8.1 the Noaa Operational Model Archive and Distribution System (Nomads)
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Hsafew2019 S2b.Pdf
How to work with SSM products From Download to Visualization Apostolos Giannakos Zentralanstalt für Meteorologie und Geodynamik (ZAMG) https://www.zamg.ac.at 1 Topics • Overview • ASCAT SSM NRT Products • ASCAT SSM CDR Products • Read and plot ASCAT SSM NRT Products • Read and plot ASCAT SSM CDR Products • Summary 2 H SAF ASCAT Surface Soil Moisture Products ASCAT SSM Near Real-Time (NRT) products – NRT products for ASCAT on-board Metop-A, Metop-B, Metop-C – Swath orbit geometry – Available 130 minutes after sensing – Various spatial resolutions • 25 km spatial sampling (50 km spatial resolution) • 12.5 km spatial sampling (25-34 km spatial resolution) • 0.5 km spatial sampling (1 km spatial resolution) ASCAT SSM Climate Data Record (CDR) products – ASCAT data merged for all Metop (A, B, C) satellites – Time series format located on an Earth fixed DGG (WARP5 Grid) – 12.5 km spatial sampling (25-34 km spatial resolution) – Re-processed every year (in January) – Extensions computed throughout the year until new release 3 Outlook: Near real-time surface soil moisture products CDOP3 H08 - SSM ASCAT NRT DIS Disaggregated Metop ASCAT NRT SSM at 1 km (pre-operational)* H101 - SSM ASCAT-A NRT O12.5 Metop-A ASCAT NRT SSM orbit 12.5 km sampling (operational) H102 - SSM ASCAT-A NRT O25 Metop-A ASCAT NRT SSM orbit 25 km sampling (operational) H16 - SSM ASCAT-B NT O12.5 Metop-B ASCAT NRT SSM orbit 12.5 km sampling (operational) H103 - SSM ASCAT-B NRT O25 Metop-B ASCAT NRT SSM orbit 25 km sampling (operational) 4 Architecture of ASCAT SSM Data Services -
Portage of AL26T1 Op4 on IBM Regatta P690 Machine
Portage of AL26T1_op4 on IBM Regatta p690 machine . François Thomas [email protected] Revised and Approved by National Institute of Meteorology Tunisie April, 5th, 2004 Part 7: Data assimilation, Variational computations 1. S ummary This document outlines the work performed at INM from February, 22nd to March, 2nd. The tasks that were performed during this stay are: • system tasks : software upgrade (AIX, LoadLeveler, Parallel Environment, compilers), disk space management, installation of a web server, etc. • compilation and link of AL26T1_OP4 • unsuccessful attempt to compile ODB • implementation of a workload management strategy using LoadLeveler, WLM and vsrac, a tool developped by IBM Montpellier • test and implementation of an asynchronous method for running a forecast with slightly outdated coupling files from Meteo France • start a port of Metview/Magics We will describe all these tasks and give four appendices regarding : • software management under AIX • AIX system backup • users management with AIX • vsrac : task placement tool 2. S ystem tasks 2.1 Sof tware upgrade The first step of the work was to upgrade the system software to the latest levels. The table below summarizes the levels currently installed on the p690 Text (Body) Software name Software name AIX 5.1 ML5 : Maintenance Level 5 Parallel Environment 3.2.0.17 Load Leveler 3.1.0.22 xlf compiler 8.1.1.4 ESSL library 3.3.0.6 The best way to get system software fixes is to use the IBM support web site : http://www- 1.ibm.com/servers/eserver/support/pseries/fixes/. This service is free of charge. See Appendix A for details about the software management under AIX. -
NOMADS User Guide V1.0
NOMADS User Guide V1.0 Table of Contents • Introduction o Explanation of "Online" and "Offline" Data o Obtaining Offline Data o Offline Order Limitations • Distributed Data Access o Functional Overview o Getting Started with OPeNDAP o Desktop Services ("Clients") o OPeNDAP Servers • Quick Start to Retrieve or Plot Data o Getting Started o Using FTP to Retrieve Data o Using HTTP to Retrieve Data o Using wget to Retrieve Data o Using GDS Clients to Plot Data o Using the NOMADS Server to Create Plots • Advanced Data Access Methods o Subsetting Parameters and Levels Using FTP4u o Obtain ASCII Data Using GDS o Scripting Wget in a Time Loop o Mass GRIB Subsetting: Utilizing Partial-file HTTP Transfers • General Data Structure o Organization of Data Directories o Organization of Data Files o Use of Templates in GrADS Data Descriptor Files • MD5 Hash Files o Purpose of MD5 Files o Identifying MD5 Files o Using md5sum on MD5 Files (Linux) o Using gmd5sum on MD5 Files (FreeBSD) o External Links • Miscellaneous Information o Building Dataset Collections o Working with Timeseries Data o Creating Plots with GrADS o Scheduled Downtime Notification Introduction The model data repository at NCEI contains both deep archived (offline) and online model data. We provide a variety of ways to access our weather and climate model data. You can access the online data using traditional access methods (web-based or FTP), or you can use open and distributed access methods promoted under the collaborative approach called the NOAA National Operational Model Archive and Distribution System (NOMADS). On the Data Products page you are presented with a table that contains basic information about each dataset, as well as links to the various services available for each dataset. -
Whither the Weather Analysis and Forecasting Process?
