncwa from dividing the weighted sum of the variable (the numerator in the averaging expression) by the weighted sum of the weights (the denominator in the averaging expression). Thus ‘-N’ tells ncwa to return just the numerator of the arithmetic expression defining the operation (see Section 3.39 [Operation Types], page 129). With this normalization option, ncwa can integrate variables. Averages are first com- puted as sums, and then normalized to obtain the average. The original sum (i.e., the numerator of the expression in Section 3.39 [Operation Types], page 129) is output if de- fault normalization is turned off (with ‘-N’). This sum is the integral (not the average) over the specified (with ‘-a’, or all, if none are specified) dimensions. The weighting variable, if specified (with ‘-w’), plays the role of the differential increment and thus permits more so- phisticated integrals (i.e., weighted sums) to be output. For example, consider the variable lev where lev = [100, 500, 1000] weighted by the weight lev_wgt where lev wgt = [10, 2, 1]. The vertical integral of lev, weighted by lev_wgt, is the dot product of lev and lev wgt. That this is is 3000.0 can be seen by inspection and verified with the integration command ncwa -N -a lev -v lev -w lev_wgt in.nc foo.nc;ncks foo.nc EXAMPLES Given file 85_0112.nc: netcdf 85_0112 { 350 NCO 5.0.2-alpha07 User Guide dimensions: lat = 64 ; lev = 18 ; lon = 128 ; time = UNLIMITED ; // (12 currently) variables: float lat(lat) ; float lev(lev) ; float lon(lon) ; float time(time) ; float scalar_var ; float three_dmn_var(lat, lev, lon) ; float two_dmn_var(lat, lev) ; float mask(lat, lon) ; float gw(lat) ; } Average all variables in in.nc over all dimensions and store results in out.nc: ncwa in.nc out.nc All variables in in.nc are reduced to scalars in out.nc since ncwa averages over all dimen- sions unless otherwise specified (with-a ‘ ’). Store the zonal (longitudinal) mean of in.nc in out.nc: ncwa -a lon in.nc out1.nc ncwa -a lon -b in.nc out2.nc The first command turns lon into a scalar and the second retains lon as a degenerate dimension in all variables. % ncks --trd -C -H -v lon out1.nc lon = 135 % ncks --trd -C -H -v lon out2.nc lon[0] = 135 In either case the tally is simply the size of lon, i.e., 180 for the 85_0112.nc file described by the sample header above. Compute the meridional (latitudinal) mean, with values weighted by the corresponding element of gw 4: ncwa -w gw -a lat in.nc out.nc Here the tally is simply the size of lat, or 64. The sum of the Gaussian weights is 2.0. Compute the area mean over the tropical Pacific: ncwa -w gw -a lat,lon -d lat,-20.,20. -d lon,120.,270. in.nc out.nc Here the tally is 64 × 128 = 8192. 4 gw stands for Gaussian weight in many climate models. Chapter 4: Reference Manual 351 Compute the area-mean over the globe using only points for which ORO < 0.5 5: ncwa -B ’ORO < 0.5’ -w gw -a lat,lon in.nc out.nc ncwa -m ORO -M 0.5 -T lt -w gw -a lat,lon in.nc out.nc It is considerably simpler to specify the complete mask cond with the single string argument to ‘-B’ than with the three separate switches ‘-m’, ‘-T’, and ‘-M’6. If in doubt, enclose the mask cond within quotes since some of the comparators have special meanings to the shell. Assuming 70% of the gridpoints are maritime, then here the tally is 0.70×8192 ≈ 5734. Compute the global annual mean over the maritime tropical Pacific: ncwa -B ’ORO < 0.5’ -w gw -a lat,lon,time \ -d lat,-20.0,20.0 -d lon,120.0,270.0 in.nc out.nc ncwa -m ORO -M 0.5 -T lt -w gw -a lat,lon,time \ -d lat,-20.0,20.0 -d lon,120.0,270.0 in.nc out.nc Further examples will use the one-switch specification of mask cond. Determine the total area of the maritime tropical Pacific, assuming the variable area contains the area of each gridcell ncwa -N -v area -B ’ORO < 0.5’ -a lat,lon \ -d lat,-20.0,20.0 -d lon,120.0,270.0 in.nc out.nc Weighting area (e.g., by gw) is not appropriate because area is already area-weighted by definition. Thus the ‘-N’ switch, or, equivalently, the ‘-y ttl’ switch, correctly integrate the cell areas into a total regional area. Mask a file to contain FillValue everywhere except where thr min <= msk var <= thr max: # Set masking variable and its scalar thresholds export msk_var=’three_dmn_var_dbl’ # Masking variable export thr_max=’20’ # Maximum allowed value export thr_min=’10’ # Minimum allowed value ncecat -O in.nc out.nc # Wrap out.nc in degenerate "record" dimension ncwa -O -a record -B "${msk_var} <= ${thr_max}" out.nc out.nc ncecat -O out.nc out.nc # Wrap out.nc in degenerate "record" dimension ncwa -O -a record -B "${msk_var} >= ${thr_min}" out.nc out.nc After the first use of ncwa, out.nc contains FillValue where ${msk_var} >= ${thr_ max}. The process is then repeated on the remaining data to filter out points where ${msk_ var} <= ${thr_min}. The resulting out.nc contains valid data only where thr min <= msk var <= thr max. 5 ORO stands for Orography in some climate models and in those models ORO < 0.5 selects ocean gridpoints. 6 Unfortunately the ‘-B’ and ‘--mask_condition’ options are unsupported on Windows (with the MVS compiler), which lacks a free, standard parser and lexer. 353 5 Contributing We welcome contributions from anyone. The project homepage at https://sf.net/ projects/nco contains more information on how to contribute. Financial contributions to NCO development may be made through PayPal (https:// www.paypal.com/xclick/business=zender%40uci.edu&item_name=NCO+development& item_number=nco_dnt_dvl&no_note=1&tax=0¤cy_code=USD). NCO has been shared for over 10 years yet only two users have contributed any money to the developers1. So you could be the third! 5.1 Contributors NCO would not exist without the dedicated efforts of the remarkable software engineers who conceive, develop, and maintain netCDF, UDUnits, and OPeNDAP. Since 1995 NCO has received support from, I believe, the entire staff of all these projects, including Russ Rew, John Caron, Glenn Davis, Steve Emmerson, Ward Fisher, James Gallagher, Ed Hartnett, and Dennis Heimbigner. In addition to their roles in maintaining the software stack on which NCO perches, Yertl-like, some of these gentlemen have advised or contributed to NCO specifically. That support is acknowledged separately below. The primary contributors to NCO development have been: Charlie Zender All concept, design and implementation from 1995–2000. Since then auto- tools, bug-squashing, CDL, chunking, documentation, anchoring, recursion, GPE, packing, regridding, CDL/XML backends, compression, NCO library re- design, ncap2 features, ncbo, ncpdq, SMP threading and MPI parallelization, netCDF4 integration, external funding, project management, science research, releases. Henry Butowsky Non-linear operations and min(), max(), total() support in ncra and ncwa. Type conversion for arithmetic. Migration to netCDF3 API. ncap2 parser, lexer, GSL-support, and I/O. Multislabbing algorithm. Variable wildcarding. JSON backend. Numerous hacks. ncap2 language. Rorik Peterson Original autotools build support. Long command-line options. Original UDUnits support. Debianization. Numerous bug-fixes. Joe Hamman Python bindings (PyNCO). Milan Klower, Rostislav Kouznetsov Quantization by rounding 1 Happy users have sent me a few gifts, though. This includes a box of imported chocolate. Mmm. Appreciation and gifts are definitely better than money. Naturally, I’m too lazy to split and sendgifts to the other developers. However, unlike some NCO developers, I have a steady "real job". My intent is to split monetary donations among the active developers and to send them their shares via PayPal. 354 NCO 5.0.2-alpha07 User Guide Daniel Wang Script Workflow Analysis for MultiProcessingSWAMP ( ). RPM support. Harry Mangalam Benchmarking. OPeNDAP configuration. Pedro Vicente Windows Visual Studio support. netCDF4 groups. CMake build-engine. Jerome Mao Multi-argument parsing. Joseph O’Rourke Routines from his book “Computational Geometry in C”. Russ Rew Advice on NCO structural algorithms. Brian Mays Original packaging for Debian GNU/Linux, nroff man pages. George Shapovalov Packaging for Gentoo GNU/Linux. Bill Kocik Memory management. Len Makin NEC SX architecture support. Jim Edwards AIX architecture support. Juliana Rew Compatibility with large PIDs. Karen Schuchardt Auxiliary coordinate support. Gayathri Venkitachalam MPI implementation. Scott Capps Large work-load testing Xylar Asay-Davis, Sterling Baldwin, Dave Blodgett, Philip Cameron-Smith, Peter Campbell, Martin Dix, Mark Flanner, Chris Golaz, Barron Henderson, Aleksandar Jelenak, Markus Liebig, Keith Lindsay, Daniel Macks, Daniel Neumann, Mike Page, Martin Schmidt, Michael Schulz, Lori Sentman, Rich Signell, Bob Simons, Gary Strand, Matthew Thompson, Qi Tang, Adrian Tompkins, George White Andrew Wittenberg, Min Xu, Remik Ziemlinski Excellent bug reports and feature requests. Filipe Fernandes, Isuru Fernando, Craig MacLachlan, Hugo Oliveira, Rich Signell, Kyle Wilcox Anaconda packaging Xylar Asay-Davis, Daniel Baumann, Nick Bower, Luk Claebs, Bas Couwenberg, Barry deFreese, Francesco Lovergine, Matej Vela Cygwin packaging Chapter 5: Contributing 355 Marco Atzeri Debian packaging Patrice Dumas, Ed Hill, Orion Poplawski RedHat packaging George Shapavalov, Patrick Kursawe, Manfred Schwarb Gentoo packaging Filipe Fernandes OpenSuse packaging Takeshi Enomoto, Alexander Hansen, Ian Lancaster, Alejandro Soto Mac OS packaging Eric Blake Autoconf/M4 help Gavin Burris, Kyle Wilcox RHEL and CentOS build scripts and bug reports. Andrea Cimatoribus NCO Spiral Logo Martin Otte, Etienne Tourigny Single bug reports and fixes Wenshan Wang CMIP5 and MODIS processing documentation, reference card Todd Mitchell Financial support Please let me know if your name was omitted! 5.2 Citation The recommended citations for NCO software are Zender, C. S. (2008), Analysis of Self-describing Gridded Geoscience Data with netCDF Operators (NCO), Environ. Modell. Softw., 23(10), 1338-1342, doi:10.1016/j.envsoft.2008.03.004. Zender, C. S. and H. J. Mangalam (2007), Scaling Properties of Common Statistical Operators for Gridded Datasets, Int. J. High Perform. Comput. Appl., 21(4), 485-498, doi:10.1177/1094342007083802. Zender, C. S. (2016), Bit Grooming: Statistically accurate precision-preserving quantization with compression, evaluated in the netCDF Operators (NCO, v4.4.8+), Geosci. Model Dev., 9, 3199-3211, doi:10.5194/gmd-9-3199-2016. Zender, C. S. (Year), netCDF Operator (NCO) User Guide, http://nco.sf.net/nco.pdf. 356 NCO 5.0.2-alpha07 User Guide Use the first when referring to overall design, purpose, and optimization of NCO, the second for the speed and throughput of NCO, the third for compressions, and the fourth for specific features and/or the User Guide itself, or in a non-academic setting. Acomplete list of NCO publications and presentations is at http://nco.sf.net#pub. This list links to the full papers and seminars themselves. 5.3 Proposals for Institutional Funding From 2004–2007, NSF funded a project (http://nco.sf.net#prp_sei) to improve Dis- tributed Data Reduction & Analysis (DDRA) by evolving NCO parallelism (OpenMP, MPI) and Server-Side DDRA (SSDDRA) implemented through extensions to OPeNDAP and netCDF4. The SSDDRA features were implemented in SWAMP, the PhD Thesis of Daniel Wang. SWAMP dramatically reduced bandwidth usage for NCO between client and server. With this first NCO proposal funded, the content of the next NCO proposal became clear. We had long been interested in obtaining NASA support for HDF-specific enhancements to NCO. From 2012–2015 the NASA ACCESS program funded us to implement support support netCDF4 group functionality. Thus NCO will grow and evade bit-rot for the foreseeable future. We are considering other interesting ideas for still more proposals. Please contact us if you wish to be involved with any future NCO-related proposals. Comments on the proposals and letters of support are also very welcome. 357 6 Quick Start Simple examples in Bash shell scripts showing how to average data with different file struc- tures. Here we include monthly, seasonal and annual average with daily or monthly data in either one file or multiple files. 6.1 Daily data in one file Suppose we have daily data from Jan 1st, 1990 to Dec. 31, 2005 in the file of in.nc with the record dimension as time. Monthly average: for yyyy in {1990..2005}; do # Loop over years for moy in {1..12}; do # Loop over months mm=$( printf "%02d" ${moy} ) # Change to 2-digit format # Average specific month yyyy-mm ncra -O -d time,"${yyyy}-${mm}-01","${yyyy}-${mm}-31" \ in.nc in_${yyyy}${mm}.nc done done # Concatenate monthly files together ncrcat -O in_??????.nc out.nc Annual average: for yyyy in {1990..2005}; do # Loop over years ncra -O -d time,"${yyyy}-01-01","${yyyy}-12-31" in.nc in_${yyyy}.nc done # Concatenate annual files together ncrcat -O in_????.nc out.nc The -O switch means to overwrite the pre-existing files (see Section 3.41 [Batch Mode], page 143). The -d option is to specify the range of hyperslabs (see Section 3.16 [Hyperslabs], page 63). There are detailed instructions on ncra (see Section 4.10 [ncra netCDF Record Averager], page 298, and ncrcat (see Section 4.11 [ncrcat netCDF Record Concatenator], page 302). NCO supports UDUnits so that we can use readable dates as time dimension (see Section 3.27 [UDUnits Support], page 98). 6.2 Monthly data in one file Inside the input file in.nc, the record dimension time is from Jan 1990 to Dec 2005. Seasonal average (e.g., DJF): ncra -O --mro -d time,"1990-12-01",,12,3 in.nc out.nc Annual average: 358 NCO 5.0.2-alpha07 User Guide ncra -O --mro -d time,,,12,12 in.nc out.nc Here we use the subcycle feature (i.e., the number after the fourth comma: ‘3’ in the seasonal example and the second ‘12’ in the annual example) to retrieve groups of records separated by regular intervals (see Section 3.19 [Subcycle], page 68). The option --mro switches ncra to produce a Multi-Record Output instead of a single-record output. For example, assume snd is a 3D array with dimensions time * latitude * longitude and time includes every month from Jan. 1990 to Dec. 2005, 192 months as total, which are 16 years. Consider the following two command lines: ncra --mro -v snd -d time,"1990-12-01",,12,3 in.nc out_mro.nc ncra -v snd -d time,"1990-12-01",,12,3 in.nc out_sro.nc In the first output file, out_mro.nc, snd is still a 3D array with dimensions time * latitude * longitude, but the length of time now is 16, meaning 16 winters. In the second output file, out_sro.nc, the length of time is only 1. It is now the average of all the 16 winters. When using ‘-d dim,min[,max]’ to specify the hyperslabs, you can leave it blank if you want to include the minimum or the maximum of the data, like we did above. 6.3 One time point one file This means if you have daily data of 30 days, there will be 30 data files. Or if you have monthly data of 12 months, there will be 12 data files. Dealing with this kind of files, you need to specify the file names in shell scripts and pass them to NCO operators. For example, your daily data files may look like snd_19900101.nc, snd_19900102.nc, snd_19900103.nc ... If you want to know the monthly average of Jan 1990, you can write like, ncra -O snd_199001??.nc out.nc You might want to use loop if you need the average of each month. for moy in {1..12}; do # Loop over months mm=$( printf "%02d" ${moy} ) # Change to 2-digit format ncra -O snd_????${mm}??.nc out_${mm}.nc done 6.4 Multiple files with multiple time points Similar as the last one, it’s more about shell scripts. Suppose you have daily data with one month of them in one data file. The monthly average is simply to apply ncra on the specific data file. And for seasonal averages, you can specify the three months by shell scripts. 