5. GSI Applications for Regional 3DVar, Hybrid 3DEnVar and Hybrid 4DEnVar¶
In this chapter, information from the previous chapters will be applied to three regional GSI cases with different data sources. These examples are to give users a clear idea of how to set up GSI with various configurations and properly check the run status and analysis results in order to determine if a particular GSI application was successful. Note that the examples here only use the WRF-ARW system - WRF-NMM runs are similar, but require different background and namelist options.
For illustrations of all the cases, it is assumed that the reader has successfully compiled GSI on a local machine. For regional case studies, users should have the following data available:
Background file
- When using WRF, WPS and real.exe will be run to create a WRF input file: wrfinput_<domain>_<yyyy-mm-dd_hh:mm:ss>
Conventional data
Real time NAM PrepBUFR data can be obtained from the server:
ftp://ftpprd.ncep.noaa.gov/pub/data/nccf/com/nam/prod
Note: An NDAS PrepBUFR file was chosen to increase the amount of data used in the analysis (compared to a NAM PrepBUFR file)
Radiance data and GPS RO data
Real time GDAS BUFR files can be obtained from the following server:
ftp://ftpprd.ncep.noaa.gov/pub/data/nccf/com/gfs/prod
Note: GDAS data was chosen to get better coverage for radiance and GPS/RO refractivity
The following cases will give users an example of a successful GSI run with various data sources. Users are welcome to download these example data from the GSI users webpage (online case) or create a new background and get the observational data from the above server. The background and observations used in this case study are as follows:
Background files: wrfinput_d01_2014-06-17_00:00:00
The horizontal grid spacing is 30-km with 51 vertical sigma levels
The terrain (left) and land mask (right) of the background used in this case study.
The terrain (left) and land mask (right) of the background used in this case study.
Conventional data: NAM PrepBUFR data from 0000 UTC 17 June 2014
- File: nam.t00z.prepbufr.tm00.nr
Radiance and GPS RO data: GDAS PREPBUFR data from 0000 UTC 17 June 2014
Files: gdas.t00z.1bamua.tm00.bufr_d
gdas.t00z.1bhrs4.tm00.bufr_d
gdas.t00z.gpsro.tm00.bufr_d
This case study was run on a Linux cluster. As of version 3.2, the BUFR/PrepBUFR files do not need to be byte-swapped to little endian format. BUFRLIB can automatically handle byte order issues.
Assume the background file is located at:
data/2014061700/arw
All the observations are located at:
data/2014061700/obs
And the GSI release version 3.6 is located at:
code/comGSI(version_number)_EnKF(version_number)
Assimilating Conventional Observations with Regional GSI¶
Run Script¶
With GSI successfully compiled and background and observational data acquired, move to the ./run directory under ./comGSIv3.5_EnKFv1.1 to run the GSI using the sample script run_gsi_regional.ksh. The run_gsi_regional.ksh script must be modified in several places before running:
- Set up batch queuing system.To run GSI with multi-processors, a job queuing section has to be added at the beginning of the run_gsi_regional.ksh script. The set up of the job queue is dependent on the machine and the job control system. More setup examples are described in section [sec3.2.2]. The following example is set up to run on a Linux cluster supercomputer with LSF. The job section is as follows:
#BSUB -P ????????? # project code #BSUB -W 00:20 # wall-clock time (hrs:mins) #BSUB -n 4 # number of tasks in job #BSUB -R "span[ptile=16]" # run 16 MPI tasks per node #BSUB -J gsi # job name #BSUB -o gsi.%J.out #BSUB -e gsi.%J.err #BSUB -q small # queue
In order to find out how to set up the job section, a good method is to use an existing MPI job script and copy the job section over. Set up the number of processors and the job queue system used. For this example, LINUX_PBS and four processors are used:
GSIPROC=4 ARCH='LINUX_PBS'
Set up the case data, analysis time, GSI fix files, GSI executable, and CRTM coefficients:
Set up analysis time:
ANAL_TIME=2014061700
Set up a working directory, which will hold all the analysis results. This directory must have correct write permissions, as well as enough space to hold the output.
WORK_ROOT=/scratch1/gsiprd_${ANAL_TIME}_prepbufrSet path to the background directory and file:
BK_ROOT=data/20140617/${ANAL_TIME}/arw BK_FILE=${BK_ROOT}/wrfinput_d01.${ANAL_TIME}Set path to the observation directory and the PrepBUFR file within the observation directory. All observations to be assimilated should be in the observation directory.