520 WEATHER AND FORECASTING VOLUME 18 FORECASTER'S FORUM Whither the Weather Analysis and Forecasting Process? LANCE F. B OSART Department of Earth and Atmospheric Sciences, The University at Albany, State University of New York, Albany, New York 5 December 2002 and 19 December 2002 ABSTRACT An argument is made that if human forecasters are to continue to maintain a skill advantage over steadily improving model and guidance forecasts, then ways have to be found to prevent the deterioration of forecaster skills through disuse. The argument is extended to suggest that the absence of real-time, high quality mesoscale surface analyses is a signi®cant roadblock to forecaster ability to detect, track, diagnose, and predict important mesoscale circulation features associated with a rich variety of weather of interest to the general public. 1. Introduction h day-1 QPF scores made by selected NCEP models, By any objective or subjective measure, weather fore- including the Nested Grid Model (NGM; Hoke et al. casting skill has improved signi®cantly over the last 40 1989), the Eta Model (Black 1994), and the Aviation years. By way of illustration, Fig. 1 shows the annual Model [AVN, now called the Global Forecast System threat score for 24-h quantitative precipitation forecast (GFS); Kanamitsu et al. (1991); Kalnay et al. (1998)]. (QPF) amounts of 1.00 in. (2.5 cm) or more over the The key point to be made from Fig. 2 is that HPC contiguous United States for 1961±2001 as produced by forecasters have been able to sustain approximately a forecasters at the Hydrometeorological Prediction Cen- 0.05 threat score advantage over the NCEP numerical ter (HPC) of the National Centers for Environmental models during this 12-yr period (and longer, not shown) Prediction (NCEP). -
Article, We Describe the Architecture of for Using Data from Numerical Atmospheric Models for Re- the Tool
Geosci. Model Dev., 5, 55–71, 2012 www.geosci-model-dev.net/5/55/2012/ Geoscientific doi:10.5194/gmd-5-55-2012 Model Development © Author(s) 2012. CC Attribution 3.0 License. A web service based tool to plan atmospheric research flights M. Rautenhaus, G. Bauer, and A. Dornbrack¨ Deutsches Zentrum fur¨ Luft- und Raumfahrt, Institut fur¨ Physik der Atmosphare,¨ Oberpfaffenhofen, Germany Correspondence to: M. Rautenhaus ([email protected]) Received: 10 August 2011 – Published in Geosci. Model Dev. Discuss.: 1 September 2011 Revised: 13 January 2012 – Accepted: 13 January 2012 – Published: 17 January 2012 Abstract. We present a web service based tool for the plan- of atmospheric research flight planning is to explore large ning of atmospheric research flights. The tool provides on- amounts of atmospheric prediction and observation data in line access to horizontal maps and vertical cross-sections of order to extract specific regions of interest. Subsequently, a numerical weather prediction data and in particular allows flight route is designed considering the scientific objectives the interactive design of a flight route in direct relation to of the campaign, the predicted atmospheric situation, and in- the predictions. It thereby fills a crucial gap in the set of strument, aircraft and legal constraints (illustrated in Fig. 1). currently available tools for using data from numerical atmo- Several efforts have been started in recent years to sup- spheric models for research flight planning. A distinct fea- port the planning of flights during aircraft-based field cam- ture of the tool is its lightweight, web service based architec- paigns. Besides the application of general purpose tools for ture, requiring only commodity hardware and a basic Internet the exploration of weather prediction and observation data connection for deployment. -
The Operational CMC–MRB Global Environmental Multiscale (GEM)
VOLUME 126 MONTHLY WEATHER REVIEW JUNE 1998 The Operational CMC±MRB Global Environmental Multiscale (GEM) Model. Part I: Design Considerations and Formulation JEAN COÃ TEÂ AND SYLVIE GRAVEL Meteorological Research Branch, Atmospheric Environment Service, Dorval, Quebec, Canada ANDREÂ MEÂ THOT AND ALAIN PATOINE Canadian Meteorological Centre, Atmospheric Environment Service, Dorval, Quebec, Canada MICHEL ROCH AND ANDREW STANIFORTH Meteorological Research Branch, Atmospheric Environment Service, Dorval, Quebec, Canada (Manuscript received 31 March 1997, in ®nal form 3 October 1997) ABSTRACT An integrated forecasting and data assimilation system has been and is continuing to be developed by the Meteorological Research Branch (MRB) in partnership with the Canadian Meteorological Centre (CMC) of Environment Canada. Part I of this two-part paper motivates the development of the new system, summarizes various considerations taken into its design, and describes its main characteristics. 1. Introduction time and space scales that are commensurate with those An integrated atmospheric environmental forecasting associated with the phenomena of interest, and this im- and simulation system, described herein, has been and poses serious practical constraints and compromises on is continuing to be developed by the Meteorological their formulation. Research Branch (MRB) in partnership with the Ca- Emphasis is placed in this two-part paper on the con- nadian Meteorological Centre (CMC) of Environment cepts underlying the long-term developmental strategy, -