359 7 CMIP5 Example The fifth phase of the Coupled Model Intercomparison Project (CMIP5 (http:// cmip-pcmdi.llnl.gov/cmip5/index.html?submenuheader=0)) provides a multi-model framework for comparing the mechanisms and responses of climate models from around the world. However, it is a tremendous workload to retrieve a single climate statistic from all these models, each of which includes several ensemble members. Not only that, it is too often a tedious process that impedes new research and hypothesis testing. Our NASA ACCESS 2011 project simplified and accelerated this process. Traditional geoscience data analysis requires users to work with numerous flat (data in one level or namespace) files. In that paradigm instruments or models produce, andthen repositories archive and distribute, and then researchers request and analyze, collections of flat files. NCO works well with that paradigm, yet it also embodies the necessary algorithms to transition geoscience data analysis from relying solely on traditional (or “flat”) datasets to allowing newer hierarchical (or “nested”) datasets. Hierarchical datasets support and enable combining all datastreams that meet user- specified criteria into a single or small number of files thathold all the science-relevant data. NCO (and no other software to our knowledge) exploits this capability now. Data and metadata may be aggregated into and analyzed in hierarchical structures. We call the resulting data storage, distribution, and analysis paradigm Group-Oriented Data Analysis and Distribution (GODAD). GODAD lets the scientific question organize the data, not the ad hoc granularity of all relevant datasets. This chapter illustrates GODAD techniques applied to analysis of the CMIP5 dataset. To begin, we document below a prototypical example of CMIP5 analysis and evaluation using traditional NCO commands on netCDF3-format model and HDF-EOS format observa- tional (NASA MODIS satellite instrument) datasets. These examples complement the NCO User Guide by detailing in-depth data analysis in a frequently encountered “real world” context. Graphical representations of the results (NCL scripts available upon request) are provided to illustrate physical meaning of the analysis. Since NCO can process hierarchical datasets, i.e., datasets stored with netCDF4 groups, we present sample scripts illustrating group-based processing as well. 7.1 Combine Files Sometimes, the data of one ensemble member will be stored in several files to reduce single file size. It is more convenient to concatenate these files into a single timeseries, andthe following script illustrates how. Key steps include: 1. Obtain number and names (or partial names) of files in a directory 2. Concatenate files along the record dimension (usually time) using ncrcat (see Section 4.11 [ncrcat netCDF Record Concatenator], page 302). #!/bin/bash # shell type shopt -s extglob # enable extended globbing #======360 NCO 5.0.2-alpha07 User Guide # Some of the models cut one ensemble member into several files, # which include data of different time periods. # We’d better concatenate them into one at the beginning so that # we won’t have to think about which files we need if we want # to retrieve a specific time period later. # # Method: # - Make sure ’time’ is the record dimension (i.e., left-most) # - ncrcat # # Input files like: # /data/cmip5/snc_LImon_bcc-csm1-1_historical_r1i1p1_185001-190012.nc # /data/cmip5/snc_LImon_bcc-csm1-1_historical_r1i1p1_190101-200512.nc # # Output files like: # /data/cmip5/snc_LImon_bcc-csm1-1_historical_r1i1p1_185001-200512.nc # # Online: http://nco.sourceforge.net/nco.html#Combine-Files # # Execute this script: bash cmb_fl.sh #====== drc_in=’/home/wenshanw/data/cmip5/’ # Directory of input files var=( ’snc’ ’snd’ ) # Variables rlm=’LImon’ # Realm xpt=( ’historical’ ) # Experiment ( could be more ) for var_id in {0..1}; do # Loop over two variables # Names of all the models (ls [get file names]; # cut [get model names]; # sort; uniq [remove duplicates]; awk [print]) mdl_set=$( ls ${drc_in}${var[var_id]}_${rlm}_*_${xpt[0]}_*.nc | \ cut -d ’_’ -f 3 | sort | uniq -c | awk ’{print $2}’ ) # Number of models (echo [print contents]; wc [count]) mdl_nbr=$( echo ${mdl_set} | wc -w ) echo "======" echo "There are" ${mdl_nbr} "models for" ${var[var_id]}. for mdl in ${mdl_set}; do # Loop over models # Names of all the ensemble members nsm_set=$( ls ${drc_in}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_*.nc | \ cut -d ’_’ -f 5 | sort | uniq -c | awk ’{print $2}’ ) # Number of ensemble members in each model nsm_nbr=$( echo ${nsm_set} | wc -w ) echo "------" echo "Model" ${mdl} "includes" ${nsm_nbr} "ensemble member(s):" Chapter 7: CMIP5 Example 361 echo ${nsm_set}"." for nsm in ${nsm_set}; do # Loop over ensemble members # Number of files in this ensemble member fl_nbr=$( ls ${drc_in}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_${nsm}_*.nc \ | wc -w ) # If there is only 1 file, continue to next loop if [ ${fl_nbr} -le 1 ] then echo "There is only 1 file in" ${nsm}. continue fi echo "There are" ${fl_nbr} "files in" ${nsm}. # Starting date of data # (sed [the name of the first file includes the starting date]) yyyymm_str=$( ls ${drc_in}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_${nsm}_*.nc\ | sed -n ’1p’ | cut -d ’_’ -f 6 | cut -d ’-’ -f 1 ) # Ending date of data # (sed [the name of the last file includes the ending date]) yyyymm_end=$( ls ${drc_in}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_${nsm}_*.nc\ | sed -n "${fl_nbr}p" | cut -d ’_’ -f 6 | cut -d ’-’ -f 2 ) # Concatenate one ensemble member files # into one along the record dimension (now is time) ncrcat -O ${drc_in}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_${nsm}_*.nc \ ${drc_in}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_\ ${nsm}_${yyyymm_str}-${yyyymm_end} # Remove useless files rm ${drc_in}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_${nsm}_\ !(${yyyymm_str}-${yyyymm_end}) done done done CMIP5 model data downloaded from the Earth System Grid Federation (ESGF (http:// pcmdi9.llnl.gov/esgf-web-fe/)) does not contain group features yet. Therefore users must aggregate flat files into hierarchical ones themselves. The following script shows how. Each dataset becomes a group in the output file. There can be several levels of groups. In this example, we employ two experiments (“scenarios”) as the top-level. The second- level comprises different models (e.g., CCSM4, CESM1-BGC). Many models are run multiple times with slight perturbed initial conditions to produce an ensemble of realizations. These ensemble members comprise the third level of the hierarchy. The script selects two variables, snc and snd (snow cover and snow depth). 362 NCO 5.0.2-alpha07 User Guide #!/bin/bash # #======# Aggregate models to one group file # # Method: # - Create files with groups by ncecat --gag # - Append groups level by level using ncks # # Input files like: # snc_LImon_CCSM4_historical_r1i1p1_199001-200512.nc # snd_LImon_CESM1-BGC_esmHistorical_r1i1p1_199001-200512.nc # # Output files like: # sn_LImon_199001-200512.nc # # Online: http://nco.sourceforge.net/nco.html#Combine-Files # # Execute this script: bash cmb_fl_grp.sh #====== # Directories drc_in=’../data/’ drc_out=’../data/grp/’ # Constants rlm=’LImon’ # Realm: LandIce; Time frequency: monthly tms=’200001-200512’ # Timeseries flt=’nc’ # File Type # Geographical weights # Can be skipped when ncap2 works on group data # Loop over all snc files for fn in $( ls ${drc_in}snc_${rlm}_*_${tms}.${flt} ); do ncap2 -O -s \ ’gw = float(cos(lat*3.1416/180.)); gw@long_name="geographical weight";’\ ${fn} ${fn} done var=( ’snc’ ’snd’ ) xpt=( ’esmHistorical’ ’historical’ ) mdl=( ’CCSM4’ ’CESM1-BGC’ ’CESM1-CAM5’ ) for i in {0..1}; do # Loop over variables for j in {0..1}; do # Loop over experiments for k in {0..2}; do # Loop over models ncecat -O --glb_mtd_spp -G ${xpt[j]}/${mdl[k]}/${mdl[k]}_ \ Chapter 7: CMIP5 Example 363 ${drc_in}${var[i]}_${rlm}_${mdl[k]}_${xpt[j]}_*_${tms}.${flt} \ ${drc_out}${var[i]}_${rlm}_${mdl[k]}_${xpt[j]}_all-nsm_${tms}.${flt} ncks -A \ ${drc_out}${var[i]}_${rlm}_${mdl[k]}_${xpt[j]}_all-nsm_${tms}.${flt} \ ${drc_out}${var[i]}_${rlm}_${mdl[0]}_${xpt[j]}_all-nsm_${tms}.${flt} done # Loop done: models ncks -A \ ${drc_out}${var[i]}_${rlm}_${mdl[0]}_${xpt[j]}_all-nsm_${tms}.${flt} \ ${drc_out}${var[i]}_${rlm}_${mdl[0]}_${xpt[0]}_all-nsm_${tms}.${flt} done # Loop done: experiments ncks -A \ ${drc_out}${var[i]}_${rlm}_${mdl[0]}_${xpt[0]}_all-nsm_${tms}.${flt} \ ${drc_out}${var[0]}_${rlm}_${mdl[0]}_${xpt[0]}_all-nsm_${tms}.${flt} done # Loop done: variables # Rename output file mv ${drc_out}${var[0]}_${rlm}_${mdl[0]}_${xpt[0]}_all-nsm_${tms}.${flt} \ ${drc_out}sn_${rlm}_all-mdl_all-xpt_all-nsm_${tms}.${flt} # Remove temporary files rm ${drc_out}sn?_${rlm}*.nc #- Rename Group: # E.g., file snc_LImon_CESM1-CAM5_historical_r1i1p1_199001-200512.nc # is now group /historical/CESM1-CAM5/CESM1-CAM5_00. # You can rename it to /historical/CESM1-CAM5/r1i1p1 to make more sense. # Note: You don’t need to write the full path of the new name. ncrename -g ${xpt}/${mdl}/${mdl}_00,r1i1p1 \ ${drc_out}${var}_${rlm}_${mdl}_all-nsm_${tms}.${flt} #------# Output file structure #------# esmHistorical #{ # CESM1-BGC #{ # CESM1-BGC_00 #{ # snc(time, lat, lon) # snd(time, lat, lon) #} #} #} # historical #{ # CCSM4 #{ 364 NCO 5.0.2-alpha07 User Guide # CCSM4_00 #{ # snc(time, lat, lon) # snd(time, lat, lon) #} # CCSM4_01 #{ # snc(time, lat, lon) # snd(time, lat, lon) #} # CCSM4_02 { ... } # CCSM4_03 { ... } # CCSM4_04 { ... } #} # CESM1-BGC #{ # CESM1-BGC_00 { ... } #} # CESM1-CAM5 #{ # r1i1p1 { ... } # CESM1-CAM5_01 { ... } # CESM1-CAM5_02 { ... } #} #} Chapter 7: CMIP5 Example 365 7.2 Global Distribution of Long-term Average Figure 7.1: Global Distribution of Long-term Average. This section illustrates how to calculate the global distribution of long-term average (see Figure 7.1) with either flat files or group filehttp://nco.sourceforge.net/nco.html# ( index-groups). Key steps include: 1. Average ensemble members of each model using nces (see Section 4.6 [nces netCDF Ensemble Statistics], page 256) 2. Average the record dimension using ncra (see Section 4.10 [ncra netCDF Record Av- erager], page 298) 3. Store results of each model as a distinct group in a single output file using ncecat (see Section 4.11 [ncrcat netCDF Record Concatenator], page 302) with the --gag option The first example shows how to process flat files. #!/bin/bash #======366 NCO 5.0.2-alpha07 User Guide # After cmb_fl.sh # Example: Long-term average of each model globally # # Input files like: # /data/cmip5/snc_LImon_bcc-csm1-1_historical_r1i1p1_185001-200512.nc # # Output files like: # /data/cmip5/output/snc/snc_LImon_all-mdl_historical_all-nsm_clm.nc # # Online: # http://nco.sourceforge.net/nco.html#Global-Distribution-of-Long_002dterm-Average # # Execute this script: bash glb_avg.sh #====== #------# Parameters drc_in=’/home/wenshanw/data/cmip5/’ # Directory of input files drc_out=’/home/wenshanw/data/cmip5/output/’ # Directory of output files var=( ’snc’ ’snd’ ) # Variables rlm=’LImon’ # Realm xpt=( ’historical’ ) # Experiment ( could be more ) fld_out=( ’snc/’ ’snd/’ ) # Folders of output files #------ for var_id in {0..1}; do # Loop over two variables # Names of all models # (ls [get file names]; cut [get the part for model names]; # sort; uniq [remove duplicates]; awk [print]) mdl_set=$( ls ${drc_in}${var[var_id]}_${rlm}_*_${xpt[0]}_*.nc | \ cut -d ’_’ -f 3 | sort | uniq -c | awk ’{print $2}’ ) # Number of models (echo [print contents]; wc [count]) mdl_num=$( echo ${mdl_set} | wc -w ) for mdl in ${mdl_set}; do # Loop over models # Average all the ensemble members of each model # Use nces file ensembles mode: --nsm_fl nces --nsm_fl -O -4 -d time,"1956-01-01 00:00:0.0","2005-12-31 23:59:9.9" \ ${drc_in}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_*.nc \ ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}\ _all-nsm_195601-200512.nc # Average along time ncra -O ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}\ _all-nsm_195601-200512.nc \ Chapter 7: CMIP5 Example 367 ${drc_out}${fld_out[var_id]}${var[var_id]}_${mdl}.nc echo Model ${mdl} done! done # Remove temporary files rm ${drc_out}${fld_out[var_id]}${var[var_id]}*historical*.nc # Store models as groups in the output file ncecat -O --gag ${drc_out}${fld_out[var_id]}${var[var_id]}_*.nc \ ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_\ all-mdl_${xpt[0]}_all-nsm_clm.nc echo Var ${var[var_id]} done! done With the use of group, the above script will be shortened to ONE LINE. # Data from cmb_fl_grp.sh # ensemble averaging nces -O --nsm_grp --nsm_sfx=’_avg’ \ sn_LImon_all-mdl_all-xpt_all-nsm_200001-200512.nc \ sn_LImon_all-mdl_all-xpt_nsm-avg.nc The input file, sn_LImon_all-mdl_all-xpt_all-nsm_200001-200512.nc, produced by cmb_fl_grp.sh, includes all the ensemble members as groups. The option ‘--nsm_grp’ de- notes that we are using group ensembles mode (http://nco.sf.net/nco.html#nsm_grp) of nces, instead of file ensembles mode (http://nco.sf.net/nco.html#nsm_fl),‘--nsm_fl’. The option ‘--nsm_sfx=’_avg’’ instructs nces to store the output as a new child group /[model]/[model name]_avg/var; otherwise, the output will be stored directly in the par- ent group /[model]/var. In the final output file, sn_LImon_all-mdl_all-xpt_nsm-avg_ tm-avg.nc, sub-groups with a suffix of ‘avg’ are the long-term averages of each model. One thing to notice is that for now, ensembles with only one ensemble member will be left untouched. 368 NCO 5.0.2-alpha07 User Guide 7.3 Annual Average over Regions Figure 7.2: Annual Average over Regions. This section illustrates how to calculate the annual average over specific regions (see Figure 7.2). Key steps include: 1. Spatial average using ncap2 (see Section 4.1 [ncap2 netCDF Arithmetic Processor], page 154) and ncwa (see Section 4.14 [ncwa netCDF Weighted Averager], page 347); 2. Change dimension order using ncpdq (see Section 4.9 [ncpdq netCDF Permute Dimen- sions Quickly], page 289); 3. Annual average using ncra (see Section 4.10 [ncra netCDF Record Averager], page 298); 4. Anomaly from long-term average using ncbo (see Section 4.3 [ncbo netCDF Binary Operator], page 225); 5. Standard deviation using ncbo (see Section 4.3 [ncbo netCDF Binary Operator], page 225) and nces (see Section 4.6 [nces netCDF Ensemble Statistics], page 256); 6. Rename variables using ncrename (see Section 4.13 [ncrename netCDF Renamer], page 341); 7. Edit attributions using ncatted (see Section 4.2 [ncatted netCDF Attribute Editor], page 218); 8. Linear regression using ncap2 (see Section 4.1 [ncap2 netCDF Arithmetic Processor], page 154); Chapter 7: CMIP5 Example 369 9. Use ncap2 (see Section 4.1 [ncap2 netCDF Arithmetic Processor], page 154) with nco script file (i.e., .nco file); 10. Move variables around using ncks (see Section 4.8 [ncks netCDF Kitchen Sink], page 263). Flat files example #!