EUMETSAT AWDP User Manual
Below you will find brief information for AWDP. AWDP generates surface winds from satellite data, using a processing chain that includes pre-processing, collocation with NWP data, inversion, quality control, and ambiguity removal. It produces wind vectors representing surface winds within the scatterometer's ground swath.
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NWP SAF
Satellite Application Facility for Numerical Weather Prediction
Document NWPSAF-KN-UD-005
Version 2.3
February 2014
AWDP User Manual and Reference Guide
Anton Verhoef, Jur Vogelzang, Jeroen Verspeek and Ad Stoffelen
KNMI, De Bilt, the Netherlands
NWP SAF
AWDP User Manual and
Reference Guide
Doc ID : NWPSAF-KN-UD-005
Version : 2.3
Date : February 2014
AWDP User Manual and Reference Guide
KNMI, De Bilt, the Netherlands
This documentation was developed within the context of the EUMETSAT Satellite Application
Facility on Numerical Weather Prediction (NWP SAF), under the Cooperation Agreement dated
29 June, 2011, between EUMETSAT and the Met Office, UK, by one or more partners within the
NWP SAF. The partners in the NWP SAF are the Met Office, ECMWF, KNMI and Météo France.
Copyright 2014, EUMETSAT, All Rights Reserved.
Change record
Version Date
1.0j Jun 2007
Author / changed by
Anton Verhoef
Remarks
First draft
1.0k
1.0.13
Oct 2007
Mar 2008
Anton Verhoef
Anton Verhoef
Adapted for AWDP version 1.0k
Adapted for AWDP version 1.0.13
1.0.14
1.0.16
1.1
2.0
Oct 2008
Dec 2008
Jan 2010
Aug 2010
Anton Verhoef
Anton Verhoef
Anton Verhoef
Anton Verhoef
First version for external review
Modified according to DRI comments
Removed a few typo’s and corrected some of the diagrams in the appendices for AWDP version
1.1
Modified for AWDP version 2.0; added section
3.5.3, changed sections 2.3, 2.3.4, 2.4, Chapter 9 and Appendix B4
2.0.01
2.2
2.3
Nov 2010 Anton Verhoef
Jun 2013 Anton Verhoef
Feb 2014 Anton Verhoef
Modified according to DRI comments
Version for AWDP version 2.2
Version for AWDP version 2.3
NWP SAF
AWDP User Manual and
Reference Guide
Doc ID : NWPSAF-KN-UD-005
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Date : February 2014
Contents
CHAPTER 1
1.1
A
IMS AND SCOPE
1.2
D
EVELOPMENT OF
1.3
T
ESTING
1.4
U
SER
M
ANUAL AND
R
EFERENCE
G
UIDE
........................................................................................ 6
1.5
C
ONVENTIONS
CHAPTER 2
2.1
W
HY USING THE
AWDP
PROGRAM
? .............................................................................................. 7
2.2
M
ODES OF USING
2.3
I
NSTALLING
2.3.1
2.3.2
2.3.3
2.3.4
2.3.5
2.4
C
OMMAND LINE OPTIONS
2.5
S
CRIPTS
2.6
T
EST DATA AND TEST PROGRAMS
................................................................................................. 19
2.7
D
OCUMENTATION
CHAPTER 3
3.1
P
URPOSE OF PROGRAM
3.2
O
UTPUT SPECIFICATION
3.3
I
NPUT SPECIFICATION
3.4
S
YSTEM REQUIREMENTS
3.5
D
ETAILS OF FUNCTIONALITY
3.5.1
3.5.2
3.5.3
3.5.4
3.5.5
3.5.6
3.5.7
3.5.8
3.5.9
3.6
D
ETAILS OF PERFORMANCE
CHAPTER 4
4.1
T
OP
L
EVEL
D
ESIGN
4.1.1
4.1.2
4.1.3
4.1.4
4.1.5
4.2
M
ODULE DESIGN FOR GENSCAT LAYER
........................................................................................ 32
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4.2.1
4.2.2
4.2.3
4.2.4
4.2.5
4.2.6
4.3
M
ODULE DESIGN FOR PROCESS LAYER
......................................................................................... 34
4.3.1
4.3.2
4.3.3
4.3.4
4.3.5
4.3.6
4.3.7
4.3.8
4.3.9
CHAPTER 5
5.1
B
ACKGROUND
5.2
R
OUTINES
5.3
A
NTENNA DIRECTION
CHAPTER 6
6.1
A
MBIGUITY
R
EMOVAL
6.2
M
ODULE AMBREM
6.3
M
ODULE
B
ATCH
M
OD
6.4
T
HE
KNMI 2DVAR
SCHEME
6.4.1
6.4.2
6.4.3
6.4.4
6.4.5
6.4.6
6.4.7
6.4.8
6.5
T
HE
P
RE
S
CAT SCHEME
CHAPTER 7
7.1
B
ACKGROUND
7.2
R
OUTINES
7.3
D
ATA STRUCTURES
CHAPTER 8
8.1
B
ACKGROUND
8.2
R
OUTINES
8.3
D
ATA STRUCTURES
8.4
L
IBRARIES
8.5
BUFR
TABLE ROUTINES
8.6
C
ENTRE SPECIFIC MODULES
CHAPTER 9
9.1
B
ACKGROUND
9.2
R
OUTINES
9.3
D
ATA STRUCTURES
9.4
L
IBRARIES
APPENDIX A
APPENDIX B1
CALLING TREE FOR INVERSION ROUTINES................................................. 83
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APPENDIX B2
CALLING TREE FOR AR ROUTINES.................................................................. 86
APPENDIX B3
CALLING TREE FOR BUFR ROUTINES ............................................................ 90
APPENDIX B4
CALLING TREE FOR GRIB ROUTINES ............................................................. 92
APPENDIX B5
CALLING TREE FOR PFS ROUTINES ................................................................ 94
APPENDIX B6
CALLING TREE FOR ICE MODEL ROUTINES ................................................ 97
APPENDIX C
ASCAT BUFR DATA DESCRIPTORS ................................................................... 98
APPENDIX D
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Preface
Software code for processing satellite data may become very complex. On the one hand, it consists of code related to the technical details of the satellite and instruments; on the other hand, the code drives complex algorithms to create the physical end products. Therefore, the EUMETSAT
Satellite Application Facility (SAF) project for Numerical Weather Prediction (NWP) has included some explicit activities aiming at enhancing the modularity, readability and portability of the processing code.
For several years, the KNMI observation research group has been developing processing code to supply Near Real Time (NRT) level 2 surface wind products based on the ERS and SeaWinds
Scatterometer level 1b Normalized Radar Cross Section data (σ
0
). This work is coordinated and supervised by Ad Stoffelen. In the beginning only an adaptation of his ERS code existed. Later
Marcos Portabella and Julia Figa added modifications and extensions to improve, e.g., the wind retrieval and quality control algorithms. In 2003, John de Vries finished the first official release of a processor within the NWP SAF. This processor was called the QuikSCAT Data Processor
(QDP).
Meanwhile, Jos de Kloe has been updating the code for ERS scatterometer wind processing. For many parts of the process steps (e.g., the BUFR handling and part of the wind retrieval) a large overlap with SeaWinds Data processing coding exists. The KNMI Scatterometer Team is working towards generic NRT scatterometer processing. As a result, a new modular processing code for
SeaWinds data was developed within the NWP SAF: the SeaWinds Data Processor (SDP) as successor of QDP.
Based on the generic code already available for SeaWinds and ERS processing, a new ASCAT
Wind Data Processor (AWDP) was developed. This document is the corresponding reference manual. I hope this manual will strongly contribute to the comprehension of future developers and of users interested in the details of the processing.
Many persons contributed (directly or indirectly) to the development of the scatterometer software at KNMI: Hans Bonekamp, Jos de Kloe, Marcos Portabella, Ad Stoffelen, Anton Verhoef, Jeroen
Verspeek, Jur Vogelzang and John de Vries are (in alphabetical order) the most important contributors.
Anton Verhoef, June 2007
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Chapter 1
Introduction
1.1 Aims and scope
The ASCAT Wind Data Processor (AWDP) is a software package written in Fortran 90 for handling data from the Advanced Scatterometer (ASCAT) and European Remote Sensing satellite
(ERS) scatterometer instruments. Details of these instruments can be found on several web sites and in several other documents, see e.g. [Portabella, 2002; Stoffelen, 1998] and information on the
ESA and EUMETSAT web sites.
AWDP generates surface winds based on level 1b or level 2 ASCAT and ERS data. It allows performing the ambiguity removal with the Two-dimensional Variational Ambiguity Removal
(2DVAR) method and it supports the Multiple Solution Scheme (MSS). The output of AWDP consists of wind vectors which represent surface winds within the ground swath of the scatterometer. Input of AWDP is Normalized Radar Cross Section (NRCS, σ
0
) data. These data may be real-time. The input files of AWDP are in BUFR or Product Format Specification (PFS, native Metop) format. BUFR input may be provided using the BUFR templates for ERS or
ASCAT; output is always written using the ASCAT BUFR template. Besides the nominal 25-km and 12.5-km products, AWDP also has the capability to generate a coastal wind product, where the backscatter data from the level 1b files are replaced by box-averaged backscatter values from the full resolution level 1 ASCAT product (SZF data). This mode of operation produces winds that are closer to the coast than the winds from the nominal level 1b data which contain backscatter values that are averaged using a Hamming filter, see [Verhoef et. al., 2012]. Currently (2013), the SZF data are not available for users in near-real time, but only off-line from the EUMETSAT Data
Centre.
Apart from the ASCAT input data, AWDP needs Numerical Weather Prediction (NWP) model winds as a first guess for the Ambiguity Removal step. These data need to be provided in GRIB edition 1 or 2.
1.2 Development of AWDP
AWDP is developed within the Numerical Weather Prediction Satellite Application Facility (NWP
SAF) and Ocean and Sea Ice Satellite Application Facility (OSI SAF) programs as code which can be run in an operational setting. The coding is in Fortran 90 and has followed the procedures specified for the NWP SAF. Special attention has been paid to robustness and readability. AWDP
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Windows machine if a Linux environment like the Windows Installer for Ubuntu (Wubi) is installed.
1.3 Testing AWDP
Modules are tested by test programs and test routines. Many test routines or test support routines are part of the modules themselves. Test programs can be compiled separately. For the AWDP program, the description of the test programs and the results of the testing are reported in [Verhoef
et. al., 2013].
1.4 User Manual and Reference Guide
This document is intended as the complete reference book for AWDP.
Chapter 2 is the user manual (UM) for the AWDP program. This chapter provides the basic
information for installing, compiling, and running AWDP. Chapter 3 contains the Product
Specification (PS) of the AWDP program. Reading the UM and the PS should provide sufficient information to the user who wants to apply the AWDP program as a black box.
The subsequent chapters are of interest to developers and users who need more specific information on how the processing is done. The Top Level Design (TLD) of the code and the
Module Design (MD) of the AWDP code can be found in Chapter 4. Several modules are very
generic for NRT scatterometer data processing. Examples are the modules for the BUFR and
GRIB handling, ambiguity removal, and parts of the wind retrieval. These generic modules are
part of the generic scatterometer (genscat) layer and are described in Chapter 5 to Chapter 9.
The appendices of this document contain a complete calling tree of the AWDP program up to and including the genscat layer. The appendices also contain a list of ASCAT BUFR data descriptors and a list of acronyms.
1.5 Conventions
Names of physical quantities (e.g., wind speed components u and v), modules (e.g. BufrMod), subroutines and identifiers are printed italic.
Names of directories and subdirectories (e.g. awdp/src), files (e.g. awdp.F90), and commands (e.g. awdp -f input) are printed in Courier. Software systems in general are addressed using the normal font (e.g. AWDP, genscat).
Hyperlinks are printed in blue and underlined (e.g. http://www.knmi.nl/scatterometer/ ).
References are in square brackets with the name of the author italic (e.g. [Stoffelen, 1998]).
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Chapter 2
AWDP User Manual
the AWDP software. The command line arguments of AWDP are discussed in section 2.4. Section
2.5 gives information on a script for running AWDP.
Please note that any questions or problems regarding the installation or use of AWDP can be addressed at the NWP SAF helpdesk at http://www.nwpsaf.org/ .
2.1 Why using the AWDP program?
Scatterometers provide valuable observational data over the world's oceans. Therefore, successful assimilation of scatterometer data in numerical weather prediction systems generally improves weather forecasts. The AWDP program has been developed to fully exploit scatterometer data. It is meant to form the key component of the observation operator for surface winds in data assimilation systems.
The general scheme of AWDP (and any other wind scatterometer data processor) is given in figure
2.1. The input of the AWDP program is the EUMETSAT ASCAT level 1b BUFR or PFS wind product (combined with ASCAT level 1 full resolution data in PFS in the case of coastal wind product processing) or the ESA ERS level 2 BUFR wind product. Besides this, GRIB input containing land-sea mask, sea surface temperature or soil temperature on level 0, and first guess winds over the globe is necessary.
The AWDP processing chain contains several steps (see figure 2.1):
1. Pre-processing. The input file is decoded and the radar backscatter (σ
0
) values are written in the data structures of AWDP. In the case of coastal product processing, the full resolution
(SZF) backscatter data are read and averaged. Some quality control on the input data is done.
2. Collocation with NWP data. The GRIB edition 1 or 2 files containing NWP data are read and the values for land fraction, sea surface temperature and first guess winds are interpolated and stored with the information of each WVC.
3. Inversion. The σ
0
values are compared to the Geophysical Model Function (GMF) by means of a Maximum Likelihood Estimator (MLE). The wind vectors that give the best description of the σ
0
values (the solutions) are retained. The MLE is also used to assign a probability to each
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Scheme (MSS) the maximum number of solutions is 144.
4. Quality Control. Solutions that lie far away from the GMF are likely to be contaminated by, e.g., sea ice or confused sea state. During Quality Control these solutions are identified and flagged.
5. Ambiguity Removal. This procedure identifies the most probable solution using some form of external information. AWDP uses a two-dimensional variational scheme (2DVAR) as default.
A cost function is minimized that consists of a background wind field and all solutions with their probability, using meteorological balance, mass conservation and continuity as constraints.
6. Quality Monitoring. The last step is to output quality indicators to an ASCII monitoring file and to write the results in a BUFR format output file.
(
σ
0
Input
values)
Input
(NWP data)
Pre-processing
NWP collocation
Inversion
Quality Control
NWP model
Ambiguity Removal
Quality Monitoring
Output wind field
Figure 2.1 AWDP processing scheme. The wind vectors and their probabilities after Quality Control may be fed directly in the Data Assimilation step of a Numerical Weather Prediction model.
Step 1, 2 and 6 of the processing chain are rather trivial; the real work is done in steps 3, 4, and 5.
Note that an undesirable dependency arises if the output wind field is assimilated into a numerical weather prediction (NWP) model: in the 2DVAR Ambiguity Removal step (i) a background wind field is used and (ii) meteorological balance constraints causing spatially correlated error.
Therefore it is recommended to feed the wind solutions and their probabilities directly into the
NWP data assimilation step after Quality Control, as indicated in figure 2.1. If this is done, the
Ambiguity Removal step can be skipped and consequently, no forecast winds are necessary in the
NWP input. No impact tests have been performed to date by assimilating AWDP outputs after ambiguity removal.
As further detailed in Chapter 3, AWDP profits from developments in
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• Inversion and output of the full probability density function of the vector wind (Multiple
Solution Scheme, MSS).
• Quality Control (QC).
• Meteorologically balanced Ambiguity Removal (2DVAR).
• Quality monitoring.
• Capability to process ASCAT data on both 25 km and 12.5 km cell spacing.
A complete specification of the AWDP program can be found in the Product Specification in
Chapter 3. The program is based on generic genscat routines for inversion, ambiguity removal, and
BUFR and GRIB file handling. These routines are discussed in more detail in Chapter 5 to Chapter
Figure 2.2 AWDP wind field retrieved for 15 April 2007, approximately 13 h UTC, at 25 km cell spacing, near Newfoundland overlaid on a GOES IR satellite image. The yellow dots are rejected WVCs. The blue and violet arrows are a 6 hours forecast from the KNMI HIRLAM model.
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2.2 Modes of using AWDP
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There are several modes to assimilate the ASCAT data in NWP models using AWDP. Anyway, the first thing to assure oneself of is the absence of biases by making scatter plots between ASCAT and NWP model first guess for at least wind speed, but wind direction and wind components would also be of interest to guarantee consistency.
The operational ASCAT wind product, available as a deliverable from the EUMETSAT OSI SAF project, could be the starting point for NWP assimilation:
1. The unique solution at every WVC may be assimilated as if it were buoy data. This is the fastest way and one exploits the data to a large extent. For a small advantage, AWDP could be installed to provide 2DVAR solutions based on the local first guess.
2. The AWDP software may be used to modify the 3DVAR or 4DVAR data assimilation system to work with the ambiguous wind solutions and their probabilities at every WVC. This represents some investment, but is applicable for all scatterometer data. The advantage with respect to option 1 in the ambiguity removal occurs only occasionally, but always in dynamic atmospheric cases (storms or cyclones) that are really relevant.
Both options can be based on AWDP in standard or MSS mode, and at various resolutions. MSS is somewhat more dependent on the first guess and balance constraints in 2DVAR than the standard
AWDP, but much less noisy. A noticeable advantage is thus obtained by using option 2 and potentially the full hi-res benefit of the ASCAT data is achieved. The mode of using AWDP thus depends on the opportunities, experience, and time the user has to experiment with ASCAT data in the NWP system under consideration.
The AWDP program can, of course, also be used to create a stand-alone wind product, e.g., for nowcasting purposes. Such a stand-alone ASCAT wind product is a deliverable of the OSI SAF project. More information on this project can be found on http://www.osi-saf.org/ .
2.3 Installing AWDP
AWDP is written in Fortran 90 (with a few low level modules in C) and is designed to run on a modern computer system under Linux or Unix. AWDP needs a Fortran 90 compiler and a C compiler for installation. AWDP comes along with a complete make system for compilation. In some cases, the Makefiles call installation scripts which are written in Bourne shell to enhance portability. When compiled, AWDP requires about 100-150 Mb disk space.
In principle, AWDP may also run under Windows. However, it needs the BUFR and GRIB API libraries from ECMWF, and this poses some restrictions on the systems supported. Under
Windows one must use a (free) Linux environment like Wubi (see http://www.ubuntu.com/download/desktop/windows-installer for more information and download).
To install AWDP, the following steps must be taken:
1. Copy the AWDP package (file AWDP<version>.tar.gz) to the directory from which
AWDP will be applied, and unzip and untar it. This will create subdirectories awdp and genscat
that contain all code needed (see section 2.3.1), and a script called InstallAWDP
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2. Download the ECMWF BUFR library file bufr_000400.tar.gz (or another version not earlier than 000240 and not later than 000387) and copy it to directory genscat/support/bufr
. Note that library versions 000388 and 000389 are not
supported. See also section 2.3.2.
3. Download the ECMWF GRIB API library file grib_api-1.11.0.tar.gz (or another version not earlier than 1.9.0) and copy it to directory genscat/support/grib. See also
4. Go to the top directory and run the ./InstallAWDP script. The script will ask for the compiler used and it will invoke the make system for compilation and linking of the software
AWDP is now ready for use, provided that the environment variables discussed in section 2.3.5
have the proper settings. See also sections 2.4 and 2.5 for directions on how to run AWDP.
2.3.1 Directories and files
All code for AWDP is stored in a file named AWDP<version>.tar.gz that is made available in the framework of the NWP SAF project. This file should be placed in the directory from which
AWDP is to be run. After unzipping (with gunzip AWDP<version>.tar.gz) and untarring
(with tar -xf AWDP<version>.tar), the AWDP package is extracted in subdirectories awdp
and genscat, which are located in the directory where the tar file was located.
Subdirectories awdp and genscat each contain a number of files and subdirectories. A copy of the release notes can also be found in the directory awdp/docs.
Tables 2.1 and 2.2 list the contents of directories awdp and genscat, respectively, together with the main contents of the various parts. Depending on the distribution, more directories may be present, but these are of less importance to the user.