I/O Analysis of Climate Applications
I/O analysis of climate applications Arne Beer, MN 6489196, Frank Röder, MN 6526113 Introduction About the paper and our goals In this paper we analyze and present the strengths and weaknesses of different data structures required by some carefully picked climate and weather prediction models. Further we investigate the bare minimum of data required by those models. Another important aspect we will elaborate is the pre- and post- processing of data and the precise moment it occurs. In the following sections we will elucidate some models and the process on how to set them up as well as running sample cases. At the end there will be a section about the overall life cycle of data. Getting started With intent of getting an overview about the richness of climate applications and their land, ice, weather and ocean modulation, we took an in-depth look at some models. The main goal was to find a model with proper documentation and a license which allowed us to use it for our research and benchmarking purposes. Most model investigated by us were too old for our objective. In many cases there were practically no documentation, let alone active support or new releases. The next obvious step was to get an good overview of up to date and easy to handle models which are still supported. For this purpose we put a spreadsheet together from which we could choose the most promising ones. We came across some good looking models, but despite a good first impression, most of them are shipped with broken scripts, bad documentation or a hidden provision, which forbids us to use them. -
Metview's Python Interface Opens New Possibilities
from Newsletter Number 162 – Winter 2019/20 COMPUTING Metview’s Python interface opens new possibilities 11.4 11.5 10.4 11.7 11.6 12.2 12.6 12.9 12.2 12.0 13.5 13.3 12.7 14.1 13.1 12.8 13.0 13.5 12.0 12.7 11.8 12.7 12.5 12.6 13.4 12.6 14.5 14 13.9 13.3 14.0 13 13.1 12.5 13.2 12 13.5 14.0 13.1 14.1 14.1 11 13.8 14.1 14.6 13.6 14.1 13.6 13.1 13.7 10 13.8 12.8 12.7 www.ecmwf.int/en/about/media-centre/media-resources doi: 10.21957/hv3sp41ir5 Iain Russell, Linus Magnusson, Martin Janousek, Sándor Kertész Metview’s Python interface opens new possibilities This article appeared in the Computing section of ECMWF Newsletter No. 162 – Winter 2019/20, pp. 36–39 Metview’s Python interface opens new possibilities Iain Russell, Linus Magnusson, Martin Janousek, Sándor Kertész Metview is ECMWF’s interactive and batch processing software for accessing, manipulating and visualising meteorological data. Metview is used extensively both at ECMWF and in the Centre’s Member and Co-operating States. A national meteorological service may for example use it to plot fields produced by the ECMWF model and a regional model, calculate some additional fields such as temperature advection, and plot vertical cross sections. The recent addition of a Python interface to Metview has expanded its range of uses and has made it accessible to more potential users. -
MP - Mechanistic Habitat Modeling with Multi-Model Climate Ensembles - Final - HMJ5.Docx
MP - Mechanistic Habitat Modeling with Multi-Model Climate Ensembles - Final - HMJ5.docx Mechanistic Habitat Modeling with Multi-Model Climate Ensembles A practical guide to preparing and using Global Climate Model output for mechanistically modeling habitat, as exemplified by the modeling of the fundamental niche of a pagophilic Pinniped species through 2100. Masters project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University, May 2013. Author: Hunter Jones Adviser: Dr. David Johnston Abstract Projections of future Sea Ice Concentration (SIC) were prepared using a 13- member ensemble of climate model output from the Coupled Model Inter- comparison Project (CMIP5). Three climate change scenarios (RCP 2.6, RCP 6.0, RCP 8.5), corresponding to low, moderate, and high climate change possibilities, were used to generate these projections for known Harp Seal whelping locations. The projections were splined and statistically downscaled via the CCAFS Delta method using satellite-derived observations from the National Sea Ice Data Center (NSIDC) to prepare a spatial representation of sea ice decline through the year 2100. Multi-Model Ensemble projections of the mean sea ice concentration anomaly for Harp Seal whelping locations under the moderate and high climate change scenarios (RCP 6.0 and RCP 8.5) show a decline of 10% to 40% by 2100 from a modern baseline climatology (average of SIC, 1988 - 2005) while sea ice concentrations under the low climate change scenario remain fairly stable. Projected year-over-year sea ice concentration variability decreases with time through 2100, but uncertainty in the prediction (model spread) increases. -
Downscaling Geopotential Height Using Lapse Rate