/bin/bash # Includes gsl_rgr.nco #======# After cmb_fl.sh # Example: Annual trend of each model over Greenland and Tibet # ( time- and spatial-average, standard deviation, # anomaly and linear regression) # # Input files: # /data/cmip5/snc_LImon_bcc-csm1-1_historical_r1i1p1_185001-200512.nc # # Output files: # /data/cmip5/outout/snc/snc_LImon_all-mdl_historical_all-nsm_annual.nc # # Online: http://nco.sourceforge.net/nco.html#Annual-Average-over-Regions # # Execute this script: bash ann_avg.sh #====== #------# Parameters drc_in=’/home/wenshanw/data/cmip5/’ # Directory of input files drc_out=’/home/wenshanw/data/cmip5/output/’ # Directory of output files var=( ’snc’ ’snd’ ) # Variables rlm=’LImon’ # Realm xpt=( ’historical’ ) # Experiment ( could be more ) fld_out=( ’snc/’ ’snd/’ ) # Folders of output files # ------ for var_id in {0..1}; do # Loop over two variables # Names of all models # (ls [get file names]; cut [get the part for model names]; # sort; uniq [remove duplicates]; awk [print]) mdl_set=$( ls ${drc_in}${var[var_id]}_${rlm}_*_${xpt[0]}_*.nc | \ cut -d ’_’ -f 3 | sort | uniq -c | awk ’{print $2}’ ) for mdl in ${mdl_set}; do # Loop over models 370 NCO 5.0.2-alpha07 User Guide # Loop over ensemble members for fn in $( ls ${drc_in}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_*.nc ); do pfx=$( echo ${fn} | cut -d’/’ -f6 | cut -d’_’ -f1-5 ) # Two regions # Geographical weight ncap2 -O -s ’gw = cos(lat*3.1415926/180.); gw@long_name="geographical weight"\ ;gw@units="ratio"’ ${fn} ${drc_out}${fld_out[var_id]}${pfx}_gw.nc # Greenland ncwa -O -w gw -d lat,60.0,75.0 -d lon,300.0,340.0 -a lat,lon \ ${drc_out}${fld_out[var_id]}${pfx}_gw.nc \ ${drc_out}${fld_out[var_id]}${pfx}_gw_1.nc # Tibet ncwa -O -w gw -d lat,30.0,40.0 -d lon,80.0,100.0 -a lat,lon \ ${drc_out}${fld_out[var_id]}${pfx}_gw.nc \ ${drc_out}${fld_out[var_id]}${pfx}_gw_2.nc # Aggregate 2 regions together ncecat -O -u rgn ${drc_out}${fld_out[var_id]}${pfx}_gw_?.nc \ ${drc_out}${fld_out[var_id]}${pfx}_gw_rgn4.nc # Change dimensions order ncpdq -O -a time,rgn ${drc_out}${fld_out[var_id]}${pfx}_gw_rgn4.nc \ ${drc_out}${fld_out[var_id]}${pfx}_gw_rgn4.nc # Remove temporary files (optional) rm ${drc_out}${fld_out[var_id]}${pfx}_gw_?.nc \ ${drc_out}${fld_out[var_id]}${pfx}_gw.nc # Annual average (use the feature of ’Duration’) ncra -O --mro -d time,"1956-01-01 00:00:0.0","2005-12-31 23:59:9.9",12,12 \ ${drc_out}${fld_out[var_id]}${pfx}_gw_rgn4.nc \ ${drc_out}${fld_out[var_id]}${pfx}_yrly.nc # Anomaly # Long-term average ncwa -O -a time ${drc_out}${fld_out[var_id]}${pfx}_yrly.nc \ ${drc_out}${fld_out[var_id]}${pfx}_clm.nc # Subtract long-term average ncbo -O --op_typ=- ${drc_out}${fld_out[var_id]}${pfx}_yrly.nc \ ${drc_out}${fld_out[var_id]}${pfx}_clm.nc \ ${drc_out}${fld_out[var_id]}${pfx}_anm.nc done rm ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_*_yrly.nc # Average over all the ensemble members Chapter 7: CMIP5 Example 371 ncea -O -4 ${drc_out}${fld_out[var_id]}${var[var_id]}_\ ${rlm}_${mdl}_${xpt[0]}_*_anm.nc ${drc_out}${fld_out[var_id]}\ ${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_all-nsm_anm.nc # Standard deviation ------for fn in $( ls ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_\ ${xpt[0]}_*_anm.nc ); do pfx=$( echo ${fn} | cut -d’/’ -f8 | cut -d’_’ -f1-5 ) # Difference between each ensemble member and the average of all members ncbo -O --op_typ=- ${fn} \ ${drc_out}${fld_out[var_id]}${var[var_id]}_\ ${rlm}_${mdl}_${xpt[0]}_all-nsm_anm.nc \ ${drc_out}${fld_out[var_id]}${pfx}_dlt.nc done # RMS ncea -O -y rmssdn ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_\ ${mdl}_${xpt[0]}_*_dlt.nc \ ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_\ ${mdl}_${xpt[0]}_all-nsm_sdv.nc # Rename variables ncrename -v ${var[var_id]},sdv \ ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_\ ${mdl}_${xpt[0]}_all-nsm_sdv.nc # Edit attributions ncatted -a standard_name,sdv,a,c,"_standard_deviation_over_ensemble" \ -a long_name,sdv,a,c," Standard Deviation over Ensemble" \ -a original_name,sdv,a,c," sdv" \ ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_\ ${mdl}_${xpt[0]}_all-nsm_sdv.nc #------ # Linear regression ------#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! # Have to change the name of variable in the commands file # of gsl_rgr.nco manually (gsl_rgr.nco is listed below) ncap2 -O -S gsl_rgr.nco \ ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_\ ${mdl}_${xpt[0]}_all-nsm_anm.nc ${drc_out}${fld_out[var_id]}${var[var_id]}\ _${rlm}_${mdl}_${xpt[0]}_all-nsm_anm_rgr.nc #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! # Get rid of temporary variables ncks -O -v c0,c1,pval,${var[var_id]},gw \ ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_\ ${xpt[0]}_all-nsm_anm_rgr.nc \ 372 NCO 5.0.2-alpha07 User Guide ${drc_out}${fld_out[var_id]}${var[var_id]}_${mdl}.nc #------ # Move the variable ’sdv’ into the anomaly files (i.e., *anm.nc files) ncks -A -v sdv \ ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_\ ${mdl}_${xpt[0]}_all-nsm_sdv.nc \ ${drc_out}${fld_out[var_id]}${var[var_id]}_${mdl}.nc rm ${drc_out}${fld_out[var_id]}${var[var_id]}_*historical* echo Model ${mdl} done! done # Store models as groups in the output file ncecat -O --gag ${drc_out}${fld_out[var_id]}${var[var_id]}_*.nc ${drc_out}${fld_out[var_id]}${var[var_id]}_\ ${rlm}_all-mdl_${xpt[0]}_all-nsm_annual.nc echo Var ${var[var_id]} done! done gsl rgr.nco // Linear Regression // Called by ann_avg.sh // Caution: make sure the variable name is // in agreement with the main script (now is ’snd’) // Online: http://nco.sourceforge.net/nco.html#Annual-Average-over-Regions // Declare variables *c0[$rgn]=0.; // Intercept *c1[$rgn]=0.; // Slope *sdv[$rgn]=0.; // Standard deviation *covxy[$rgn]=0.; // Covariance *x = double(time); for (*rgn_id=0;rgn_id<$rgn.size;rgn_id++) // Loop over regions { gsl_fit_linear(time,1,snd(:,rgn_id),1,$time.size, \ &tc0, &tc1, &cov00, &cov01,&cov11,&sumsq); // Linear regression function c0(rgn_id) = tc0; // Output results c1(rgn_id) = tc1; covxy(rgn_id) = gsl_stats_covariance(time,1,\ $time.size,double(snd(:,rgn_id)),1,$time.size); // Covariance function sdv(rgn_id) = gsl_stats_sd(snd(:,rgn_id), \ 1, $time.size); // Standard deviation function } Chapter 7: CMIP5 Example 373 // P value------*time_sdv = gsl_stats_sd(time, 1, $time.size); *r_value = covxy/(time_sdv*sdv); *t_value = r_value/sqrt((1-r_value^2)/($time.size-2)); pval = abs(gsl_cdf_tdist_P(t_value, $time.size-2) - \ gsl_cdf_tdist_P(-t_value, $time.size-2)); //------ // Write RAM variables to disk //------// Usually NCO writes the outputs directly to disk // Using RAM variables, declared by *, will shorten running time // Output the final results using ram_write() //------ram_write(c0); ram_write(c1); With the group feature, all the loops over experiments, models and ensemble members can be omitted. As we are working on implementing group feature in all NCO operators, some functions (e.g., regression and standard deviation over ensemble members) may have to wait until the new versions. #!/bin/bash # #======# Group data output by cmb_fl_grp.sh # Annual trend of each model over Greenland and Tibet # Time- and spatial-average, standard deviation and anomaly # No regression yet (needs ncap2) # # Input files: # sn_LImon_all-mdl_all-xpt_all-nsm_200001-200512.nc # # Online: http://nco.sourceforge.net/nco.html#Annual-Average-over-Regions # # Execute this script: bash ann_avg_grp.sh #======# Input and output directory drc=’../data/grp/’ # Constants pfx=’sn_LImon_all-mdl_all-xpt_all-nsm’ tms=’200001-200512’ # Time series # Greenland ncwa -O -w gw -d lat,60.0,75.0 -d lon,300.0,340.0 -a lat,lon \ ${drc}${pfx}_${tms}.nc \ ${drc}${pfx}_${tms}_grl.nc 374 NCO 5.0.2-alpha07 User Guide # Tibet ncwa -O -w gw -d lat,30.0,40.0 -d lon,80.0,100.0 -a lat,lon \ ${drc}${pfx}_${tms}.nc \ ${drc}${pfx}_${tms}_tbt.nc # Aggregate 2 regions together ncecat -O -u rgn ${drc}${pfx}_${tms}_???.nc \ ${drc}${pfx}_${tms}_rgn2.nc # Change dimensions order ncpdq -O -a time,rgn ${drc}${pfx}_${tms}_rgn2.nc \ ${drc}${pfx}_${tms}_rgn2.nc # Remove temporary files (optional) rm ${drc}${pfx}_${tms}_???.nc #Annual average ncra -O --mro -d time,,,12,12 ${drc}${pfx}_${tms}_rgn2.nc \ ${drc}${pfx}_${tms}_rgn2_ann.nc # Anomaly #------# Long-term average ncwa -O -a time ${drc}${pfx}_${tms}_rgn2_ann.nc \ ${drc}${pfx}_${tms}_rgn2_clm.nc # Subtract ncbo -O --op_typ=- ${drc}${pfx}_${tms}_rgn2_ann.nc \ ${drc}${pfx}_${tms}_rgn2_clm.nc \ ${drc}${pfx}_${tms}_rgn2_anm.nc #------ # Standard Deviation: inter-annual variability # RMS of the above anomaly ncra -O -y rmssdn ${drc}${pfx}_${tms}_rgn2_anm.nc \ ${drc}${pfx}_${tms}_rgn2_stddev.nc Chapter 7: CMIP5 Example 375 7.4 Monthly Cycle Figure 7.3: Monthly Cycle. This script illustrates how to calculate the monthly anomaly from the annual average (see Figure 7.3). In order to keep only the monthly cycle, we will subtract the annual average of each year from the monthly data, instead of subtracting the long-term average. This is a little more complicated in coding since we need to loop over years. Flat files example #!/bin/bash #======# After cmb_fl.sh # Example: Monthly cycle of each model in Greenland # # Input files: # /data/cmip5/snc_LImon_bcc-csm1-1_historical_r1i1p1_185001-200512.nc # # Output files: # /data/cmip5/snc/snc_LImon__all-mdl_historical_all-nsm_GN_mthly-anm.nc # # Online: http://nco.sourceforge.net/nco.html#Monthly-Cycle # # Execute this script: bash mcc.sh #====== #------# Parameters drc_in=’/home/wenshanw/data/cmip5/’ # Directory of input files drc_out=’/home/wenshanw/data/cmip5/output/’ # Directory of output files var=( ’snc’ ’snd’ ) # Variables 376 NCO 5.0.2-alpha07 User Guide rlm=’LImon’ # Realm xpt=( ’historical’ ) # Experiment ( could be more ) fld_out=( ’snc/’ ’snd/’ ) # Folders of output files #------ for var_id in {0..1}; do # Loop over two variables # names of all models # (ls [get file names]; cut [get the part for model names]; # sort; uniq [remove duplicates]; awk [print]) mdl_set=$( ls ${drc_in}${var[var_id]}_${rlm}_*_${xpt[0]}_*.nc | \ cut -d ’_’ -f 3 | sort | uniq -c | awk ’{print $2}’ ) for mdl in ${mdl_set}; do ## Loop over models # Average all the ensemble members of each model ncea -O -4 -d time,"1956-01-01 00:00:0.0","2005-12-31 23:59:9.9" \ ${drc_in}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_*.nc \ ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_all-nsm.nc # Greenland # Geographical weight ncap2 -O -s \ ’gw = cos(lat*3.1415926/180.); \ gw@long_name="geographical weight";gw@units="ratio"’ \ ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_all-nsm.nc \ ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_all-nsm.nc ncwa -O -w gw -d lat,60.0,75.0 -d lon,300.0,340.0 -a lat,lon \ ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_all-nsm.nc \ ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_all-nsm_GN.nc # Anomaly------for moy in {1..12}; do # Loop over months mm=$( printf "%02d" ${moy} ) # Change to 2-digit format for yr in {1956..2005}; do # Loop over years # If January, calculate the annual average if [ ${moy} -eq 1 ]; then ncra -O -d time,"${yr}-01-01 00:00:0.0","${yr}-12-31 23:59:9.9" \ ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_\ ${xpt[0]}_all-nsm_GN.nc ${drc_out}${fld_out[var_id]}${var[var_id]}_\ ${rlm}_${mdl}_${xpt[0]}_all-nsm_GN_${yr}.nc fi # The specific month ncks -O -d time,"${yr}-${mm}-01 00:00:0.0","${yr}-${mm}-31 23:59:9.9" \ ${drc_out}${fld_out[var_id]}${var[var_id]}_\ ${rlm}_${mdl}_${xpt[0]}_all-nsm_GN.nc \ Chapter 7: CMIP5 Example 377 ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_\ all-nsm_GN_${yr}${mm}.nc # Subtract the annual average from the monthly data ncbo -O --op_typ=- ${drc_out}${fld_out[var_id]}${var[var_id]}_\ ${rlm}_${mdl}_${xpt[0]}_all-nsm_GN_${yr}${mm}.nc \ ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_\ all-nsm_GN_${yr}.nc ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_\ ${mdl}_${xpt[0]}_all-nsm_GN_${yr}${mm}_anm.nc done # Average over years ncra -O ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_\ ${xpt[0]}_all-nsm_GN_????${mm}_anm.nc \ ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_\ ${xpt[0]}_all-nsm_GN_${mm}_anm.nc done #------ # Concatenate months together ncrcat -O ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_\ ${xpt[0]}_all-nsm_GN_??_anm.nc \ ${drc_out}${fld_out[var_id]}${var[var_id]}_${mdl}.nc echo Model ${mdl} done! done rm -f ${drc_out}${fld_out[var_id]}${var[var_id]}*historical* # Store models as groups in the output file ncecat -O --gag -v ${var[var_id]} \ ${drc_out}${fld_out[var_id]}${var[var_id]}_*.nc \ ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_all-mdl_\ ${xpt[0]}_all-nsm_GN_mthly-anm.nc echo Var ${var[var_id]} done! done Using group feature and hyperslabs (http://nco.sourceforge.net/nco.html# Hyperslabs) of ncbo, the script will be shortened. #!/bin/bash #======# Monthly cycle of each ensemble member in Greenland # # Input file from cmb_fl_grpsh # sn_LImon_all-mdl_all-xpt_all-nsm_199001-200512.nc # Online: http://nco.sourceforge.net/nco.html#Monthly-Cycle 378 NCO 5.0.2-alpha07 User Guide # # Execute this script in command line: bash mcc_grp.sh #======# Input and output directory drc=’../data/grp/’ # Constants pfx=’sn_LImon_all-mdl_all-xpt_all-nsm_200001-200512’ # Greenland ncwa -O -w gw -d lat,60.0,75.0 -d lon,300.0,340.0 -a lat,lon \ ${drc}${pfx}.nc ${drc}${pfx}_grl.nc # Anomaly from annual average of each year for yyyy in {2000..2005}; do # Annual average ncwa -O -d time,"${yyyy}-01-01","${yyyy}-12-31" \ ${drc}${pfx}_grl.nc ${drc}${pfx}_grl_${yyyy}.nc # Anomaly ncbo -O --op_typ=- -d time,"${yyyy}-01-01","${yyyy}-12-31" \ ${drc}${pfx}_grl.nc ${drc}${pfx}_grl_${yyyy}.nc \ ${drc}${pfx}_grl_${yyyy}_anm.nc done # Monthly cycle for moy in {1..12}; do mm=$( printf "%02d" ${moy} ) # Change to 2-digit format ncra -O -d time,"2000-${mm}-01",,12 \ ${drc}${pfx}_grl_????_anm.nc ${drc}${pfx}_grl_${mm}_anm.nc done # Concatenate 12 months together ncrcat -O ${drc}${pfx}_grl_??_anm.nc \ ${drc}${pfx}_grl_mth_anm.nc 7.5 Regrid MODIS Data In order to compare the results between MODIS and CMIP5 models, one usually regrids one or both datasets so that the spatial resolutions match. Here, the script illustrates how to regrid MODIS data. Key steps include: 1. Regrid using bilinear interpolation (see Section 4.1.21 [Bilinear interpolation], page 193) 2. Rename variables, dimensions and attributions using ncrename (see Section 4.13 [ncre- name netCDF Renamer], page 341). Main Script #!/bin/bash Chapter 7: CMIP5 Example 379 # include bi_interp.nco #======# Example for # - regrid (using bi_interp.nco): the spatial resolution of MODIS data # is much finer than those of CMIP5 models. In order to compare # the two, we can regrid MODIS data to comform to CMIP5. # # Input files (Note: the .hdf files downloaded have to be converted to .nc at # the present): # /modis/mcd43c3/MCD43C3.A2000049.005.2006271205532.nc # # Output files: # /modis/mcd43c3/cesm-grid/MCD43C3.2000049.regrid.nc # # Online: http://nco.sourceforge.net/nco.html#Regrid-MODIS-Data # # Execute this script: bash rgr.sh #====== var=( ’MCD43C3’ ) # Variable fld_in=( ’monthly/’ ) # Folder of input files fld_out=( ’cesm-grid/’ ) # Folder of output files drc_in=’/media/grele_data/wenshan/modis/mcd43c3/’ # Directory of input files for fn in $( ls ${drc_in}${fld_in}${var}.*.nc ); do # Loop over files sfx=$( echo $fn | cut -d ’/’ -f 8 | cut -d ’.’ -f 2 ) # Part of file names # Regrid ncap2 -O -S bi_interp.nco ${fn} ${drc_in}${fld_out}${var}.${sfx}.regrid.nc # Keep only the new variables ncks -O -v wsa_sw_less,bsa_sw_less ${drc_in}${fld_out}${var}.${sfx}.regrid.nc \ ${drc_in}${fld_out}${var}.${sfx}.regrid.nc # Rename the new variables, dimensions and attributions ncrename -O -d latn,lat -d lonn,lon -v latn,lat -v lonn,lon \ -v wsa_sw_less,wsa_sw -v bsa_sw_less,bsa_sw -a missing_value,_FillValue \ ${drc_in}${fld_out}${var}.