OBS_ROOT=data/20140617/${ANAL_TIME}/obs PREPBUFR=${OBS_ROOT}/nam.t${HH}z.prepbufr.tm00.nrSet up the GSI system used for this case, including the paths of fix files and the CRTM coefficients as well as the location of the GSI executable and the namelist file:
CRTM_ROOT=data/fix/CRTM_2.2.3 GSI_ROOT=/comGSIv3.5_EnKFv1.1 FIX_ROOT=${GSI_ROOT}/fix GSI_EXE=${GSI_ROOT}/run/gsi.exe GSI_NAMELIST=${GSI_ROOT}/run/comgsi_namelist.shSet which background and background error file to use:
bk_core=ARW bkcv_option=NAM if_clean=clean
This example uses the ARW NetCDF background; therefore
bk_coreis set to ’ARW.’ The regional background error covariance file is used in this case, as set bybkcv_option=NAM. Finally, the run scripts are set to clean the run directory to delete all temporary intermediate files.
Run GSI and Check the Run Status¶
Once the run script is set up properly for the case and machine, and the anavinfo file has been updated with the same number of vertical levels as the background (please see section [sec3.1] for more details), GSI can be run through the run script. On our test machine, the GSI run is submitted as follows:
$ bsub < run_gsi_regional.ksh
While the job is running, move to the working directory and check the details. Given the following working directory setup:
WORK_ROOT=/scratch1/gsiprd_${ANAL_TIME}_prepbufr
Go to directory /scratch1 to check the GSI run directory.
A directory named gsiprd_2014061700_prepbufr should have been created. This directory is the run directory for this GSI case study. While GSI is still running, the contents of this directory should include files such as:
imgr_g12.TauCoeff.bin ssmi_f15.SpcCoeff.bin
imgr_g13.SpcCoeff.bin ssmi_f15.TauCoeff.bin
imgr_g13.TauCoeff.bin ssmis_f16.SpcCoeff.bin
These are CRTM coefficient files that have been linked to this run directory through the GSI run script. Additionally, many other files are linked or copied to this run directory or generated during the run, such as:
stdout: standard output filewrf_inout: background filegsiparm.anl: GSI namelistprepbufr: prepBUFR file for conventional observationconvinfo: data usage control for conventional databerror_stats: background error fileerrtable: observation error file
The presence of these files indicates that the GSI run scripts have successfully set up a run environment for GSI and that the GSI executable is running. While GSI is still running, checking the content of the standard output file (stdout) can monitor the status of the GSI analysis:
$ tail stdout
[mhu@yslogin2 testarw_conv]$ tail stdout
pcgsoi: gnorm(1:2),b= 4.971613638691962543E-04 4.971613637988174387E-04 6.539937387039491679E-01
cost,grad,step,b,step? = 2 21 2.283071832605468444E+04 2.229711559527815246E-02 4.235685495878949780E+01 6.539937387039491679E-01 good
pcgsoi: gnorm(1:2),b= 2.427998455757554152E-04 2.427998455925739387E-04 4.883723137754825139E-01
cost,grad,step,b,step? = 2 22 2.283069726786290266E+04 1.558203598942562579E-02 5.370146211199837438E+01 4.883723137754825139E-01 good
pcgsoi: gnorm(1:2),b= 1.349871612172568163E-04 1.349871612633241600E-04 5.559606553423739328E-01
cost,grad,step,b,step? = 2 23 2.283068422915619885E+04 1.161839753224414365E-02 6.137769018004394894E+01 5.559606553423739328E-01 good
pcgsoi: gnorm(1:2),b= 9.116990149652581994E-05 9.116990145686963470E-05 6.753968350377786978E-01
cost,grad,step,b,step? = 2 24 2.283067594395604101E+04 9.548293119533240308E-03 5.219011413742769179E+01 6.753968350377786978E-01 good
pcgsoi: gnorm(1:2),b= 5.783146215365002791E-05 5.783146214669085264E-05 6.343262545796939378E-01
cost,grad,step,b,step? = 2 25 2.283067118578847294E+04 7.604700004184913528E-03 5.599317818817987558E+01 6.343262545796939378E-01 good
