2009:029 MASTER'S THESIS Collocating Satellite-Based Radar and Radiometer Measurements to Develop an Ice Water Path Retrieval Gerrit Holl Luleå University of Technology Master Thesis, Continuation Courses Space Science and Technology Department of Space Science, Kiruna 2009:029 - ISSN: 1653-0187 - ISRN: LTU-PB-EX--09/029--SE Master's Thesis Collocating satellite-based radar and radiometer measurements to develop an ice water path retrieval Gerrit Holl June 11, 2009 Approximate footprints for different sensors 4480 CloudSat MHS 4460 HIRS AMSU−A 4440 4420 4400 4380 UTM y−pos (km) 4360 4340 4320 4300 390 400 410 420 430 440 450 460 470 480 UTM x−pos (km) Abstract Remote sensing satellites can roughly be divided in operational satellites and scientific satellites. Generally speaking, operational satellites have a long lifetime and often several near-identical copies, whereas scientific satellites are unique and have a more limited lifetime, but produce more advanced data. An example of a scientific satellite is the CloudSat, a NASA satellite flying in the so-called "A-Train" formation with other satellites. Examples of operational satellites are the NOAA and MetOp meteorological satellite series. CloudSat carries a 94 GHz nadir viewing radar instrument measuring pro- files of clouds. The NOAA-15 to NOAA-18 and MetOp-A satellites carry radiometers at various frequencies ranging from the infrared (3.76 µm) to around 183 GHz (≈ 1:6 mm). The full range is covered by the High Res- olution Infrared Radiation Sounder (HIRS) and the Advanced Microwave Sounding Units (AMSU-A and AMSU-B). On newer satellites, AMSU-B has been replaced by the Microwave Humidity Sounder (MHS) with nearly the same characteristics. Those instruments scan the atmosphere at angles from approximately −50◦ to +50◦ perpendicular to the ground track. The large amount of data from operational satellites is interesting to the scientific community, particularly when combined with measurements from a scientific satellite. The degree project focuses on this combination and consists of two parts: • The first part of the project involves searching for collocations 1 between the CloudSat radar and one of the NOAA or MetOp-A instruments. A collocation between two instruments is defined to occur when both look at the same place at the same time (within pre-set thresholds). This has been done with software developed by the student. • Those collocations are then used to find the relation between the radi- 1Two different spellings of the word collocation are found: either co + location, which makes colocation, or con + location which makes collocation. This text uses the spelling collocation. ii Abstract iii ances and physical data (such as Ice Water Path (IWP)) derived from CloudSat measurements. For the tropical ocean, this relation has been compared with data from models. Additionally, an artificial neural network has been trained to retrieve IWP. Acknowledgements This thesis work would not have existed in this form without the help of a large number of people. First of all, I would like to thank Stefan Buehler, my supervisor and teacher in a course on remote sensing. It was your idea to look for collocated satellite data. By your supervising, I have learnt a lot about atmospheric remote sensing, a field about which I had little to no knowledge when I started. Your feedback along the way was highly valuable. You were always easy to reach and it was impressive how fast you always replied to e-mails. Tack Stefan! I would like to thank Carlos Jim´enez(at Observatoire de Paris) for giving valuable feedback about neural networks. Bengt Rydberg (Chalmers Univer- sity of Technology) and Ajil Kottayil (SAT-group) have helped by providing plots I used to validate my collocations. Jasna Pittman (National Center for Atmospheric Research) and collaborators have done similar research. A poster published by Pittman and collaborators was useful for comparing dif- ferent results. I have used some freely available code that was not written by my- self. I would like to thank Patrick Eriksson and the atmlab commu- nity for land sea mask.m, Alexandre Schimel for wgs2utm.m, Matt G. for ignoreNaN.m David Dean for hist2d.m, and the Python community for the Python programming language. The following figures were not made by me: (2.1) (NASA/JPL), (2.2) (USGS), (2.3), (2.5), (2.6), (2.7), (2.8) (ESA), (2.4) (Arash Houshangyour), (2.9) (Viju Oommen John), (4.4) (Wikipedia contributor Colin M.L. Bur- nett). My thanks to all members of the SAT-group: Stefan Buehler, Salomon Eliasson, Erik Johansson, Ajil Kottayil, Thomas Kuhn, Oliver Lemke, Math- ias Milz, Isaac Moradi and Simon Ostman.