Name Contents
doc
Documentation, including this document execs
Link to awdp executable, shell script for running AWDP src
Source code for AWDP program and supporting routines test
Example BUFR and GRIB input files for testing purposes.
Table 2.1 Contents of directory awdp.
Name Contents
ambrem ambrem/twodvar inversion main
Ambiguity removal routines
KNMI 2DVAR ambiguity removal routines
Inversion and quality control routines
Dummy subdirectory to facilitate the make system support support/BFGS support/convert
General purpose routines sorted in subdirectories
Minimization routines needed in 2DVAR support/bufr
BUFR tables (in subdirectory) and file handling routines support/Compiler_Features
Compiler specific routines, mainly command line handling
Conversion between wind speed/direction and u and v
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support/datetime support/ErrorHandler support/file support/grib support/num support/pfs support/singletonfft support/sort
Date and time conversion routines
Error handling routines
File handling routines
GRIB file handling routines
Numerical definitions and number handling routines
PFS file handling routines
FFT routines needed in minimization
Sorting routines
Table 2.2 Contents of directory genscat.
Directories awdp and genscat and their subdirectories contain various file types:
• Fortran 90 source code, recognizable by the .F90 extension;
• C source code, recognizable by the .c extension;
• Files and scripts that are part of the make system for compilation like Makefile_thisdir,
Makefile
, use_, Objects.txt and Set_Makeoptions (see 2.3.4 for more details);
• Scripts for the execution of AWDP in directory awdp/execs;
• Look-up tables and BUFR tables needed by AWDP;
• Files with information like Readme.txt.
After compilation, the subdirectories with the source code will also contain the object codes of the various modules and routines.
2.3.2 Installing the BUFR library
AWDP needs the ECMWF BUFR library for its input and output operations. Only ECMWF is allowed to distribute this software. It can be obtained free of charge from ECMWF at the web page http://www.ecmwf.int/products/data/software/bufr.html
. The package contains scripts for compilation and installation. The reader is referred to this site for assistance in downloading and installing the BUFR Library.
Directory genscat/support/bufr contains the shell script make.bufr.lib. It unzips, untars, and compiles the BUFR library file which is downloaded from ECMWF and placed into this directory. This script is part of the genscat make system and it is automatically invoked when compiling genscat. The current version is tested with BUFR version 000400, earlier versions between 000240 and 000387 can also be used. Note that library versions 000388 and 000389 are not supported.
BUFR file handling at the lowest level is difficult to achieve. Therefore some routines were coded
in file bufrio.c in subdirectory genscat/support/bufr. Compilation is done within the
genscat make system and requires no further action from the user (see 2.3.4).
2.3.3 Installing the GRIB API library
AWDP needs the ECMWF GRIB API library for its input operations. Only ECMWF is allowed to distribute this software. It can be obtained free of charge from ECMWF at the web page
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. The package contains scripts for compilation and installation. The reader is referred to this site for assistance in downloading and installing the GRIB API Library.
Directory genscat/support/grib contains the shell script make.grib.lib. It unzips, untars, and compiles the GRIB API library file which is downloaded from ECMWF and placed into this directory. This script is part of the genscat make system and it is automatically invoked when compiling genscat. The current version is tested with GRIB API version 1.11.0, but later versions (or earlier, but not earlier than 1.9.0) can be used. However, AWDP is not tested with later versions.
By default, the library for handling GRIB messages that are compressed using the JPEG algorithm, is not linked in the compilation process. This option can be activated by adding the link option
-lopenjpeg
to the Makeoptions file in directory genscat:
LINKFLAGS = $(LIB) –lopenjpeg
After this change, the software in directories genscat/support/grib and awdp/src needs to be recompiled using the commands make clean and make.
2.3.4 Manual compilation and linking
Compilation and linking of AWDP under Linux or Unix is done in three steps:
1. Set the compiler environment variables according to the choice entered on request. This can be done by running the appropriate use_* scripts in directory genscat.
2. Go to directory genscat and type make.
3. Go to directory awdp and type make to produce the executable awdp in directory awdp/src
.
Before activating the make system, some environment variables identifying the compiler should be set. These variables are listed in table 2.4. The environment variables in table 2.4 can be set by using one of the use_* scripts located in directory genscat. Table 2.5 shows the properties of these scripts. The scripts are available in Bourne shell (extension .bsh) and in C shell (extension
.csh
). Note that if one of the environment variables is not set, the default f90 and cc commands on the Unix platform will be invoked. Note that in the top directory a script called InstallAWDP is provided that asks the user which compiler he wants to use and invokes the appropriate use_* script (step 1 above), after which the compilation in the genscat and awdp directories is performed (steps 2 and 3 above).
Variable Function
$GENSCAT_F77
$GENSCAT_F90
$GENSCAT_CC
$GENSCAT_LINK
Reference to Fortran 77 compiler
Reference to Fortran 90 compiler
Reference to C compiler
Reference to linker for Fortran objects
$GENSCAT_CLINK
Reference to linker for C objects
$GENSCAT_SHLINK
Reference to linker for shared objects
Table 2.4 Environment variables for compilation and linking.
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Remarks compiler compiler
use_gfortran gfortran gcc
GNU-GCC 4.0 compiler collection use_ifort ifort icc
Intel Fortran and C compilers use_pgf90 pgf90 gcc
Portland Fortran compiler
Table 2.5 Properties of the use_* scripts.
Example: To select the GNU g95 compiler under Bourne, Bash or Korn shell type
“. use_g95.bsh”, the dot being absolutely necessary in order to apply the compiler selection to the current shell. Under C shell the equivalent command reads “source use_g95.csh”.
If the user wants to use a Fortran or C compiler not included in table 2.6, he can make his own version of the use_* script, or set the environment variables for compilation and linking manually.
AWDP is delivered with a complete make system for compilation and linking under Unix or
Linux. The make system is designed as portable as possible, and system dependent features are avoided. As a consequence, some tasks must be transferred to shell scripts. The make system consists of two parts: one for AWDP and one for genscat. The genscat part should be run first. For compilation and linking of the genscat part, the user should move to the genscat directory and simply enter make.
The Makefile refers to each subdirectory of genscat, invoking execution of the local
Makefile
and, in cases where a subdirectory contains code as well as a subdirectory containing code, Makefile_thisdir. The Makefiles need supplementary information from the files
Objects.txt
which are present in each directory containing code. The settings for the compilers are located in file Makeoptions in directory genscat. This file is generated by the
Bourne shell script Set_Makeoptions which is called automatically by the genscat make system. The local Makefile in subdirectory genscat/support/bufr calls the script make.bufr.lib
for compilation of the BUFR library (see 2.3.2). It also contains the Fortran
program test_modules that generates the binary BUFR tables B and D from the ASCII tables already present, and is executed automatically by the make system. Program test_modules can also be used to test the genscat BUFR module. The Makefile in subdirectory genscat/support/bufr/bufr_tables
calls some shell scripts, which make symbolic links (using the ln -s command) of the generic binary BUFR tables B and D under different names. There are four different naming conventions in BUFR version 000240 to 000280, and binary files are generated for each of them. Further information on the make system is given in the inline comments in the scripts and Makefiles.
Compilation and linking of the AWDP part is done in a similar manner: go to the awdp directory and enter make. As with genscat, the make system will execute Makefiles in every subdirectory of awdp
. The result is the executable awdp in directory awdp/src and a symbolic link to this executable in awdp/execs. AWDP is now ready for use. The make system of AWDP doesn’t need any further files except the genscat file Makeoptions. This is the reason why genscat should be compiled first.
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When recompiling (part of) AWDP or genscat with the make system, for instance when installing a new version of the BUFR library, one should be sure to enter make clean first. To recompile part of the software invoke the make system where needed. The compiler settings from file
Makeoptions
in directory genscat will be used again. If a change in these settings is necessary, type make clean in the genscat directory and Makeoptions will be removed.
Don’t forget to rerun the use_* commands to select the right compiler.
2.3.5 Environment variables
AWDP needs a number of environment variables to be set. These are listed in table 2.3 together with their possible values.
Name Value
$BUFR_TABLES genscat/support/bufr/bufr_tables/
$GRIB_DEFINITION_PATH genscat/support/grib/definitions
$INVERSION_LUTSDIR genscat/inversion
$LUT_FILENAME_C_VV
Any existing, writable path + file name
Table 2.3 Environment variables for AWDP.
The $BUFR_TABLES variable guides AWDP to the BUFR tables needed to read the input and write the output. The $GRIB_DEFINITION_PATH variable is necessary for a proper functioning of the GRIB decoding software.
The variable $INVERSION_LUTSDIR should point to a directory containing some look up tables
(extension .asc) that are used by the inversion software. The necessary tables are delivered with genscat.
The variable $LUT_FILENAME_C_VV points AWDP to the correct C band GMF lookup table at
VV polarisation. It should contain a file name including a valid path. If the file does not exist, it will be created when the inversion is invoked for the first WVC. In order to prevent confusion, it is advised to use standard file names <path>/cmod5.dat, <path>/cmod5_5.dat,
<path>/cmod5_n.dat
, or <path>/cmod6.dat, since the inversion software uses the file name to determine which CMOD version is used.
2.4 Command line options
The AWDP program is started from directory awdp/execs with the command
awdp [options/modes] -f <BUFR/PFS file> [-nwpfl <file>]
with <> indicating obligatory input, and [] indicating non-obligatory input. The following command line options are available.
-f <input file>
Process a BUFR or PFS input file with name input file.
AWDP detects if the input file is in BUFR format. If not, it attempts to read the input as PFS file. The BUFR input file should either have the
ASCAT or the ERS format. The PFS file should contain 25 or 12.5 km level 1b data, not full resolution level 1b data.
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Example: awdp -f ascat_20070426_test_250.l1_bufr will process this file. The results will be written to a new BUFR file, see below in this section for the output file naming convention. It is possible to concatenate multiple BUFR input files into one using the Unix cat command, but PFS files must be processed one by one.
-nwpfl <file>
Read a list of GRIB file names in the file named file.
The files in the list are read and the GRIB edition 1 or 2 data are used in the wind processing. The most convenient way to construct a file list is to use the Unix command ls -1 GRIB file pattern > file. If no
GRIB data are used, only the land masking which is present in the level 1b beam information will be used. No ice screening will be performed (unless the -icemodel option is used). Ambiguity removal will be performed only if model winds are already present in the input BUFR file (i.e., in case of reprocessing of a level 2 file) or if the -armeth 1strank option is used (i.e., selection of the 1 st
rank wind solution). If level 2 data are reprocessed and no NWP data are read, the qual_sigma0 flag which was set in the initial processing is evaluated and it will be used to determine if a WVC contains suitable backscatter data for wind inversion.
Several options for the processing can be invoked.
-szffl <file>
Read a list of full resolution PFS file names in the file named file.
The files in the list are read and the full resolution PFS data (SZF) are used to replace the 25-km/12.5-km beam data. This option is intended to produce a coastal wind product. The beam data (σ
0
values, incidence and azimuth angles) which are read from the BUFR or PFS level 1b input file, are replaced by average values of the data from the full resolution file which are located within a certain radius (typically 10 to 20 kilometers)
from the WVC location. See section 3.5.3 for more information.
-stressparam
Get stress-related parameters derived from GRIB files.
This option is intended for research activities. More information can be found in the Fortran code of AWDP.
-noinv
Switch off inversion (default is switched on).
-icemodel <IM>
Choose ice screening method to be used: 0 (default), 1 or 2.
The value 0 results in no ice screening, except when a GRIB file containing sea surface temperature is read. The value 1 invokes a simple
(non-Bayesian) ice model which does not keep history of the water or ice state of each location. Value 2 invokes the Bayesian ice model which keeps the history of each location and uses this history to determine the sea or ice state of a WVC. The ice screening can be combined with the ice screening which is done in the GRIB collocation. In this case, the SST of the GRIB file will be used to assign a WVC as ‘surely water’ when the
SST is above a certain value.
-noamb
Switch off ambiguity removal (default is switched on).
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This option is useful if the selection of the scatterometer wind solution is left to the data assimilation procedure of a Numerical Weather Prediction model. In other words: the NWP model is fed with a number of solutions and their probability, and finds the best value when comparing with other data sources.
-nowrite
Do not produce BUFR output (default is switched on).
-ignorel1flags
Ignore the setting of level 1b σ
0
related flags in BUFR input.
If this option is switched on, the value of the flags and quality indicators in the beam information, including the sigma0 usability and land fraction, is neglected.
-cmod <N>
Choose CMOD version 4, 5, 55, 5n (default) or 6.
With this option, the user can choose between GMF version CMOD4,
CMOD5, CMOD5.5 (CMOD5 + 0.5 m/s), CMOD5n (CMOD5 for neutral winds) or CMOD6. The GMF table is generated by the program and written to a binary file named c-vv2.dat in the current directory, if it does not yet exist. Alternatively, the user may specify a file name
(including path) in the environment variable $LUT_FILENAME_C_VV. If
$LUT_FILENAME_C_VV
is present, it will be used to store the GMF. A second file with the same name and extension .zspace is also generated.
Note: the old GMF files need to be removed if new files need to be generated, i.e., if a different GMF version is requested.
-calval
Perform
A calibration of the σ
0
. values is performed. See [Verspeek,, Stoffelen,
Verhoef and Portablla, 2012] for more details. Calibration coefficients are applied dependent on the satellite (Metop A or B) and the level 1 data processing version.
-mss
Use the Multiple Solution Scheme for Ambiguity Removal.
If the Multiple Solution Scheme (MSS) is switched on, AWDP internally works with 144 different solutions for the wind vector. If MSS is switched off, AWDP calculates two solutions at most. MSS is switched off as default.
-armeth <meth>
Choose ambiguity removal method.
Valid methods are: 1strank - the wind solution with the lowest distance to the GMF (residual) is selected, bgclosest - the wind solution closest
to the background model wind is selected, prescat - see section 6.5,
2dvar
- 2DVAR, see section 6.4. The default is 2dvar.
-par, -ana, -tc, -varqc, -ocf, -research, -orpm
Various options intended for research activities.
More information can be found in the Fortran code of AWDP and genscat.
-binof <file>
Write selected data of each WVC to a binary output file.
Data are written to a binary file <file>. This option is intended for research activities. More information on the file format can be found in the
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-writeall
-handleall
Fortran code of AWDP.
Write all data to BUFR output, including level 2 input data.
In the normal near-real time processing, a mixture of level 1b and (recent) level 2 data is fed into AWDP in order to provide more data, which is beneficial for ambiguity removal. Only those data rows which were level 1b input, are written to the level 2 output file. This option overrides this behaviour and writes all rows to the output file.
Perform NWP collocation, inversion, ambiguity removal and output writing in all WVCs.
By default, these steps are done only for WVCs which are level 1b input, see the description at the –writeall option. This option is useful for reprocessing level 2 data.
-nws <N>
-subc <SC>
Write N wind solutions in BUFR output (default 4).
The number of wind solutions to be written into the ASCAT BUFR format is flexible due to the use of the so-called delayed replication and can be chosen between 1 (providing only the selected wind solution) and 144
(providing all wind solutions in MSS processing).
Set id of sub-centre in each WVC of the BUFR output to SC.
By default it is copied from input.
-mon
Switch on the monitoring function.
The monitoring results are written in an ASCII file with the name monitoring_report.txt
. By default, no monitoring file is produced.
-verbosity <L>
Set the verbosity level to L (default is 0).
If the verbosity level is -1 or smaller, no output is written to the standard output except error messages. If the verbosity level equals 0 only some top level processing information is written to output. If the verbosity level is 1 or greater, also additional information is given.
The normal mode of operation of AWDP is wind processing, i.e., a BUFR or PFS file is read and the various processing steps are performed. Note that by default, AWDP does not recalculate data that are already present in the input. For example, if a WVC already contains model winds then the
GRIB collocation will not be done for this WVC; if a WVC already contains wind solutions then the wind inversion will not be performed. This behaviour is desired when near-real time processing is performed and a mixture of level 1b and level 2 files is fed into AWDP. If one wants to perform reprocessing of level 2 files, the behaviour of AWDP can be changed by the command line options, e.g. the -handleall option.
Besides the wind processing, some other modes of operation are available. If one of the modes is invoked, AWDP internally sets some of the options in order to obtain the desired result. Note that these modes are always used in combination with the -f <input file> option.
-mononly
Write the monitoring file without any processing.
-properties
Write some properties of the last row of the input file.
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The acquisition date and time and the sub-centre id are written to a small
ASCII output file properties.txt.
-writeonly
Write all data to BUFR output without processing.
This mode is useful to copy an input file to BUFR output without processing.
Running the command awdp without any command line options will display a list of all available command line options with a short explanation on the console. Running the command awdp with an illegal option will produce the same output, but preceded by an error message.
The output will be written into a BUFR file with the name
INSTR_YYYYMMDD_HHMMSS_SAT_ORBIT_srv_o_SMPL(_CONT).l2_bufr
, where
• INSTR is the instrument, ascat or scatt.
• YYYYMMDD_HHMMSS is the acquisition date and time (UTC) of the first data in the file.
• SAT is the satellite (6 characters), ers1__, ers2__, metopa or metopb.
• ORBIT is the orbit number (5 digits) of the first data in the file, 00000 for ERS data.
• SMPL is the WVC sampling (cell spacing), 250 for 25 km and 125 for 12.5 km.
• _CONT (contents) is omitted if the data contains both wind and soil moisture data. Otherwise it is set to _ovw (Ocean Vector Winds) or _ssm.
Example: ascat_20070426_095102_metopa_02681_srv_o_250_ovw.l2_bufr
2.5 Scripts
Directory awdp/execs contains a Bourne shell script awdp_run for running awdp with the correct environment variables. The script can be invoked with all valid command line options for awdp. In the same directory, there is also a script awdp_gui.py available. This script provides a convenient graphical user interface and builds and runs an AWDP command line depending on settings of available radio buttons, check boxes et cetera. This script requires Python to be installed on your system. It may be necessary to change some of the environment variables set in the top part of the script.
2.6 Test data and test programs
Directory awdp/tests contains two BUFR files for testing the AWDP executable.. File ascat_20070426_test_250.l1_bufr
contains ASCAT level 1b data from 26 April 2007,
9:51 to 10:29 UTC with 25 km cell spacing. The same data, but on 12.5 km cell spacing is available in file ascat_20070426_test_125.l1_bufr. The files ECMWF*.grib contain the necessary NWP data (SST, land-sea mask and wind forecasts) to perform the NWP collocation step.
The user can test the proper functioning of AWDP using the files in the awdp/tests directory.
To do this, first create a small file containing a list of NWP files:
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ls -1 ECMWF_200704260000_0* > nwpflist
Then run AWDP on 25-km and 12.5-km cell spacing:
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../execs/awdp_run -f ascat_20070426_test_250.l1_bufr -mon –calval
-nwpfl nwpflist
../execs/awdp_run -f ascat_20070426_test_125.l1_bufr –mon -calval
-nwpfl nwpflist
The result should be two ASCAT level 2 files in BUFR format, called ascat_20070426_095102_metopa_02681_srv_o_250_ovw.l2_bufr and ascat_20070426_095100_metopa_02681_srv_o_125_ovw.l2_bufr
, respectively.
Figure 2.3 shows the global coverage of the test run on 25 km. The colours indicate the magnitude of the wind speed as indicated by the legend. The result on 12.5 km should be very similar to this.
Directory awdp/tests also contains an ERS BUFR file in ESA format, called scatt_20070426_test_250.l1
_bufr in ESA BUFR format. The data are from the same date as the ASCAT data in this directory and they can be processed using the same ECMWF files:
../execs/awdp_run -f scatt_20070426_test_250.l1_bufr – mon -nwpfl nwpflist
The result should be an output file in ASCAT BUFR format, called scatt_20070426_063627_ers2___00000_srv_o_250_ovw.l2_bufr
Figure 2.3 Global coverage of the test run. Wind speed results for the 25 km product are shown.
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Directory genscat/support/bufr contains a test program named test_modules. It is invoked by the genscat make system to construct the BUFR tables required by AWDP, but it can also be used to test the genscat BUFR module. The program is used as follows:
test_modules [BUFRinput]
where BUFRinput is the BUFR input file.