The above output shows that GSI is in the inner iteration stage. It may take several minutes to finish the GSI run. Once GSI has finished running, the number of files in the directory will be greatly reduced from those during the run stage. This is because the run script was set to clean the working directory after a successful run. The important analysis result files and configuration files will remain. Please check Section [sec3.3] for more details on GSI run results. Upon successful completion of GSI, the run directory will look as follows:
anavinfo fort.202 fort.214 fort.228 satbias_ang.out
berror_stats fort.203 fort.215 fort.229 satbias_in
convinfo fort.204 fort.217 fort.230 satbias_out
diag_conv_anl.2014061700 fort.205 fort.218 gsi.exe satbias_out.int
diag_conv_ges.2014061700 fort.206 fort.219 gsiparm.anl satbias_pc
errtable fort.207 fort.220 l2rwbufr satbias_pc.out
fit_p1.2014061700 fort.208 fort.221 list_run_directory satinfo
fit_q1.2014061700 fort.209 fort.223 ozinfo stdout
fit_rad1.2014061700 fort.210 fort.224 pcpbias_out stdout.anl.2014061700
fit_t1.2014061700 fort.211 fort.225 pcpinfo wrfanl.2014061700
fit_w1.2014061700 fort.212 fort.226 prepbufr wrf_inout
fort.201 fort.213 fort.227 prepobs_prep.bufrtable
Check for Successful GSI Completion¶
It is important to always check for a successful completion of the GSI analysis. However, completion of the GSI run without crashing does not guarantee a successful analysis. First, it is necessary to check the stdout file in the run directory to make sure GSI completed each step without any obvious problems. The following are several important steps to check:
- Read in the anavinfo and namelist filesThe following lines show that GSI started normally and has read in the anavinfo and namelist files:
gsi_metguess_mod*init_: 2D-MET STATE VARIABLES: ps z gsi_metguess_mod*init_: 3D-MET STATE VARIABLES: u v ... ... control_vectors*init_anacv: ALL CONTROL VARIABLES sf vp ps t q oz ... ... GSI_4DVAR: nobs_bins = 1 SETUP_4DVAR: l4dvar= F SETUP_4DVAR: l4densvar= F SETUP_4DVAR: winlen= 3.00000000000000 SETUP_4DVAR: winoff= 3.00000000000000 SETUP_4DVAR: hr_obsbin= 3.00000000000000 SETUP_4DVAR: nobs_bins= 1 ... ... &SETUP GENCODE = 78.0000000000000 , FACTQMIN = 0.000000000000000E+000, FACTQMAX = 0.000000000000000E+000, CLIP_SUPERSATURATION = F, FACTV = 1.00000000000000 , FACTL = 1.00000000000000 , FACTP = 1.00000000000000 , FACTG = 1.00000000000000 , ... ...
- Read in the background fieldThe following lines in standard output file, immediately following the namelist section, indicate that GSI is reading the background fields. Checking the range of the ’max’ and ’min’ values will indicate if certain background fields are normal.
dh1 = 3 iy,m,d,h,m,s= 2014 6 17 0 0 0 dh1 = 3 RMS errore_var = SMOIS ndim1 = 3 ordering = XYZ staggering = N/A start_index = 1 1 1 0 end_index = 332 215 4 0 WrfType = 104 ierr = 0 RMS errore_var = T ndim1 = 3 dh1 = 3 ............... RMS errore_var = U ndim1= 3 WrfType = 104 WRF_REAL= 104 ierr = 0 ordering = XYZ staggering = N/A start_index = 1 1 1 0 end_index = 333 215 50 0 k,max,min,mid U= 1 18.50961 -17.84097 -0.8667576 k,max,min,mid U= 2 18.68178 -18.39229 -0.8647658 k,max,min,mid U= 3 19.28049 -19.42709 -0.8610985 k,max,min,mid U= 4 19.60607 -21.29182 -0.8547171 k,max,min,mid U= 5 21.58153 -24.50086 -0.8405453
- Read in observational dataSkipping through a majority of the content towards the middle of the standard output file, the following lines will appear:
OBS_PARA: ps 1429 3190 4655 6774 OBS_PARA: t 2564 5200 7057 11128 OBS_PARA: q 2346 4626 6148 8128 OBS_PARA: pw 65 80 63 49 OBS_PARA: uv 3358 6453 8091 11998
This table is an important step to check if the observations have been read in, which types of observations have been read in, and the distribution of observations in each sub domain. At this point, GSI has read in all the data needed for the analysis. Following this table is the inner iteration information. - Inner iterationThe inner iteration step in the standard output file will look like this:
GLBSOI: START pcgsoi jiter= 1 pcgsoi: gnorm(1:2),b= 1.131520548923311509E+02 1.131520548923311509E+02 0.000000000000000000E+00 Begin J table inner/outer loop 0 1 J term J surface pressure 5.7012207042385944E+03 temperature 6.4242087278840627E+03 wind 1.6782607330525603E+04 moisture 3.5878183830232451E+03 ----------------------------------------------------- J Global 3.2495855145671507E+04 End Jo table inner/outer loop 0 1 Initial cost function = 3.249585514567150676E+04 Initial gradient norm = 1.063729546888358080E+01 cost,grad,step,b,step? = 1 0 3.249585514567150676E+04 1.063729546888358080E+01 2.548553547231620442E+01 0.000000000000000000E+00 good pcgsoi: gnorm(1:2),b= 9.711890653545569307E+01 9.711890653545563623E+01 8.583043995786756586E-01 cost,grad,step,b,step? = 1 1 2.961211443694752961E+04 9.854892517701838273E+00 2.514521805501437157E+01 8.583043995786756586E-01 good pcgsoi: gnorm(1:2),b= 8.597091659074136771E+01 8.597091659074150982E+01 8.852129791983981422E-01 cost,grad,step,b,step? = 1 2 2.717003835484893352E+04 9.272050290563644381E+00 1.036575164309021346E+01 8.852129791983981422E-01 good pcgsoi: gnorm(1:2),b= 5.683515872824821002E+01 5.683515872824790449E+01 6.610975081120487040E-01 ...Following the namelist set up, similar information will be repeated for each inner loop. In this case, two outer loops with 50 inner loops in each outer loop are shown. The last iteration looks like this:
... cost,grad,step,b,step? = 2 43 2.283066393607995633E+04 1.877001128106250809E-04 2.191568845986752123E+01 1.034537274286373876E+00 good pcgsoi: gnorm(1:2),b= 1.819672329827284049E-08 1.819678976283112548E-08 5.164945106960998622E-01 cost,grad,step,b,step? = 2 44 2.283066393530783535E+04 1.348952308210814343E-04 4.180909725254741716E+01 5.164945106960998622E-01 good pcgsoi: gnorm(1:2),b= 1.127913513618353988E-08 1.127903191777782680E-08 6.198386233002942669E-01 cost,grad,step,b,step? = 2 45 2.283066393454704667E+04 1.062032727187987424E-04 2.399192596022169610E+01 6.198386233002942669E-01 good PCGSOI: WARNING **** Stopping inner iteration *** gnorm 0.996812222890348903E-10 less than 0.100000000000000004E-09 update_guess: successfully complete
At the 45th iterationi, GSI met the stop threshold before getting to the maximum iteration number (50). As a quick check, the J value should descend with each iteration. Here, J has a value of 3.249585514567150676E+04 at the beginning and a value of 2.283066393454704667E+04 for the final iteration. Therefore, the value has reduced by about one third, which is an expected reduction. - Write out analysis resultsThe final step of the GSI analysis procedure looks very similar to the portion where the background fields were read in:
... ... max,min psfc= 102799.9 66793.78 max,min MU= 2799.898 -1195.195 RMS errore_var=MU ordering=XY WrfType,WRF_REAL= 104 104 ndim1= 2 staggering= N/A start_index= 1 1 1 0 end_index1= 332 215 50 0 k,max,min,mid T= 1 321.6157 270.8151 309.3634 k,max,min,mid T= 2 321.7112 270.9660 309.4070 k,max,min,mid T= 3 321.4324 271.2166 309.3831 k,max,min,mid T= 4 321.2418 271.6100 309.3864 k,max,min,mid T= 5 321.6863 272.2831 309.3308 k,max,min,mid T= 6 320.9441 272.8387 309.1977 k,max,min,mid T= 7 320.8511 273.1703 309.0908 ... ...
As an indication that GSI has successfully run, several lines will appear at the bottom of the file:
ENDING DATE-TIME JUL 05,2016 11:30:38.156 187 TUE 2457575 PROGRAM GSI_ANL HAS ENDED. * . * . * . * . * . * . * . * . * . * . * . * . * . * . * . * . * . * . * . * .
After carefully investigating each portion of the standard output file, it can be concluded that GSI successfully ran through every step and there were no run issues. A more complete description of the standard output file can be found in Section [sec4.1]. However, it cannot be concluded that GSI successfully produced an analysis until more diagnosis has been completed.