${sfx}.regrid.nc echo $sfx done. done bi interp.nco // Bilinear interpolation // Included by rgr.sh // Online: http://nco.sourceforge.net/nco.html#Regrid-MODIS-Data defdim("latn",192); // Define new dimension: latitude 380 NCO 5.0.2-alpha07 User Guide defdim("lonn",288); // Define new dimension: longitude latn[$latn] = {90,89.0576 ,88.1152 ,87.1728 ,86.2304 ,85.288 ,\ 84.3456 ,83.4031 ,82.4607 ,81.5183 ,80.5759 ,79.6335 ,78.6911 ,\ 77.7487 ,76.8063 ,75.8639 ,74.9215 ,73.9791 ,73.0367 ,72.0942 ,\ 71.1518 ,70.2094 ,69.267 ,68.3246 ,67.3822 ,66.4398 ,65.4974 ,\ 64.555 ,63.6126 ,62.6702 ,61.7277 ,60.7853 ,59.8429 ,58.9005 ,\ 57.9581 ,57.0157 ,56.0733 ,55.1309 ,54.1885 ,53.2461 ,52.3037 ,\ 51.3613 ,50.4188 ,49.4764 ,48.534 ,47.5916 ,46.6492 ,45.7068 ,\ 44.7644 ,43.822 ,42.8796 ,41.9372 ,40.9948 ,40.0524 ,39.11 ,\ 38.1675 ,37.2251 ,36.2827 ,35.3403 ,34.3979 ,33.4555 ,32.5131 ,\ 31.5707 ,30.6283 ,29.6859 ,28.7435 ,27.8011 ,26.8586 ,25.9162 ,\ 24.9738 ,24.0314 ,23.089 ,22.1466 ,21.2042 ,20.2618 ,19.3194 ,\ 18.377 ,17.4346 ,16.4921 ,15.5497 ,14.6073 ,13.6649 ,12.7225 ,\ 11.7801 ,10.8377 ,9.89529 ,8.95288 ,8.01047 ,7.06806 ,6.12565 ,\ 5.18325 ,4.24084 ,3.29843 ,2.35602 ,1.41361 ,0.471204,-0.471204,\ -1.41361,-2.35602,-3.29843,-4.24084,-5.18325,-6.12565,-7.06806,\ -8.01047,-8.95288,-9.89529,-10.8377,-11.7801,-12.7225,-13.6649,\ -14.6073,-15.5497,-16.4921,-17.4346,-18.377 ,-19.3194,-20.2618,\ -21.2042,-22.1466,-23.089 ,-24.0314,-24.9738,-25.9162,-26.8586,\ -27.8011,-28.7435,-29.6859,-30.6283,-31.5707,-32.5131,-33.4555,\ -34.3979,-35.3403,-36.2827,-37.2251,-38.1675,-39.11 ,-40.0524,\ -40.9948,-41.9372,-42.8796,-43.822 ,-44.7644,-45.7068,-46.6492,\ -47.5916,-48.534 ,-49.4764,-50.4188,-51.3613,-52.3037,-53.2461,\ -54.1885,-55.1309,-56.0733,-57.0157,-57.9581,-58.9005,-59.8429,\ -60.7853,-61.7277,-62.6702,-63.6126,-64.555 ,-65.4974,-66.4398,\ -67.3822,-68.3246,-69.267 ,-70.2094,-71.1518,-72.0942,-73.0367,\ -73.9791,-74.9215,-75.8639,-76.8063,-77.7487,-78.6911,-79.6335,\ -80.5759,-81.5183,-82.4607,-83.4031,-84.3456,-85.288,-86.2304,\ -87.1728,-88.1152,-89.0576,-90}; // Copy of CCSM4 latitude lonn[$lonn] = {-178.75,-177.5,-176.25,-175,-173.75,-172.5,-171.25,\ -170,-168.75,-167.5,-166.25,-165,-163.75,-162.5,-161.25,-160,\ -158.75,-157.5,-156.25,-155,-153.75,-152.5,-151.25,-150,-148.75,\ -147.5,-146.25,-145,-143.75,-142.5,-141.25,-140,-138.75,-137.5,\ -136.25,-135,-133.75,-132.5,-131.25,-130,-128.75,-127.5,-126.25,\ -125,-123.75,-122.5,-121.25,-120,-118.75,-117.5,-116.25,-115,\ -113.75,-112.5,-111.25,-110,-108.75,-107.5,-106.25,-105,-103.75,\ -102.5,-101.25,-100,-98.75,-97.5,-96.25,-95,-93.75,-92.5,-91.25,\ -90,-88.75,-87.5,-86.25,-85,-83.75,-82.5,-81.25,-80,-78.75,-77.5,\ -76.25,-75,-73.75,-72.5,-71.25,-70,-68.75,-67.5,-66.25,-65,-63.75,\ -62.5,-61.25,-60,-58.75,-57.5,-56.25,-55,-53.75,-52.5,-51.25,-50,\ -48.75,-47.5,-46.25,-45,-43.75,-42.5,-41.25,-40,-38.75,-37.5,\ -36.25,-35,-33.75,-32.5,-31.25,-30,-28.75,-27.5,-26.25,-25,-23.75,\ -22.5,-21.25,-20,-18.75,-17.5,-16.25,-15,-13.75,-12.5,-11.25,-10,\ -8.75,-7.5,-6.25,-5,-3.75,-2.5,-1.25,0,1.25,2.5,3.75,5,6.25,7.5,\ 8.75,10,11.25,12.5,13.75,15,16.25,17.5,18.75,20,21.25,22.5,23.75,\ 25,26.25,27.5,28.75,30,31.25,32.5,33.75,35,36.25,37.5,38.75,40,\ 41.25,42.5,43.75,45,46.25,47.5,48.75,50,51.25,52.5,53.75,55,56.25,\ Chapter 7: CMIP5 Example 381 57.5,58.75,60,61.25,62.5,63.75,65,66.25,67.5,68.75,70,71.25,72.5,\ 73.75,75,76.25,77.5,78.75,80,81.25,82.5,83.75,85,86.25,87.5,88.75,\ 90,91.25,92.5,93.75,95,96.25,97.5,98.75,100,101.25,102.5,103.75,\ 105,106.25,107.5,108.75,110,111.25,112.5,113.75,115,116.25,117.5,\ 118.75,120,121.25,122.5,123.75,125,126.25,127.5,128.75,130,131.25,\ 132.5,133.75,135,136.25,137.5,138.75,140,141.25,142.5,143.75,145,\ 146.25,147.5,148.75,150,151.25,152.5,153.75,155,156.25,157.5,\ 158.75,160,161.25,162.5,163.75,165,166.25,167.5,168.75,170,171.25,\ 172.5,173.75,175,176.25,177.5,178.75,180}; // Copy of CCSM4 longitude *out[$time,$latn,$lonn]=0.0; // Output structure // Bi-linear interpolation bsa_sw_less=bilinear_interp_wrap(bsa_sw,out,latn,lonn,lat,lon); wsa_sw_less=bilinear_interp_wrap(wsa_sw,out,latn,lonn,lat,lon); // Add attributions latn@units = "degree_north"; lonn@units = "degree_east"; latn@long_name = "latitude"; lonn@long_name = "longitude"; bsa_sw_less@hdf_name = "Albedo_BSA_shortwave"; bsa_sw_less@calibrated_nt = 5; bsa_sw_less@missing_value = 32767.0; bsa_sw_less@units = "albedo, no units"; bsa_sw_less@long_name = "Global_Albedo_BSA_shortwave"; wsa_sw_less@hdf_name = "Albedo_WSA_shortwave"; wsa_sw_less@calibrated_nt = 5; wsa_sw_less@missing_value = 32767.0; wsa_sw_less@units = "albedo, no units"; wsa_sw_less@long_name = "Global_Albedo_WSA_shortwave"; 7.6 Add Coordinates to MODIS Data Main Script #!/bin/bash #======# Example for # - regrid (using bi_interp.nco): the spatial resolution of MODIS data # is much finer than those of CMIP5 models. In order to compare # the two, we can regrid MODIS data to comform to CMIP5. # - add coordinates (using coor.nco): there is no coordinate information # in MODIS data. We have to add it manually now. # # Input files: # /modis/mcd43c3/cesm-grid/MCD43C3.2000049.regrid.nc 382 NCO 5.0.2-alpha07 User Guide # # Output files: # /modis/mcd43c3/cesm-grid/MCD43C3.2000049.regrid.nc # # Online: http://nco.sourceforge.net/nco.html#Add-Coordinates-to-MODIS-Data # # Execute this script: bash add_crd.sh #====== var=( ’MOD10CM’ ) # Variable fld_in=( ’snc/nc/’ ) # Folder of input files drc_in=’/media/grele_data/wenshan/modis/’ # directory of input files for fn in $( ls ${drc_in}${fld_in}${var}*.nc ); do # Loop over files sfx=$( echo ${fn} | cut -d ’/’ -f 8 | cut -d ’.’ -f 2-4 ) # Part of file names echo ${sfx} # Rename dimension names ncrename -d YDim_MOD_CMG_Snow_5km,lat -d XDim_MOD_CMG_Snow_5km,lon -O \ ${drc_in}${fld_in}${var}.${sfx}.nc ${drc_in}${fld_in}${var}.${sfx}.nc # Add coordinates ncap2 -O -S crd.nco ${drc_in}${fld_in}${var}.${sfx}.nc \ ${drc_in}${fld_in}${var}.${sfx}.nc done crd.nco // Add coordinates to MODIS HDF data // Included by add_crd.sh // Online: http://nco.sourceforge.net/nco.html#Add-Coordinates-to-MODIS-Data lon = array(0.f, 0.05, $lon) - 180; lat = 90.f- array(0.f, 0.05, $lat); 7.7 Permute MODIS Coordinates MODIS orders latitude data from 90◦N to -90◦N, and longitude from -180◦E to 180◦E. However, CMIP5 orders latitude from -90◦N to 90◦N, and longitude from 0◦E to 360◦E. This script changes the MODIS coordinates to follow the CMIP5 convention. #!/bin/bash ##======## Example for ## - permute coordinates: the grid of MODIS is ## from (-180 degE, 90 degN), the left-up corner, to ## (180 degE, -90 degN), the right-low corner. However, CMIP5 is ## from (0 degE, -90 degN) to (360 degE, 90 degN). The script ## here changes the MODIS grid to CMIP5 grid. Chapter 7: CMIP5 Example 383 ## ## Input files: ## /modis/mcd43c3/cesm-grid/MCD43C3.2000049.regrid.nc ## ## Output files: ## /modis/mcd43c3/cesm-grid/MCD43C3.2000049.regrid.nc ## ## Online: http://nco.sourceforge.net/nco.html#Permute-MODIS-Coordinates ## ## Execute this script: bash pmt_crd.sh ##====== ##------## Permute coordinates ## - Inverse lat from (90,-90) to (-90,90) ## - Permute lon from (-180,180) to (0,360) for fn in $( ls MCD43C3.*.nc ); do # Loop over files sfx=$( echo ${fn} | cut -d ’.’ -f 1-3 ) # Part of file names echo ${sfx} ## Lat ncpdq -O -a -lat ${fn} ${fn} # Inverse latitude (NB: there is ’-’ before ’lat’) ## Lon ncks -O --msa -d lon,0.0,180.0 -d lon,-180.0,-1.25 ${fn} ${fn} ## Add new longitude coordinates ncap2 -O -s ’lon=array(0.0,1.25,$lon)’ ${fn} ${fn} done 385 8 Parallel This section will describe NCO scripting strategies. Many techniques can be used to exploit script-level parallelism, including GNU Parallel and Swift. ls *historical*.nc | parallel ncks -O -d time,"1950-01-01","2000-01-01" {} 50y/{} 387 9 CCSM Example This chapter illustrates how to use NCO to process and analyze the results of a CCSM climate simulation. ************************************************************************ Task 0: Finding input files x************************************************************************ The CCSM model outputs files to a local directory like: /ptmp/zender/archive/T42x1_40 Each component model has its own subdirectory, e.g., /ptmp/zender/archive/T42x1_40/atm /ptmp/zender/archive/T42x1_40/cpl /ptmp/zender/archive/T42x1_40/ice /ptmp/zender/archive/T42x1_40/lnd /ptmp/zender/archive/T42x1_40/ocn within which model output is tagged with the particular model name /ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0001-01.nc /ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0001-02.nc /ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0001-03.nc ... /ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0001-12.nc /ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0002-01.nc /ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0002-02.nc ... or /ptmp/zender/archive/T42x1_40/lnd/T42x1_40.clm2.h0.0001-01.nc /ptmp/zender/archive/T42x1_40/lnd/T42x1_40.clm2.h0.0001-02.nc /ptmp/zender/archive/T42x1_40/lnd/T42x1_40.clm2.h0.0001-03.nc ... ************************************************************************ Task 1: Regional processing ************************************************************************ A common task in data processing is often creating seasonal cycles. Imagine a 100-year simulation with its 1200 monthly mean files. Our goal is to create a single file containing 12 months of data. Each month in the output file is the mean of 100 input files. Normally, we store the "reduced" data in a smaller, local directory. 388 NCO 5.0.2-alpha07 User Guide caseid=’T42x1_40’ #drc_in="${DATA}/archive/${caseid}/atm" drc_in="${DATA}/${caseid}" drc_out="${DATA}/${caseid}" mkdir -p ${drc_out} cd ${drc_out} Method 1: Assume all data in directory applies for mth in {1..12}; do mm=‘printf "%02d" $mth‘ ncra -O -D 1 -o ${drc_out}/${caseid}_clm${mm}.nc \ ${drc_in}/${caseid}.cam2.h0.*-${mm}.nc done # end loop over mth Method 2: Use shell ’globbing’ to construct input filenames for mth in {1..12}; do mm=‘printf "%02d" $mth‘ ncra -O -D 1 -o ${drc_out}/${caseid}_clm${mm}.nc \ ${drc_in}/${caseid}.cam2.h0.00??-${mm}.nc \ ${drc_in}/${caseid}.cam2.h0.0100-${mm}.nc done # end loop over mth Method 3: Construct input filename list explicitly for mth in {1..12}; do mm=‘printf "%02d" $mth‘ fl_lst_in=’’ for yr in {1..100}; do yyyy=‘printf "%04d" $yr‘ fl_in=${caseid}.cam2.h0.${yyyy}-${mm}.nc fl_lst_in="${fl_lst_in} ${caseid}.cam2.h0.${yyyy}-${mm}.nc" done # end loop over yr ncra -O -D 1 -o ${drc_out}/${caseid}_clm${mm}.nc -p ${drc_in} \ ${fl_lst_in} done # end loop over mth Make sure the output file averages correct input files! ncks --trd -M prints global metadata: ncks --trd -M ${drc_out}/${caseid}_clm01.nc The input files ncra used to create the climatological monthly mean will appear in the global attribute named ’history’. Use ncrcat to aggregate the climatological monthly means ncrcat -O -D 1 \ Chapter 9: CCSM Example 389 ${drc_out}/${caseid}_clm??.nc ${drc_out}/${caseid}_clm_0112.nc Finally, create climatological means for reference. The climatological time-mean: ncra -O -D 1 \ ${drc_out}/${caseid}_clm_0112.nc ${drc_out}/${caseid}_clm.nc The climatological zonal-mean: ncwa -O -D 1 -a lon \ ${drc_out}/${caseid}_clm.nc ${drc_out}/${caseid}_clm_x.nc The climatological time- and spatial-mean: ncwa -O -D 1 -a lon,lat,time -w gw \ ${drc_out}/${caseid}_clm.nc ${drc_out}/${caseid}_clm_xyt.nc This file contains only scalars, e.g., "global mean temperature", used for summarizing global results of a climate experiment. Climatological monthly anomalies = Annual Cycle: Subtract climatological mean from climatological monthly means. Result is annual cycle, i.e., climate-mean has been removed. ncbo -O -D 1 -o ${drc_out}/${caseid}_clm_0112_anm.nc \ ${drc_out}/${caseid}_clm_0112.nc ${drc_out}/${caseid}_clm_xyt.nc ************************************************************************ Task 2: Correcting monthly averages ************************************************************************ The previous step appoximates all months as being equal, so, e.g., February weighs slightly too much in the climatological mean. This approximation can be removed by weighting months appropriately. We must add the number of days per month to the monthly mean files. First, create a shell variable dpm: unset dpm # Days per month declare -a dpm dpm=(0 31 28.25 31 30 31 30 31 31 30 31 30 31) # Allows 1-based indexing Method 1: Create dpm directly in climatological monthly means for mth in {1..12}; do mm=‘printf "%02d" ${mth}‘ ncap2 -O -s "dpm=0.0*date+${dpm[${mth}]}" \ ${drc_out}/${caseid}_clm${mm}.nc ${drc_out}/${caseid}_clm${mm}.nc done # end loop over mth 390 NCO 5.0.2-alpha07 User Guide Method 2: Create dpm by aggregating small files for mth in {1..12}; do mm=‘printf "%02d" ${mth}‘ ncap2 -O -v -s "dpm=${dpm[${mth}]}" ~/nco/data/in.nc \ ${drc_out}/foo_${mm}.nc done # end loop over mth ncecat -O -D 1 -p ${drc_out} -n 12,2,2 foo_${mm}.nc foo.nc ncrename -O -D 1 -d record,time ${drc_out}/foo.nc ncatted -O -h \ -a long_name,dpm,o,c,"Days per month" \ -a units,dpm,o,c,"days" \ ${drc_out}/${caseid}_clm_0112.nc ncks -A -v dpm ${drc_out}/foo.nc ${drc_out}/${caseid}_clm_0112.nc Method 3: Create small netCDF file using ncgen cat > foo.cdl << ’EOF’ netcdf foo { dimensions: time=unlimited; variables: float dpm(time); dpm:long_name="Days per month"; dpm:units="days"; data: dpm=31,28.25,31,30,31,30,31,31,30,31,30,31; } EOF ncgen -b -o foo.nc foo.cdl ncks -A -v dpm ${drc_out}/foo.nc ${drc_out}/${caseid}_clm_0112.nc Another way to get correct monthly weighting is to average daily output files, if available. ************************************************************************ Task 3: Regional processing ************************************************************************ Let’s say you are interested in examining the California region. Hyperslab your dataset to isolate the appropriate latitude/longitudes. ncks -O -D 1 -d lat,30.0,37.0 -d lon,240.0,270.0 \ ${drc_out}/${caseid}_clm_0112.nc \ ${drc_out}/${caseid}_clm_0112_Cal.nc The dataset is now much smaller! To examine particular metrics. Chapter 9: CCSM Example 391 ************************************************************************ Task 4: Accessing data stored remotely ************************************************************************ OPeNDAP server examples: UCI DAP servers: ncks --trd -M -p http://dust.ess.uci.edu/cgi-bin/dods/nph-dods/dodsdata in.nc ncrcat -O -C -D 3 \ -p http://dust.ess.uci.edu/cgi-bin/dods/nph-dods/dodsdata \ -l /tmp in.nc in.nc ~/foo.nc Unidata DAP servers: ncks --trd -M -p http://thredds-test.ucar.edu/thredds/dodsC/testdods in.nc ncrcat -O -C -D 3 \ -p http://thredds-test.ucar.edu/thredds/dodsC/testdods \ -l /tmp in.nc in.nc ~/foo.nc NOAA DAP servers: ncwa -O -C -a lat,lon,time -d lon,-10.,10. -d lat,-10.,10. -l /tmp -p \ http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.dailyavgs/surface \ pres.sfc.1969.nc ~/foo.nc LLNL PCMDI IPCC OPeNDAP Data Portal: ncks --trd -M -p http://username:[email protected]/cgi-bin/dap-cgi.py/ipcc4/sresa1b/ncar_ccsm3_0 pcmdi.ipcc4.ncar_ccsm3_0.sresa1b.run1.atm.mo.xml Earth System Grid (ESG): http://www.earthsystemgrid.org caseid=’b30.025.ES01’ CCSM3.0 1% increasing CO2 run, T42_gx1v3, 200 years starting in year 400 Atmospheric post-processed data, monthly averages, e.g., /data/zender/tmp/b30.025.ES01.cam2.h0.TREFHT.0400-01_cat_0449-12.nc /data/zender/tmp/b30.025.ES01.cam2.h0.TREFHT.0400-01_cat_0599-12.nc ESG supports password-protected FTP access by registered users NCO uses the .netrc file, if present, for password-protected FTP access Syntax for accessing single file is, e.g., ncks -O -D 3 \ -p ftp://climate.llnl.gov/sresa1b/atm/mo/tas/ncar_ccsm3_0/run1 \ -l /tmp tas_A1.SRESA1B_1.CCSM.atmm.2000-01_cat_2099-12.nc ~/foo.nc # Average surface air temperature tas for SRESA1B scenario # This loop is illustrative and will not work until NCO correctly # translates ’*’ to FTP ’mget’ all remote files for var in ’tas’; do for scn in ’sresa1b’; do for mdl in ’cccma_cgcm3_1 cccma_cgcm3_1_t63 cnrm_cm3 csiro_mk3_0 \ gfdl_cm2_0 gfdl_cm2_1 giss_aom giss_model_e_h giss_model_e_r \ 392 NCO 5.0.2-alpha07 User Guide iap_fgoals1_0_g inmcm3_0 ipsl_cm4 miroc3_2_hires miroc3_2_medres \ miub_echo_g mpi_echam5 mri_cgcm2_3_2a ncar_ccsm3_0 ncar_pcm1 \ ukmo_hadcm3 ukmo_hadgem1’; do for run in ’1’; do ncks -R -O -D 3 -p ftp://climate.llnl.gov/${scn}/atm/mo/${var}/${mdl}/run${run} -l ${DATA}/${scn}/atm/mo/${var}/${mdl}/run${run} ’*’ ${scn}_${mdl}_${run}_${var}_${yyyymm}_${yyyymm}.nc done # end loop over run done # end loop over mdl done # end loop over scn done # end loop over var cd sresa1b/atm/mo/tas/ukmo_hadcm3/run1/ ncks -H -m -v lat,lon,lat_bnds,lon_bnds -M tas_A1.nc | m bds -x 096 -y 073 -m 33 -o ${DATA}/data/dst_3.75x2.5.nc # ukmo_hadcm3 ncview ${DATA}/data/dst_3.75x2.5.nc # msk_rgn is California mask on ukmo_hadcm3 grid # area is correct area weight on ukmo_hadcm3 grid ncks -A -v area,msk_rgn ${DATA}/data/dst_3.75x2.5.nc \ ${DATA}/sresa1b/atm/mo/tas/ukmo_hadcm3/run1/area_msk_ukmo_hadcm3.nc Template for standardized data: ${scn}_${mdl}_${run}_${var}_${yyyymm}_${yyyymm}.nc e.g., raw data ${DATA}/sresa1b/atm/mo/tas/ukmo_hadcm3/run1/tas_A1.nc becomes standardized data Level 0: raw from IPCC site--no changes except for name Make symbolic link name match raw data Template: ${scn}_${mdl}_${run}_${var}_${yyyymm}_${yyyymm}.nc ln -s -f tas_A1.nc sresa1b_ukmo_hadcm3_run1_tas_200101_209911.nc area_msk_ukmo_hadcm3.nc Level I: Add all variables (not standardized in time) to file containing msk_rgn and area Template: ${scn}_${mdl}_${run}_${yyyymm}_${yyyymm}.nc /bin/cp area_msk_ukmo_hadcm3.nc sresa1b_ukmo_hadcm3_run1_200101_209911.nc ncks -A -v tas sresa1b_ukmo_hadcm3_run1_tas_200101_209911.nc \ sresa1b_ukmo_hadcm3_run1_200101_209911.nc ncks -A -v pr sresa1b_ukmo_hadcm3_run1_pr_200101_209911.nc \ sresa1b_ukmo_hadcm3_run1_200101_209911.nc If already have file then: mv sresa1b_ukmo_hadcm3_run1_200101_209911.nc foo.nc /bin/cp area_msk_ukmo_hadcm3.nc sresa1b_ukmo_hadcm3_run1_200101_209911.nc Chapter 9: CCSM Example 393 ncks -A -v tas,pr foo.nc sresa1b_ukmo_hadcm3_run1_200101_209911.nc Level II: Correct # years, months Template: ${scn}_${mdl}_${run}_${var}_${yyyymm}_${yyyymm}.nc ncks -d time,...... file1.nc file2.nc ncrcat file2.nc file3.nc sresa1b_ukmo_hadcm3_run1_200001_209912.nc Level III: Many derived products from level II, e.g., A. Global mean timeseries ncwa -w area -a lat,lon \ sresa1b_ukmo_hadcm3_run1_200001_209912.nc \ sresa1b_ukmo_hadcm3_run1_200001_209912_xy.nc B. Califoria average timeseries ncwa -m msk_rgn -w area -a lat,lon \ sresa1b_ukmo_hadcm3_run1_200001_209912.nc \ sresa1b_ukmo_hadcm3_run1_200001_209912_xy_Cal.nc 395 10 References • [ZeM07] Zender, C. S., and H. J. Mangalam (2007), Scaling Properties of Common Statistical Operators for Gridded Datasets, Int. J. High Perform. Comput. Appl., 21(4), 485-498, doi:10.1177/1094342007083802. • [Zen08] Zender, C. S. (2008), Analysis of Self-describing Gridded Geoscience Data with netCDF Operators (NCO), Environ. Modell. Softw., 23(10), 1338-1342, doi:10.1016/j.envsoft.2008.03.004. • [WZJ07] Wang, D. L., C. S. Zender, and S. F. Jenks (2007), DAP-enabled Server-side Data Reduction and Analysis, Proceedings of the 23rd AMS Conference on Interac- tive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Paper 3B.2, January 14-18, San Antonio, TX. American Meteorological Society, AMS Press, Boston, MA. • [ZMW06] Zender, C. S., H. Mangalam, and D. L. Wang (2006), Improving Scaling Properties of Common Statistical Operators for Gridded Geoscience Datasets, Eos Trans. AGU, 87(52), Fall Meet. Suppl., Abstract IN53B-0827. • [ZeW07] Zender, C. S., and D. L. Wang (2007), High performance distributed data reduction and analysis with the netCDF Operators (NCO), Proceedings of the 23rd AMS Conference on Interactive Information and Processing Systems (IIPS) for Mete- orology, Oceanography, and Hydrology, Paper 3B.4, January 14-18, San Antonio, TX. American Meteorological Society, AMS Press, Boston, MA. • [WZJ06] Wang, D. L., C. S. Zender, and S. F. Jenks (2006), Server-side netCDF Data Reduction and Analysis, Eos Trans. AGU, 87(52), Fall Meet. Suppl., Abstract IN53B-0826. • [WZJ073] Wang, D. L., C. S. Zender, and S. F. Jenks (2007), Server-side parallel data reduction and analysis, in Advances in Grid and Pervasive Computing, Second International Conference, GPC 2007, Paris, France, May 2-4, 2007, Proceedings. IEEE Lecture Notes in Computer Science, vol. 4459, edited by C. Cerin and K.-C. Li, pp. 744-750, Springer-Verlag, Berlin/Heidelberg, doi:10.1007/978-3-540-72360-8 67. • [WZJ074] Wang, D. L., C. S. Zender and S. F. Jenks (2007), A System for Scripted Data Analysis at Remote Data Centers, Eos Trans. AGU, 88(52), Fall Meet. Suppl., Abstract IN11B-0469. • [WZJ081] Wang, D. L., C. S. Zender and S. F. Jenks (2008), Cluster Workflow Execution of Retargeted Data Analysis Scripts, Proceedings of the 8th IEEE Int’l Symposium on Cluster Computing and the Grid (IEEE CCGRID ’08), pp. 449-458, Lyon, France, May 2008. • [WZJ091] Wang, D. L., C. S. Zender, and S. F. Jenks (2009), Efficient Clustered Server-side Data Analysis Workflows using SWAMP, Earth Sci. Inform., 2(3), 141-155, doi:10.1007/s12145-009-0021-z. • [PFT88] Press, Flannery, Teukolsky, and Vetterling (1988), Numerical Recipes in C, Cambridge Univ. Press, New York, NY. 397 General Index " --amwg_links ...... 235 " (double quote) ...... 221 --apn ...... 18, 143, 285 --append ...... 18, 143, 285 --area_wgt ...... 266 # --atm2ocn ...... 309 --auxiliary ...... 74 #include ...... 155 --auxiliary lon_min,lon_max,lat_min,lat_max...... 74 $ --b2l...... 309 --bfr_sz_hnt ...... 123 $ (wildcard character)...... 51 --big2ltl ...... 309 --binary ...... 265 --bnr...... 265 % --calendar ...... 265 % (modulus) ...... 213 --case ...... 232 --caseid ...... 232 --cb ...... 97 ’ --cdl...... 272 --cell_methods ...... 150 ’ (end quote) ...... 221 --cf_var ...... 331 --cf_variable ...... 331 * --chk_map ...... 266 --chk_nan ...... 271 * ...... 225 --chunk_byte ...... 104 * (filename expansion) ...... 51 --chunk_cache ...... 104 * (multiplication) ...... 213 --chunk_cache sz ...... 105 * (wildcard character)...... 51 --chunk_dimension ...... 104 --chunk_map...... 104 --chunk_min...... 104 + --chunk_policy ...... 104 + ...... 225 --chunk_scalar ...... 104 + (addition) ...... 213 --climatology ...... 231 + (wildcard character)...... 51 --climatology_information...... 97 --climatology_mode...... 231 --cll_mth ...... 150 – --clm_bnd ...... 97 - ...... 225 --clm_md ...... 231 - (subtraction) ...... 213 --clm_nfo ...... 97 --3 ...... 42 --cln_lgb ...... 265 --4 ...... 42 --cnf ...... 240, 310 --5 ...... 42 --cnk_byt ...... 104 --6 ...... 42 --cnk_csh ...... 104 --64bit_data...... 42 --cnk_csh sz ...... 105 --64bit_offset ...... 42 --cnk_dmn ...... 104 --7 ...... 42 --cnk_map ...... 104 --a2o...... 309 --cnk_map cnk_map ...... 108 --abc...... 265 --cnk_min ...... 104 --add_depth...... 311 --cnk_plc ...... 104 --add_dpt ...... 311 --cnk_scl ...... 104 --add_fill_value ...... 310, 317 --config ...... 240, 310 --add_fll ...... 310, 317 --configuration ...... 240, 310 --alg_typ ...... 306 --coords ...... 53, 149 --algorithm...... 306 --crd ...... 53, 149 --allow_no_overlap...... 309 --create_ram ...... 18, 124 --alphabetize ...... 265 --create_share ...... 125 398 NCO 5.0.2-alpha07 User Guide --csn...... 238 --drc_prv_rgr ...... 245 --csn_lst ...... 238 --drc_rgr ...... 236 --d2f ...... 233, 310 --drc_rgr_prv ...... 245 --d2s ...... 233, 310 --drc_rgr_xtn ...... 245 --data ...... 274 --drc_tmp ...... 329 --date_format ...... 265 --drc_xtn ...... 245 --date_string ...... 312 --drc_xtn_rgr ...... 245 --datestamp...... 265 --dst_fl ...... 311 --days-per-file ...... 233 --dst_msk ...... 317 --dbg...... 310 --dst_rgn ...... 328 --dbg_lvl ...... 233, 310 --dt_fmt ...... 265 --dbg_lvl debug-level ...... 16, 24, 31 --dt_sng ...... 312 --dbl...... 136 --dt_sng=dt_sng ...... 312 --dbl_flt ...... 233, 310 --dvn_flg ...... 311 --dbl_sgl ...... 233, 310 --end...... 233 --dcm_md ...... 231 --end_month...... 238 --debug ...... 310 --end_mth ...... 238 --debug-level debug-level ...... 16, 24 --end_year ...... 233 --debug_level...... 233, 310 --end_yr ...... 233 --dec_md ...... 231 --ensemble_file ...... 257 --dec_mode ...... 231 --ensemble_group ...... 257 --december_mode ...... 231 --ensemble_suffix ...... 257 --deflate ...... 121, 235 --esmf_extrap_method ...... 312 --depth ...... 311 --esmf_extrap_num_src_pnts ...... 312 --depth_file ...... 233, 311 --esmf_extrap_type...... 312 --dest_grid...... 312 --esmf_mth ...... 312 --destination_file...... 311 --esmf_opt ...... 335 --destination_grid...... 312 --esmf_options ...... 335 --dev_nll ...... 311 --esmf_pnt_src_nbr...... 312 --devnull ...... 311 --esmf_typ ...... 312 --dfl...... 235 --esmf_typ=esmf_typ ...... 312 --dfl_lvl ...... 121, 235 --exclude ...... 48, 241, 285, 336 --dimension --exclude_variables...... 241, 336 dim,[min],[max],[stride],[subcycle] ..... 68 --extended ...... 245 --dimension --extended_regridded ...... 245 dim,[min],[max],[stride],[subcycle],[interleave--extensive] .. 69 ...... 336 --dimension dim,[min],[max],stride ...... 66 --extensive_variables ...... 336 --dimension --extra_variables ...... 240 dim,[min][,[max][,[stride]]] .. 63, 71, 74, 98 --extrapolation_method ...... 335 --dir_in ...... 234, 314 --extrapolation_type ...... 335 --dir_out ...... 237, 320 --family ...... 233 --dir_rgr ...... 236 --family_name ...... 233 --dir_tmp ...... 329 --file_format ...... 42 --diskless_all...... 24, 124 --file_format_ncremap ...... 313 --dmn dim,[min],[max],[stride],subcycle] .. 68 --file_list...... 145 --dmn --fill_empty ...... 310 dim,[min],[max],[stride],subcycle],[interleave--fill_value] .. 69 ...... 319 --dmn dim,[min],[max],stride...... 66 --fix_rec_dmn all ...... 47 --dmn dim,[min][,[max][,[stride]]] .... 63, 71, --fix_rec_dmn dim ...... 271 74, 98 --fl_bnr ...... 265 --double_float ...... 233, 310 --fl_fmt ...... 42 --dpf...... 233 --fl_fmt_ncremap ...... 313 --dpf=dpf ...... 233 --fl_lst_in...... 145 --dpt...... 311 --fl_out fl_out...... 37 --dpt_fl=dpt_fl ...... 233, 311 --fl_prn ...... 280 --drc_in ...... 234, 314 --fl_spt ...... 154 --drc_out ...... 237, 320 --fll_mpt ...... 310 --drc_prv ...... 245 --fll_val ...... 319 General Index 399 --flt...... 136 --link_flag...... 235 --fml...... 233 --lnk_flg ...... 235 --fml_nm ...... 233 --local output-path ...... 37 --fmt_val ...... 280 --lrg2sml ...... 309 --fnc_tbl ...... 215 --map...... 77, 86, 238, 316 --fortran ...... 63 --map cnk_map ...... 108 --frac_b_nrm ...... 266, 271 --map pck_map ...... 290 --gaa...... 143 –map-file ...... 86 --gaa key=val ...... 32 --map_file ...... 316 --gag ...... 254, 361 --map_fl ...... 316 --glb...... 143 --mask-value mask_val...... 349 --glb att_nm=att_val...... 143 --mask-variable mask_var ...... 347 --glb_att_add ...... 143 --mask_apply ...... 310, 317 --glb_avg ...... 236 --mask_comparator mask_comp...... 348 --global_average ...... 236 --mask_condition mask_cond ...... 347, 348 --gpe gpe_dsc ...... 53 --mask_destination ...... 317, 318 --grd_dst ...... 312 --mask_dst ...... 317 --grd_sng ...... 313 ‘--mask_out’...... 93 --grd_src ...... 328 --mask_out ...... 318 --grid_dest...... 312 --mask_source ...... 318 --grid_gen ...... 313 --mask_src ...... 318 --grid_generation ...... 313 --mask_value mask_val...... 349 --grid_source ...... 328 --mask_variable mask_var ...... 347 --grid_string ...... 313 --md5_dgs ...... 122 --group grp ...... 48 --md5_digest ...... 122 --grp grp ...... 48 --md5_write_attribute ...... 122 --grp_xtr_var_xcl ...... 48 --md5_wrt_att ...... 122 --gxvx...... 48 --mdl...... 236 --hdf_unpack ...... 126 --mdl_nm ...... 236 --hdf_upk ...... 126 --mesh ...... 317 ‘--hdf4’ ...... 13 --mesh_file...... 317 --hdn...... 272 --metadata ...... 279 --hdr_pad hdr_pad ...... 29 --Metadata ...... 278 --header_pad hdr_pad ...... 29 --missing_value ...... 319 --hidden ...... 272 --mk_rec_dmn dim...... 255, 274 --hieronymus ...... 274 --mlt_map ...... 316 --history ...... 144, 234 --mode ...... 231 --history_name ...... 234 --model ...... 236 --hst...... 144 --model_name ...... 236 --hst_nm ...... 234 --mono ...... 307 --in_dir ...... 314 --month_end...... 238 --in_drc ...... 234, 314 --month_start ...... 238 --in_file ...... 314 --mpas_depth ...... 233, 311 --in_fl ...... 314 --mpas_fl ...... 233, 311 --inp_std ...... 239 --mpi_nbr ...... 316 --input ...... 234, 314 --mpi_nbr=mpi_nbr ...... 316 --input_file ...... 314 --mpi_number ...... 316 --interpolation_method ...... 335 --mpi_pfx ...... 316 --interpolation_type ...... 335 --mpi_pfx=mpi_pfx ...... 316 --intersection ...... 50 --mpi_prefix ...... 316 --job_nbr ...... 234, 315 --mrd...... 102 --job_number ...... 234, 315 --mro ...... 68, 69 --jobs...... 234, 315 --msa...... 71 --jsn...... 275 --msa_user_order ...... 71 --jsn_fmt ...... 275 --msa_usr_rdr ...... 71 --json ...... 275 --msh...... 317 --l2s...... 309 --msh_fl ...... 317 --lcl output-path ...... 37 --msh_fl=msh_fl ...... 317 400 NCO 5.0.2-alpha07 User Guide --msk_apl ...... 310, 317 --no_frm ...... 236, 319 --msk_app ...... 317 --no_frm_trm ...... 236, 319 --msk_cmp_typ mask_comp ...... 348 --no_glb_mtd ...... 254 --msk_cnd mask_cond ...... 347 --no_inp_std ...... 239, 329 --msk_cnd_sng mask_cond ...... 348 ‘--no_mask’ ...... 93 --msk_dst ...... 317 ‘--no_msk’ ...... 93 --msk_dst=msk_dst ...... 317 --no_multimap ...... 316 --msk_nm mask_var ...... 347 --no_native...... 237 ‘--msk_out’ ...... 93 --no_nm_prn ...... 281, 287 --msk_out ...... 318 --no_ntv ...... 237 --msk_out=msk_out ...... 318 --no_ntv_tms ...... 237 --msk_src ...... 318 --no_permute ...... 326 --msk_src=msk_src ...... 318 --no_rec_dmn dim ...... 271 --msk_val mask_val ...... 349 --no_redirect...... 239, 329 --msk_var mask_var ...... 347 --no_stagger ...... 93, 237, 319 --mss_val ...... 319 --no_staggered_grid...... 237, 319 --mss_val=mss_val ...... 319 --no_standard_input...... 239, 329 --mtd...... 279 --no_stdin ...... 239, 329 --Mtd...... 278 --no_stg ...... 93, 237, 319 --mth_end ...... 238 --no_stg_grd ...... 93, 237, 319 --mth_srt ...... 238 --no_tmp_fl ...... 18, 125 --multimap ...... 316 --noblank ...... 279 --multiple_record_dimensions ...... 102 --nomultimap ...... 316 --name_dst ...... 319 --normalize...... 260 --name_short_destination...... 319 --nrm...... 260 --name_short_source ...... 319 --nsm_fl ...... 257 --name_src ...... 319 --nsm_grp ...... 257 --ncml ...... 281 --nsm_sfx ...... 257 --nco ...... 236, 319 --nsx...... 50 --nco_opt ...... 236, 319 --ntp_mth ...... 335 --nco_options...... 236, 319 --omp_num_threads thr_nbr ...... 30 --netcdf4 ...... 42 --op_rlt mask_comp ...... 348 --nintap loop ...... 34 --op_typ op_typ...... 129, 225 --nm_dst ...... 319 --open_ram...... 18, 24, 124 --nm_dst=nm_dst ...... 319 --open_share ...... 125 --nm_sht_dst ...... 319 --operation op_typ ...... 129, 225 --nm_sht_src ...... 319 --orphan_dimensions ...... 280 --nm_src ...... 319 --out_dir ...... 320 --nm_src=nm_src ...... 319 --out_drc ...... 237, 320 --no-abc ...... 265 --out_file ...... 320 --no-alphabetize ...... 265 --out_fl ...... 320 --no-blank ...... 279 --out_msk ...... 318 --no_abc ...... 265 --output ...... 237, 320 --no_alphabetize ...... 265 --output fl_out...... 37 --no_amwg ...... 235 --output_file ...... 320 --no_amwg_links ...... 235 --overwrite ...... 18, 143 --no_area ...... 236, 319 --ovr ...... 18, 143 --no_blank ...... 279, 286 --pack_policy pck_plc...... 289 --no_cell_measures ...... 236, 319 --par_md ...... 237, 325 --no_cell_methods ...... 150 --par_typ ...... 237, 325 --no_cll ...... 236, 319 --parallel ...... 237, 325 ‘--no_cll_msr’ ...... 92 --parallel_mode ...... 237, 325 --no_cll_msr ...... 