If omitted, the program uses as default input the file testreading.bufr in directory genscat/support/bufr
. The output is written to a BUFR file named testwriting.bufr
. The directory also contains a shell script named run_test_modules that sets the environment variables required and executes the program. Further information can be found in the comment lines of the source code of test_modules.
Directory genscat/support/grib contains test programs named test_read_GRIB1, test_read_GRIB2
and test_read_GRIB3. The programs can be run from the command line and read in the GRIB file testfile.grib in directory genscat/support/grib.
Some properties of this file are written to ASCII output files. Note that the environment variable
$GRIB_DEFINITION_PATH
needs to be set to directory
(…)/genscat/support/grib/definitions
.
Subdirectories Compiler_Features, convert, ErrorHandler, singletonfft, file,
BFGS
, num, sort and datetime of genscat/support contain test programs for the module in that subdirectory. The test programs write their result to the standard output. In some cases, a copy of the output is contained in the .output files for comparison. Table 2.6 gives an overview of the genscat test programs.
Subdirectory Program name Output file Remarks
Part of make system grib test_read_GRIB* handling
Compiler_Features convert
TestCompiler_Features test_convert
- test_convert.output
ErrorHandler TestErrorHandler - singletonfft TestSingleton -
Command line handling
Wind speed conversion
Fast Fourier Transform
BFGS Test_BFGS - Minimization datetime TestDateTimeMod TestDateTimeMod.output Date and time conversion
Table 2.6 Test programs in genscat/support.
2.7 Documentation
Directory awdp/doc contains documentation on AWDP, including this document. Further information can be found in the readme text files, and in the comments in scripts, Makefiles and source code.
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Chapter 3
AWDP product specification
3.1 Purpose of program AWDP
The ASCAT Wind Data Processor (AWDP) program has been developed to fully exploit σ
0
data from the ASCAT scatterometer instrument on the Metop satellites or the AMI scatterometer instrument on the European Remote Sensing (ERS) satellites, to generate surface winds. AWDP may be used for real-time data processing. The main application of AWDP is to form the core of an Observation Operator for ASCAT scatterometer data within an operational Numerical Weather
Prediction System.
Program AWDP is also a level 2 data processor. It reads data from the EUMETSAT level 1b
ASCAT BUFR or PFS product or from the ESA ERS scatterometer BUFR product. AWDP applies algorithms for inversion, quality control, and Ambiguity Removal at various spatial resolutions. These methods are mainly developed and published by KNMI. The output of AWDP is a BUFR file in ASCAT BUFR format.
3.2 Output specification
The wind vectors generated by AWDP represent the instantaneous mean surface wind at 10 m anemometer height in a 2D array of Wind Vector Cells (WVCs) with specified size (25
× 25 km 2 or 12.5
× 12.5 km 2
, depending on the cell spacing of the input product). These WVCs are part of the ground swath of the instrument.
In conventional mode, the wind output for every WVC consists of up to 4 ambiguities (wind vector alternatives, with varying probabilities). The selected wind vector is indicated by a selection index. For every WVC additional parameters are stored. These are e.g.: latitude, longitude, time information, orbit and node numbers, background wind vector, cell quality flag, and information on the scatterometer beams including σ
0
and K
p
data. The output file is structured according to the same conventions as the ASCAT level 1b input, also if ERS data are processed.
The ASCAT BUFR format consist of three main sections: one section containing level 1b information which is copied from the input data, one section containing Surface Soil Moisture
(SSM) level 2 information, which is also copied from the input, and one section containing level 2 wind data, which is calculated in AWDP. The ASCAT BUFR data descriptors are listed in
Appendix C.
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Input of AWDP is the ASCAT level 1b BUFR or PFS Data Product. These products are created by
EUMETSAT; see [WMO, 2007] and [Figa-Saldaña and Wilson, 2005]. Alternatively, the ERS scatterometer wind product in BUFR can be used as input; see [UK Met Office, 2001].
It is also possible to reprocess level 2 ASCAT or ERS data in ASCAT BUFR format, and treat them as if they are level 1b data. To achieve this, some command line options need to be set; see
Apart from the scatterometer data, GRIB files containing NWP output with global coverage are necessary for the wind processing. At least three wind forecasts with forecast time intervals of 3 hours are necessary to perform interpolation with respect to time and location. Apart from this,
GRIB fields of Sea Surface Temperature and Land Sea Mask are necessary for land and ice masking.
3.4 System requirements
Table 3.1 shows the platform and compiler combinations for which AWDP has been tested.
However, the program is designed to run on any Unix (Linux) based computer platform with a
Fortran compiler and a C compiler. The equivalent of a modern personal computer will suffice to provide a timely NRT wind product. AWDP requires about 100-150 MB disk space when installed and compiled.
Platform
Fedora Linux work station
SunOS Unix
SGI Altix
Fortran compiler
Portland pgf90
GNU g95
GNU gfortran
Sun Fortran
Intel Fortran compiler
C compiler
GNU gcc
Sun C
Intel C compiler
Table 3.1 Platform and compiler combinations for which AWDP has been tested.
AWDP may also run in other environments, provided that the environment variables discussed in section 2.2 are set to the proper values, and that the BUFR library is properly installed. For
Windows a Linux environment like Wubi is needed.
3.5 Details of functionality
3.5.1 BUFR IO and coding
Data sets of near-real time meteorological observations are generally coded in the Binary
Universal Form for Representation (BUFR). BUFR is a machine independent data representation system (but it contains binary data, so care must be taken in reading and writing these data under different operating systems). A BUFR message (record) contains observational data of any sort in a self-descriptive manner. The description includes the parameter identification and its unit, decimal, and scaling specifications. The actual data are in binary code. The meta data are stored in
BUFR tables. These tables are therefore essential to decode and encode the data.
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BUFR tables are issued by the various meteorological centres. The largest part of the data descriptors specified in the BUFR tables follows the official BUFR descriptor standards maintained by the World Meteorological Organization (WMO, http://www.wmo.int/ ). However, for their different observational products meteorological centres do locally introduce additional descriptors in their BUFR tables.
Appendix C contains a listing of the data descriptors of the BUFR data input and the BUFR data output of the AWDP program in the ASCAT BUFR product format. For more details on BUFR, the reader is referred to [Dragosavac, 1994].
ECMWF maintains a library of routines for reading (writing) and decoding (encoding) the binary
BUFR messages. This library forms the basis of the genscat BUFR module and hence the AWDP
program BUFR interface, see Chapter 8.
3.5.2 Product resolution
An important feature of the AWDP program is that it may produce a level 2 wind product on different resolutions. The resolution of the level 2 wind product is the same as that of the level 1b input product. ASCAT data are available in two different resolutions: 50 km resolution with 25 km cell spacing (also known as the ASCAT operational product, SZO) and 25 km resolution with
12.5 km cell spacing (also known as the ASCAT research product, SZR).
3.5.3 Use of full resolution data
AWDP offers the possibility to replace the backscatter values in the level 1b product by box averaged σ
0
data that are acquired using the full resolution ASCAT level 1 data (SZF). The beam data (σ
0
values, incidence and azimuth angles) which are read from the BUFR or PFS level 1b input file, are replaced by average values of the data from the full resolution file which are located within a certain radius (typically 10 to 20 kilometers) from the WVC location. In this way, a coastal product can be created. See [Verhoef et. al., 2012] for more information on how the box averaged σ
0
data are composed. It is important to notice that the full resolution data which are fed into AWDP (using the –szffl option) must span a time starting at least 150 seconds before the first data in the level 1b WVCs and ending no less than 150 seconds after the last data in the level 1b WVCs.
AWDP needs a high-resolution land-sea mask in order to determine if a full resolution backscatter measurement is over land or sea. This information is used in coastal areas to skip backscatter data over land and to use only backscatter data over sea in the averaging of full resolution data. The high resolution land-sea mask should be available in GRIB format and should have a resolution of approximately 15 km or better. The file containing the land-sea mask should be present in the list
of GRIB files supplied with the –nwpfl command line option (see section 2.4).
3.5.4 WVC triplet completion and row merging
AWDP sorts the WVC rows in the input file by their acquisition date and time and merges WVC information if duplicate rows occur. The duplicate information is considered and the output will contain as much useful information as is available in the input WVCs. This is especially useful if direct readout data from different ground stations is processed. Sometimes a WVC from one
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3.5.5 Quality control
The quality of every WVC is controlled. Before processing the beam data, checks are done on the completeness and usability of the σ
0
data. After the wind inversion step, the distance of the first rank wind solutions to the GMF is considered. If this value is too large, the wind solutions are flagged. The K
p
values are also considered. If one of the three beam K
p
values is above a threshold which is wind speed dependent, the wind information is flagged [Stoffelen, 1998].
3.5.6 Inversion
In the inversion step of wind retrieval, the radar backscatter observations in terms of the
Normalized Radar Cross Sections (σ
0
’s) are converted into a set of ambiguous wind vector solutions. In fact, a Geophysical Model Function (GMF) is used to map a wind vector (specified in term of wind speed and wind direction) to a σ
0
value. The GMF depends not only on wind speed and wind direction but also on the measurement geometry (relative azimuth and incidence angle) and beam parameters (frequency and polarization). Currently, the CMOD5 GMF which was developed for ERS is in use, see [Hersbach, Stoffelen and de Haan, 2007], but improvements are under study.
The AWDP program also includes the Multiple Solution Scheme (MSS). In MSS mode, a large number of wind vector solutions is produced, typically 144. The wind vector solutions are ranked according to their probability based on the MLE and constitute the full wind vector probability
density function. Subsequently, the 2DVAR Ambiguity Removal method, see, e.g., section 3.5.8,
is applied with a much larger set of wind vector solutions. The output BUFR format can accommodate any number of wind solutions due to the use of the so-called delayed descriptor replication. Details on the KNMI inversion approach can be found in [Portabella, 2002]. For
SeaWinds, MSS compares better to an independent NWP model reference and buoys than conventional two or four-solution schemes [Portabella and Stoffelen, 2004; Vogelzang et al.,
2008], but for ERS and ASCAT this needs to be investigated further.
Technical information on the KNMI inversion approach can be found in Chapter 5.
3.5.7 Ice screening
A Bayesian sea ice detection algorithm was developed for ASCAT [Belmonte et al., 2012] and this algorithm is implemented in AWDP. It is based on the probabilistic distances to ocean and sea ice geophysical model functions. When a combination of backscatter measurements is close to the wind GMF, the probability that the WVC is covered with water is high. On the other hand, when the measurement is close to the sea ice GMF, the probability that the WVC contains ice is high.
Each satellite overpass over the poles will yield new measurements which contribute to an ice map containing the ice probabilities.
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3.5.8 Ambiguity Removal
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The Ambiguity Removal (AR) step of the wind retrieval is the selection of the most probable surface wind vector among the available wind vector solutions, the so-called ambiguities. Various methods have been developed for AR. More information on Ambiguity Removal is given in
Chapter 6. The default method implemented in AWDP is the KNMI 2DVAR AR scheme. A
description of its implementation can be found in section 6.4. The Multiple Solution Scheme
(MSS) offers the possibility to postpone AR to the NWP step in order to treat all information from models and measurements in the same manner. Further details on the algorithms and their validation can be found in the reports [de Vries and Stoffelen, 2000; de Vries, Stoffelen and
Beysens, 2005].
The performance of 2DVAR with meteorological balance constraints was tested and optimized for
ERS data. It was found to be superior to other schemes.
3.5.9 Monitoring
For the automatic ingestion of observations into their NWP systems, meteorological centres require quality checks on the NRT products. For the ASCAT wind product a monitor flag is under development, analogous to the one developed for the SeaWinds Wind Product. This flag indicates that several measures on the level of corruption of the output BUFR files are above a specified threshold. Onset of the flag indicates that the input should be rejected for ingestion by the NWP system. Details on the monitor developed can be found in the NWP SAF document [de Vries,
Stoffelen and Beysens, 2005].
3.6 Details of performance
AWDP is delivered with two example BUFR input files containing data from 26 April 2007. They ascat_20070426_test_125.l1_bufr
(12.5 km cell spacing) and contain approximately half an orbit of data. Moreover, a set of ECMWF GRIB files containing the necessary NWP output is supplied. Table 3.2 gives the approximate times needed for processing these files under various options on a personal workstation with a 2.66 GHz Pentium 4 processor under Linux using the
GNU g95 Fortran compiler.
Cell spacing (m) MSS? Inversion AR BUFR IO GRIB IO Total
(seconds) (seconds) (seconds) (seconds) (seconds)
25000 No 33 8 3 1 46
12500 No 3 160
12500 Yes 8 3 263
Table 3.2 Approximate times needed by AWDP to process example BUFR files under various input resolutions and options.
As can be seen from table 3.2, the use of MSS results in slightly larger processing times needed for inversion and in much larger processing times needed for AR. The computation time, of course, increases with increasing resolution.
The choice of platform, compiler and compiler settings will result in a large variation in the
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Chapter 4
In this chapter, the design of the AWDP program is described in detail. Readers to whom only a summary will suffice are referred to the Top Level Design (TLD) in section 4.1. Readers who really want to know the very detail should not only read the complete chapter, but also the documentation within the code.
4.1 Top Level Design
4.1.1 Main program
The main program, AWDP, (file awdp in the awdp/src directory) is a Unix (Linux) executable which processes ASCAT BUFR or PFS or ERS BUFR input files. The main output consists of
BUFR files. The output BUFR messages are always in the ASCAT BUFR format, for a list of descriptors see appendix C. The user may provide arguments and parameters according to Unix command line standards. The purpose of the different options is described in the User Manual
When executed, the AWDP program logs information on the standard output. The detail of this information may be set with the verbosity flag. The baseline of processing is described in Figure
4.1, but note that not all of these steps are always invoked. Some of them will be skipped, depending on the command line options. A more detailed representation of the AWDP structure is given in Appendices A and B.
The first step is to process the arguments given at the command line using the genscat
Compiler_Features module. Next, the AWDP program reads the input file specified in the arguments. The BUFR messages or PFS records are read and mapped onto the AWDP data
structure, see subsection 4.1.3. As part of the pre-processing a similar AWDP data structure is
created for the output. Subsequently, the input data are sorted with respect to data acquisition time, duplicate rows are merged and the output data structure is filled with level 1b (σ
0
related) data.
Then, the NWP GRIB data (wind forecasts, land-sea mask and sea surface temperature) are read and the data are collocated with the Wind Vector Cells. The next steps are the inversion and the ambiguity removal. These steps are performed on the output data. The program ends with the postprocessing step (which includes some conversions and the monitoring) and the mapping of the output data structure onto BUFR messages of the BUFR output file. The different stages in the processing correspond directly to specific modules of the code. These modules form the process
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Process arguments
Read input data
Pre-processing
Read full resolution data
Read/collocate GRIB data
Inversion
Ice screening
Ambiguity Removal
Post-processing
Write output BUFR message
Figure 4.1 Baseline of the ASCAT Wind Data Processor
4.1.2 Layered model structure
AWDP is a Fortran 90 program consisting of several Fortran 90 modules which are linked after their individual compilation. The AWPD program is set up from two layers of software modules.
The purpose of the layer structure is to divide the code into generic scatterometer processing software and ASCAT specific software. Details on the individual modules can be found in sections
The first layer (the process layer) consists of modules which serve the main steps of the process.
Module name
Τasks Comments
awdp_data awdp_bufr awdp_pfs awdp_prepost awdp_grib
Definition of data structures
BUFR file handling
PFS file handling
Sorting of input
Quality control
Post processing
Monitoring
Clean up
GRIB file handling
Collocation of GRIB data
Interface to genscat/support/bufr
Interface to genscat/support/pfs
Duplicate rows are merged
Usability of input data is determined
Setting of flags
Deallocation of used memory
Interface to genscat/support/grib
NWP data are interpolated w.r.t. time and location
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Module name
Τasks Comments
awdp_inversion
genscat/inversion
awdp_ambrem
Ambiguity Removal
awdp_icemodel
Ice screening
Interface to genscat/ambrem
Interface to genscat/icemodel
Table 4.1 AWDP process modules.
Each module contains code for performing one or more of the specific tasks. These tasks are
briefly described in table 4.1. A more elaborate description is given in section 4.3. The first
module listed, awdp_data is a general support module. This module is used by the other modules of the process layer for the inclusion of definitions of the data structures and the support routines.
The second module layer is the genscat layer. The genscat module classes (i.e., groups of modules) used in the AWDP program are listed in table 4.2. The genscat package is a set of generic modules which can be used to assemble processors as well as pre, and post-processing tools for different scatterometer instruments available to the user community. A short description of the main
(interface) modules is given in section 4.2. The most important classes of modules are related to
the inversion processing step (Chapter 5), the Ambiguity Removal step (Chapter 6), the BUFR file
handling (Chapter 8), and the GRIB file handling (Chapter 9). The genscat modules are located in
subdirectory genscat.
In addition, genscat contains a large support class to convert and transform meteorological, geographical, and time data, to handle file access and error messages, sorting, and to perform more complex numerical calculations on minimization and Fourier transformation. Many routines are co-developed for ERS, ASCAT and SeaWinds data processing.
Module class Tasks
Ambrem
Ambiguity Removal
Description
2DVAR and other schemes, see Chapter 6
Inversion
IceModel
Support
Wind retrieval
Ice screening
BUFR support
PFS support
Inversion in one cell, see Chapter 5
Uses ice line and wind cone for ice discrimination
BufrMod, based on ECMWF library
Reading of PFS files
GRIB support
FFT, minimization
Error handling
File handling
gribio_module, based on ECMWF library
Support for 2DVAR
Print error messages
Finding, opening and closing free file units
Conversion
Sorting
Date and time
Conversion of meteorological quantities
Sorting of ambiguities to their probability
General purpose
Table 4.2 genscat module classes.
4.1.3 Data Structure
Along track, the ASCAT swath is divided into rows. Within a row (across track), the ASCAT orbit is divided into cells, also called Wind Vector Cells (WVCs) or nodes. This division in rows and cells forms the basis of the main data structures within the AWDP package. In fact, both the input and the output structure are one dimensional arrays of the row data structure, row_type. These arrays represent just a part of the swath. Reading and writing (decoding and encoding) ASCAT
BUFR files corresponds to the mapping of a BUFR message to an instance of the row_type and
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The main constituent of the row_type is the cell data structure, cell_type, see figure 4.2. Since most of the processing is done on a cell-by-cell basis the cell_type is the pivot data structure of the processor. row_type cell_type beam_type ambiguity_type
Figure 4.2 Schematic representation of the nested data definitions in the row_type data structure.
The σ
0
related level 1b data of a cell are stored in a data structure called beam_type. Every cell contains three instances of the beam_type, corresponding to the fore, middle and aft beams.
A cell may also contain an array of instances of the ambiguity_type data structure. This array stores the results of a successful wind retrieval step, the wind ambiguities (level 2 data). Details of all the data structures and methods working on them are described in the next sections.
4.1.4 Quality flagging and error handling
Important aspects of the data processing are to check the validity of the data and to check the data quality. In the AWDP program two flags are set for every WVC, see table 4.3. The flags themselves do not address a single aspect of the data, but the flags are composed of several bits each addressing a specific aspect of the data. A bit is set to 0 (1) in case the data is valid (not valid) with respect to the corresponding aspect. In order to enhance the readability of the code, each flag is translated to a data type consisting of only booleans (false = valid, true = invalid). On input and output these data types are converted to integer values by set and get routines.
Flag Tasks Description
wvc_quality Quality checking In BUFR output process_flag Range checking Not in BUFR output
Table 4.3 Flags for every WVC (attributes of cell_type).
Apart from the flags on WVC level, also the beams contain quality indicators. Most of them are implemented as real values ranging from 0 to 1, where 0 stands for good quality and 1 for
degraded quality. See section 4.3.1 for more information on this.
4.1.5 Verbosity
Every routine in a module may produce some data and statements for the log of the processor. To
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-verbosity
. In general, there are three levels of verbosity specified:
≤ -1: be as quiet as possible;
0: only report top level processing information;
≥ 1: report additional information.