KMITL Sci. Tech. J. Vol. 12 No. 2 Jul.-Dec. 2012 Downscaling Geopotential Height Using Lapse Rate Chiranya Surawut1 and Dusadee Sukawat2* 1Earth System Science, King Mongkut ’s University of Technology Thonburi, Bangkok, Thailand 2*Department of Mathematics, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand Abstract In order to utilize the output from a global climate model, relevant information for the area of interest must be extracted. This is called "downscaling". In this paper, the derivation of a local geopotential height in terms of lapse rate is presented. The main assumptions are hydrostatic balance, perfect gas, constant gravity, and constant lapse rate. Two sets of data are required for this method, simulation outputs from the Education Global Climate Model (EdGCM) and the observed data at the points of interest (Chiangmai, Bangkok, Ubon Ratchathani, Phuket and Songkla). The results show that downscaling of the geopotential heights by using lapse rate dynamic equation are closer to the observed data than the geopotential heights from EdGCM. Keywords: Downscaling, Geopotential height, Lapse rate 1. Introduction Climate change data are simulation outputs from global climate models (GCMs). Outputs of GCMs are coarse resolution, so it must be downscaled for use in regional applications by downscaling [1]. The starting point for downscaling is a larger scale atmospheric or coupled oceanic atmospheric model run from a GCM [2]. There are two major kinds of downscaling, statistical and dynamical methods. Statistical downscaling methods use historical data and archived forecasts to produce downscaled information from large scale forecasts. Dynamical downscaling methods involve dynamical models of the atmosphere nested within the grids of the large scale forecast models [1]. -
Bias Removal and Model Consensus Forecasts of Maximum and Minimum Temperatures Using the Graphical Forecast Editor 1. Introducti
Bias Removal and Model Consensus Forecasts of Maximum and Minimum Temperatures Using the Graphical Forecast Editor Jeffrey T. Davis National Weather Service Office Tucson, Arizona 1. Introduction Operational Numerical Weather Prediction (NWP) models have inherent biases that need to be removed either objectively or subjectively before used in official National Weather Service (NWS) forecasts. The recent shift of the NWS to the Interactive Forecast Preparation System (IFPS) to create and distribute digital weather forecasts has led to more of a dependency on the direct use of NWP models. This dependency on raw model output is mainly due to the gridded format needed to initialize digital weather forecasts. The primary tool for creating these digital forecasts is the Graphical Forecast Editor (GFE) (Lefebvre, 1995). The GFE has two design features that account for the objective and subjective adjustments of the native NWP model grid. The front-end feature is referred to as “Smart Initialization” which is used to objectively derive sensible weather elements and downscale the coarser resolution models to a higher 2.5 km or 5 km grid spacing. The second feature is interactive and accounts for both subjective and objective adjustments through the use of simple graphical editing tools and “Smart Tools”. The baseline GFE design lacks the capability to objectively remove model biases in the initialization process. This is not to say that the GFE should have this capability, but high resolution, bias-corrected model grids are not currently included in the overall IFPS design. As a result, forecasters are tasked with subjectively correcting for these model biases using the GFE tools. -
Metview in a Pythonic World EGOWS 2019, KNMI, De Bilt, the Netherlands
Metview in a Pythonic World EGOWS 2019, KNMI, De Bilt, The Netherlands Iain Russell Development Section, ECMWF Thanks to Linus Magnusson and Martin Janousek © ECMWF November 13, 2019 What is Metview? • Workstation software for researchers and operational analysts – Runs on UNIX, from laptops to supercomputers • Retrieve/manipulate/visualise/examine meteorological data • Batch mode or graphical user interface • Can access MARS, either locally or through the Web API • Open Source under Apache Licence 2.0 • Metview is a co-operation project with INPE (Brazil) EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS 2 Built on top of ECMWF software packages Metview Data decoding Regridding Plotting Data Access ecCodes Other (NetCDF, (GRIB, ODB_API Geopoints MIR Magics MARS CDS Files WMS URL BUFR) , CSV) EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS 3 Using Metview • Icon-based user interface – interactive investigation of data – icons represent data, settings and processes – icons can be chained together – output from one is input to another • e.g. filter fields from a certain date, then pass that to the Cross Section icon • Powerful Python/Macro scripting language – more serious computations – batch or interactive usage EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS 4 Generating Python code from the GUI EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS 5 Generating Python code from the GUI EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS 6 Data Formats • GRIB, BUFR, NetCDF, ODB, Geopoints, CSV • Plot • Examine • Filter, regrid, masking