Diagnose GSI Analysis Results¶
Check Analysis Fit to Observations¶
The analysis uses observations to correct the background fields to fit to the observations under certain constraints. The easiest way to confirm the GSI analysis results fit the observations better than the background is to check a set of files with names fort.2??, where ?? is a number from 01 to 19 or larger than 20. In the run scripts, several “fort” files have also been renamed as fit_t1 (q1, p1, rad1, w1).YYYYMMDDHH. Please check Section [sec4.5.1] for a detailed explanation of the fit files. Here, we illustrate how to use these fit files.
fit_t1.2014061700 (fort.203)
This file shows how the background and analysis fields fit to temperature observations. The contents of this file show five data types were used in the analysis: 120, 130, 132, 180, and 182. Also included are the number of observations, bias, and RMS error of observation minus background (o-g 01) or analysis (o-g 03) on each level for the three data types. The following is part of the file, only showing data types 120 and 180:
ptop 1000.0 900.0 800.0 600.0 400.0 300.0 250.0 200.0 150.0 100.0 50.0 0.0 it obs type styp pbot 1200.0 1000.0 900.0 800.0 600.0 400.0 300.0 250.0 200.0 150.0 100.0 2000.0 ---------------------------------------------------------------------------------------------------------------------------------------------- o-g 01 t 120 0000 count 107 350 357 866 1153 719 252 450 551 884 745 7188 o-g 01 t 120 0000 bias 0.80 0.32 -0.10 -0.12 -0.15 -0.20 -0.24 -0.60 -0.22 0.15 -0.10 -0.07 o-g 01 t 120 0000 RMS error 2.06 1.55 0.83 0.77 0.69 0.66 0.73 1.20 1.44 1.65 1.65 1.23 o-g 01 t 120 0000 cpen 0.81 0.49 0.23 0.33 0.33 0.30 0.36 0.79 0.91 0.98 0.79 0.58 o-g 01 t 120 0000 qcpen 0.81 0.49 0.23 0.33 0.33 0.30 0.36 0.79 0.91 0.98 0.79 0.58 o-g 01 t 180 0000 count 339 35 0 0 0 0 0 0 0 0 0 374 o-g 01 t 180 0000 bias 0.17 1.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.26 o-g 01 t 180 0000 RMS error 1.66 4.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.01 o-g 01 t 180 0000 cpen 0.63 7.18 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.25 o-g 01 t 180 0000 qcpen 0.63 7.18 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.25 o-g 01 t 180 0001 count 1344 15 0 0 0 0 0 0 0 0 0 1359 o-g 01 t 180 0001 bias 0.82 4.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.86 o-g 01 t 180 0001 RMS error 2.07 5.44 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.13 o-g 01 t 180 0001 cpen 0.47 23.37 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.73 o-g 01 t 180 0001 qcpen 0.47 23.37 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.73 o-g 01 all count 1792 405 358 871 1172 725 325 800 651 884 745 9482 o-g 01 all bias 0.69 0.53 -0.10 -0.12 -0.15 -0.19 -0.09 -0.50 -0.04 0.15 -0.10 0.08 o-g 01 all RMS error 1.99 2.14 0.83 0.77 0.69 0.67 0.84 1.32 1.58 1.65 1.65 1.45 o-g 01 all cpen 0.52 1.91 0.23 0.33 0.36 0.31 0.44 0.97 1.18 0.98 0.79 0.68 o-g 01 all qcpen 0.52 1.91 0.23 0.33 0.36 0.31 0.44 0.97 1.18 0.98 0.79 0.68 ---------------------------------------------------------------------------------------------------------------------------------------------- o-g 03 t 120 0000 count 107 350 357 866 1153 719 252 450 551 884 745 7188 o-g 03 t 120 0000 bias 0.58 0.29 -0.04 -0.02 -0.04 -0.02 0.01 -0.16 -0.04 0.06 0.04 0.01 o-g 03 t 120 0000 RMS error 1.72 1.35 0.70 0.61 0.49 0.43 0.50 0.79 1.14 1.40 1.59 1.05 o-g 03 t 120 0000 cpen 0.57 0.33 0.14 0.19 0.16 0.12 0.18 0.34 0.57 0.72 0.73 0.39 o-g 03 t 120 0000 qcpen 0.57 0.33 0.14 0.19 0.16 0.12 0.18 0.34 0.57 0.72 0.73 0.39 o-g 03 t 180 0000 count 339 35 0 0 0 0 0 0 0 0 0 374 o-g 03 t 180 0000 bias -0.24 0.21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.19 o-g 03 t 180 0000 RMS error 1.55 2.83 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.71 o-g 03 t 180 0000 cpen 0.34 2.57 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.55 o-g 03 t 180 0000 qcpen 0.34 2.57 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.55 o-g 03 t 180 0001 count 1344 16 0 0 0 0 0 0 0 0 0 1360 o-g 03 t 180 0001 bias 0.30 1.97 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.32 o-g 03 t 180 0001 RMS error 1.75 2.88 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.77 o-g 03 t 180 0001 cpen 0.27 6.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.34 o-g 03 t 180 0001 qcpen 0.27 6.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.34 o-g 03 all count 1792 406 358 871 1172 725 325 800 651 884 745 9483 o-g 03 all bias 0.21 0.34 -0.04 -0.02 -0.04 -0.02 0.04 -0.13 0.06 0.06 0.04 0.05 o-g 03 all RMS error 1.71 1.61 0.69 0.61 0.49 0.43 0.61 0.94 1.26 1.40 1.59 1.22 o-g 03 all cpen 0.30 0.75 0.14 0.19 0.18 0.14 0.24 0.49 0.76 0.72 0.73 0.42 o-g 03 all qcpen 0.30 0.75 0.14 0.19 0.18 0.14 0.24 0.49 0.76 0.72 0.73 0.42