Of course, errors are logged in any case. Table 4.4 gives a (incomplete) list of verbosity parameters. They are not all set by the command line option as some of them serve testing and debugging purposes.
Ambrem2Dvar TDVverbosity
AmbremBGclosest BGverbosity
BatchMod BatchVerbosity
Ambrem AmbremVerbosity awdp_bufr BufrVerbosity awdp_grib GribVerbosity awdp_icemodel dbgLevel
Table 4.4 Verbosity parameters.
4.2 Module design for genscat layer
4.2.1 Module inversion
The module inversion contains the genscat inversion code. Module post-inversion contains some routines specific for ERS and ASCAT inversion and quality control. The modules are located in
subdirectory genscat/inversion.. Details of this module are described in Chapter 5. In the
AWDP program, the inversion module is only used in the awdp_inversion module, see section
4.2.2 Module ambrem
The module ambrem is the main module of the genscat Ambiguity Removal code. It is located in
subdirectory genscat/ambrem. Details of this module are described in Chapter 6. In the
AWDP program, the ambrem module is only used in the awdp_ambrem module, see section 4.3.7.
4.2.3 Module icemodel
The module icemodel contains the genscat ice screening code. It is located in subdirectory genscat/icemodel
. In the AWDP program, the icemodel module is only used in the
awdp_icemodel module, see section 4.3.8.
4.2.4 Module Bufrmod
Genscat contains several support modules. In particular, the BufrMod module is the Fortran 90
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. Details of this module are described in Chapter 8. In the AWDP
program, the BufrMod module is only used in the awdp_bufr module, see subsection 4.3.2.
4.2.5 Module gribio_module
The gribio_module module is the Fortran 90 wrapper around the GRIB library used for GRIB input and collocation of the NWP data with the scatterometer data. It is located in subdirectory genscat/support/grib
. Details of this module are described in Chapter 9. In the AWDP
program, the gribio_module module is used in the awdp_grib and awdp_pfs modules, see
4.2.6 Support modules
Subdirectory genscat/support contains more support modules besides Bufrmod and
gribio_module. The KNMI 2DVAR Ambiguity Removal method requires minimization of a cost function and numerical Fourier transformation. These routines are located in subdirectories BFGS
and singletonfft, respectively, and are discussed in more detail in section 6.4.
Subdirectory Compiler_Features contains module Compiler_Features for handling some compiler specific issues, mainly with respect to command line argument handling. The
Makefile
in this directory compiles on of the available source files, depending on the Fortran compiler used.
Subdirectory convert contains module convert for the conversion of meteorological and geographical quantities, e.g. the conversion of wind speed and direction into u and v components and vice versa..
Subdirectory datetime contains module DateTimeMod for date and time conversions. AWDP only uses routines GetElapsedSystemTime (for calculating the running time of the various processing steps), and julian2ymd and ymd2julian (for conversion between Julian day number and day, month and year). Module DateTimeMod needs modules ErrorHandler and numerics.
Subdirectory ErrorHandler contains module ErrorHandler for error management. This module is needed by module DateTimeMod.
Subdirectory file contains module LunManager for finding, opening and closing free logical units in Fortran. AWDP uses only routines get_lun and free_lun for opening and closing of a logical unit, respectively.
Subdirectory num contains module numerics for defining data types and handling missing values, for instance in the BUFR library. This module is needed by many other modules.
Subdirectory pfs contains module pfs_ascat for opening, reading and closing of files in PFS format.
Subdirectory sort, finally, contains module SortMod for sorting the rows according to their acquisition date and time, or the wind vector solutions according to their probability.
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The process layer consists of the modules awdp_data, awdp_bufr, awdp_pfs, awdp_prepost,
awdp_grib, awdp_inversion, awdp_icemodel and awdp_ambrem. The routines present in these modules are described in the next sections.
4.3.1 Module awdp_data
The module awdp_data contains all the important data types relevant for the processing.
Elementary data types are introduced for the most basic data structures of the processing. These are e.g. wind_type and time_type. Using these data types (and of course the standard types as integer, real etc.), more complex (composed) data types are derived. Examples are beam_type,
ambiguity_type, cell_type, and row_type. A complete description of all types is given below. The attributes of all these types have intentionally self-documenting names.
Ambiguity data: The ambiguity_type data type contains information on an individual ambiguity
(wind vector solution). The attributes are listed in table 4.5. The routine init_ambiguity() sets all ambiguity data to missing. The routine print_ambiguity() may be used to print all ambiguity data.
Attribute Type Description
prob
real
conedistance
real
Probability of wind vector solution
Distance of solution to the GMF
Table 4.5 Ambiguity data structure.
Beam data: Every WVC contains three beams. The information of every beam is stored in the data type beam_type. The attributes are listed in table 4.6. Most of the attributes are explained in detail in [Wilson, Figa-Saldaña and O’Clerigh, 2004]. The routine init_beam() sets all beam data to missing and the routine test_beam checks if the data in the beam are within valid ranges. The routine print_beam() may be used to print all beam data.
Attribute Type Description
identifier incidence
integer real
Beam number: 1 = fore, 2 = mid, 3 = aft
Incidence angle (degrees, 0 is vertical, 90 is horizontal)
azimuth sigma0 noise_val
real Radar look angle (degrees, counted clockwise from the south)
) in dB real Noise value in %
s0_usability synt_data_quantity synt_data_quality
integer Usability real real
Flag related to the quality of the Kp estimate
Amount of synthetic data in σ
0
(0..1)
Quality of used synthetic data in σ
0
(0..1)
orbit_quality solar_reflec telemetry
real real real
Satellite orbit and attitude quality (0..1)
Solar array reflection contamination in σ
0
(0..1)
Telemetry quality (0..1)
extrapol_ref_pres land_frac
real Presence real Land fraction in σ
0
(0..1)
Table 4.6 Beam data structure.
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Cell Data: The cell_type data type is a key data type in the AWDP program, because many processing steps are done on a cell by cell basis. The attributes are listed in table 4.7. The routine
init_cell() sets the cell data to missing values. Also the flags are set to missing. The routine
test_cell() tests the validity of data. This routine sets the cell process flag. The routine print_cell() may be used to print the cell data.
Attribute Type Description
centre_id sub_centre_id software_id_l1b satellite_id
integer integer integer
Identification of originating/generating centre
Identification of originating/generating sub-centre
Software identification of level 1 processor
sat_instruments sat_motion
integer real
Satellite instrument identifier
Direction of motion of satellite
time time_type Date and time of data acquisition
lat
real Latitude of WVC
lon pixel_size_hor orbit_nr node_nr height_atmosphere
real real integer real
Longitude of WVC
Distance between WVCs (meters)
Across track cell number
Height of atmosphere used
loss_unit_lenght
real Loss per unit length of atmosphere
beam_collocation beam_collocation_type Beam collocation flag
beam (3) beam_type
Beam data
full_res full_res_type Averaged full resolution data
software_id_sm database_id surface_sm surface_sm_err sigma0_40 sigma0_40_err slope_40 slope_40_err
integer integer real real real real real real
sm_sensitivity dry_backscatter
real real
wet_backscatter
real
mean_surface_sm
real
rain_fall_detect sm_corr_flag sm_proc_flag sm_quality
real integer integer real
Soil moisture information
Soil moisture information
Soil moisture information
Soil moisture information
Soil moisture information
Soil moisture information
Soil moisture information
Soil moisture information
Soil moisture information
Soil moisture information
Soil moisture information
Soil moisture information
Soil moisture information
Soil moisture information
Soil moisture information
Soil moisture information
snow_cov_frac froz_land_frac inund_wet_frac topo_complexity
real real real real
software_id_wind
integer
generating_app
integer
model_wind wind_type ice_prob
real
Soil moisture information
Soil moisture information
Soil moisture information
Soil moisture information
Software identification of level 2 wind processor
Generating application of model information
Model wind used for Ambiguity Removal
Probability of ice
ice_age
real Ice age A-parameter
wvc_quality wvc_quality_type WVC quality flag
num_ambigs selection skill
integer integer real
Number of ambiguities present in WVC
Index of selected wind vector
Parameter used for PreScat Ambiguity Removal
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ambig (0..144) ambiguity_type
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Description
Array of wind ambiguities
process_flag process_flag_type Processing flag
level_of_input
integer Level of input data (1 or 2)
Table 4.7 Cell data structure.
All soil moisture information is read from the input BUFR file into the cell data structure and not used within the program. It is written to the output BUFR file at the end of the processing.
Full resolution data: The full_res_type contains average full resolution data, read from a PFS file, which are used to replace the 25-km or 12.5-km beam data. The attributes are listed in table 4.8.
The routine init_full_res() sets the full resolution averaged data to zero. The routine
print_full_res() may be used to print the full resolution data.
count_tot lat lon count_fore
integer Number of full res measurements used real Mean value of full res lats real Mean value of full res lons integer Number of full res fore beams used
incidence_fore
real
azimuth_fore
real
sigma0_fore
real
sigma0_sq_fore real
Mean value of full res values
Mean value of full res values
Mean value of full res values
Sum of squares
land_frac_fore
real
count_mid
integer
Mean value of full res values
Number of full res mid beams used
incidence_mid
real
azimuth_mid
real
Mean value of full res values
Mean value of full res values
sigma0_mid
real
sigma0_sq_mid real
Mean value of full res values
Sum of squares
land_frac_mid
real
count_aft
integer
Mean value of full res values
Number of full res aft beams used
incidence_aft
real
azimuth_aft
real
sigma0_aft
real
sigma0_sq_aft
real
land_frac_aft
real
Mean value of full res values
Mean value of full res values
Mean value of full res values
Sum of squares
Mean value of full res values
Table 4.8 Full res data structure.
Ice model data: The icemodel_type contains information related to the ice screening. The attributes are listed in table 4.9. The routine init_icemodel() sets the ice model data to missing values. The routine print_icemodel() may be used to print the ice data.
Attribute Type Description
class ii jj a b
integer Code for WVC being ice or wind integer Coordinate on the ice map integer Coordinate on the ice map
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Attribute Type Description
c dIce
real Distance to the ice line
wind_sol
integer Wind solution to be used
Table 4.9 Ice model data structure.
NWP stress parameter data: The nwp_stress_param_type data type contains information relevant for the ice screening and wind stress calculations (stress calculation is not yet implemented in AWDP). The attributes are listed in table 4.10. The routine
init_nwp_stress_param() sets the NWP stress parameter data to missing values. The routine
print_nwp_stress_param () may be used to print the stress data.
u v t q
Attribute Type Description
real real
Eastward (zonal) wind component
Northward (meridional) wind component
sst chnk sp
real Sea surface temperature
Table 4.10 NWP stress parameter data structure.
Row data: The data of a complete row of the swath is stored in the data type row_type, see table
4.11. A complete row corresponds to a single BUFR message in the AWDP output. The level 1
BUFR data may contain more than one row per BUFR message..
Attribute Type Description
time_stamp
integer
num_cells
integer
Time stamp of row data in seconds, used for sorting
Actual number of WVC’s
Table 4.11 Row data structure.
Time data: The time_type data type contains a set of 6 integers representing both the date and the time, see table 4.12. The routine init_time() sets the time entries to missing values. The routine
test_time() tests the validity of the date and time specification (see also the cell process flag). The routine print_time() can be used to print the time information.
Attribute Type Description
year month day hour minute second
integer integer integer integer integer integer
19XX or 20XX
1 – 12
1 – 31
0 – 23
0 – 59
0 – 59
Table 4.12 Time data structure.
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Wind Data: The wind_type data type contains the wind speed and wind direction, see table 4.13.
The routine init_wind() sets the wind vector to missing. The routine print_wind() may be used to print the wind vector. The routine test_wind() tests the validity of the wind specification, see also the cell process flag.
Attribute Type Description
speed dir
Table 4.13 Wind data structure.
Some special data types are introduced for the data (quality) flags. These are discussed below.
Beam collocation flag: The beam_collocation_type data type is used to indicate whether data of the three beams is originating from a single ground station or from multiple ground stations
(collocated data). This is relevant for so-called direct readout data from different ground stations which maybe merged into one single product. In a WVC, e.g. the fore beam information from one ground station may be combined with the mid and aft beam information from another ground station, in order to make a complete WVC. The attributes are listed in table 4.14. The routine
get_beam_collocation() converts an integer value to the logical beam collocation structure. The routine set_beam_collocation () converts a logical beam collocation structure to an integer value.
missing collocation
0
Bit
Description
Flag not set (all bits on)
1 Beam information originates from different ground stations
Table 4.14 Beam collocation flag bits.
K p
estimate quality flag: The kp_estim_qual_type data type contains the flag indicating the quality of the K
p
estimate. Each one of the three beams in a WVC contain an instance of this flag.
The attributes are listed in table 4.15. The function get_kp_estim_qual() interprets an integer flag
(BUFR input) to an instance of kp_estim_qual_type. The function set_kp_estim_qual() transforms an instance of kp_estim_qual_type to an integer flag.
missing estim_qual
Description
Flag not set (all bits on) quality estimate
Table 4.15 K
p
estimate quality flag bits (Fortran).
Wind Vector Cell quality flag: Every WVC contains a flag for its quality. Therefore the cell_type contains an instance of the wvc_quality_type. Table 4.16 gives an overview of its attributes. The function get_wvc_quality() interprets an integer flag (BUFR input) to an instance of
wvc_quality_type. The function get_wvc_quality() transforms an instance of wvc_quality_type to an integer flag. The routine print_wvc_quality() may be used to print the bit values of the flag.
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2
Bit
Description
missing qual_sigma0 azimuth kp
Flag not set (all bits on) good available for wind retrieval
21 2097152 Poor azimuth diversity among σ
0
20 1048576 Any beam noise content above threshold
19 524288 Product monitoring not used
monflag monvalue knmi_qc var_qc land ice inversion
18 262144 Product monitoring flag
17 131072 KNMI quality control fails
16
15
14
13
65536 Variational quality control fails
32768 Some portion of wind vector cell is over land
16384 Some portion of wind vector cell is over ice
8192 Wind inversion not successful
large small rain_fail rain_detect no_background redundant gmf_distance
12
11
10
8
7
6
4096 Reported wind speed is greater than 30 m/s
2048 Reported wind speed is less than or equal to 3 m/s
1024 Rain flag not calculated
9 512 detected
256 No meteorological background used
128 Data are redundant
64 Distance to GMF too large
Table 4.16 Wind Vector Cell quality flag bits (Fortran).
Cell process flag: Besides a cell quality flag, every WVC contains a process flag. The process flag checks on aspects that are important for a proper processing, but are not available as a check in the cell quality flag. The cell process flag is set by the routine test_cell, which calls routines test_time,
test_beam and test_wind.
Table 4.17 lists the attributes of the process_flag_type. The process flag is only available internally in AWDP. The routine print_process_flag() may be used to print the bit values of the flag.
Attribute Description
satellite_id sat_instruments sat_motion time latlon pixel_size_hor node_nr beam (3) model_wind ambiguity selection
Invalid satellite id
Invalid satellite instrument id
Invalid satellite direction of motion
Invalid date or time specification
Invalid latitude or longitude
Invalid cell spacing
Invalid across track cell number
Invalid data in one of the beams
Invalid background wind
Invalid ambiguities
Invalid wind selection
Table 4.17 Cell process flag bits (Fortran).
Table 4.18 provides an overview of all routines and their calls in module awdp_data.
get_beam_collocation init_cell Convert integer beam collocation to logical structure
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Convert integer K
p
estimate quality to logical structure
init_ambiguity Initialise ambiguity structure
init_nwp_stress_param init_cell Initialise NWP stress parameters structure
print_ambiguity Print ambiguity structure
print_full_res Print full resolution structure
print_icemodel Print ice model structure
print_nwp_stress_param
Print NWP stress parameters structure
print_process_flag
Print process flag structure
print_wvc_quality
Print quality flag structure
set_beam_collocation
Convert logical beam collocation to integer
set_knmi_flag Sets/unsets KNMI QC flag depending on other flag settings
set_kp_estim_qual
Convert logical K
p
estimate quality to integer
set_wvc_quality Convert logical WVC quality to integer
Table 4.18 Routines in module awdp_data
4.3.2 Module awdp_bufr
The module awdp_bufr maps the AWDP data structure on BUFR messages and vice versa. A list of the BUFR data descriptors can be found in appendix C. Satellite and instrument identifiers are listed in tables 4.19 and 4.20. Note that the first Metop mission is Metop 2, which is also known as
Metop A. The awdp_bufr module uses the genscat module BufrMod, see subsection 4.2.3 for the
interface with the BUFR routine library.
Satellite Value
ERS-1 1
ERS-2 2
Metop 1 = Metop B
Metop 2 = Metop A
Metop 3 = Metop C
3
4
5
Table 4.19 BUFR satellite identifiers.
Instrument Parameter Value
AMI/scatt
sat_instr_ers
142
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Instrument Parameter Value
ASCAT
sat_instr_ascat
190
Table 4.20 BUFR instrument identifiers.
Table 4.21 provides an overview of the different routines and their calls in this module. The genscat support routines ymd2julian() and julian2ymd() are used to provide each row in AWDP with a date/time stamp that can be used for sorting easily.
ascat_bufr_to_row_data read_bufr_file ASCAT BUFR message into one or more row_types
read_bufr_file write_bufr_file write_bufr_file
AWDP
AWDP
Read a complete BUFR file into row_types
Write all row_types into a complete BUFR file
Table 4.21 Routines in module awdp_bufr
Note that the acquisition date and time of ERS data are modified when they are read in routine
ers_bufr_to_row_data. An ERS BUFR message contains 19 rows of data which all have the same date and time of acquisition. This would cause problems in AWDP when the rows are sorted with respect to the acquisition date and time. Therefore, the date and time of each ERS row are recalculated assuming that the 10 th
(middle) row of the ERS BUFR message contains the ‘true’ acquisition time and that subsequent rows are 3.766 seconds apart. The time corrections are rounded to an integer number of seconds. Hence, in the first row, 34 seconds are subtracted from the acquisition time, in the second row 30 seconds, et cetera, until in the last (19 th
) row, 34 seconds are added to the acquisition time.
4.3.3 Module awdp_pfs
The module awdp_pfs maps the records in a PFS file on the AWDP data structure. It also contains a routine to read in a full resolution PFS file and use the data to calculate averaged beam data which are used to replace 25/12.5-km row data.
Table 4.22 provides an overview of the different routines and their calls in this module. Several routines from the pfs_ascat module in genscat are called from this module to handle the PFS data.
Appendix B5 shows the calling trees of the routines in module pfs_ascat that are used in AWDP.
ascat_pfs_to_row_data read_pfs_file ASCAT PFS record into one row_type
read_full_res_data
AWDP Read full resolution PFS data and replace beam data
read_pfs_file
AWDP Read a complete PFS level 1b file into row_types
Table 4.22 Routines in module awdp_pfs
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4.3.4 Module awdp_prepost
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Module awdp_prepost contains the routines to do all the pre and post processing. Pre processing consists of the procedures between the reading of the BUFR input and the wind retrieval for the output product. This includes sorting and merging, and assessments of the quality of the input data.
Post processing consists of the procedure between the ambiguity removal step and the BUFR encoding of the output. The post processing includes the monitoring of the wind data and the setting of some of the flags in the output product.
Description
calibration
merge_rows preprocess Merge the data of two input rows
monitoring postprocess Monitoring
postprocess
AWDP Main routine of the post processing
pre_inversion_qc preprocess Perform quality checks on input data
preprocess process_cleanup
AWDP Main routine of the pre processing
write_binary_output postprocess Write WVC data to a binary output file
write_properties postprocess Write some properties of the data into a text file
Table 4.23 Routines of module awdp_prepost.
Table 4.23 lists the tasks of the individual routines. AWDP calls preprocess() to sort the rows with respect to the acquisition data and time. It also checks on the appearance of double rows, that is, rows which are less than half the nominal cell distance (pixel size on horizontal in the input data) apart. If preprocess() finds a double row it merges the two rows into one row. In that case the number of input rows will be reduced. Once the input rows are sorted and merged, an output row structure is allocated ant the input data are copied into the output rows.