236, 319 --parallel_type ...... 237, 325 --no_cll_mth ...... 150 --path input-path...... 34, 37 --no_coords ...... 53, 149 --pck_map pck_map ...... 290 --no_crd ...... 53, 149 --pck_plc pck_plc ...... 289 --no_dmn_var_nm ...... 281, 287 --pdq...... 326 --no_formula_terms ...... 236, 319 --pdq_opt ...... 326 General Index 401 --pdq_typ ...... 320 --rgn_dst ...... 328 --per_record_weights ...... 299 --rgn_src ...... 328 --permute ...... 326 ‘--rgr area_out=area_nm’ ...... 92 --ppc ...... 111, 237 ‘--rgr col_nm=col_nm’ ...... 90 --ppc key=val ...... 32 ‘--rgr frc_nm=frc_nm’ ...... 92 --ppc=ppc_prc ...... 237 ‘--rgr grd_ttl=grd_ttl’ ...... 78 --ppc_prc ...... 237 ‘--rgr grid=scrip_grid’ ...... 78 --prc_typ ...... 320 ‘--rgr ilev_dmn_nm=ilev_dmn_nm’ ...... 91 --precision...... 237 ‘--rgr ilev_nm=ilev_nm’ ...... 91 --precision_preserving_compression ...... 111 --rgr infer ...... 83 --preserve ...... 327 --rgr key=val...... 32, 77, 86 --preserve=prs_stt...... 327 ‘--rgr lat_bnd_nm=lat_bnd_nm’...... 91 --preserve_statistic ...... 327 ‘--rgr lat_dmn_nm=lat_dmn_nm’...... 90 --previous ...... 245 ‘--rgr lat_drc=lat_drc’ ...... 80 --previous_end ...... 244 ‘--rgr lat_nbr=lat_nbr’ ...... 80 --previous_regridded ...... 245 ‘--rgr lat_nm=lat_nm’ ...... 90 --previous_start ...... 244 ‘--rgr lat_nrt=lat_nrt’ ...... 80 --print ...... 280 ‘--rgr lat_typ=lat_typ’ ...... 78 --prm...... 326 ‘--rgr lat_weight=lat_wgt_nm’...... 93 --prm_ints ...... 299 ‘--rgr latlon=lat_nbr,lon_nbr’ ...... 80 --prm_opt ...... 326 ‘--rgr lev_dmn_nm=lev_dmn_nm’...... 91 --prm_typ ...... 320 ‘--rgr lev_nm=lev_nm’ ...... 91 --prn...... 280 ‘--rgr lon_bnd_nm=lon_bnd_nm’...... 91 --prn_fl ...... 280 ‘--rgr lon_dmn_nm=lon_dmn_nm’...... 90 --prn_fnc_tbl ...... 215 ‘--rgr lon_nbr=lon_nbr’ ...... 80 --prn_lgb ...... 265 ‘--rgr lon_nm=lon_nm’ ...... 90 --procedure...... 320 ‘--rgr lon_typ=lon_typ’ ...... 78 --promote_ints ...... 299 ‘--rgr msk_nm=msk_nm’ ...... 93 --prs_stt ...... 327 --rgr nfr ...... 83 --prv_drc ...... 245 ‘--rgr no_area’...... 92 --prv_yr_end ...... 244 ‘--rgr no_area_out’ ...... 92 --prv_yr_srt ...... 244 ‘--rgr no_mask’...... 93 --prw...... 299 ‘--rgr no_msk_out’ ...... 93 --pseudonym ...... 11 ‘--rgr plev_nm=plev_nm’ ...... 91 --pth input-path ...... 34, 37 ‘--rgr scrip=scrip_grid’ ...... 78 --quantize ...... 111, 237 --rgr skl ...... 86 --quench ...... 280 ‘--rgr --quiet ...... 280, 287 snwe=lat_sth,lat_nrt,lon_wst,lon_est’ ... 80 --rad...... 280 --rgr ugrid ...... 83 --ram_all ...... 24, 124 ‘--rgr ‘--rcd_nm ulm_nm’ ...... 253 wesn=lon_wst,lon_est,lat_sth,lon_nrt’ ... 80 --rec_apn ...... 67 --rgr_drc ...... 236 --record_append ...... 67 --rgr_map...... 86, 238, 316 --redirect ...... 239 --rgr_opt ...... 238, 327 --regional_destination ...... 328 --rgr_rnr ...... 87 --regional_source ...... 328 --rgr_var ...... 331 --regrid ...... 236 --rnr ...... 87, 327 --regrid_algorithm...... 306 --rnr_thr ...... 327 --regrid_map ...... 238, 316 --rph_dmn ...... 280 --regrid_options ...... 238, 327 --rrg_bb_wesn=bb_wesn ...... 322 --regridded_extended ...... 245 --rrg_dat_glb=dat_glb ...... 322 --regridded_previous ...... 245 --rrg_grd_glb=grd_glb ...... 322 --remove_native ...... 237 --rrg_grd_rgn=grd_rgn ...... 322 --renormalization_threshold...... 87, 327 --rrg_rnm_sng=rnm_sng ...... 322 --renormalize ...... 87, 327 --rth_dbl ...... 136 --retain ...... 42 --rth_flt ...... 136 --retain_all_dimensions ...... 280 --rtn...... 42 --revision ...... 16, 151 --scr...... 281 402 NCO 5.0.2-alpha07 User Guide --script ...... 154 --traditional...... 263, 281 --script-file ...... 154 --trd ...... 263, 281 --seasons ...... 238 ‘--trr’ ...... 32 --seasons=csn_lst ...... 238 --trr key=val ...... 32 --secret ...... 281 --tsk_nbr ...... 316 --sgs_frc=sgs_frc ...... 323 --ugrid ...... 330 --sgs_msk=sgs_msk ...... 323 --ugrid_file ...... 330 --sgs_nrm=sgs_nrm ...... 323 --ugrid_fl ...... 330 --share_all...... 125 --ugrid_fl=ugrid_fl ...... 330 --skeleton ...... 328 --uio...... 125 --skeleton_file ...... 328 ‘--ulm_nm ulm_nm’ ...... 253 --skl...... 328 --unbuffered_io ...... 125 --skl_fl ...... 328 --union ...... 48, 50 --skl_fl=skl_fl ...... 328 --unique_suffix ...... 330 --sng_fmt ...... 280, 286 --units ...... 281 --source_grid ...... 328 --unn ...... 48, 50 --spt...... 154 --unpack ...... 290, 330 --src_grd ...... 328 --unq_sfx ...... 330 --src_msk ...... 318 --upk...... 330 --src_rgn ...... 328 --upk_inp ...... 330 --srt_mth ...... 238 --val_fmt ...... 280 --srt_yr ...... 238 --val_var ...... 281, 287 --srun_cmd ...... 316 --value_format ...... 280 --srun_command ...... 316 --var ...... 240, 331 --ssh...... 281 --var_cf ...... 331 --stagger ...... 93 --var_extra...... 240 --standard_input ...... 239 --var_lst ...... 240, 331 --start ...... 238 --var_rgr ...... 331 --start_month ...... 238 --var_xtn ...... 336 --start_year ...... 238 --var_xtr ...... 240 --std_flg ...... 239 --variable var ...... 48, 285 --stdin ...... 239 --variable_list ...... 240, 331 --stg...... 93 --variables ...... 240, 331 --stg_grd ...... 93 --variables_extra ...... 240 --string ...... 280, 286 --vars...... 240, 331 --suffix ...... 330 --verbosity...... 331 --suppress_global_metadata ...... 254 --verbosity_level ...... 331 --task_nbr ...... 316 --version ...... 16, 151, 240, 310 --tempest_options ...... 335 --vertical_file ...... 331 --template ...... 311 --vrb...... 331 --template_file ...... 311 --vrb=vrb_lvl ...... 331 --thr ...... 239, 329 --vrb_lvl ...... 331 --thr_nbr ...... 239, 329 --vrs ...... 16, 151, 240, 310 --thr_nbr thr_nbr ...... 30 --vrt...... 331 --thr_rnr ...... 327 --vrt_crd ...... 331 --thread_number ...... 239, 329 --vrt_fl=vrt_fl ...... 331 --threads ...... 239, 329 --vrt_ntp ...... 335 --threads thr_nbr ...... 30 --vrt_ntp=vrt_ntp ...... 335 --timesteps_per_day ...... 240 --vrt_xtr ...... 335 --tmp...... 329 --vrt_xtr=vrt_xtr ...... 335 --tmp_dir ...... 329 --weight weight ...... 347 --tmp_drc ...... 329 --weight wgt1[,wgt2]...... 260 --tpd...... 240 --weight_command ...... 336 --tpd_out ...... 240 --weight_generator...... 336 --tpdtpd_out ...... 240 --weight_options ...... 335 --tpl...... 311 --wgt_cmd ...... 336 --tpl_fl ...... 311 --wgt_gnr ...... 336 --tps_opt ...... 335 --wgt_opt ...... 335 General Index 403 --wgt_var weight ...... 347 -f fml_nm ...... 233 --wgt_var wgt1[,wgt2]...... 260 -G gpe_dsc ...... 53 --write_tmp_fl ...... 18 -g grd_dst ...... 312 --wrt_tmp_fl...... 18 -G grd_sng ...... 313 --xcl ...... 48, 241, 285, 336 -g grp ...... 48 --xcl_ass_var ...... 53 -h ...... 144, 218 --xcl_var ...... 241, 336 -H ...... 145, 274 --xml...... 281 -h hst_nm ...... 234 --xml_no_location ...... 282 -I ...... 348 --xml_spr_chr ...... 282 -i drc_in ...... 234 --xml_spr_nmr ...... 282 -I in_drc ...... 314 --xtn_drc ...... 245 -i in_fl ...... 314 --xtn_lst ...... 336 -j job_nbr ...... 234, 315 --xtn_var ...... 336 -l ...... 235 --xtr_ass_var ...... 53 -L ...... 121, 235 --xtr_mth ...... 335 -l output-path ...... 37, 39 --xtr_nsp ...... 312 -m ...... 279 --xtr_nsp=xtr_nsp ...... 312 -M ...... 44, 254, 278, 316 --xtr_xpn ...... 312 -M cnk_map ...... 108 --xtr_xpn=xtr_xpn ...... 312 -m map_fl ...... 316 --year_end ...... 233 -m mask_var ...... 347 --year_start ...... 238 -m mdl_nm ...... 236 --ypf_max ...... 241 -M pck_map ...... 290 --ypf_max ypf_max ...... 241 -N ...... 130, 260, 349 --yr_end_prv ...... 244 -n loop...... 21, 23, 34 --yr_srt_prv ...... 244 -n nco_opt ...... 236, 319 -0 ...... 63 -O ...... 18, 143 -3 ...... 13, 42, 313 -o drc_out ...... 237 -4 ...... 13, 42, 313 -O drc_rgr ...... 236 -5 ...... 42, 313 -o fl_out ...... 22, 37 -6 ...... 42, 313 -O out_drc ...... 320 -7 ...... 42, 313 -o out_fl ...... 320 -a ...... 285 -P ...... 280 -A...... 18, 143, 285 -p input-path ...... 34, 39 ‘-A’ ...... 294 -p par_typ ...... 237, 325 -a alg_typ ...... 306 -P pck_plc ...... 289 -a dec_md ...... 231 -P prc_typ ...... 320 ‘-b’ ...... 212, 227 -q ...... 280 -b ...... 265 -Q ...... 280, 287 -B mask_cond ...... 347, 348 -r ...... 16, 151 -c ...... 53, 149 -R ...... 42 -C...... 53, 149, 212 -r rgr_map ...... 238 -c caseid ...... 232 -R rgr_opt ...... 238, 327 -C clm_md ...... 231 -r rnr_thr ...... 327 -D ...... 16 -s ...... 280, 286 -D dbg_lvl ...... 233, 310 -s grd_src ...... 328 -D debug-level ...... 16, 24, 31 -s srt_yr ...... 238 -d dim,[min],[max],[stride],[subcycle] .... 68 -S yr_srt_prv ...... 244 -d -t thr_nbr ...... 26, 30, 239, 329 dim,[min],[max],[stride],[subcycle],[interleave-T] ..tmp_drc69 ...... 329 -d dim,[min],[max],stride ...... 66 -u ...... 281 -d dim,[min][,[max][,[stride]]] .. 63, 71, 74, 98 -U ...... 290, 330 -d dim,[min][,[max]]...... 347 ‘-u ulm_nm’ ...... 253 -d dst_fl ...... 311 -u unq_sfx ...... 330 -e end_yr ...... 233 ‘-v’ ...... 294 -E yr_end_prv ...... 244 -V ...... 281, 287 -f ...... 215 -V rgr_var ...... 331 -F ...... 63 -v var ...... 48, 285 404 NCO 5.0.2-alpha07 User Guide -v var_lst ...... 240, 331 -v xtn_lst ...... 336 _ChunkSizes...... 272 -w weight ...... 347 _DeflateLevel ...... 272 -w wgt_cmd ...... 336 _Endianness...... 272 -W wgt_opt ...... 335 _FillValue .. 87, 103, 126, 218, 222, 261, 292, 310, -w wgt1[,wgt2]...... 260 317, 345 -x...... 48, 285, 287 _Fletcher32...... 272 -X ...... 74 _Format ...... 272 -x drc_prv ...... 245 _IsNetcdf4 ...... 272 -X drc_xtn ...... 245 _NCProperties ...... 272 -X lon_min,lon_max,lat_min,lat_max ...... 74 _NOFILL ...... 272 -y drc_rgr_prv...... 245 _Shuffle ...... 272 -Y drc_rgr_xtn...... 245 _SOURCE_FORMAT ...... 272 -y op_typ...... 129, 225 _Storage ...... 272 _SuperblockVersion...... 272 ...... 341 @ . (wildcard character)...... 51 @ (attribute) ...... 155 .bashrc ...... 283 @ (separator character) ...... 54 .netrc...... 37 .rhosts ...... 37 \ / \ (backslash) ...... 221 / ...... 225 \" (protected double quote) ...... 221 / (division) ...... 213 \’ (protected end quote) ...... 221 /*...*/ (comment) ...... 155 \? (protected question mark) ...... 221 // (comment)...... 155 \\ (ASCII \, backslash) ...... 221 \\ (protected backslash) ...... 221 \a (ASCII BEL, bell)...... 221 : \b (ASCII BS, backspace) ...... 221 : (separator character) ...... 54 \f (ASCII FF, formfeed) ...... 221 \n (ASCII LF, linefeed) ...... 221 \n (linefeed) ...... 286 ; \r (ASCII CR, carriage return)...... 221 \t (ASCII HT, horizontal tab) ...... 221 ; (end of statement) ...... 155 \t (horizontal tab)...... 286 \v (ASCII VT, vertical tab) ...... 221 < A areaCell ...... 146 a2o ...... 309 areaTriangle ...... 146, 306 aave...... 306 arithmetic operators...... 103, 104, 347 abs ...... 213 arithmetic processor ...... 154 absolute value ...... 213 ARM conventions ...... 150 accurate_conservative_ ARM conventions ...... 302 nonmonotone_fv2se ...... 307 array...... 162 accurate_conservative_ array function ...... 162 nonmonotone_se2fv ...... 307 array indexing ...... 155 accurate_monotone_ array storage ...... 155 nonconservative_se2fv ...... 307 array syntax ...... 155 ACME conventions ...... 146 arrays ...... 162 acos ...... 213 arrival value ...... 260 acosh ...... 213 ASCII...... 221 add ...... 225 asin ...... 213 add_depth.py ...... 233, 311 asinh ...... 213 add offset...... 27 asort...... 182 add_offset...... 126, 254, 289, 302 assignment statement ...... 155 adding data ...... 225, 260 asynchronous file access ...... 37 addition...... 213, 225, 260 atan ...... 213 Adrian Tompkins ...... 354 aggregation ...... 361 atanh...... 213 Alejandro Soto...... 355 attribute concatenation ...... 166 Aleksandar Jelenak ...... 354 attribute inheritance ...... 166 Alexander Hansen ...... 355 attribute names ...... 218, 341 alg typ ...... 306 attribute propagation ...... 166 alias...... 226 attribute syntax ...... 155 alias ...... 283 attribute, units...... 98 alias...... 290 attributes...... 218 ‘all’...... 106 attributes, appending ...... 219 alphabetization ...... 265 attributes, creating ...... 219 alphabetize output ...... 285 attributes, deleting...... 219 alternate invocations ...... 225 attributes, editing ...... 219, 368, 378 AMWG ...... 93 attributes, global ...... 22, 143, 144, 145, 151, 219, Anaconda...... 10 223, 263, 264, 285, 342, 345 anchor ...... 49 attributes, modifying ...... 219, 368, 378 anchoring ...... 49 attributes, nappending ...... 219 ancillary variables convention ...... 148, 149 attributes, overwriting...... 219, 368, 378 attribute ...... 148, 287 ancillary_variables attributesncap2 ...... 165 Andrea Cimatoribus ...... 355 autoconf ...... 16 Andrew Wittenberg...... 354 autoconversion ...... 46 angleEdge ...... 146, 306 automagic ...... 9, 21 annual average...... 368 annual average from daily data...... 357 automatic type conversion...... 134, 214 annual average from monthly data ...... 357 auxiliary coordinates ...... 74, 90, 149 anomalies ...... 227, 368, 375 average ...... 129, 300, 348, 357, 358, 365, 368, 375 ANSI...... 8 averaging data...... 103, 256, 298, 347 ANSI C ...... 214 avg ...... 129 appending data ...... 212, 264 avg() ...... 175 appending to files ...... 18, 143, 285 avgsqr ...... 129 appending variables ...... 18, 19, 154, 294 axes ...... 90 AR4 ...... 257 Azure CI ...... 239, 329 arccosine function...... 213 arcsine function...... 213 arctangent function ...... 213 area ...... 92, 146, 306 area-averaging ...... 368 area nm ...... 92 406 NCO 5.0.2-alpha07 User Guide B C Barron Henderson ...... 354 C index convention ...... 63 Barry deFreese...... 354 C language .... 9, 104, 136, 137, 155, 156, 221, 280 Bas Couwenberg ...... 354 C Shell...... 66, 229 base_time ...... 150 c++ ...... 8 bash ...... 52, 283 C++...... 8 Bash shell...... 226, 283 C format ...... 27 C89 ...... 8, 9 Bash Shell ...... 229 C99 ...... 9, 10, 114 batch mode ...... 143 cache size ...... 105 bb wesn ...... 322 caching ...... 125 beer ...... 141 calendar dates ...... 265 benchmarks...... 31 CAM ...... 93, 121 Bessel function ...... 196 CAM-FV grid ...... 77 BIL format...... 32 CAM-FV grid ...... 81 bilin...... 306 CAM3 ...... 139 bilinear ...... 306 caseid ...... 232 bilinear interpolation...... 378 cc ...... 8 Bill Kocik ...... 354 CC ...... 8 binary climatology (climo) ...... 243 CCM Processor ...... 34, 300, 302 binary format...... 265 CCSM ...... 356 binary operations ...... 26, 225 CCSM ...... 387 binary Operators ...... 157 CCSM conventions ...... 146 BIP format ...... 32 CDF5 ...... 42 Bit-Grooming ...... 112, 237 CDF5 files ...... 42 bitmask ...... 113 CDL ...... 272 blacklist ...... 33 ceil ...... 213 ceiling function ...... 213 blank ...... 279 cell measures convention ...... 148, 149 blin...... 306 cell methods convention ...... 150 blocksize...... 105 cell-based grid ...... 75 Bob Simons...... 354 cell_area ...... 92 Boolean values...... 299 cell_measures attribute ...... 148, 236, 319 bounds...... 91 cell_methods ...... 92, 150 bounds attribute ...... 113, 147, 287 cellMask ...... 146 bounds convention ...... 147 cellsOnCell ...... 146, 306 bounds_lat ...... 306 cellsOnEdge ...... 146, 306 bounds_lon ...... 306 cellsOnVertex...... 146, 306 Bourne Shell ...... 66, 229 CF...... 90 Brian Mays ...... 354 CF compliance checker ...... 60 broadcasting ...... 228 CF conventions ..... 53, 74, 100, 113, 146, 227, 287 broadcasting groups ...... 13, 227, 228 CF-JSON ...... 275 broadcasting variables ...... 156, 226, 262, 347 cfchecker ...... 60 BSD ...... 31 cgll...... 307 BSQ format ...... 32 change_miss() ...... 174 Buffer sizes ...... 123 char() ...... 178 buffering ...... 26 characters, special ...... 221 bugs, reporting ...... 15 Charlie Zender ...... 1, 353 chocolate ...... 353 Burrows-Wheeler algorithm ...... 119 Chris Barker...... 108 byte() ...... 178 Chris Golaz...... 354 ...... 119 bzip2 Chrysalis ...... 239, 329 chunk cache size ...... 105 chunking ...... 13, 104, 279 chunking map ...... 