For example, data type 120 has 1153 observations in layer 400.0-600.0 hPa, a bias of -0.15, and a RMS error of 0.69. The last column shows the statistics for the whole atmosphere. There are several summary lines for all data types, which is indicated by “all” in the data types column. For summary O-B (which is “o-g 01” in the file), there are 9482 observations in total, for a bias of 0.08, and a RMS error of 1.45.Skipping ahead in the “fort” file, “o-g 03” columns (under “it”) show the observation minus analysis (O-A) information. Under the summary (“all”) rows, it can be seen that there were 9483 total observations, a bias of 0.05, and a RMS error of 1.22. This shows that from the background to the analysis, one more observation data point is being used because of the recalculation of the innovation and the gross check after each outer loop, the bias reduced from 0.08 to 0.05, and the RMS error reduced from 1.45 to 1.22. This is about a 16% reduction, which is a reasonable value for a large-scale analysis.fit_w1.2014061700 (fort.202)
This file demonstrates how the background and analysis fields fit to wind observations. This file (as well as fit_q1) is formatted the same way as fort.203. Therefore, only the summary lines for O-B and O-A will be shown here to gain a quick view of the fit to observations:
ptop 1000.0 900.0 800.0 600.0 400.0 300.0 250.0 200.0 150.0 100.0 50.0 0.0 it obs type styp pbot 1200.0 1000.0 900.0 800.0 600.0 400.0 300.0 250.0 200.0 150.0 100.0 2000.0 ---------------------------------------------------------------------------------------------------------------------------------------------- o-g 01 all count 1597 1703 1839 2930 1213 828 290 687 533 694 798 14513 o-g 01 all bias 0.27 0.84 0.68 0.61 0.56 0.45 0.67 0.91 0.48 0.83 1.21 0.64 o-g 01 all RMS error 2.50 2.65 2.52 3.11 4.02 3.98 4.37 4.31 5.32 5.41 4.77 3.59 ---------------------------------------------------------------------------------------------------------------------------------------------- o-g 03 all count 1608 1695 1843 2931 1212 828 290 687 533 694 798 14520 o-g 03 all bias 0.23 0.42 0.26 0.30 0.37 0.33 0.22 0.37 0.32 0.67 1.22 0.39 o-g 03 all RMS error 2.27 2.16 1.94 2.23 2.74 2.82 3.64 3.31 4.22 4.43 4.41 2.90
O-B: 14513 observations in total, bias is 0.64, and RMS error is 3.59
O-A: 14520 observations in total, bias is 0.39, and RMS error is 2.90The total bias was reduced from 0.64 to 0.39 and the RMS error was reduced from 3.59 to 2.90 ( 20% reduction).fit_q1.2014061700 (fort.204)
This file demonstrates how the background and analysis fields fit to moisture observations (relative humidity). The summary lines for O-B and O-A are as follows:
ptop 1000.0 950.0 900.0 850.0 800.0 700.0 600.0 500.0 400.0 300.0 0.0 0.0 it obs type styp pbot 1200.0 1000.0 950.0 900.0 850.0 800.0 700.0 600.0 500.0 400.0 300.0 2000.0 ---------------------------------------------------------------------------------------------------------------------------------------------- o-g 01 all count 543 186 182 211 146 457 406 520 621 623 0 3895 o-g 01 all bias 1.17 -3.68 -2.47 -1.30 -3.55 0.19 0.64 -1.80 -4.28 -5.55 0.00 -2.05 o-g 01 all RMS error 9.09 10.63 9.03 9.34 12.73 12.30 14.53 15.27 16.45 16.01 0.00 13.66 ---------------------------------------------------------------------------------------------------------------------------------------------- o-g 03 all count 543 186 182 211 146 457 406 520 621 623 0 3895 o-g 03 all bias -0.39 -0.88 -0.68 0.45 -0.51 0.06 0.13 -0.10 -0.70 -1.90 0.00 -0.53 o-g 03 all RMS error 5.48 5.19 4.37 5.73 8.13 9.31 12.19 13.82 13.01 12.36 0.00 10.64
O-B: 3895 observations in total, bias is -2.05, and RMS error is 13.66
O-A: 3895 observations in total, bias is -0.53, and RMS error is 10.64 The total bias and RMS error were reduced.fit_p1.2014061700 (fort.201)