The routine pre_inversion_qc() which is called by preprocess() performs land flagging and checks the setting of flags in the level 1b beam information. If the input data is of inferior quality, the
qual_sigma0 flag in the wvc_quality is set, which prevents further processing of this WVC. Also the land fractions present in the beam information in the level 1b product are considered: if any land fraction in the fore, mid or aft beam exceeds 0.02, the qual_sigma0 flag in wvc_quality is set, as well. The land flag in wvc_quality is set whenever any level 1b land fraction is above zero.
The next step is the calibration of the σ
0
’s in calibrate_s0. Calibration coefficients for each level 1 processing version and instrument (ASCAT on Metop A or Metop B) have been obtained using the so-called NWP Ocean Calibration (NOC). Note that the calibration is done again in the reverse order after the post processing in order to write the σ
0
’s to output as plain copies of the input σ
0
’s.
More information about the calibration can be found in [Verspeek et al., 2012].
The monitoring, which is performed as part of the post processing, calculates some statistics from the wind product and writes them to an ASCII file called monitoring_report.txt. The monitoring parameters are listed in table 4.24. They are calculated separately for three different regions of each swath (left and right). Note that the monitoring is invoked only if the –mon command line option is set.
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Parameter Description
observation land
Number of Wind Vector Cells in output = N1
Fraction of WVCs with land flag set
ice
Fraction of WVCs with ice flag set
background
Fraction of WVCs containing model winds
backscatter_info
Fraction of WVCs containing sufficient valid σ
0
’s for inversion =N2
knmi_flag wind_retrieval
Ratio number of WVCs with KNMI QC flag set / N2
Fraction of N2 that actually contains wind solutions = N3
wind_selection big_mle avg_mle var_qc rank_1_skill avg_wspd_diff rms_diff_wspd wspd_ge_4 rms_diff_dir rms_diff_u rms_diff_v
Fraction of N3 that actually contains a wind selection = N4
Number of WVCs containing a wind solution but no MLE value
Averaged (over N4) MLE value of 1 st
wind selection
Fraction of N4 that has the Variational QC flag set
Fraction of N4 where the first wind solution is the chosen one
Averaged (over N4) difference between observed and model wind speeds
RMS (over N4) difference between observed and model wind speeds
Fraction of N4 where the selected wind speed is ≥ 4 m/s = N5
RMS (over N5) difference between observed and model wind directions
RMS (over N5) difference between observed and model wind u components
RMS (over N5) difference between observed and model wind v components
rms_diff_vec_len
RMS (over N5) vector length between observed and model winds
ambiguity
Fraction of N5 where the chosen solution is not the one closest to the model wind
Table 4.24 Parameters in monitoring output.
4.3.5 Module awdp_grib
The module awdp_grib reads in ECMWF GRIB files and collocates the model data with the scatterometer measurements. The awdp_grib module uses the genscat module gribio_module, see
subsection 4.2.5 for the interface with the GRIB routine library.
Table 4.25 provides an overview of the routines and their calls in this module. The genscat support routines uv_to_speed() and uv_to_dir() are used to convert NWP wind components into wind speed and direction.
get_grib_data
AWDP Get land mask, ice mask and background winds using GRIB data
init_grib_processing get_grib_data Initialise module
Table 4.25 Routines in module awdp_grib
NWP model sea surface temperature and land-sea mask data are used to provide information about possible ice or land presence in the WVCs. WVCs with a sea surface temperature below 272.16 K
(-1.0 °C) are assumed to be covered with ice and the ice and qual_sigma0 flags in wvc_quality are
set. Note that this step is omitted if the Bayesian ice screening is used; see section 4.3.7.
Land presence within each WVC is determined using the land-sea mask available from the model data. The weighted mean value of the land fractions of all model grid points within 80 km of the
WVC centre is calculated and if this mean value exceeds a threshold of 0.02, the qual_sigma0 flag in wvc_quality is set. The land flag in wvc_quality is set if the calculated land fraction is above zero.
NWP forecast wind data are necessary in the ambiguity removal step of the processing. Wind
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WVC.
4.3.6 Module awdp_inversion
Module awdp_inversion serves the inversion step in the wind retrieval. The inversion step is done cell by cell. The actual inversion algorithm is implemented in the genscat modules inversion and
post_inversion, see subsection 4.2.1. Table 4.26 provides an overview of the routines and their
calls in this module.
Routine Call Description
init_inversion invert_wvcs Initialisation
invert_wvcs
AWDP Loop over all WVCs and perform inversion
Table 4.26 Routines of module awpd_inversion.
4.3.7 Module awdp_ambrem
Module awdp_ambrem controls the ambiguity removal step of the AWDP program. The actual
ambiguity removal schemes are implemented in the genscat module ambrem, see section 4.2.2.
The default method is the KNMI 2DVAR scheme. Table 4.27 lists the tasks of the individual routines.
Routine Call Description
remove_ambiguities
AWDP Main routine of ambiguity removal
Table 4.27 Routines of module awpd_ambrem.
The ambiguity removal scheme works on a so-called batch. The batch is defined in the fill_batch() routine. For the AWDP program a batch is just a set of rows. The size of the batch is determined by the resolution of the structure functions and the number of FFT. The genscat routine
remove_ambiguities() performs the actual ambiguity removal. Finally select_wind() passes the selection to the output WVCs.
4.3.8 Module awdp_icemodel
Module awdp_icemodel performs the ice screening of the wind product. The ice screening works on the principle that WVCs over water yield wind solutions which are close to the GMF (‘cone’).
If a WVC is over ice, the σ
0
triplets from fore, mid and aft beam will be close to the so-called ice line. Hence, there is a possibility to discriminate between water (wind) and ice WVCs. The implementation of this principle is described in more detail in [Belmonte et al., 2012]. The ice screening is done directly after the ambiguity removal step. Table 4.28 provides an overview of the routines and their calls in this module.
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Routine Call Description
bayesianIcemodel ice_mode
Implementation of the Bayesian ice model
Calculate ice coordinates and distance to ice line
Calculate distance to ice line from given σ
0
’s
calcIceCoord iceGMF
not used
iceLine iceGMF (not used)
ice_model
AWDP
nonbayesianIceModel ice_mode
Calculate a priori ice probability
Calculate the σ
0
values from the ice coordinates
Calculate the ice line origin and slope
Main routine of ice screening
Implementation of the basic ice model without history
updateIcePixel scat2iceMap Update one ice pixel
Table 4.28 Routines of module awdp_icemodel.
4.3.9 Module awdp
Module awdp is the main program of AWDP. It processes the command line options and controls the flow of the wind processing by calling the subroutines performing the subsequent processing steps. If any process step returns with an error code, the processing will be terminated.
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Chapter 5
5.1 Background
In the inversion step of the wind retrieval, the radar backscatter observations in terms of the normalized radar cross-sections (σ
0
's) are converted into a set of ambiguous wind vector solutions.
In fact, a Geophysical Model Function (GMF) is used to map a wind vector (specified in term of wind speed and wind direction) to the σ
0
values. The GMF further depends not only on wind speed and wind direction, but also on the measurement geometry (relative azimuth and incidence angle), and beam parameters (frequency, polarisation). A maximum likelihood estimator (MLE) is used to select a set of wind vector solutions that optimally match the observed σ
0
's. The wind vector solutions correspond to local minima of the MLE function
MLE
=
1
N
N
∑
i
=
1
(
σ
obs
0
(
i
)
−
σ
GMF
0
K p
(
i
)
)
2
,
(5.1)
With N the number of independent σ
0
measurements available within the wind vector cell, and K
p
the covariance of the measurement error. Following a Bayesian approach, K
p
is a constant representing the noise in all three ERS or ASCAT beams together [Stoffelen and Portabella,
2006]. This selection depends on the number of independent σ
0
values available within the wind vector cell. The MLE can be regarded upon as the distance between an actual scatterometer measurement and the GMF in N-dimensional measurement space. The MLE is related to the probability P that the GMF at a certain wind speed and direction represents the measurement by
P
∝
e
−
MLE
.
(5.2)
Therefore, wind vectors with low MLE have a high probability of being the correct solution. On the other hand, wind vectors with high MLE are not likely represented by any point on the GMF.
Details on the inversion problem can be found in [Stoffelen and Portabella, 2006; Portabella,
2002]. The AWDP program includes the Multiple Solution Scheme (MSS), see [Portabella and
Stoffelen, 2001].
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5.2 Routines
The inversion module class contains two modules named inversion and post_inversion. They are located in subdirectory genscat/inversion. Tables 5.1 and 5.2 list all routines in the modules. Appendix B.1 shows the calling tree for the inversion routines.
Routine Call Routine
invert_one_wvc
AWDP
fill_wind_quality_code invert_one_wvc save_inv_input read_inv_input
not used not used
save_inv_output
not used
do_parabolic_winddir_search invert_one_wvc
calc_normalisation invert_one_wvc
INTERPOLATE interpolate1d
Call
generic
calc_sigma0 interpolated2d calc_sigma0 interpolate2dv calc_sigma0 interpolate3d calc_sigma0 read_LUT calc_sigma0 create_LUT_C_VV calc_sigma0 print_message init_inv_input
see B.1
AWDP
my_mod my_min init_inv_output invert_one_wvc my_max init_inv_settings_to_default
AWDP
my_average write_inv_settings_to_file get_inv_settings set_inv_settings
not used
AWDP
AWDP not used see B.1 see B.1 see B.1
get_indices_lowest_local_minimum invert_one_wvc my_index_max my_exit
see B.1 see B.1
find_minimum_cone_dist invert_one_wvc print_input_data_of_inversion check_input_data calc_dist_to_cone_center
not used
convert_sigma_to_zspace invert_one_wvc get_ers_noise_estimate calc_var_s0 get_dynamic_range get_GMF_version_used calc_sigma0
not used not used see B.1
calc_sigma0_cmod4 create_LUT_C_VV f1 calc_sigma0_cmod4 calc_sigma0_cmod5_5 create_LUT_C_VV calc_sigma0_cmod5_n create_LUT_C_VV calc_sigma0_cmod6 create_LUT_C_VV
Table 5.1 Routines in module inversion.
Routine Call
normalise_conedist_ers_ascat
AWDP
calc_kp_ers_ascat normalise_conedist_ers_ascat calc_geoph_noise_ers_ascat calc_kp_ers_ascat normalise_conedist_prescat_mode
AWDP
get_ers_noise_estimate normalise_conedist_prescat_mode check_ers_ascat_inversion_data check_wind_solutions_ers_ascat
see B.1
AWDP
remove_one_solution check_wind_solutions_ers_ascat calc_probabilities
AWDP
Table 5.2 Routines of module post_inversion.
To establish the MLE function (1), the radar cross section according to the GMF, σ
0
GMF
, must be calculated. This is done in routine calc_sigma0. The GMF used is read as a Look Up Table (LUT) from a binary file. The value for σ
0
GMF
is obtained from interpolation of this table. The
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interpolate2d, interpolate2dv, or interpolate3d, depending on the type of interpolation needed.
For C-band at VV polarization the GMF (CMODx, see [Hersbach, Stoffen and de Haan, 2007]) is given in analytical form (routines calc_sigma0_cmodxxx). If a C-band LUT is not present it will be created by routine create_LUT_C_VV. This routine calls one of the routines calc_sigma0_cmodxxx that contain the analytical expressions of the CMOD4 or CMOD5 algorithm. There is a parameter in the inversion settings type that is used to determine which CMOD function is to be used.
Routines get_lun and free_lun from module LunManager in subdirectory genscat/support/file
are needed when reading and creating the LUTs.
Note that module post_inversion uses some tables for the normalisation of MLEs and noise values.
These tables are read from ASCII files which are present in direction genscat/inversion.
The environment variable $INVERSION_LUTSDIR should contain the proper directory name.
5.3 Antenna direction
The output wind direction of inversion routines are generally given in the meteorological convention, see table 5.3. The inversion routine uses a wind direction that is relative to the antenna direction. The convention is that if the wind blows towards the antenna then this relative wind direction equals to 0. Therefore, it is important to be certain about the convention of your antenna
(azimuth) angle.
For ERS and ASCAT, the radar look angle (antenna angle or simply azimuth) equals 0 if the antenna is orientated towards the south. The radar look angle increases clockwise. Therefore, the antenna angle needs a correction of 180 degrees.
Meteorological Oceanographic Mathematical u v Description
0
90
180
270
180
270
0
90
270
180
90
0
0
-1
0
1
-1 Wind blowing from the north
0
1
0
Wind blowing from the east
Wind blowing from the south
Wind blowing from the west
Table 5.3 Conventions for the wind direction.
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Chapter 6
6.1 Ambiguity Removal
Ambiguity Removal (AR) schemes select a surface wind vector among the different surface wind vector solutions per cell for the set of wind vector cells in consideration. The goal is to set a unique, meteorological consistent surface wind field. The surface wind vector solutions per cell, simply called ambiguities, result from the wind retrieval process step.
Whenever the ambiguities are ranked, a naive scheme would be to select the ambiguity with the first rank (e.g., the highest probability, the lowest distance to the wind cone). In general, such a persistent first rank selection will not suffice to create a realistic surface wind vector field: scatterometer measurements tend to generate ambiguous wind solutions with approximately equal likelihood (mainly due to the ~180
° invariance of stand alone scatterometer measurements).
Therefore additional spatial constraints and/or additional (external) information are needed to make sensible selections.
A common way to add external information to a WVC is to provide a background surface wind vector. The background wind acts as a first approximation for the expected mean wind over the cell. In general, a NWP model wind is interpolated for this purpose. Whenever a background wind is set for the WVC, a second naive Ambiguity Removal scheme is at hand: the Background
Closest (BC) scheme. The selected wind vector is just the minimizer of the distance (e.g., in the least squares sense) to the background wind vector. This scheme may produce far more realistic wind vector fields than the first rank selection, since the background surface wind field is meteorologically consistent.
However, background surface winds have their own uncertainty. Therefore, sophisticated schemes for Ambiguity Removal take both the likelihood of the ambiguities and the uncertainty of the background surface wind into account. Examples are the KNMI Two-Dimensional Variational
(2DVAR) scheme and the PreScat scheme.
The implementation of these schemes is described in sections 6.4 and 6.5.
6.2 Module ambrem
Module Ambrem is the interface module between the various ambiguity removal methods and the different scatterometer data processors. Table 6.1 provides an overview of the different routines
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ambrem. More elaborate Ambiguity Removal methods have an interface module, see table 6.2.
Figure 6.1 shows schematically the interdependence of the various modules for Ambiguity
Removal.
InitAmbremModule
AWDP
InitAmbremMethod
AWDP
DoAmbrem
AWDP
Initialization of module Ambrem
Initialization of specified AR scheme
Execution of specified AR scheme
InitDummyBatch
not used
ExitAmbremMethod
memory
Table 6.1 Routines of module Ambrem.
Routine Description Documentation
Ambrem2DVAR
Interface to KNMI 2DVAR method
AmbremBGClosest
Interface to Background Closest method
AmbremPrescat
Interface to Prescat method
Table 6.2 Interface modules for different Ambiguity Removal schemes.
6.3 Module BatchMod
After the wind retrieval step, the Ambiguity Removal step is performed on selections of the available data. In general, these selections are just a compact part of the swath or a compact part of the world ocean. The batch module BatchMod facilitates these selections of data. In fact, a batch data structure is introduced to create an interface between the swath related data and the data structures of the different AR methods. Consequently, the attributes of the batch data structures are a mixture of swath items and AR scheme items. Figure 6.2 gives a schematic overview of the batch data structure. Descriptions of the attributes of the individual batch data components are given in table 6.3.
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Ambrem2DVAR ambrem
AmbremPreScat
BatchMod
AmbremBGclosest
TwoDvar
CostFunction StrucFunc convert
TwoDvarData BFGSMod SingletonFFT
Figure 6.1 Interdependence of the modules for Ambiguity Removal. The connections from module
ambrem to module BatchMod and from module Ambrem2DVAR to convert are not drawn.
BatchType
BatchRowType
BatchCellType
BatchQualFlagType
BatchAmbiType
Figure 6.2 Schematic representation of the batch data structure.
Attribute Type
NrRows
Integer
BatchType
Description
Number of rows in batch
Attribute Type
RowNr
Integer
BatchRowType
Description
Row number within orbit
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Number of cells in batch (max 76)
NrCells
BatchCellType
Attribute Type
NodeNr
Integer
lat lon
Description
Node number within orbit row
Real Latitude
Real Longitude
ubg vbg
NrAmbiguities
Real
Real u-component of background wind v-component of background wind
BatchAmbiType
Attribute Type
selection uana vana f gu gv
Integer
Real
Real
Real
Description
Index of selected ambiguity u-component of analysis wind v-component of analysis wind
Contribution of this cell to cost function
Table 6.3 Batch data structures.
To check the quality of the batch a quality flag is introduced for instances of the BatchCellType.
The flag is set by routine TestBatchCell(). The attributes of this flag of type BatchQualFlagType are listed in table 6.4.
Module BatchMod contains a number of routines to control the batch structure. The calls and tasks of the various routines are listed in table 6.5. The batch structure is allocatable because it is only active between the wind retrieval and the ambiguity removal step.
Attribute Description
Missing
Quality flag not set
Node
Lat
Lon
Incorrect node number specification
Incorrect latitude specification
Incorrect longitude specification
Ambiguities
Selection
Background
Analysis
Threshold
Cost
Gradient
Invalid ambiguities
Invalid selection indicator
Incorrect background wind specification
Incorrect analysis
Threshold overflow
Invalid cost function value
Invalid gradient value
Table 6.4 Batch quality flag attributes.
Routine Call Description
AllocRowsAndCellsAndInitBatch Processor Allocation of batch
AllocAndInitBatchRow AllocRowsAndCellsAndInitBatch Allocation of batch rows
AllocAndInitBatchCell AllocAndInitBatchRow Allocation of batch cells
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Routine Call Description
AllocRowsOnlyAndInitBatch
not used
InitBatchModule Ambrem
Initialization module
InitBatch AllocRowsAndCellsAndInitBatch Initialization batch
InitBatchRow InitBatch
Initialization of batch rows
InitBatchCell InitBatchRow Initialization of batch cells
InitbatchAmbi InitBatchCell Initialization of batch ambiguities
DeallocBatch
Processor Deallocation of batch
DeallocBatchRows DeallocBatch
DeallocBatchCells DeallocBatchRows
DeallocBatchAmbis DeallocBatchCells
Deallocation of batch rows
Deallocation of batch cells
Deallocation of batch ambiguities
TestBatch
Processor
TestBatchRow TestBatch
TestBatchCell TestBatchRow
TestBatchQualFlag
Processor
getBatchQualFlag setBatchQualFlag
PrnBatchQualFlag
not used not used not used
Test complete batch
Test complete batch row
Test batch cell
Print the quality flag
Table 6.5 Routines of module BatchMod.
6.4 The KNMI 2DVAR scheme
6.4.1 Introduction
The purpose of the KNMI 2DVAR scheme is to make an optimal selection provided the
(modelled) likelihood of the ambiguities and the (modelled) uncertainty of the background surface wind field. First, an optimal estimated surface wind vector field (analysis) is determined based on variational principles. This is a very common method originating from the broad discipline of Data
Assimilation. The optimal surface wind vector field is called the analysis. Second, the selected wind vector field (the result of the 2DVAR scheme) consists of the wind vector solutions that are closest to the analysis wind vector. For details on the KNMI 2DVAR scheme formulation the reader is referred to [Vogelzang, 2007]. Information on 2DVAR can also be found in [Stoffelen, de
Haan, Quilfen and Schyberg, 2000; de Vries, Stoffelen and Beysens, 2005; de Vries and Stoffelen,
2000].
The calculation of the cost function and its gradient is rather complex matter. The reader who is only interested in how the 2DVAR scheme is assembled into the genscat module class ambrem is
the minimization should also read the subsequent subsections. Subsection 6.4.3 forms an
introduction to the cost function. It is recommended to first read this section, because it provides
necessary background information to understand the code. Subsection 6.4.7 on the actual
minimization and subsection 6.4.8 on Fast Fourier Transforms are in fact independent of the cost
function itself. The reader might skip these subsections.