105, 108 chunking policy ...... 105 chunksize ...... 105 citation ...... 355 General Index 407 clang ...... 8 contents ...... 7 CLASSIC files ...... 42 contributing ...... 353 client-server...... 39 contributors ...... 353 Climate and Forecast Metadata Convention ... 100 coordinate limits ...... 63 climate model ...... 17, 20, 36, 254, 350 coordinate variable...... 98, 129, 149, 227, 348 climatology ...... 230 coordinate variables...... 343 climatology attribute...... 113, 148, 287 coordinates ...... 74, 149 climatology convention ...... 148 coordinates ...... 381 climo ...... 230 coordinates attribute ...... 113, 287 clipping operators...... 159 coordinates convention ...... 149 clm md ...... 231 coordinates, modifying ...... 382 CMake ...... 10 core dump ...... 15 CMIP ...... 253, 257 core dump ...... 23, 287 CMIP5 ...... 359 cos ...... 213 ‘cnk_all’ ...... 106 cosh ...... 213 ‘cnk_dmn’ ...... 108 cosine function...... 213 ‘cnk_g2d’ ...... 106 covariance ...... 211 ‘cnk_g3d’ ...... 106 cp ...... 49 ‘cnk_lfp’ ...... 108 Craig MacLachlan ...... 354 cnk map...... 108 Cray ...... 8, 23 ‘cnk_nc4’ ...... 108 crontab ...... 232 ‘cnk_nco’ ...... 108 cs2cs...... 307 ‘cnk_prd’ ...... 108 csh ...... 52 ‘cnk_r1d’ ...... 106 Csh shell ...... 283 ‘cnk_rd1’ ...... 108 csn lst ...... 238 ‘cnk_rew’ ...... 108 cubed-sphere grid ...... 87 ‘cnk_scl’ ...... 108 current climatology (climo) ...... 243 ‘cnk_xpl’ ...... 106 CWL ...... 239, 326, 329 ‘cnk_xst’ ...... 106, 108 cxx ...... 8 CO_Latitude...... 306 Cygwin ...... 10, 11 CO_Longitude ...... 306 col nm...... 90 Comeau...... 8 D command line options ...... 31 d2f ...... 233, 292, 310 command line switches ...... 17, 29, 37, 153 daily data...... 357, 358 comments ...... 155 Daniel Baumann ...... 354 Common Workflow Language ...... 239, 329 Daniel Macks, ...... 354 como ...... 8 Daniel Neumann ...... 354 Compaq ...... 8 Daniel Wang...... 353 comparator ...... 348 DAP ...... 39 compatability ...... 8 dat glb ...... 322 compilers ...... 37 data access protocol...... 39 complementary error function ...... 213 data safety ...... 17, 341 compliance checker...... 60 data, missing ...... 87, 103, 218 compression...... 121, 279 date...... 146 concatenation...... 19, 253, 293, 302 date formats ...... 265 concurrent access ...... 125 date_written ...... 147 Conda ...... 10, 304 datesec ...... 146 conditional Operator ...... 158 Dave Blodgett ...... 354 config.guess...... 16 dbg lvl...... 16, 24, 31, 233, 310 configure.eg...... 16 ‘dbl_flt’ ...... 291 conservative regridding...... 87 dcEdge...... 146, 306 conservative_monotone_fv2se ...... 307 DDRA ...... 356 conservative_monotone_fv2se_alt...... 307 Debian ...... 15 conservative_monotone_se2fv ...... 307 debug-level ...... 16, 24 conserve ...... 306 debugging ...... 16, 24, 31 conserve2nd...... 306 DEC ...... 8 constraint expressions ...... 40 dec md ...... 231 408 NCO 5.0.2-alpha07 User Guide Decimal Significant Digits ...... 112, 237 duration ...... 68 defdim() ...... 159 dvEdge...... 146, 306 defining dimensions in ncap2 ...... 159 dwe ...... 306 DEFLATE ...... 115 DWE ...... 306 deflation ...... 13, 121, 279 dynamic linking...... 11 degenerate dimension...... 108, 132, 212, 227, 255, 261, 297, 298, 347, 350 degrees_east...... 90 E degrees_north ...... 90 delete (groups)...... 54 E3SM conventions...... 146 delete_miss() ...... 174 ECMWF ERA5 grid ...... 81 demotion ...... 134 ECMWF IFS grid...... 81 Dennis Heimbigner...... 353 Ed Hartnett ...... 353 Depth ...... 233, 311 Ed Hill ...... 355 derived fields...... 154, 155 eddy covariance ...... 211 dgll...... 307 edgeMask ...... 306 digest...... 122 edgesOnCell ...... 146, 306 Digital ...... 8 edgesOnEdge ...... 146, 306 dimension limits ...... 63 edgesOnVertex...... 146, 306 dimension names ...... 341 editing attributes ...... 218 dimension order ...... 368 egrep...... 51 dimensions, growing ...... 211 Elliptic integrals ...... 196 disaggregate ...... 57 end yr ...... 233 disjoint files...... 19 ensemble ...... 20, 256 diskless files ...... 124 ensemble average ...... 256 dismember ...... 57 ensemble concatenation ...... 253 distance-weighted extrapolation ...... 306, 312 ENVI ...... 32 distance_weighted ...... 306 Equi-angular grid ...... 81 Distributed Data Reduction & Analysis ...... 356 Equi-Angular grid ...... 77 Distributed Oceanographic Data System ...... 39 equiangular grid ...... 87 divide ...... 225 ERDDAP ...... 275 dividing data ...... 225 erf ...... 213 division ...... 213 erfc ...... 213 djf ...... 231 Eric Blake ...... 355 DJF ...... 231 error function...... 213 ‘dmn’...... 108 error tolerance ...... 17 documentation ...... 7 ERWG ...... 304 DODS ...... 39, 42 ESMF ...... 77, 86 DODS_ROOT ...... 39 ESMF_RegridWeightGen ...... 304 DOE ...... 32 esmf typ...... 312 dot product...... 349 Etienne Tourigny ...... 355 double() ...... 178 exclusion ...... 48, 285, 287 double-precision ...... 214 execution time ...... 11, 18, 26, 29, 104, 218, 342 dpf ...... 233 exists() ...... 177 dpt ...... 311 exp ...... 213 dpt fl ...... 233, 311 exponent ...... 141 drc in ...... 234 exponentiation ...... 213 drc out ...... 237 exponentiation function ...... 213 drc prv ...... 245 expressions ...... 155 drc rgr ...... 236 extended climatology (climo)...... 243 drc rgr prv ...... 245 extended file format...... 44 drc rgr xtn ...... 245 extended regular expressions .. 22, 51, 212, 219, 223 drc xtn ...... 245 extensive variable ...... 92 DSD ...... 112 extraction ...... 48, 285, 287 dsort...... 182 extrapolation ...... 312 dst fl ...... 311 dt sng ...... 312 dtos...... 306 General Index 409 F G f90 ...... 10 g++ ...... 8 features, requesting ...... 15 g++ ...... 10 File buffers ...... 123 ‘g2d’...... 106 file combination...... 359 ‘g3d’...... 106 file deletion ...... 42 GAG...... 253 file multiplication ...... 262 Galerkin methods ...... 307 file removal ...... 42 gamma...... 9, 213 gamma function...... 196, 213 file retention ...... 42 Gary Strand ...... 354 files, multiple ...... 35 Gaussian grid ...... 77, 87 files, numerous input ...... 21 Gaussian weight ...... 93 Filipe Fernandes ...... 354, 355 Gaussian weights ...... 350 filters ...... 283 Gavin Burris...... 355 findgen-equivalent ...... 162 Gayathri Venkitachalam ...... 354 fix record dimension ...... 253, 255, 274 gcc ...... 8 fixed dimension ...... 253, 255, 271, 274 gcc ...... 10 flags ...... 211 GCM ...... 17, 121, 139 flatten (groups) ...... 54 GenerateOfflineMap...... 304 flattening ...... 47 GenerateOverlapMesh...... 304, 309 float...... 214 geographical weight ...... 375 float() ...... 178 George Shapavalov...... 355 floor ...... 213 George Shapovalov...... 354 floor function ...... 213 George White...... 354 ‘flt_byt’ ...... 291 get_miss() ...... 174 ‘flt_dbl’ ...... 291 getdims() ...... 177 ‘flt_sht’ ...... 291 gethostname ...... 10 fml nm ...... 233 getopt...... 31 for() ...... 181 getopt.h ...... 31 force append...... 143 getopt_long ...... 31 force overwrite ...... 143 getuid...... 10 Glenn Davis ...... 353 foreword ...... 1 global attribute ...... 223, 342 formula_terms attribute...... 149, 236, 319, 333 global attributes .. 22, 143, 144, 145, 151, 219, 223, Fortran ...... 137, 300, 303 263, 264, 285, 342, 345 Fortran index convention...... 63 global_latitude0 ...... 306 FORTRAN format...... 27 global_longitude0 ...... 306 Francesco Lovergine ...... 354 globbing ...... 22, 34, 52, 212, 226, 300, 302 frc ...... 92 GMT ...... 147 frc nm ...... 92 gmtime() ...... 147 ftp ...... 10, 37 GNU ...... 31, 51 FTP ...... 42 gnu-win32 ...... 10 funding ...... 356 GNU/Linux ...... 23 FV...... 93 GNUmakefile ...... 10 FV grid...... 77 God ...... 101 FV grid ...... 87 GODAD ...... 359 fv2fv...... 307 grd dst ...... 312 fv2fv_flx ...... 307 grd glb ...... 322 fv2fv_stt ...... 307 grd rgn ...... 322 fv2se_alt ...... 307 grd src ...... 328 fv2se_flx ...... 307 grd ttl ...... 78 fv2se_stt ...... 307 Gregorian dates...... 265 grep -E ...... 51 grid, CAM-FV ...... 77 grid, Equi-Angular ...... 77 grid, Fixed...... 77 grid, FV ...... 77 grid, Gaussian ...... 77 410 NCO 5.0.2-alpha07 User Guide grid, Offset ...... 77 HPSS ...... 38 grid-file ...... 86 hsi ...... 38 grid_mapping attribute ...... 149, 287 hst nm ...... 234 gridcell_area ...... 306 HTML ...... 7 gridfile...... 77 HTTP protocol...... 39 group aggregation ...... 253, 361 Huffman coding...... 111 group attributes ...... 345 Hugo Oliveira...... 354 group names ...... 341 hyai ...... 146, 148, 333 group path...... 54 hyam ...... 146, 333 group, aggregation ...... 373 hybi ...... 146, 148, 333 group, anomaly ...... 373 hybm ...... 146, 333 group, dimension permutation ...... 373 hybrid coordinate system ...... 160 group, spatial averaging...... 373 hyperbolic arccosine function...... 213 group, standard deviation ...... 373 hyperbolic arcsine function...... 213 group, temporal averaging ...... 373 hyperbolic arctangent function ...... 213 groups ...... 13, 223 hyperbolic cosine function...... 213 groups, averaging ...... 367 hyperbolic sine function...... 213 groups, creating ...... 361 hyperbolic tangent ...... 213 groups, moving ...... 56 hyperslab ...... 63, 106, 253, 256, 298, 302, 347 groups, renaming ...... 56 hyperslabs ...... 163 growing dimensions ...... 211 GSL ...... 9, 195, 203 gsl sf bessel Jn ...... 196 I gsl sf gamma ...... 196 I/O ...... 41, 63, 72 gsl_sf_legendre_Pl...... 197 I/O block size ...... 123 gunzip ...... 115 I18N...... 29 gw ...... 93, 146, 306, 350 Ian Lancaster ...... 355 gzip ...... 111, 115 IBM ...... 8 icc ...... 8 ID Quoting ...... 215 H IDL ...... 17 h4_ncgen ...... 272 IEEE...... 114, 136 H4CF ...... 15 IEEE 754 ...... 113 h4tonccf ...... 15 IEEE NaN ...... 222 Harry Mangalam ...... 354 if() ...... 169 has_miss() ...... 174 ilev...... 148 hash ...... 122 ilev dmn nm ...... 91 HDF ...... 12, 42, 272, 356 ilev nm ...... 91 HDF unpacking ...... 127 ilimit...... 24 hdf_name ...... 14 illegal names ...... 14 HDF4 ...... 13, 272 implicit conversion ...... 136 HDF4_UNKNOWN...... 13 in drc ...... 314 HDF5...... 12, 13, 115 in fl ...... 314 HDF5_USE_FILE_LOCKING ...... 230, 304 include ...... 182 hdp ...... 272 including files ...... 155 help ...... 15 incremental climatology (climo) ...... 243 Henry Butowsky ...... 353 index convention ...... 63 ‘hgh_byt’ ...... 291 indexToCellID...... 146, 306 ‘hgh_sht’ ...... 291 indexToEdgeID...... 146, 306 hidden attributes ...... 272 indexToVertexID ...... 146, 306 hidden features ...... 281 indgen-equivalent ...... 162 Hierarchical Data Format ...... 12 indicator option ...... 77, 113, 143 highorder_fv2se ...... 307 inexact conversion ...... 214 highorder_se2fv ...... 307 infer ...... 83, 93, 312, 330 history ...... 23, 37, 144, 151, 218, 254, 285 Info...... 7 history_of_appended_files...... 144 input files ...... 22, 34, 37 hncgen ...... 272 input-path ...... 34, 39 HP...... 8 installation ...... 8, 16 General Index 411 int() ...... 178 lat nrt ...... 80 int64()...... 178 lat sth ...... 80 intbilin_se2fv ...... 307 lat typ ...... 78 integration...... 349 lat_vertices ...... 91, 306 integrity ...... 122 lat wgt nm ...... 93 Intel ...... 8 lat wst ...... 80 interleave ...... 69 latCell ...... 146, 306 Internationalization ...... 29 latEdge ...... 146, 306 interoperability ...... 127 latitude ...... 90, 306 interpolation ...... 260, 378 Latitude ...... 306 intersection ...... 48, 50 latitude_bnds ...... 306 introduction ...... 7 latitude0 ...... 306 invert_map ...... 182 LatitudeCornerpoints ...... 306 IPCC ...... 257, 356 latt_bounds...... 306 irregular grids ...... 191 latu_bounds...... 306 ISO ...... 8 latVertex ...... 146, 306 Isuru Fernando ...... 354 LD_LIBRARY_PATH ...... 11 Least Significant Digit ...... 112 least_significant_digit ...... 115 J left hand casting ...... 26, 160 James Gallagher ...... 353 Legendre polynomial ...... 197 JavaScript ...... 275 Lempel-Ziv deflation ...... 121 Jeff Whitaker ...... 115 Len Makin...... 354 Jerome Mao ...... 354 lev ...... 148 jfd ...... 231 lev dmn nm ...... 91 JFD ...... 231 lev nm ...... 91 Jim Edwards ...... 354 lexer...... 154 job nbr ...... 234, 315 ‘lfp’...... 108 Joe Hamman ...... 353 LFS ...... 23, 47 John Caron ...... 115, 353 LHS ...... 160 Joseph O’Rourke ...... 354 libnco...... 8 JSN...... 275 libraries ...... 11 JSON ...... 275 linkers ...... 37 jsonlint ...... 278 Linux...... 215 Juliana Rew ...... 354 LLVM ...... 8 ln ...... 213 ln -s ...... 226, 290 K log ...... 213 Karen Schuchardt...... 354 log10 ...... 213 Keith Lindsay ...... 354 logarithm, base 10 ...... 213 kitchen sink ...... 263 logarithm, natural ...... 213 kiteAreasOnVertex ...... 146, 306 lon ...... 91, 306 Kyle Wilcox ...... 354, 355 LON ...... 306 lon bnd nm...... 91 lon_bnds ...... 91, 146, 306 L lon dmn nm ...... 90 L10N ...... 29 lon nbr ...... 80 large datasets ...... 23, 30 lon nm ...... 90 Large File Support ...... 23, 47 lon typ ...... 78 lat ...... 91, 306 lon_vertices ...... 91, 306 LAT ...... 306 lonCell ...... 146, 306 lat bnd nm ...... 91 lonEdge ...... 146, 306 lat_bnds ...... 91, 146, 306 long double ...... 214 lat dmn nm ...... 90 long options...... 31, 296 lat drc ...... 80 long-term average ...... 365 lat est ...... 80 longitude ...... 74 lat nbr ...... 80 longitude ...... 90, 306 lat nm...... 90 Longitude ...... 306 412 NCO 5.0.2-alpha07 User Guide longitude_bnds ...... 306 maxLevelEdgeTop ...... 306 longitude0 ...... 306 MD5 digest ...... 122 LongitudeCornerpoints ...... 306 mdl nm ...... 236 lont_bounds...... 306 mean ...... 129 lonu_bounds...... 306 mebs...... 129 lonVertex ...... 146, 306 mebs()...... 176 Lori Sentman ...... 354 median ...... 284 lossy compression ...... 111 memory available ...... 24, 124 lrint()...... 136 memory leaks ...... 26 lround()...... 136 memory requirements...... 24, 50, 124 LSD ...... 112 merging files ...... 19, 264 Luk Claes ...... 354 meshDensity ...... 146, 306 metadata ...... 279 metadata optimization ...... 29 M metadata, global ...... 254, 278 mabs...... 129 mibs...... 129 mabs() ...... 176 mibs() ...... 176 Macintosh ...... 8 Michael Decker ...... 60 Make-Weight-Files (MWF) ...... 321 Michael Prather ...... 141 make bounds() function...... 216 Michael Schulz...... 354 Makefile ...... 8, 10, 40 Microsoft...... 8, 10 malloc() ...... 26 Microsoft Visual Studio ...... 10 Manfred Schwarb ...... 355 Mike Folk...... 12 mantissa...... 141 Mike Page ...... 354 manual type conversion ...... 134 Milan Klower ...... 353 map ...... 86 min ...... 129 map-file ...... 86, 266 Min Xu...... 354 ‘map_dmn’ ...... 108 min() ...... 175 map fl ...... 316 minimum ...... 129, 284 ‘map_lfp’ ...... 108 missing value ...... 310, 317 ‘map_nc4’ ...... 108 missing values...... 87, 103, 130, 218, 261, 279 ‘map_nco’ ...... 108 missing values ncap2 ...... 173 ‘map_prd’ ...... 108 missing(), mask_miss() ...... 174 ‘map_rd1’ ...... 108 missing_value ...... 87, 103, 126, 345 ‘map_rew’ ...... 108 MKS units ...... 98, 101 ‘map_scl’ ...... 108 mode ...... 284 ‘map_xst’ ...... 108 MODIS ...... 378, 381 mapping_file...... 93 modulus ...... 213 Marco Atzeri ...... 354 mono...... 307 Mark Flanner...... 354 mono_fv2se ...... 307 Markus Liebig ...... 354 mono_se2fv ...... 307 Martin Dix ...... 354 monotonic coordinates ...... 27 Martin Otte ...... 355 monotr_fv2se ...... 307 Martin Schmidt...... 354 monthly average ...... 357, 375 Martin Schultz ...... 60 monthly data ...... 357, 358 mask ...... 93 move groups ...... 56 mask ...... 191, 211 MPAS ...... 222 mask condition...... 348, 351 MPAS ...... 233, 311, 331 masked average ...... 347 MPAS conventions ...... 146 Mass Store System ...... 37 mpi nbr ...... 316 Matej Vela...... 354 mpi pfx ...... 316 mathematical functions ...... 213 MRO ...... 68 Matlab ...... 17 MSA ...... 