This file demonstrates how the background and analysis fields fit to surface pressure observations. Because surface pressure is a two-dimensional field, the table is formatted differently than the three-dimensional fields shown above. Once again, only the summary lines will be shown for O-B and O-A to gain a quick view of the fit to observations:
-------------------------------------------------- pressure levels (hPa)= 0.0 2000.0 it obs type stype count bias RMS error cpen qcpen o-g 01 all 13890 0.1912 0.7931 0.4105 0.4105 -------------------------------------------------- o-g 03 all 13916 0.0403 0.6764 0.2921 0.2921
O-B: 13890 observations in total, bias is 0.1912, and RMS error is 0.7931
O-A: 13916 observations in total, bias is 0.0403, and RMS error is 0.6764
Both the total bias and RMS error were reduced.These statistics show that the analysis results fit to the observations closer than the background, which is what we would expect. How close the analysis fits to the observations is based on the ratio of background error variance and observation error.
Check the Minimization¶
In addition to the minimization information in the standard output file, GSI writes more detailed information into a file called “fort.220.” The content of “fort.220” is explained in the Advanced GSI Users Guide. Below is an example of a quick check of the cost function trend and the norm of gradient. The values should get smaller with each iteration.
In the run directory, information on the cost function and norm of the gradient can be dumped into an output file by using the following command:
$ grep 'cost,grad,step,b' fort.220 | sed -e 's/cost,grad,step,b,step? = //g' | sed -e 's/good//g' > cost_gradient.txt
The file cost_gradient.txt includes six columns, however only the first four columns are needed and are explained below. The first five and last five lines read are:
1 0 3.249585514567150676E+04 1.063729546888358080E+01 2.548553547231620442E+01 0.000000000000000000E+00
1 1 2.961211443694752961E+04 9.854892517701838273E+00 2.514521805501437157E+01 8.583043995786756586E-01
1 2 2.717003835484893352E+04 9.272050290563644381E+00 1.036575164309021346E+01 8.852129791983981422E-01
1 3 2.627888518494048549E+04 7.538909651153024249E+00 1.948409284114243079E+01 6.610975081120487040E-01
1 4 2.517150367563822510E+04 5.715354258100989071E+00 2.423238066649372513E+01 5.747370998254690555E-01
... ...
2 41 2.283066394128102547E+04 2.700371175626514980E-04 4.661917669555924704E+01 3.046618826707093719E-01
2 42 2.283066393788155256E+04 1.845403996927800583E-04 5.290241773629001187E+01 4.670206697407201513E-01
2 43 2.283066393607995633E+04 1.877001128106250809E-04 2.191568845986752123E+01 1.034537274286373876E+00
2 44 2.283066393530783535E+04 1.348952308210814343E-04 4.180909725254741716E+01 5.164945106960998622E-01
2 45 2.283066393454704667E+04 1.062032727187987424E-04 2.399192596022169610E+01 6.198386233002942669E-01
The first column is the outer loop number and the second column is the inner iteration number. The third column is the cost function, and the forth column is the norm of the gradient. It can be seen that both the cost function and norm of the gradient are descending.
To get a complete picture of the minimization process, the cost function and norm of the gradient can be plotted using an included NCL script located here:
./util/Analysis_Utilities/plot_ncl/GSI_cost_gradient.ncl.
The plot is shown as Fig.[fig:costgrad_ch5]:
The cost function (y-axes) and norm of the gradient (y-axes) change with each iteration (x-axes).
[fig:costgrad_ch5]
The above plots demonstrate that both the cost function and norm of the gradient descend very fast in the first ten iterations in both outer loops and drop very slowly afterward.
Check the Analysis Increment¶
The analysis increment gives us an idea of where and how much the background fields have been modified by the observations through the analysis. Another useful graphics tool that can be used to look at the analysis increment is located here:
./util/Analysis_Utilities/plot_ncl/Analysis_increment.ncl.
The graphic below shows the analysis increment at the 15th sigma (vertical) level on the analysis grid. Notice that the scales are different for each of the plots.