6.4.2 Data structure, interface and initialisation
The main module of the 2DVAR scheme is TwoDvar. Within the genscat ambiguity removal module class, the interface with the 2DVAR scheme is set by module Ambrem2DVAR. Table 6.6 lists its routines that serve the interface with TwoDvar.
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Routine Call Description
Do2DVARonBatch DoAmbrem
Apply 2DVAR scheme on batch
Set_WVC_Orientations BatchInput2DVAR Sets the observation orientation
GetBatchSize2DVAR
Determine maximum size of batch
Table 6.6 Routines of module Ambrem2DVAR.
These routines are sufficient to couple the 2DVAR scheme to the processor. The actual 2DVAR processing is done by the routines of module TwoDvar itself. These routines are listed in table 6.7.
Figures B2.1-B2.6 show the complete calling tree of the AR routines.
Routine Call Description
InitTwodvarModule
Initialization of module TwoDvar
ExitTwodvarModule ExitAmbremMethod Deallocation of module TwoDvar
Table 6.7 Routines of module TwoDvar.
The Obs2dvarType data type is the main data structure for the observed winds. Its attributes are listed in table 6.8. The TDV_Type data type contains all parameters that have to do with the
2DVAR batch grid: dimensions, sizes, and derived parameters. These data structures are defined in module TwoDvarData and the routines in this module are listed in table 6.10.
Attribute Type Description
alpha cell row igrid jgrid lat
Wll
Wlr
Wul
Wur ubg vbg
Integer
Integer
Real
Real
Real
Real
Real
Real
Store batch cell number
Store batch row number
Latitude to determine structure function
Weight lower left
Weight lower right
Weight upper left
Weight upper right
Background EW wind component
NrAmbiguities
incr() AmbiIncrType Ambiguity increments
uAnaIncr vAnaIncr selection f gu gv
Real Cost function at observation
Real df/du
Real df/dv
Table 6.8 The Obs2dvarType data structure.
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Attribute Type
delta
Real
delta_p delta_q
Real
Real
N1
H1
K1
N2
Integer
Integer
Integer
Integer
H2
K2
Ncontrol
Integer
Integer
Integer
Description
2DVAR grid size in position domain
2DVAR grid size in frequency domain
2DVAR grid size in frequency domain
Dimension 1 of 2DVAR grid
N1/2
H1+1;number of nonnegative frequencies
Dimension 2 of 2DVAR grid
N2/2
H2+1;number of nonnegative frequencies
Size of control vector
Table 6.9 The TDV_Type data structure.
Routine Call Description
Set_HelmholzCoefficients TDV_Init
Set Helmholz transformation coefficients
Set_CFW TDV_Init Set cost function weights
BatchOutput2DVAR
InitOneObs2dvar InitObs2dvar
Initialization of single observation
TestObs2dvar Do2DVAR
Test single observation
Prn2DVARQualFlag Do2DVAR set2DVARQualFlag TestObs2DVAR get2DVARQualFlag
not used
Print observation quality flag
Convert observation quality flag to integer
Convert integer to observation quality flag
Table 6.10 Routines in module TwoDvarData.
The quality status of an instance of Obs2dvarType is indicated by the attribute QualFlag which is an instance of TwoDvarQualFlagType. The attributes of this flag are listed in table 6.11.
Attribute Description
missing wrong
Flag values not set
Invalid 2DVAR process
Lat
Invalid latitude
Background
Invalid background wind increment
Ambiguities
Invalid ambiguity increments
Selection
Invalid selection
Analyse
Cost
Invalid analysis wind increment
Invalid cost function specification
gradient weights grid
Invalid gradient specification
Invalid interpolation weights
Invalid grid indices
Table 6.11 Attributes of 2DVAR observation quality flag.
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6.4.3 Reformulation and transformation
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The minimization problem to find the analysis surface wind field (the 2D Variational Data
Assimilation problem) may be formulated as min
v
J
(
v
) ,
J
(
v
)
=
J obs
(
v
)
+
J bg
(
v
)
, (6.1) where v is the surface wind field in consideration and J the total cost function consisting of the observational term J
obs
and the background term J
bg
. The solution, the analysis surface wind field, may be denoted as v
a
. Being just a weighted least squares term, the background term may be further specified as
J bg
(
v
)
=
[
v
−
v bg
]
T B
−
1 [
v
−
v bg
]
, (6.2) where B is the background error covariance matrix. The J
obs
term of the 2DVAR scheme is not simply a weighted least squares term.
Such a formulation does not closely match the code of the 2DVAR scheme. In fact, for scientific and technical reasons several transformations are applied to reformulate the minimization problem.
Description of these transformations is essential to understand the different procedures within the code. The interested reader is referred to [Vogelzang 2007].
6.4.4 Module CostFunction
Module CostFunction contains the main procedure for the calculation of the cost function and its gradient. It also contains the minimization procedure. Table 6.12 provides an overview of the routines.
Routine Call Description
Jb Jt Background term of cost function
Jo Jt Observational term of cost function
JoScat Jo
Single observation contribution to the cost function
Unpack_ControlVector Jo
Pack_ControlVector Jo
Uncondition Jo
Uncondition_adj Jo
Unpack of control vector
Pack of control vector (or its gradient)
Several transformations of control vector
Adjoint of Uncondition.
Minimise Do2DVAR (TwoDvar) Minimization
DumpAnalysisField Do2DVAR
Write analysis field to file
Table 6.12 Routines of module CostFunction.
6.4.5 Adjoint method
The minimization of cost function is done with a quasi-Newton method. Such a method requires an accurate approximation of the gradient of the cost function. The adjoint method is just a very economical manner to calculate this gradient. For introductory texts on the adjoint method and adjoint coding, see, e.g., [Talagrand, 1991; Giering, 1997]. For detailed information on the adjoint
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model in 2DVAR see [Vogelzang 2007].
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6.4.6 Structure Functions
Module StrucFunc contains the routines to calculate the covariance matrices for the stream function,
ψ
, and the velocity potential,
χ
. Its routines are listed in table 6.13.
Routine Call Description
InitStrucFunc SetCovMat Initialize the structure functions
StrucFuncPsi SetCovMat Calculate
ψ
χ
Table 6.13 Routines of module StrucFunc.
Routine InitStrucFunc sets the structure function parameters to a default value.
6.4.7 Minimization
The minimization routine used is LBFGS. This is a quasi Newton method with a variable rank for the approximation of the Hessian written by J. Nocedal. A detailed description of this method is given by [Liu and Nocedal 1989]. Routine LBFGS is freeware and can be obtained from web page http://www.netlib.org/opt/index.html
, file lbfgs_um.shar. The original Fortran 77 code has been adjusted to compile under Fortran 90 compilers. Routine LBFGS and its dependencies are located in module BFGSMod.F90 in directory genscat/support/BFGS. Table 6.14 provides an overview of the routines in this module.
Routine LBFGS uses reverse communication. This means that the routine returns to the calling routine not only if the minimization process has converged or when an error has occurred, but also when a new evaluation of the function and the gradient is needed. This has the advantage that no restrictions are imposed on the form of routine Jt calculating the cost function and its gradient.
The formal parameters of LBFGS have been extended to include all work space arrays needed by the routine. The work space is allocated in the calling routine minimise. The rank of LBFGS affects the size of the work space. It has been fixed to 3 in routine minimise, because this value gave the best results (lowest values for the cost function at the final solution).
Routine Call Description
MCSRCH LBFGS Line search routine.
MCSTEP MCSRCH Calculation of step size in line search.
Table 6.14 Routines in module BFGSMod.
Some of the error returns of the line search routine MCSRCH have been relaxed and are treated as a normal return. Further details can be found in the comment in the code itself.
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Routines daxpy and ddot were rewritten in Fortran 90. These routines, originally written by J.
Dongarra for the Linpack library, perform simple operations but are highly optimized using loop unrolling. Routine ddot, for instance, is faster than the equivalent Fortran 90 intrinsic function
dot_product.
6.4.8 SingletonFFT_Module
Module SingletonFFT_Module in directory genscat/support/singletonfft contains the multi-variate complex Fourier routines needed in the 2DVAR scheme. A mixed-radix Fast Fourier
Transform algorithm based on the work of R.C. Singleton is implemented.
Routine Call Description
Uncondition_adj
fft SingletonFFT2d
SFT_Permute fft
SFT_PermuteSinglevariate SFT_Permute
SFT_PermuteMultivariate SFT_Permute
SFT_PrimeFactors fft
SFT_Base2 fft
SFT_Base3 fft
SFT_Base4 fft
SFT_Base5 fft
SFT_BaseOdd fft
Main FFT routine
Permute the results
Support routine
Support routine
Get the factors making up N
Base 2 FFT
Base 3 FFT
Base 4 FFT
Base 5 FFT
General odd-base FFT
Table 6.15 Fourier transform routines.
Table 6.15 gives an overview of the available routines. The figures in Appendix B2 shows the calling tree of the FT routines relevant for 2DVAR.
Remark: the 2DVAR implementation can be made more efficient by using a real-to-real FFT routine rather than a complex-to-complex one as implemented now. Since AWDP satisfies the requirements in terms of computational speed, this has low priority.
6.5 The PreScat scheme
The PreScat ambiguity removal scheme can be invoked within AWDP by the use of command line option –armeth prescat. More information on this scheme can be found in [Stoffelen, de
Haan, Quilfen and Schyberg, 2000]. Currently, the PreScat scheme can be used only in combination with ERS data.
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Chapter 7
Module iceModelMod is part of the genscat support modules. It contains all the routines for initialising, reading, writing and printing of the SSM/I grids for the North Pole and South Pole region.
7.1 Background
The -icemodel option in AWDP basically fills the fields Ice Probability (BUFR item 87) and
Ice Age (BUFR item 88). Also it can output graphical maps of ice model related parameters on an
SSM/I grid for the North Pole and for the South Pole region.
Each time the Metop satellite passes over the pole region the corresponding ice map is updated with the new ASCAT data. A spatial and temporal averaging is performed in order to digest the new information. After the overpass, at the end of processing an entire BUFR file, the updated information on the ice map is put back into the BUFR structure. Optionally graphical maps are plotted, which can be controlled by optional input parameters for routine printIceMap. The graphical filenames have encoded the North Pole/South Pole, the date/time as well as the parameter name. The most important ones are: print_a: file [N|S][yyyymmddhhmmss].ppm contains the ice subclass and the a-ice parameter on a grey-scale for points classified as ice. print_t: file [N|S][yyyymmddhhmmss]t.ppm contains the ice class. print_sst: file [N|S][yyyymmddhhmmss]sst.ppm contains the sea surface temparature print_postprob: file [N|S][yyyymmddhhmmss]postprob.ppm contains the a-posteriori ice probability.
Typically at least two days of ASCAT data are needed to entirely fill the ice map with data and give meaningful ice model output. Because AWDP handles only one BUFR file at a time, a script is needed that calls AWDP several times. After each AWDP-run a binary restart file is written to disk containing the information of an icemap (latestIceMapN.rst for the North Pole and latestIceMapS.rst
for the South Pole). With the next call of awdp, these restart files are read in again. Environment variable $RESTARTDIR contains the directory for the ice model restart files.
Optionally sea surface temperature (SST) data from GRIB files can be used to further improve the
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quality of the ice algorithm (the use_sst logical must be turned on).
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Processing l1b input with the use of NWP data and SST data can be done with the following command line options: awdp –f <bufr file> -nwpfl <gribfilelist> -icemodel 2 –mon handleall
Reprocessing of level 2 input with only running the ice model on top of it can be done with the following command line options: awdp –f <bufr file> -icemodel 2 –noinv –noamb –mon -handleall
The SSM/I grids are widely used for representation of ice related parameters. A good description as well as some software routines can be found on the website of the National Snow and Ice Data
Centre (NSIDC): http://www.nsidc.org/data/docs/daac/ae_si25_25km_tb_and_sea_ice.gd.html
.
A more detailed description of the Bayesian statistics method and ice model is given in [Belmonte
et al., 2012].
7.2 Routines
Table 7.1 provides an overview of the routines in module iceModelMod.
Routine Call Description
calcPoly3
AWDP Calculate a 3 rd
order polynomial
ExpandDateTime ij2latlon
AWDP not used
Converts a date/time to a real
Calculate lat lon values from SSM/I grid coordinates
initIceMap inv_logit latlon2ij logit
AWDP not used
AWDP not used
Initialise ice map
Calculate the inverse of the logit of p: 1/(1+exp(-p))
Calculate SSM/I grid coordinates from lat lon values
Calculate the logit of p: ln(p/(1-p))
MAPLL latlon2ij Convert from lat/lon to polar stereographic coordinates
MAPXY ij2latlon (not used) Convert from polar stereographic to lat/lon coordinates
printClass
not used
print_ice_age_ascat
not used
Print the ice class (sea or ice)
Print ice age map to graphical .ppm file
printIceAscat printIceMap Print ASCAT ice map to graphical .ppm file
printIcePixel
AWDP Print contents of an ice pixel
printIceQscat printIceMap Print QuikSCAT ice map to graphical .ppm file
printppm_qc
not used Print WVC quality flag contents to graphical .ppm file
printppmvar printIceMap Print variable to .ppm file, mapped on gray scale
printppmvars
not used Print three variables to .ppm file, mapped to an RGB scale
printSubclass printIceMap Print the ice subclass to a .ppm file
RW_IceMap wT
AWDP
AWDP
Read or write an ice map from/to a binary restart file
Calculate the moving time average function
Table 7.1 Routines of module iceModelMod.
7.3 Data structures
There are two important data structures defined in this module. The first contains all relevant data of one pixel on the ice map (IcePixel). The second one contains basically a two-dimensional array
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North Pole region or the South Pole region.
Attribute Type Description
aIce
real A ice parameter
aIceAves aSd
real A ice parameter standard deviation
class subClass sst pXgivenIce pXgivenOce pYgivenIce pYgivenOce
real Sea surface temperature (K) real real real real
Pice pIceGivenX
real
pIceGivenXave
real
sumWeightST
real
landmask
A-posteriori ice probability
Average of a-posteriori ice probability
Sum of weight factors
timePixelNow
DateTime Date/time of measurement
timePixelPrev
DateTime Date/time of previous measurement
Table 7.2 Attributes for the IcePixel data type.
Attribute Type Description
nPixels nLines pole use_sst
integer integer integer integer
Number of pixels for the ice map
Number of lines for the ice map
Indicator for Northpole or Southpole
Use SST value in ice screening
timeMapNow
DateTime
timeMapPrev
DateTime
xy
Date/time of latest ice map update
Date/time of previous ice map update
IcePixel(nPixels, nLines) Pointer to the ice map contents
Table 7.3 Attributes for the IceMapType data type.
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Chapter 8
Module BufrMod is part of the genscat support modules. The current version is a Fortran 90 wrapper around the ECMWF BUFR library (see http://www.ecmwf.int/ ). The goal of this support module is to provide a comprehensive interface to BUFR data for every Fortran 90 program using it. In particular, BufrMod provides all the BUFR functionality required for the scatterometer processor based on genscat. Special attention has been paid to testing and error handling.
8.1 Background
The acronym BUFR stands for Binary Universal Form for the Representation of data. BUFR is maintained by the World Meteorological Organization WMO and other meteorological centres. In brief, the WMO FM-94 BUFR definition is a binary code designed to represent, employing a continuous binary stream, any meteorological data. It is a self defining, table driven and very flexible data representation system. It is beyond the scope of this document to describe BUFR in detail. Complete descriptions are distributed via the websites of WMO ( http://www.wmo.int/ ) and of the European Centre for Medium-range Weather Forecasts ECMWF ( http://www.ecmwf.int/ ).
Module BufrMod is in fact an interface. On the one hand it contains (temporary) definitions to set the arguments of the ECMWF library functions. On the other hand, it provides self explaining
routines to be incorporated in the wider Fortran 90 program. Section 8.2 describes the routines in
module BufrMod. The public available data structures are described in section 8.3. BufrMod uses
two libraries: the BUFR software library of ECMWF and bufrio, a small library in C for file
handling at the lowest level. These libraries are discussed in some more detail in section 8.4.
8.2 Routines
Table 8.1 provides an overview of the routines in module BufrMod. The most important ones are described below.
InitAndSetNrOfSubsets set_BUFR_fileattributes open_BUFR_file get_BUFR_nr_of_messages
AWDP
AWDP
Opens a BUFR file
Inquiry of BUFR file
get_BUFR_message
AWDP Reads instance of BufrDataType from file
get_expected_BUFR_msg_size get_BUFR_message
Inquiry of BUFR file
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PrintBufrErrorCode ExpandBufrMessage,
EncodeBufrData
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get_bufrfile_size_c get_file_size Support routine in C
encode_table_b CheckBufrTables encode_table_d CheckBufrTables
AWDP Closes a BUFR file
close_BUFR_file
BufrReal2Int
BufrInt2Real save_BUFR_message bufr_msg_is_valid set_bufr_msg_to_invalid
PrintBufrData
GetPosBufrData
GetRealBufrData
GetIntBufrData
GetRealBufrDataArr
GetIntBufrDataArr
GetRealAllBufrDataArr
CloseBufrHelpers missing_real missing_int int2real do_range_check_int do_range_check_real
AddRealDataToBufrMsg
AddIntDataToBufrMsg
PrintBufrModErrorCode
not used not used not used not used not used not used not used not used not used not used not used not used not used not used not used not used not used not used
encode_table_d
Table 8.1 Routines of module BufrMod.
Reading (decoding): Routine get_BUFR_message() reads a single BUFR message from the
BUFR file and creates an instance of BufrDataType.
Writing (encoding): Routine save_BUFR_message() saves a single BUFR message to the BUFR file. The data should be provided as an instance of BufrDataType.
Checking and Printing: The integer parameter BufrVerbosity controls the extent of the log statements while processing the BUFR file. The routines PrintBufrData() and CheckBufrData() can be used to respectively print and check instances of BufrDataType.
Open and Close BUFR files: The routine open_BUFR_file() opens the BUFR file for either reading (writemode=.false.) or writing (writemode=.true.). Routine set_BUFR_fileattributes() determines several aspects of the BUFR file and saves these data in an instance of
bufr_file_attr_data, see table 8.5. Routine get_BUFR_nr_of_messages() is used to determine the number of BUFR messages in the file. Finally, routine close_BUFR_file() closes the BUFR file.
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As said before, the underlying encoding and decoding routines originate from the ECMWF BUFR library. Appendix B3 shows the calling trees of the routines in module BufrMod that are used in
AWDP.
8.3 Data structures
The data type closest to the actual BUFR messages in the BUFR files is the BufrMessageType, see table 8.2. These are still encoded data. Every BUFR message consists of 5 sections and one supplementary section. After decoding (expanding) the BUFR messages, the data are transferred into an instance of BufrSectionsType, see table 8.3, which contains the data and meta data in integer values subdivided in these sections.
Attribute Type Description
buff size
integer array integer
nr_of_words
integer
BUFR message, all sections
Size in bytes of BUFR message
Idem, now size in words
Table 8.2 Attributes for the BufrMessageType data type.
Attribute Type Description
ksup(9)
ksec(3) integer Supplementary info and items selected from the other sections integer Expanded section 0 (indicator)
ksec1(40) integer Expanded section 1 (identification)
ksec2(4096) integer Expanded section 2 (optional)
ksec3(4)
ksec4(2) integer Expanded section 3 (data description) integer Expanded section 4 (data)
Table 8.3 Attributes for the BufrSectionsType data type.