¨ On the weekly group meetings, you have all helped by considering the questions arising from my work. You were and are good colleagues to have, both at work and outside work. My mother, Tinelot Wittermans, has proofread my thesis and suggested iv Acknowledgements v corrections to the language. Thanks go to all the teachers and assistants for all the courses I have read throughout my studies. Thanks to Sven Molin, Victoria Barabash and others, for making the Erasmus Mundus Master Course in Space Science and Technology possible. Also thanks to the administrative staff at the Department of Space Science (IRV), Anette Sn¨allfot-Br¨andstr¨omand Maria Winneb¨ack, and to the head of the department, Hans Weber. I also want to thank my science teachers during secondary education, Hans van Dijk and Hans van Riet, for stimulating my interest in science in general and physics in particular. Without your interesting science lessons at the Pieter Nieuwland College, I am not sure if I would have chosen to study Applied Physics in my Bachelor. Thanks to all my friends in the SpaceMaster programme for an experience I will never forget. The international environment, the time spent together in W¨urzburgand Kiruna, all the trips and parties, particularly in the first year, have provided me with memories that will stay with me for a very long time. Thanks to my friends and family for sharing joy and sorrow in good and bad times, for supporting me and for simply being there. Finally, thanks to my girlfriend, Catherine Dieval, for enriching my life. Table of Contents 1 Introduction 1 1.1 Scientific background . 2 1.2 Tools . 3 2 Satellites and sensors 4 2.1 CloudSat . 4 2.1.1 Cloud Profiling Radar . 5 2.2 NOAA15 { NOAA-18, MetOp-A . 7 2.2.1 Radiometers . 9 2.2.1.1 HIRS . 11 2.2.1.2 AMSU . 13 2.2.1.3 Channel summary . 17 3 Finding collocations 18 3.1 Introduction . 18 3.2 Method . 18 3.2.1 Input data . 18 3.2.2 Preprocessing . 19 3.2.2.1 Finding matching granules . 19 3.2.2.2 Converting units . 20 3.2.2.3 Checking for data validity . 20 3.2.2.4 Checking temporal overlap . 21 3.2.3 Collocation algorithm . 21 3.2.3.1 Implementation . 23 3.2.3.1.1 HIRS and AMSU-A . 24 3.2.4 Postprocessing . 25 vi TABLE OF CONTENTS vii 3.3 Verification and statistics . 26 4 Using collocations 35 4.1 Validating simulated ice water path . 35 4.2 Comparing ice water path retrievals . 38 4.3 Using NN to develop IWP retrieval . 40 4.3.1 Adding HIRS . 42 5 Conclusions and outlook 44 A Complete software description 46 A.1 Python source file . 46 A.1.1 relate hdf.py . 46 A.2 MATLAB source files . 46 A.2.1 Core program . 47 A.2.1.1 find overlap.m . 47 A.2.1.2 compare granule.m . 47 A.2.1.3 compare date.m . 47 A.2.1.4 find all overlap.m . 48 A.2.2 Post-processing . 48 A.2.2.1 find mean CSIWP per AMSU pixel.m . 48 A.2.2.2 fill missing noaa18 amsua.m . 48 A.2.2.3 fill mspps.m . 48 A.2.2.4 get data from overlap.m . 48 A.2.2.5 remove doubles.m . 48 A.2.2.6 remove all doubles.m . 49 A.2.3 Helper functions . 49 A.2.3.1 COLNO.m . 49 A.2.3.2 calc distance.m . 49 A.2.3.3 land or sea.m . 49 A.2.3.4 put in bins.m . 49 A.2.3.5 satboxplot.m . 49 A.2.3.6 for each granule.m . 49 A.2.3.7 find datafile by date.m . 49 A.2.3.8 find datafile by unixtime.m . 50 viii TABLE OF CONTENTS A.2.3.9 find short distance.m . 50 A.2.4 Reader function . 50 A.2.4.1 extract from overlap.m . 50 A.2.5 Statistics and verification . 50 A.2.5.1 make stats colloc.m . 50 A.2.5.2 find gridded average IWP month.m . 50 A.2.5.3 find IWPcorrelated channels . 50 A.2.6 Functions by others . 51 A.2.6.1 ignoreNaN.m . 51 A.2.6.2 wgs2utm.m . 51 A.2.6.3 hist2d.m . 51 A.2.7 Plotter functions . 51 A.2.7.1 plot footprints.m . 51 A.2.7.2 plot example dlat dlong.m . 51 A.2.7.3 plot hist2d dist int.m . 51 A.2.7.4 plot hist2d logIWP BT.m . 51 A.2.7.5 plot scatter CSIWP NESDISIWP.m . 51 A.2.7.6 plot extract from overlap.m . 52 A.2.7.7 find lowestlat by interval.m . 52 A.2.7.8 plot average BT month midlat.m . 52 A.2.7.9 plot hist2d latitude angle.m .