71 Matthew Thompson ...... 354 msh fl ...... 317 max ...... 129 msk_*...... 146 max() ...... 175 msk dst ...... 317 maximum ...... 129, 284 msk nm ...... 93 maxLevelCell ...... 146 msk out ...... 318 General Index 413 msk src ...... 318 NC_USHORT ...... 12, 291 msrcp...... 38 nc3tonc4 ...... 14 msrcp ...... 42 ‘nc4’...... 108 msread...... 38 ncadd...... 225 MSS ...... 37 ncap...... 154 mss val ...... 319 ncap2 ...... 9 MTA ...... 32 ncap2 ...... 26, 30, 142 multi-arguments ...... 77, 113, 143 ncap2...... 154 Multi-arguments ...... 32 ncap2...... 289 multi-file operators .. 24, 35, 37, 254, 257, 298, 302 NCAR...... 17, 121 multi-hyperslab ...... 71 NCAR MSS ...... 37 Multi-Record Operator...... 68 ncatted ...... 51 multiplication ...... 213, 225, 262 ncatted ...... 103 multiply ...... 225 ncatted ...... 104, 143, 144 multiplying data ...... 225, 260 ncatted ...... 218 multislab ...... 71 ncattget ...... 218, 283 mv ...... 49 ncavg...... 283 MVS ...... 8 ncbo...... 104 MVS ...... 10 ncbo...... 225 MWF-mode ...... 321 ncclimo ...... 230 ncdiff ...... 225 ncdismember ...... 57, 60 N ncdivide ...... 225 n2s ...... 80 ncdmnlst ...... 283 naked characters ...... 226 ncdmnsz ...... 283 NaN ...... 222 ncdump ...... 45, 272, 279, 281 NaN ...... 271 ncecat...... 20 NaNf...... 271 ncecat ...... 253 NARR (North American NCEP2 grid ...... 81, 314 Regional Reanalysis)a ...... 180 nces ...... 21, 104 NASA ...... 356 nces...... 256 NASA CMG grid ...... 81 ncextr ...... 263 NASA EOSDIS ...... 21 ncflint...... 21, 104 NASA MERRA2 grid ...... 81 ncflint ...... 260 National Virtual Ocean Data System ...... 39 ncgen...... 272 nav_lat ...... 306 ncgen-hdf ...... 272 nav_lon ...... 306 ncgrplst ...... 283 nbnd ...... 91 ncks ...... 44 nc__enddef()...... 29 ncks ...... 122, 212, 263 NC_BYTE ...... 291 NCL...... 17, 295, 304 NC_CHAR ...... 65, 168, 227, 291 ncl_convert2nc...... 14, 295 NC_DISKLESS...... 124 nclst...... 283 NC_DOUBLE ...... 214, 291 ncmax...... 283 NC_FLOAT ...... 291 ncmdn...... 283 NC_FORMATX_DAP2 ...... 44 ncmin...... 283 NC_FORMATX_DAP4 ...... 44 NcML...... 281 NC_FORMATX_NC_HDF4 ...... 44 ncmult ...... 225 NC_FORMATX_NC_HDF5 ...... 44 ncmultiply ...... 225 NC_FORMATX_NC3 ...... 44 ‘nco’...... 108 NC_FORMATX_PNETCDF ...... 44 nco ...... 306 NC_INT ...... 291 NCO availability ...... 7 NC_INT64 ...... 12, 291 NCO homepage ...... 7 NC_SHARE ...... 125 nco script file ...... 368 NC_SHORT ...... 291 NCO User Guide ...... 7 NC_STRING ...... 168 nco.config.log.${GNU_TRP}.foo ...... 16 NC_UBYTE ...... 12, 291 nco.configure.${GNU_TRP}.foo ...... 16 NC_UINT...... 12, 291 nco.make.${GNU_TRP}.foo ...... 16 NC_UINT64 ...... 12, 291 nco_cns ...... 306 414 NCO 5.0.2-alpha07 User Guide nco_con ...... 306 nm dst ...... 319 nco_conserve ...... 306 nm src ...... 319 nco_dwe ...... 306 no stagger ...... 93 nco_input_file_list ...... 22, 145 NO_NETCDF_2 ...... 12 nco_input_file_number...... 22, 145 noclean ...... 330 nco_nearest_neighbor ...... 306 nohup...... 232 nco_openmp_thread_number ...... 30 non-coordinate grid properties ...... 147 nco opt ...... 236, 319 non-rectangular grids ...... 191 ncol ...... 90 non-standard grids ...... 191 ncpack ...... 289 normalization ...... 260, 349 ncpdq ...... 20, 30 North American Regional ncpdq ...... 106, 233, 254, 289, 302, 310 Reanalysis (NARR) ...... 180 ncpdq...... 326 Not-a-Number ...... 222 ncra ...... 21, 104 NRA ...... 356 ncra ...... 212, 298 nrnet...... 38 ncrcat ...... 20, 30 nsd ...... 306 ncrcat ...... 302 NSD ...... 112 ncrecsz ...... 283 NSF ...... 356 ncremap ...... 304 nstod...... 306 ncrename ...... 103, 341 NT (Microsoft operating system) ...... 10 ncrng...... 283 NUL ...... 221 NCSA ...... 12 NUL ...... 292 ncsub...... 225 NUL-termination ...... 221 ncsubtract ...... 225 null operation...... 262 nctypget ...... 283 number literals ncap2 ...... 168 ncunits ...... 283 Number of Significant Digits ...... 112, 237 ncunpack ...... 289 number_miss() ...... 174 ncvardmnlatlon ...... 283 number_of_significant_digits ...... 115 ncvardmnlst...... 283 numerator ...... 130, 349 ncwa ...... 21, 30, 104 nv ...... 91 ncwa ...... 212, 347 NVODS ...... 39 ndims()...... 176 ‘nxt_lsr’ ...... 291 nds ...... 306 ndtos...... 306 nearbyint ...... 213 O nearest integer function (exact) ...... 213 oceanography ...... 39 nearest integer function (inexact) ...... 213 octal dump ...... 45 nearest-neighbor extrapolation ...... 306 od ...... 45 nearestdtos...... 306 OMP_NUM_THREADS ...... 30 neareststod...... 306 on-line documentation...... 7 NEC ...... 8 open source ...... 1, 39 nEdgesOnCell ...... 146, 306 Open-source Project for a Network nEdgesOnEdge ...... 146, 306 Data Access Protocol ...... 39 Neil Davis ...... 136 OPeNDAP...... 39 nesting ...... 155 OpenMP...... 24, 26, 30, 86 netCDF...... 7 operation types...... 129, 298, 348 netCDF2 ...... 12, 42 operator speed ...... 11, 18, 26, 29, 104, 218, 342 NETCDF2_ONLY...... 12 operators ...... 5 netCDF3 ...... 12, 42 Options, multi-argument ...... 32 netCDF3 classic file format ...... 44 Options, truncating ...... 32 netCDF4 ...... 12, 42, 102 OptIPuter ...... 356 netCDF4 classic file format ...... 44 Orion Poplawski ...... 355 netCDF4 file format ...... 44 ORO ...... 146, 350 NETCDF4 files ...... 42 orphan dimensions ...... 280 NETCDF4_CLASSIC files...... 42 OS ...... 8 NETCDF4_ROOT...... 15 out drc ...... 320 Nick Bower ...... 354 output file...... 22, 37 NINTAP ...... 34, 300, 302 output-path...... 39 General Index 415 overview ...... 26 portability ...... 8 overwriting files ...... 18, 143 positional arguments ...... 37 POSIX ...... 31, 51 pow ...... 213 P power ...... 213 P0 ...... 333 power function...... 213 pack() ...... 176 PPC ...... 112 pack(x) ...... 126 ppc prc ...... 237 pack byte() ...... 176 prc typ ...... 320 pack int() ...... 176 ‘prd’...... 108 pack short() ...... 176 precision...... 214 packing ...... 41, 106, 126, 254, 289, 302 Precision...... 233, 310 packing map ...... 290 preprocessor tokens ...... 10 packing policy ...... 289 presentations...... 7 papers ...... 26 previous climatology (climo) ...... 243 par typ ...... 237, 325 print file ...... 280 print() ...... 170 parallel ...... 385 ncap2 Parallel ...... 385 printf...... 8 parallelism ...... 30, 356 printf()...... 221, 280, 286 parser ...... 154 printing files contents ...... 263 printing variables ...... 263 pasting variables ...... 19 Processor ...... 300, 302 patc...... 306 Processor, CCM ...... 34 patch...... 306 promotion ...... 134, 136, 214, 299 pathcc...... 8 proposals ...... 356 pathCC...... 8 provenance ...... 22, 23, 144, 254 PathScale ...... 8 prs stt...... 327 Patrice Dumas...... 355 PS ...... 333 Patrick Kursawe ...... 355 pseudonym ...... 11 pattern matching ...... 22, 51, 219, 223 publications...... 7 PayPal ...... 353 push...... 166 PBS ...... 239 Python ...... 232 pch ...... 306 pck map...... 290 pck plc ...... 289 Q pdq opt ...... 326 peak memory usage ...... 24, 124 Qi Tang ...... 354 Pedro Vicente ...... 354 QLogic ...... 8 per-record-weights ...... 299 quadruple-precision ...... 214 performance ...... 11, 18, 26, 29, 104, 218, 342 quantization ...... 111, 112 Perl ...... 17, 23, 221 quench ...... 280 permute ...... 326 Quick Start ...... 357 permute dimensions...... 289 quotes ...... 52, 212, 226, 296 permute() ...... 165 Peter Campbell ...... 354 pgcc ...... 8 R pgCC ...... 8 ‘r1d’...... 106 PGI ...... 8 RAG ...... 253 Philip Cameron-Smith ...... 354 RAM ...... 24, 124 philosophy ...... 17 RAM disks ...... 18, 124 pipes...... 22, 224 RAM files ...... 18, 124 ‘plc_all’ ...... 106 RAM variables ...... 125, 178 ‘plc_g2d’ ...... 106 ram delete() ...... 179 ‘plc_g3d’ ...... 106 ram write()...... 179 ‘plc_r1d’ ...... 106 random walk...... 141 ‘plc_xpl’ ...... 106 range ...... 284 ‘plc_xst’ ...... 106 rank ...... 156, 226, 227, 228, 347 plev nm ...... 91 rcp ...... 10, 37 PnetCDF file format ...... 44 RCS ...... 151 416 NCO 5.0.2-alpha07 User Guide ‘rd1’...... 108 RRG-mode...... 322 re-dimension ...... 289 rrg_bb_wesn...... 322 re-order dimensions ...... 289 rrg_dat_glb...... 322 record aggregation ...... 253 rrg_grd_glb...... 322 record append ...... 67 rrg_grd_rgn...... 322 record average ...... 298, 299 rrg_rnm_sng...... 322 record concatenation ...... 302 running average...... 298 record dimension... 19, 63, 111, 253, 255, 256, 271, Russ Rew ...... 353, 354 274, 293, 297, 298, 302 record variable ...... 63, 293 rectangular grids...... 191 S recursion ...... 49 S1_Latitude...... 306 recursive ...... 49 S1_Longitude ...... 306 regex...... 51 s2n ...... 80 regression...... 368 safeguards ...... 17, 341 regressions archive ...... 16 sanitize ...... 33 regrid ...... 304, 378 scale_factor ...... 126, 254, 289, 302 regridding ...... 86 scale format ...... 27 Regridding ReGional Data (RRG) ...... 322 scaling ...... 26, 262 regular expressions ...... 22, 34, 51, 212, 219, 223 Scientific Data Operators ...... 356 remap...... 182 ‘scl’...... 108 remap ...... 304 Scott Capps ...... 354 Remik Ziemlinski ...... 354 scp ...... 10, 37 remote files...... 10, 37 SCRIP ...... 77, 86 rename groups ...... 56 scrip grid ...... 78 renaming attributes ...... 341, 378 script file ...... 154 renaming dimensions ...... 341, 378 SDO ...... 356 renaming groups ...... 341 se2fv_alt ...... 307 renaming variables ...... 341, 368, 378 se2fv_flx ...... 307 renormalized regridding ...... 87 se2fv_stt ...... 307 reporting bugs ...... 15 se2se...... 307 reshape variables ...... 289 seasonal average ...... 300, 357 restrict ...... 9 security ...... 33, 122 reverse data ...... 296 SEIII ...... 356 reverse dimensions ...... 289, 295, 296 semi-colon ...... 155 reverse() ...... 165 separator ...... 282 ‘rew’...... 108 server ...... 23, 39, 42 rgn dst ...... 328 Server-Side Distributed Data rgn src ...... 328 Reduction & Analysis ...... 356 rgr map ...... 238 server-side processing...... 40, 356 rgr opt ...... 238, 327 set_miss() ...... 173 rgr var ...... 331 sftp ...... 10, 37 Rich Signell ...... 115, 354 SGI ...... 8 rint ...... 213 SGS-mode ...... 323 rint() ...... 114 sgs frc ...... 323 rll2rll ...... 307 sgs msk ...... 323 rms ...... 129 sgs nrm ...... 323 rmssdn ...... 129 Sh shell...... 283 rmssdn() ...... 176 shared access ...... 125 rnm sng ...... 322 shared memory machines...... 24 rnr thr ...... 88, 327 shared memory parallelism ...... 30 root-mean-square ...... 129 shell ...... 22, 52, 99, 212, 224, 226, 283 Rorik Peterson...... 353 SI...... 92 Rostislav Kouznetsov ...... 353 signedness ...... 27 round ...... 213 significand ...... 113, 114 rounding ...... 111, 141 simple_fill_miss()...... 175 rounding functions ...... 213 sin...... 213 RPM...... 15 sine function ...... 213 General Index 417 single-precision ...... 214 Swift ...... 385 sinh ...... 213 switches ...... 31 size() ...... 176 symbolic links ...... 10, 20, 23, 226, 290 skeleton ...... 86, 328 synchronous file access ...... 37 skl fl ...... 328 synonym ...... 11 slat...... 93, 237, 306, 319 syntax ...... 155 SLD (Swath-like Data) ...... 180 System calls ...... 123 slon...... 93, 237, 306, 319 SLURM ...... 239, 326, 329 SMP ...... 30 snwe ...... 80 T solar zenith angle function ...... 217 tabs...... 129 solid angle ...... 92 tabs() ...... 176 sort...... 182 Takeshi Enomoto ...... 355 sort alphabetically ...... 265, 285 source code ...... 7 tan ...... 213 source_file ...... 93 tanh ...... 213 spatial distribution ...... 365 Tempest2 ...... 307 special attributes ...... 272 TempestRemap...... 86, 304, 307 special characters ...... 221 temporary files ...... 17, 125 speed ...... 11, 18, 24, 26, 29, 104, 218, 342 temporary output files ...... 17, 125, 341 sqravg ...... 129 Terraref...... 32 sqravg() ...... 175 TEXinfo...... 7 sqrt...... 129 thr nbr ...... 30, 239, 329 sqrt ...... 213 threads ...... 24, 26, 30 square root function ...... 213 time ...... 99, 150 srt yr ...... 238 time-averaging ... 212, 300, 357, 358, 365, 368, 375 SSDDRA ...... 356 time_bnds ...... 91 SSH ...... 10, 42 time_offset...... 150 sshort()...... 178 time_written ...... 147 stagger ...... 93 timestamp ...... 144 staggered-grid ...... 93 TLAT...... 306 standard deviation ...... 129, 133, 368 TLON...... 306 standard input .... 22, 224, 231, 254, 257, 298, 302 TLONG...... 306 standard_name ...... 74, 90 tmp drc ...... 329 stat() system call ...... 123 Todd Mitchell ...... 355 statement...... 155 total...... 129 static linking...... 11 tpd ...... 240 stdin.... 22, 145, 231, 232, 254, 257, 298, 302, 314 TR ...... 86 steradian ...... 92 traditional ...... 263 Sterling Baldwin ...... 354 transpose...... 63, 293, 296 Steve Emmerson ...... 353 TREFHT ...... 139 stod...... 306 trunc ...... 213 stride ...... 64, 66, 71, 99, 298, 300, 302, 303 trunc() ...... 136 strings ...... 221 truncate (groups) ...... 54 Stu Muller ...... 354 stub ...... 39 Truncating options...... 32 sub-cycle ...... 68 truncation function ...... 213 Sub-gridscale (SGS) data ...... 323 truth condition...... 348, 351 subcycle ...... 68, 300 ttl ...... 129 subsetting ...... 48, 53, 149, 285, 287 ttl() ...... 176 subtract ...... 225 type conversion ...... 134 subtracting data ...... 225 type() ...... 177 subtraction ...... 213, 225 summary ...... 5 Sun ...... 8 swap space ...... 23, 24, 124 Swath-like Data (SLD) ...... 180 418 NCO 5.0.2-alpha07 User Guide U W ubyte() ...... 178 w_stag ...... 93, 237, 306, 319 UDUnits...... 8, 98, 146, 187 Ward Fisher ...... 353 UDUNITS2_XML_PATH ...... 99 Weather and Research Forecast UGRID ...... 83 (WRF) Model ...... 180 ugrid fl ...... 330 weighted average...... 256, 258, 299, 347, 375 uint()...... 178 weighted_fill_miss() ...... 175 ULAT...... 306 weights ...... 256, 258, 299, 347 ulimit...... 23 weightsOnEdge...... 146, 306 ULON...... 306 Wenshan Wang ...... 355 ULONG...... 306 wesn ...... 80 unary operations ...... 26 wget ...... 37 unbuffered I/O ...... 125 wgt_*...... 146 underlying file format ...... 44 wgt cmd ...... 336 UNICOS ...... 23 wgt opt ...... 335 Unidata ...... 8, 12, 98 where() ...... 179 union ...... 48, 50 while() ...... 181 union of files ...... 19 whitelist ...... 33 unit64() ...... 178 whitespace ...... 99 units ...... 90, 98, 99, 223, 262 wildcards ...... 34, 51, 219, 223 UNIX ...... 8, 10, 22, 31, 34, 283 WIN32...... 10 unlimited dimension ...... 253 Windows ...... 8, 10 unmap...... 182 wrapped coordinates ...... 65, 74, 192, 288 unpack()...... 176 wrapped filenames ...... 36 unpack(x) ...... 126 WRF ...... 193 unpacking ...... 41, 126, 254, 289, 302 WRF (Weather and Research unq sfx ...... 330 Forecast Model) ...... 180 unstructured grid ...... 75 WWW documentation ...... 7 URL ...... 37 User Guide ...... 7 ushort() ...... 178 X X axis...... 90 V xargs ...... 22, 37, 224 value list ...... 167 xCell...... 146 value listncap2 ...... 167 xEdge ...... 146, 306 var lst ...... 240, 331 XLAT...... 306 var xtr ...... 240 XLAT_M ...... 306 variable names...... 341 xlc ...... 8 variables, appending ...... 368 xlC ...... 8 variance ...... 129 XLONG...... 306 version ...... 151 XLONG_M ...... 306 vertexMask ...... 146, 306 XML...... 281 Vertical coordinate...... 331 XP (Microsoft operating system) ...... 10 verticesOnCell ...... 146, 306 ‘xpl’...... 106 verticesOnEdge ...... 146, 306 ‘xst’ ...... 106, 108 Vista (Microsoft operating system) ...... 10 xtn lst...... 336 vpointer ...... 189 xtr nsp ...... 312 vrb lvl ...... 331 xtr xpn...... 312 vrt ...... 331 xVertex ...... 146, 306 vrt fl ...... 331 Xylar Asay-Davis ...... 354 vrt ntp ...... 335 vrt xtr ...... 335 General Index 419 Y yVertex ...... 146, 306 Y axis...... 90 yCell...... 146 yEdge ...... 146, 306 Z Yorick...... 17, 27 zCell...... 146 ypf max ...... 241 zEdge ...... 146, 306 yr end prv ...... 244 zlib...... 111 yr srt prv ...... 244 zVertex ...... 146, 306