Nsec0 nsec0size
Nsec1 nsec1size kEditionNumber
Kcenter
integer integer ksup ( 9) dimension section 0 ksec0( 1) size section 0
nBufrLength
integer ksec0( 2) length BUFR
nBufrEditionNumber
integer ksec0( integer integer ksup ( 1) dimension section 1 ksec1( 1) size section 1
kUpdateNumber kOptional ktype ksubtype
integer ksec1( 7) local use
kLocalVersion kyear kmonth kday khour kminute
integer ksec1( 9) century year integer ksec1(10) integer ksec1(11) integer ksec1(12) integer ksec1(13)
kMasterTableNumber
integer ksec1(14)
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kMasterTableVersion
integer ksec1(15)
ksubcenter
integer ksec1(16)
klocalinfo()
Nsec2
integer ksec1(17:40) integer ksup ( 2) dimension section 2
nsec2size
key(46)
Nsec3 nsec3size
integer integer integer integer ksec2( 1) size section 2 ksec2( 2: ) key ksup ( 3) dimension section 3 ksec3( 1) size section 3
Kreserved3 ksubsets kDataFlag
Nsec4 nsec4size kReserved4 nelements nsubsets
integer integer integer integer ksec3( 3) number of reserved subsets ksec3( 4) compressed (0,1) observed (0,1) ksup ( 4) dimension section 4 ksec4( 1) size section 4
nvals nbufrsize ktdlen ktdexl ktdlst() ktdexp() values() cvals() cnames() cunits()
integer integer integer integer integer integer integer array integer array real array character array character array character array ksup ( 5) actual number of elements ksup ( 6) actual number of subsets ksup ( 7) actual number of values ksup ( 8) actual size of BUFR message
Actual number of data descriptors
Actual number of expanded data descriptors
List of data descriptors
List of expanded data descriptors
List of values
List of CCITT IA no. 5 elements
List of expanded element names
List of expanded element units
Table 8.4 Attributes of the BUFR message data type BufrDataType.
The next step is to bring the section data to actual dimensions, descriptions and values of data which can be interpreted as physical parameters. Therefore, instances of BufrSectionsType are transferred to instances of BufrDataType, see table 8.4. The actual data for input or output in a
BUFR message should be an instance of the BufrDataType data type. Some meta information on the BUFR file is contained in the self explaining bufr_file_attr_data data type, see table 8.5.
nr_of_BUFR_mesasges bufr_filename bufr_fileunit file_size file_open writemode
integer integer integer logical logical
is_cray_blocked
integer
list_of_BUFR_startpointers()
integer
message_is_valid()
logical
Number of BUFR messages
Fortran unit of BUFR file
Size of BUFR file
Open status of BUFR file
Reading or writing mode of BUFR file
Cray system blocked?
Pointers to BUFR messages
Validity of BUFR messages
Table 8.5 Attributes of the bufr_file_attr_data data type for BUFR files.
8.4 Libraries
Module BufrMod uses two libraries: the BUFR software library of ECMWF and bufrio, a small
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library in C for file handling at the lowest level.
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The BUFR software library of ECMWF is used as a basis to encode and decode BUFR data. This software library is explained in [Dragosavac, 1994].
Library bufrio contains routines for BUFR file handling at the lowest level. Since this is quite hard to achieve in Fortran, these routines are coded in C. The routines of bufrio are listed in table 8.6.
The source file (bufrio.c) is located in subdirectory genscat/support/bufr.
Routine Call
bufr_open open_BUFR_file bufr_split open_BUFR_file bufr_read_allsections get_BUFR_message bufr_get_section_sizes get_BUFR_message
Description
Open file
Find position of start of messages in file
Read BufrMessageType from BUFR file
bufr_write_allsections save_BUFR_message bufr_close close_BUFR_file bufr_error
see appendix B.3
Write BufrMessageType to BUFR file
Error handling
Table 8.6 Routines in library bufrio.
8.5 BUFR table routines
BUFR tables are used to define the data descriptors. The presence of the proper BUFR tables is checked before calling the reading and writing routines. If absent, it is tried to create the needed
BUFR tables from the text version, available in genscat.
8.6 Centre specific modules
BUFR data descriptors are integers. These integers consist of class numbers and numbers for the described parameter itself. These numbers are arbitrary. To establish self documenting names for the BUFR data descriptors for a Fortran 90 code several centre specific modules are created. These modules are listed in table 8.7. Note that these modules are just cosmetic and not essential for the encoding or decoding of the BUFR data. They are not used in AWDP.
Module Description
WmoBufrMod
WMO standard BUFR data description
KnmiBufrMod
KNMI BUFR data description
EcmwfBufrMod ECMWF BUFR data description
Table 8.7 Fortran 90 BUFR modules.
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Chapter 9
Module gribio_module is part of the genscat support modules. The current version is a Fortran 90 wrapper around the ECMWF GRIB API library (see http://www.ecmwf.int/ ). The goal of this support module is to provide a comprehensive interface to GRIB data for every Fortran 90 program using it. In particular, gribio_module provides all the GRIB functionality required for the scatterometer processor based on genscat. Special attention has been paid to testing and error handling.
9.1 Background
The acronym GRIB stands for GRIdded Binary. GRIB is maintained by the World Meteorological
Organization WMO and other meteorological centres. In brief, the WMO FM-92 GRIB definition is a binary format for efficiently transmitting gridded meteorological data. It is beyond the scope of this document to describe GRIB in detail. Complete descriptions are distributed via the websites of WMO ( http://www.wmo.int/ ) and of the European Centre for Medium-range Weather Forecasts
ECMWF ( http://www.ecmwf.int/ ).
Module gribio_module is in fact an interface. On the one hand it contains (temporary) definitions to set the arguments of the ECMWF library functions. On the other hand, it provides self
explaining routines to be incorporated in the wider Fortran 90 program. Section 9.2 describes the
routines in module gribio_module. The available data structures are described in section 9.3. The
gribio_module uses two libraries: from the GRIB software library of ECMWF. This is discussed in
some more detail in section 9.4.
9.2 Routines
Table 9.1 provides an overview of the routines in module gribio_module. The most important ones are described below.
Routine Call Description
init_GRIB_module dealloc_all_GRIB_messages
AWDP Clear all GRIB info from memory and close GRIB files
set_GRIB_filelist get_from_GRIB_filelist inquire_GRIB_filelist
AWDP
AWDP,
get_colloc_from_GRIB_filelist
AWDP,
Open all necessary GRIB files
Retrieve GRIB data for a given lat and lon
Inquiry of GRIB file list
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Routine Call Description
get_colloc_from_GRIB_filelist get_analyse_dates_and_times,
get_colloc_from_GRIB_filelist
AWDP Retrieve time interpolated GRIB data for a given lat and lon
Inquiry of GRIB file list get_GRIB_msgnr get_field_from_GRIB_file,
get_from_GRIB_file,
get_from_GRIB_filelist,
inquire_GRIB_filelist
display_req_GRIB_msg_properties get_GRIB_msgnr,
get_from_GRIB_filelist
display_GRIB_message_properties get_GRIB_msgnr,
get_from_GRIB_filelist
Prints GRIB message info
Prints GRIB message info
get_from_GRIB_file,
set_GRIB_filelist, information from all messages in this file
add_to_GRIB_filelist read_GRIB_header_info open_GRIB_file
Read header part of a GRIB message
extract_data_from_GRIB_message get_from_GRIB_file, get_from_GRIB_filelist
Interpolate data from four surrounding points for a given lat and lon
get_from_GRIB_file, get_from_GRIB_filelist dealloc_GRIB_message open_GRIB_file, dealloc_all_GRIB_messages,
Clear GRIB message from memory
get_analyse_dates_and_times get_field_from_GRIB_file
get_colloc_from_GRIB_filelist Helper routine
check_proximity_to_analyse get_colloc_from_GRIB_filelist Helper routine
get_field_from_GRIB_file get_from_GRIB_file add_to_GRIB_filelist
not used not used not used
Table 9.1 Routines of module gribio_module.
Reading: Routine set_GRIB_filelist reads GRIB messages from a list of files, decodes them and makes the data accessible in a list of GRIB messages in memory.
Retrieving: Routine get_from_GRIB_filelist() returns an interpolated value (four surrounding grid points) from the GRIB data in the list of files/messages for a given GRIB parameter, latitude and longitude. It is also possible to get a weighted value of all grid points lying within a circle around the latitude and longitude of interest. This is used in the land fraction calculation in AWDP. The land fraction is calculated by scanning all grid points of the land-sea mask lying within 80 km from the centre of the WVC. Every grid point found yields a land fraction (between 0 and 1). The land fraction of the WVC is calculated as the average of the grid land fractions, where each grid land fraction has a weight of 1/r
2
, r being the distance between the WVC centre and the model grid point.
Routine get_colloc_from_GRIB_filelist() returns an interpolated value (four surrounding grid points) from the GRIB data in the list of files/messages for a given GRIB parameter, latitude, longitude, and time. The list of messages must contain a sequence of forecasts (e.g. +3 hrs, +6 hrs,
+9 hrs, et cetera). At least three forecasts need to be provided; ideally two lying before the sensing time and one after.
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1 2 ^ 3
In this diagram, the 1, 2, and 3 mean the three forecast steps with intervals of three hours between them. The ^ is the sensing time. The software will perform a cubic time interpolation. Note that the 1, 2 and 3 in the diagram may correspond to +3, +6 and +9 forecasts, but also e.g. to +9, +12 and +15. If more forecasts are provided, e.g. like this:
----|-----|-----|-----|-----|----
1 2 3 ^ 4 5 the software will use forecast steps 2, 3, and 4, i.e., it will pick the most usable values by itself. If one forecast before, and two after are provided:
----|-----|-----|----
1 ^ 2 3 the software will still work, and use all three forecasts.
Checking and Printing: The integer parameter GribVerbosity controls the extent of the log statements while processing the GRIB data.
As said before, the underlying encoding and decoding routines originate from the ECMWF GRIB library. Appendix B4 shows the calling trees of the routines in module gribio_module that are used in AWDP.
9.3 Data structures
Some meta information on the GRIB file is contained in the self explaining grib_file_attr_data data type, see table 9.2.
The decoded GRIB messages in the GRIB files, with their meta information, are contained in the
grib_message_data, see table 9.3.
nr_of_GRIB_messages grib_filename grib_fileunit file_size
integer Number of messages in this file character array Name of GRIB file integer integer
Unit number in file table
Size of GRIB file in bytes
file_open list_of_GRIB_message_ids
integer array
list_of_GRIB_level list_of_GRIB_level_type
integer array integer array
Message ids assigned by GRIB API
Key to information in messages
Key to information in messages
Key to information in messages
list_of_GRIB_date list_of_GRIB_hour
integer array integer array
list_of_GRIB_analyse
integer array
list_of_GRIB_derived_date
integer array
list_of_GRIB_derived_hour
integer array
Key to information in messages
Key to information in messages
Key to information in messages
Key to information in messages
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list_of_GRIB_par_id list_of_GRIB_vals_sizes
integer array integer array
Key to information in messages
Size of data values arrays
Table 9.2 Attributes for the grib_file_attr_data data type.
Attribute Type Description
message_pos_in_file
integer Position of message in GRIB file
message_id
integer Message id assigned by GRIB API
date time derived_date derived_time total_message_size vals_size is_decoded nr_lon_points
real real real real mod(time/24) integer integer
Date when data are valid
Time when data are valid date + time/24
Size of message
Size of data values array
nr_lat_points nr_grid_points lat_of_first_gridpoint
grid
lat_of_last_gridpoint
grid
lon_of_first_gridpoint
grid
lon_of_last_gridpoint
grid
lat_step lon_step real_values
real array, pointer Decoded real data values
Table 9.3 Attributes for the grib_message_data data type.
Attribute Type Description
grib_file_attributes
attributes
list_of_GRIB_msgs
grib_message_data array List of messages in file
Table 9.4 Attributes of the list_of_grib_files_type data type for GRIB files.
9.4 Libraries
Module gribio_module uses two libraries: from the GRIB API software library of ECMWF: libgrib_api.a
and libgrib_api_f90.a. The GRIB API software library of ECMWF is used as a basis to decode GRIB data. This software library is explained on http://www.ecmwf.int/ .
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References
• Belmonte, M., J. Verspeek, A. Verhoef and A. Stoffelen, 2012,
Bayesian sea ice detection with the Advanced Scatterometer, IEEE Transactions on
Geoscience and Remote Sensing, 2012, 50, 7, 2649-2657, doi:10.1109/TGRS.2011.2182356.
• Dragosavac, M., 1994,
BUFR User Guide and Reference Manual. ECMWF. (Available on http://www.ecmwf.int/ )
• Figa-Saldaña, J., and Wilson, J.J.W., 2005,
ASCAT Level 1 Product Format Specification, Issue 6, Rev 5, EUMETSAT,
EPS.MIS.SPE.97233 (Available on http://www.eumetsat.int/ ).
• Giering, R., 1997,
Tangent linear and Adjoint Model Compiler, Users manual. Max-Planck- Institut fuer
Meteorologie.
• Hersbach, H., Stoffelen, A. and de Haan, S., 2007,
An improved C-band scatterometer ocean geophysical model function: CMOD5, Journal of Geophysical Research, 112.
• Liu, D.C., and Nocedal, J., 1989
On the limited memory BFGS method for large scale optimization methods. Mathematical
Programming, 45, pp. 503-528.
• UK Met Office, 2001
ERS Products WMO FM94 BUFR Format, ER-IS-UKM-GS-0001, Version 4, Issue 2.
• Portabella, M., 2002,
Wind field retrieval from satellite radar systems, PhD thesis, University of Barcelona.
(Available on http://www.knmi.nl/scatterometer/publications/ ).
• Portabella, M. and Stoffelen, A., 2001,
Rain Detection and Quality Control of SeaWinds, Journal of Atm. Oceanic Technol., 18, pp. 1171-1183.
• Portabella, M. and Stoffelen, A., 2004,
A probabilistic approach for SeaWinds Data Assimilation, Quart. J. Royal Meteor. Soc.,
130, pp. 127-152.
• Stoffelen, A. and M. Portabella, 2006,
On Bayesian Scatterometer Wind Inversion, IEEE Transactions on Geoscience and
Remote Sensing, 44, 6, 1523-1533, doi:10.1109/TGRS.2005.862502
.
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• Stoffelen, A., de Haan, S., Quilfen, Y., and Schyberg, H., 2000,
ERS scatterometer ambiguity removal scheme comparison, OSI SAF report. (Available on http://www.knmi.nl/scatterometer/publications/ ).
• Stoffelen, A.C.M., 1998,
Scatterometry, PhD thesis, University of Utrecht, ISBN 90-393-1708-9. (Available on http://www.knmi.nl/scatterometer/publications/ ).
• Talagrand, O., 1991,
The use of adjoint equations in numerical modeling of the atmospheric circulation. In:
Automatic Differentiation of Algorithms: Theory, Implementation and Application, A.
Griewank and G. Corliess Eds. pp. 169-180, Philadelphia, Penn: SIAM.
• Verhoef, A., M. Portabella and A. Stoffelen, 2012,
High-resolution ASCAT scatterometer winds near the coast, IEEE Transactions on
Geoscience and Remote Sensing, 2012, 50, 7, 2481-2487, doi:10.1109/TGRS.2011.2175001.
• Verhoef, A., Vogelzang, J., Verspeek, J. and Stoffelen, A., 2013,
AWDP Test Report, Report NWPSAF-KN-TV-005, UKMO, UK.
• Verspeek, J., A. Stoffelen, A. Verhoef and M. Portabella, 2012,
Improved ASCAT Wind Retrieval Using NWP Ocean Calibration, IEEE Transactions on
Geoscience and Remote Sensing, 2012, 50, 7, 2488-2494, doi:10.1109/TGRS.2011.2180730.
• Vogelzang, J., 2007,
Two dimensional variational ambiguity removal (2DVAR). Report NWPSAF-KN-TR-004,
UKMO, UK. (Available on http://www.knmi.nl/scatterometer/publications/ ).
• Vogelzang, J., Stoffelen, A., Verhoef, A., de Vries, J. and Bonekamp, H., 2008,
Validation of two-dimensional variational ambiguity removal on SeaWinds scatterometer
data, submitted to J. Atm. Oceanic Technol.
• de Vries, J. and Stoffelen, A., 2000,
2D Variational Ambiguity Removal. KNMI, Feb 2000. (Available on http://www.knmi.nl/scatterometer/publications/ ).
• de Vries, J., Stoffelen, A., and Beysens, J., 2005,
Ambiguity Removal and Product Monitoring for SeaWinds. KNMI. (Available on http://www.knmi.nl/scatterometer/publications/ ).
• Wilson, J.J.W, Figa-Saldaña, J., and O’Clerigh, E., 2004,
ASCAT Product Generation Function Specification, Issue 6, Rev 5, EUMETSAT,
EUM.EPS.SYS.SPE.990009 (Available on http://www.eumetsat.int/ ).
• WMO, 2007,
Additions to BUFR/CREX Tables for pre-operational implementation endorsed by CBS for
full operational status on 7 November 2007 (updated 04/01/07), pages 55-60 (available on http://www.wmo.int/web/www/WMOCodes/Updates/BUFRCREX/Preoperational050107.
doc )
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Appendix A
Calling tree for AWDP
The figures in this appendix show the calling tree for the AWDP program. Routines in white boxes are part of the AWDP process layer. Routines in black boxes are part of genscat. An arrow (
→) before a routine name indicates that this part of the calling tree is a continuation of a branch in a previous figure. The same arrow after a routine name indicated that this branch will be continued in a following figure. awdp iargc_genscat getarg_genscat write_usage read_bufr_file (
→) read_pfs_file (
→) preprocess (
→) read_full_res_data (
→) calibrate_s0 get_grib_data (
→) invert_wvcs (
→) ice_model (
→) remove_ambiguities (
→) calibrate_s0 postprocess (
→) write_bufr_file (
→) process_cleanup
GetElapsedSystemTime
Figure A.1 Calling tree for program awdp (top level). White boxes are cut here and will be continued in one of the first level or second level calling trees in the next figures. Black boxes with light text indicate genscat routines.
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(
→) read_bufr_file
GetElapsedSystemTime init_bufr_processing set_BUFR_fileattributes open_BUFR_file (
→) get_BUFR_nr_of_messages get_BUFR_message (
→) ers_bufr_to_row_data init_cell (
→) get_wvc_quality
BufrReal2Int get_beam_collocation set_knmi_flag test_cell (
→) ymd2julian julian2ymd ascat_bufr_to_row_data init_cell (
→) get_wvc_quality
BufrReal2Int get_beam_collocation get_kp_estim_qual test_cell (
→) close_BUFR_file (
→) ymd2julian
Figure A.2 Calling tree for routine read_bufr_file (first level).
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(
→) read_pfs_file
GetElapsedSystemTime get_lun open_pfs_ascat_file (
→) ascat_pfs_to_row_data read_pfs_ascat_data_record (
→) get_pfs_ascat_grid_node (
→) init_cell (
→) get_beam_collocation get_kp_estim_qual test_beam init_beam
WVC_Orientation test_cell (
→) close_pfs_ascat_file free_lun ymd2julian
Figure A.3 Calling tree for routine read_pfs_file (first level).
(
→) preprocess
GetElapsedSystemTime
GetSortIndex merge_rows copy_cell set_knmi_flag init_cell (
→) copy_cell test_cell (
→) pre_inversion_qc
Figure A.4 Calling tree for routine preprocess (first level).
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(
→) read_full_res_data
GetElapsedSystemTime init_grib_processing init_GRIB_module set_GRIB_filelist (
→) inquire_GRIB_filelist (
→) get_lun open_pfs_ascat_file (
→) read_pfs_ascat_data_record (
→) get_pfs_ascat_fres_node (
→) get_distance get_from_GRIB_filelist (
→) test_cell (
→) close_pfs_ascat_file free_lun init_l2_sm init_l2_wind (
→) get_wvc_quality
Figure A.5 Calling tree for routine read_full_res_data (first level).
(
→) get_grib_data
GetElapsedSystemTime init_grib_processing init_GRIB_module set_GRIB_filelist (
→) inquire_GRIB_filelist (
→) get_from_GRIB_filelist (
→) get_colloc_from_GRIB_filelist (
→) test_cell (
→) dealloc_all_GRIB_messages (
→)
Figure A.6 Calling tree for routine get_grib_data (first level).
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(
→) invert_wvcs
GetElapsedSystemTime init_inversion init_inv_settings_to_default get_inv_settings set_inv_settings invert_node init_inv_input invert_one_wvc (
→) normalise_conedist_prescat_mode (
→) normalise_conedist_ers_ascat (
→) check_wind_solutions_ers_ascat (
→) calc_probabilities
GetSortIndex test_cell (
→)
Figure A.7 Calling tree for routine invert_wvcs (first level).
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(
→) ice_model
GetElapsedSystemTime nonbayesianIcemodel calcIcelineParms calcPoly3 getClass set_knmi_flag test_cell (
→) bayesianIcemodel initIceMap
RW_IceMap scat2iceMap latlon2ij (
→) calcIceCoord (
→) met2uv
SetIntegerDate
SetIntegerTime updateIcePixel (
→) printIcePixel (
→) print_icemodel print_wvc_quality print_cell (
→) calc_pIceGivenX
ExpandDateTime wT (
→) calc_aAve
ExpandDateTime wT (
→) calc_aSd
ExpandDateTime wT (
→) calcSubClass smooth iceMap2scat set_knmi_flag printIceMap (
→)
Figure A.8 Calling tree for routine ice_model (first level).
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(
→) remove_ambiguities
GetElapsedSystemTime
InitAmbremModule
InitBatchModule
InitAmbremMethod
InitAmbremBGclosest
InitTwodvarModule (
→)
InitDummyMethod
GetMaxBatchSize fill_batch get_distance
AllocRowsAndCellsAnd…
InitBatch
AllocAndInitBatchRow
InitBatchRow
InitBatchCell
AllocAndInitBatchCell
InitBatchCell
InitBatchAmbi speeddir_to_u speeddir_to_v
TestBatch
TestBatchRow
TestBatchCell
DoAmbrem (
→) select_wind
TestBatchCell test_cell (
→)
DeallocBatch
DeallocBatchRows
DeallocBatchCells
DeallocBatchAmbis
ExitAmbremMethod
ExitTwodvarModule
TDV_Exit
Figure A.9 Calling tree for routine remove_ambiguities (first level). The full name of the 12 th
routine is
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AllocRowsAndCellsAndInitBatch.
(
→) postprocess
GetElapsedSystemTime monitoring speeddir_to_u speeddir_to_v get_lun free_lun write_properties get_lun free_lun write_binary_output get_lun free_lun
Figure A.10 Calling tree for routine postprocess (first level).
(
→) write_bufr_file
GetElapsedSystemTime init_bufr_processing set_BUFR_file_attributes open_BUFR_file (
→)
InitAndSetNrOfSubsets row_to_bufr_data
BufrInt2Real set_beam_collocation set_kp_estim_qual set_wvc_quality save_BUFR_message (
→) close_BUFR_fille (
→)
Figure A.11 Calling tree for routine write_bufr_file (first level).
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(
→) init_cell init_time get_beam_collocation init_beam get_kp_estim_qual init_full_res get_l2_sm init_l2_wind (
→)
Figure A.12 Calling tree for routine init_cell (second level).
(
→) init_l2_wind init_wind get_wvc_quality init_icemodel init_process_flag
Figure A.13 Calling tree for routine init_l2_wind (second level).
(
→) test_cell test_time test_beam test_wind
Figure A.14 Calling tree for routine test_cell (second level).
(
→) print_cell print_time print_beam print_wind print_wvc_quality print_ambiguity print_process_flag
Figure A.15 Calling tree for routine PrintCell (second level).
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(
→) calcIceCoord calcIcelineParms calcPoly3
Figure A.16 Calling tree for routine calcIceCoord (second level).
(
→)updateIcePixel
ExpandDateTime getClass getPx
Figure A.17 Calling tree for routine updateIcePixel (second level).
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Appendix B1
Calling tree for inversion routines
The figures in this appendix show the calling tree for the inversion routines in genscat. All routines are part of genscat, as indicated by the black boxes. An arrow (
→) before a routine name indicates that this part of the calling tree is a continuation of a branch in a previous figure. The same arrow after a routine name indicates that this branch will be continued in a following figure.
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(
→) invert_one_wvc init_inv_settings_to_default init_inv_output print_message check_input_data convert_sigma_to_zspace calc_normalisation print_input_data_of_inversion my_exit print_message calc_var_s0 find_minimum_cone_dist (
→) my_min my_average my_max get_indices_lowest_local_minimum my_index_max print_message do_parabolic_winddir_search get_parabolic_minimum my_exit
GetSortIndex
SortWithIndex calc_sign_MLE calc_sigma0 (
→) fill_wind_quality_code (
→)
Figure B1.1 Calling tree for inversion routine invert_one_wvc.
(
→) find_minimum_cone_dist calc_cone_distance calc_sigma0 (
→) get_parabolic_minimum my_exit
Figure B1.2 Calling tree for inversion routine find_minimum_cone_dist
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(
→) calc_sigma0 read_LUT get_lun free_lun create_LUT_C_VV get_lun calc_sigma0_cmod4
Get_Br_from_Look_Up_Table f1 calc_sigma0_cmod5 (_5, _n) free_lun test_for_identical_LUTs my_exit
INTERPOLATE
Figure B1.3 Calling tree for inversion routine calc_sigma0. Routine INTERPOLATE is an interface that can have the values interpolate1d, interpolate2d, interpolate2dv or interpolate3d. There are several equivalent routines to calculate the CMOD backscatter, like calc_sigma0_cmod5, calc_sigma0_cmod5_5,
calc_sigma0_cmod5_n.
(
→) normalize_conedist_prescat_mode check_ers_ascat_inversion_data get_ers_noise_estimate
Figure B1.4 Calling tree for inversion routine normalize_conedist_prescat_mode.
(
→) normalise_conedist_ers_ascat check_ers_ascat_inversion_data calc_kp_ers_ascat calc_geoph_noise_ers_ascat
Figure B1.5 Calling tree for inversion routine normalize_conedist_ers_ascat.
(
→) check_wind_solutions_ers_ascat remove_one_solution calc_dist_to_cone_center calc_sigma0 (
→)
Figure B1.6 Calling tree for inversion routine check_wind_solutions_ers_ascat.
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Appendix B2
Calling tree for AR routines
The figures in this appendix show the calling tree for the Ambiguity Removal routines in genscat.
All routines are part of genscat, as indicated by the black boxes. An arrow (
→) before a routine name indicates that this part of the calling tree is a continuation of a branch in a previous figure.
The same arrow after a routine name indicates that this branch will be continued in a following figure.
(
→) InitTwodvarModule
TDV_Init
Set_CFW
Set_HelmholzCoefficients
Figure B2.1 Calling tree for AR routine InitTwodvarModule.
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(
→) DoAmbrem
TestBatch
TestBatchRow
AmbRem1stRank
DoAmbremBGclosestOnBatch uv_to_dir
DoAmbremPreScatOnBatch
DoAmbremBGclosestOnBatch
TestBatchCell uv_to_dir
Do2DVARonBatch
BatchInput2DVAR
TestBatchCell
InitObs2DVAR (
→)
Set_WVC_Orientations
WVC_Orientation rotuv
PrintObs2DVAR
Do2DVAR (
→)
BatchOutput2DVAR rotuv
InitObs2DVAR (
→)
DeallocObs2DVAR
DoDummyMeth
Figure B2.2 Calling tree for AR routine DoAmbrem.
(
→) InitObs2dvar
InitOneObs2dvar
TestObs2dvar set2DVARQualFlag
Figure B2.3 Calling tree for AR routine InitObs2dvar.
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Date : February 2014
(
→) Do2DVAR
TestObs2dvar set2DVARQualFlag
Prn2DVARQualFlag
SetCovMat
StrucFuncPsi
StrucFuncChi
SingletonFFT2d (
→)
Jt (
→)
Minimise
Jt (
→)
LBFGS daxpy ddot
LB1
MCSRCH
MCSTEP
TestObs2dvar set2DVARQualFlag
DumpAnalysisField
Figure B2.4 Calling tree for AR routine Do2DVAR.
(
→) Jt
Jb
Jo
Unpack_ControlVector
Uncondition
SingletonFFT2d (
→)
JoScat
Uncondition_adj
SingletonFFT2d (
→)
Pack_ControlVector
Figure B2.5 Calling tree for AR routine Jt (calculation of cost function).
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(
→) SingletonFFT2d fft
SFT_PrimeFactors
SFT_Permute
SFT_PermuteSinglevariate
SFT_PermuteMultivariate
SFT_Base2
SFT_Base3
SFT_Base4
SFT_Base5
SFT_BaseOdd
SFT_Rotate
Figure B2.6 Calling tree for AR routine SingletonFFT2D.
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Appendix B3
Calling tree for BUFR routines
The figures in this appendix show the calling tree for the BUFR file handling routines in genscat.
Routines in black boxes are part of genscat. Routines in grey boxes with names completely in capitals belong to the ECMWF BUFR library. Other routines in grey boxes belong to the bufrio library (in C). An arrow (
→) before a routine name indicates that this part of the calling tree is a continuation of a branch in a previous figure. The same arrow after a routine name indicates that this branch will be continued in a following figure.
(
→) open_BUFR_file bufr_open bufr_error bufr_split
Figure B3.1 Calling tree for BUFR file handling routine open_BUFR_file.
(
→) close_BUFR_file bufr_close bufr_error
Figure B3.2 Calling tree for BUFR handling routine close_BUFR_file.
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(
→) get_BUFR_message get_expected_BUFR_msg_size bufr_read_allsections bufr_error bufr_get_section_sizes bufr_swap_allsections
ExpandBufrMessage
BUS012
PrintBufrErrorCode
CheckBufrTables
Doc ID : NWPSAF-KN-UD-005
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Date : February 2014 get_file_size encode_table_b encode_table_d
BUFREX
FillBufrSecData
BUSEL
Figure B3.3 Calling tree for BUFR handling routine get_BUFR_message.
(
→) save_BUFR_message
EncodeBufrData
CheckBufrData
FillBufrData
BUFREN
PrintBufrErrorCode bufr_swap_allsections bufr_write_allsections bufr_error
Figure B3.4 Calling tree for BUFR file handling routine save_BUFR_file.
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Appendix B4
Calling tree for GRIB routines
The figures in this appendix show the calling tree for the GRIB file handling routines in genscat.
Routines in black boxes are part of genscat. Routines in grey boxes belong to the ECMWF GRIB
API library. An arrow (
→) before a routine name indicates that this part of the calling tree is a continuation of a branch in a previous figure. The same arrow after a routine name indicates that this branch will be continued in a following figure.
(
→) set_GRIB_filelist open_GRIB_file grib_open_file grib_multi_support_on grib_new_from_file read_GRIB_header_info grib_get
Figure B4.1 Calling tree for GRIB file handling routine set_GRIB_filelist.
(
→) inquire_GRIB_filelist get_GRIB_msgnr display_req_GRIB_msg_properties display_GRIB_message_properties
Figure B4.2 Calling tree for GRIB file handling routine inquire_GRIB_filelist.
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(
→) get_from_GRIB_filelist get_GRIB_msgnr display_req_GRIB_msg_properties display_GRIB_message_properties display_req_GRIB_msg_properties display_GRIB_message_properties get_GRIB_data_values grib_get grib_is_missing grib_set get_angle_distance extract_data_from_GRIB_message
Figure B4.3 Calling tree for GRIB file handling routine get_from_GRIB_filelist.
(
→) get_colloc_from_GRIB_filelist convert_to_derived_datetime conv_date_to_daycount get_analyse_date_and_times inquire_GRIB_filelist (
→) check_proximity_to_analyse conv_date_to_daycount inquire_GRIB_filelist (
→) get_from_GRIB_filelist (
→)
Figure B4.4 Calling tree for GRIB file handling routine get_colloc_from_GRIB_filelist.
(
→) dealloc_all_GRIB_messages dealloc_GRIB_message grib_release grib_close_file
Figure B4.5 Calling tree for GRIB file handling routine dealloc_all_GRIB_messages.
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Appendix B5
Calling tree for PFS routines
The figures in this appendix show the calling tree for the PFS (native Metop format) file handling routines in genscat. All routines are part of genscat, as indicated by the black boxes. An arrow (
→) before a routine name indicates that this part of the calling tree is a continuation of a branch in a previous figure. The same arrow after a routine name indicates that this branch will be continued in a following figure.
(
→) open_pfs_ascat_file read_rec read_string_from_file get_uint get_str get_num strne skip_nrec skip_rec read_string_from_file get_uint streq
Figure B5.1 Calling tree for PFS file handling routine open_pfs_ascat_file.
(
→) read_pfs_ascat_data_record read_string_from_file get_uint
Figure B5.2 Calling tree for PFS file handling routine read_pfs_ascat_mdr.
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(
→) get_pfs_ascat_grid_node
AWDP User Manual and
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get_szo_node get_time
Doc ID : NWPSAF-KN-UD-005
Version : 2.3
Date : February 2014 get_ushort get_uint ymd2julian julian2ymd get_ushort1 get_ushort get_int1 get_int get_uint1 get_uint get_int3 get_int get_ushort3 get_ushort get_short3 get_short get_uchar3 get_szr_node get_smo_node get_smr_node get_szf_grid_node ymd2julian calc_asc
Figure B5.3 Calling tree for PFS file handling routine get_pfs_ascat_node. The calling tree for
get_szr_node, get_smo_node, get_smr_node and get_szf_grid_node is identical to to the one of
get_szo_node.
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(
→) get_pfs_ascat_fres_node get_time
Doc ID : NWPSAF-KN-UD-005
Version : 2.3
Date : February 2014 get_ushort get_uint ymd2julian julian2ymd get_int1 get_int get_ushort1 get_ushort get_uint1 get_uint get_uchar1 ymd2julian calc_asc
Figure B5.4 Calling tree for PFS file handling routine get_pfs_ascat_szf_node.
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Appendix B6
Calling tree for ice model routines
The figures in this appendix show the calling tree for the ice model routines in genscat. All routines are part of genscat, as indicated by the black boxes. An arrow (
→) before a routine name indicates that this part of the calling tree is a continuation of a branch in a previous figure. The same arrow after a routine name indicates that this branch will be continued in a following figure.
(
→) wT
ExpandIntegerDate
ExpandIntegerTime ymd2julian
Figure B6.1 Calling tree for routine printIcePixel (second level).
(
→) printIceMap printIceAscat get_lun free_lun printIceQscat get_lun free_lun printSubclass get_lun free_lun printppmvar get_lun free_lun
Figure B6.2 Calling tree for routine printIceMap (second level).
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Appendix C
ASCAT BUFR data descriptors
15
17
18
19
20
21
23
29
30
31
32
33
25
26
27
28
35
37
39
40
4
5
6
Number Descriptor Parameter
1
2
001033
001034
Identification Of Originating/Generating Centre
Identification Of Originating/Generating Sub-Centre
001007
002019
001012
Satellite Identifier
Satellite Instruments
Direction Of Motion Of Moving Observing Platform
005033
006034
010095
021157
021150
008085
002134
021063
021158
021159
021160
021161
021162
021163
021164
021165
008085
002134
021063
021158
Pixel Size On Horizontal-1
Cross Track Cell Number
Height Of Atmosphere Used
Loss Per Unit Length Of Atmosphere Used
Beam Collocation
Beam Identifier
Antenna Beam Azimuth
Radiometric Resolution (Noise Value)
ASCAT Kp Estimate Quality
ASCAT Sigma-0 Usability
ASCAT Use Of Synthetic Data
ASCAT Synthetic Data Quality
ASCAT Satellite Orbit And Attitude Quality
ASCAT Solar Array Reflection Contamination
ASCAT Telemetry Presence And Quality
ASCAT Extrapolated Reference Function
Beam Identifier
Antenna Beam Azimuth
Radiometric Resolution (Noise Value)
ASCAT Kp Estimate Quality
98
Code Table
Degree
Degree dB
%
Code Table
Code Table
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
Code Table
Degree
Degree dB
%
Code Table
Unit
Code Table
Code Table
Numeric
Code Table
Code Table
Degree True
Year
Month
Day
Hour
Minute
Second
Degree
Degree
M
Numeric
Numeric m dB/m
Flag Table
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Number Descriptor Parameter
49
51
57
58
59
60
61
53
54
55
56
41
42
43
44
45
46
47
65
66
68
69
70
75
76
77
81
021159
021160
021161
021162
021163
021164
021165
008085
002134
021063
021158
021159
021160
021161
021162
021163
021164
021165
040001
040002
021151
021152
021153
040004
040005
040006
040009
ASCAT Sigma-0 Usability
ASCAT Use Of Synthetic Data
ASCAT Synthetic Data Quality
ASCAT Satellite Orbit And Attitude Quality
ASCAT Solar Array Reflection Contamination
ASCAT Telemetry Presence And Quality
ASCAT Extrapolated Reference Function
Unit
Code Table
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
Beam Identifier
Antenna Beam Azimuth
Radiometric Resolution (Noise Value)
ASCAT Kp Estimate Quality
ASCAT Sigma-0 Usability
ASCAT Use Of Synthetic Data
ASCAT Synthetic Data Quality
ASCAT Satellite Orbit And Attitude Quality
ASCAT Solar Array Reflection Contamination
ASCAT Telemetry Presence And Quality
ASCAT Extrapolated Reference Function
Surface Soil Moisture (Ms)
Estimated Error In Surface Soil Moisture
%
% dB
Estimated Error In Sigma0 At 40 Deg Incidence Angle dB
Slope At 40 Deg Incidence Angle
Estimated Error In Slope At 40 Deg Incidence Angle dB/Degree dB/Degree dB dB
Mean Soil Moisture
Rain Fall Detection
Soil Moisture Correction Flag
Soil Moisture Processing Flag
Inundation And Wetland Fraction dB
Numeric
Numeric
Flag Table
Flag Table
%
%
%
%
%
Code Table
Degree
Degree dB
%
Code Table
Code Table
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
85
86
89
90
91
92
93
94
011082
011081
021155
021101
021102
031001
011012
011011
Model Wind Speed At 10 m
Model Wind Direction At 10 m
Wind Vector Cell Quality
Number Of Vector Ambiguities
Index Of Selected Wind Vector
Delayed Descriptor Replication Factor
Wind Speed At 10 m
Wind Direction At 10 m m/s
Degree True
Numeric dB
Flag Table
Numeric
Numeric
Numeric m/s
Degree True
Numeric
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Number Descriptor Parameter
96 021104 Likelihood Computed For Solution
97
98
011012
011011
Wind Speed At 10 m
Wind Direction At 10 m
100 021104 Likelihood Computed For Solution
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Unit
Numeric m/s
Degree True
Numeric
Numeric
Table C.1 List of data descriptors. Note that descriptor numbers 93-96 can be repeated 1 to 144 times, depending on the value of the Delayed Descriptor Replication Factor (descriptor number 92)
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Appendix D
Acronyms
Name Description
AMI Active Microwave Instrument, scatterometer on ERS-1 and ERS-2 satellites
ASCAT
BUFR
C-band
ERS
Advanced SCATterometer on Metop
Binary Universal Form for the Representation of data
Radar wavelength at about 5 cm
European Remote Sensing satellites
ECMWF European Centre for Medium-range Weather Forecasts
EUMETSAT European Organization for the Exploitation of Meteorological Satellites genscat generic scatterometer software routines
HIRLAM
KNMI
Ku-band
LSM
LUT
Metop
MSS
NRCS
NWP
OSI
PFS
RFSCAT
RMS
SAF
SST
WVC
High resolution Local Area Model
Koninklijk Nederlands Meteorologisch Instituut (Royal Netherlands Meteorological
Institute)
Radar wavelength at about 2 cm
Land Sea Mask
Look up table
Meteorological Operational Satellite
Multiple Solution Scheme
Normalized Radar Cross-Section (σ
0
Numerical Weather Prediction
Ocean and Sea Ice
)
Product Format Specification (native Metop file format)
Rotating Fan beam Scatterometer
Root Mean Square
Satellite Application Facility
Sea Surface Temperature
Wind Vector Cell, also called node or cell
Table D.1 List of acronyms.
101
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Key features
Processes ASCAT and ERS data
Generates surface winds
2DVAR Ambiguity Removal
Multiple Solution Scheme (MSS)
BUFR/GRIB file handling
Frequently asked questions
AWDP generates surface winds based on level 1b or level 2 ASCAT and ERS data.
AWDP requires EUMETSAT ASCAT level 1b BUFR/PFS data or ESA ERS level 2 BUFR data, and GRIB data containing land-sea mask, SST, and first guess winds.
The AWDP processing chain includes pre-processing, NWP collocation, inversion, quality control, ambiguity removal, and quality monitoring.