Analysis increment at the 15th level
The analysis increment indicates that conventional observations are mostly located within the continental United States and that data availability over the ocean is very sparse.
Assimilating Radiance Data with Regional GSI¶
Run Script¶
Adding radiance data into the GSI analysis is straightforward after
having already run GSI with conventional data. The same run script from
the above section can be used to run GSI with radiance data (with or
without PrepBUFR data). The key step to adding the radiance data is
linking the radiance BUFR files to the GSI run directory with the names
listed in the &OBS_INPUT section of the GSI namelist. The following
example adds the two radiance BUFR files:
AMSU-A: gdas1.t00z.1bamua.tm00.bufr_d HIRS4: gdas1.t00z.1bhrs4.tm00.bufr_d
The location of these radiance BUFR files is already included in the
scripts variable OBS_ROOT, therefore the following two lines can be
inserted below the link to the prepBUFR data in the script
run_gsi_regional.ksh:
ln -s ${OBS_ROOT}/gdas1.t${HH}z.1bamua.tm00.bufr_d amsuabufr
ln -s ${OBS_ROOT}/gdas1.t${HH}z.1bhrs4.tm00.bufr_d hirs4bufr
If radiance data is desired in addition to conventional prepBUFR data, the following link to the prepBUFR data should be kept as is:
ln -s ${PREPBUFR} ./prepbufr
Alternatively, to analyze radiance data without conventional prepBUFR data, this line can be commented out in the script run_gsi_regional.ksh:
# ln -s ${PREPBUFR} ./prepbufr
In the following example, both radiance and conventional observations will be assimilated.
In order to link the correct name for the radiance BUFR file, the
namelist section &OBS_INPUT should be referenced. This section has a
list of data types and BUFR file names that can be used in GSI. The
1st column "dfile" is the file name recognized by GSI. The
2nd column "dtype" and 3rd column "dplat" are
the data type and data platform that are included in the file listed in
“dfile,” respectively. For example, the following line tells us the
AMSU-A observation from NOAA-15 should be read from a BUFR file named
“amsuabufr”:
! dfile dtype dplat dsis dval dthin dsfcalc
amsuabufr amsua n15 amsua_n15 10.0 2 0
With radiance data assimilation, data thinning and bias correction need to be checked carefully. The following is a brief description of these two:
Radiance data thinning
Radiance data thinning is found in the namelist section
&OBS_INPUT. The following is a part of namelist in that section:dmesh(1)=120.0,dmesh(2)=60.0,dmesh(3)=30,time_window_max=1.5,ext_sonde=.true. ! dfile dtype dplat dsis dval dthin dsfcalc amsuabufr amsua n15 amsua_n15 10.0 2 0
The first line of&OBS_INPUTlists multiple mesh grids as elements of the arraydmesh(three mesh grids in the above example). For the line specifying data type, the 2nd to last element of that line is used to specify the choice ofdthin. This selects the mesh grid to be used for thinning. The data thinning option for NOAA-15 AMSU-A observations is set to 60 km because the value ofdthinis two, corresponding todmesh(2)=60 km. For more information about radiance data thinning, please refer to the Advanced GSI Users Guide.Radiance data bias correction
Radiance data bias correction is very important for successful radiance data assimilation. In the sample run scripts, there are two files related to bias correction:
# for satellite bias correction cp ${FIX_ROOT}/gdas1.t00z.abias.20150617 ./satbias_in cp ${FIX_ROOT}/gdas1.t00z.abias_pc.20150617 ./satbias_pcFor this case, the GDAS bias correction files were downloaded and saved in the fix directory as examples. For other cases, the run script should link to corresponding bias correction coefficient files. The first line sets the path to the bias coefficient file, and the second copies the bias correction coefficients for passive (monitored) channels into the working directory. These two coefficient files are usually calculated from within GSI in the previous cycle. Two files are provided in ./fix as examples of the bias correction coefficients. For the best results, it is necessary for the user to generate his or her own bias files. The details of radiance data bias correction are discussed in the Advanced GSI Users Guide. Please note that GSI releases prior to v3.5 have coefficients for mass bias correction and angle bias correction calculated separately.Once these links are set, we are ready to run GSI.
Run GSI and Check Run Status¶
The process for running GSI is the same as described in section 1.1.2. Once run_gsi_regional.ksh has been submitted, move into the run directory to check the GSI analysis results. For the current case, the run directory will look almost as it did for the conventional data case, the exception being the two links to the radiance BUFR files and new diag files for the radiance data types used. Following the same steps as in section 1.1.2, check the stdout file to see if GSI has run through each part of the analysis process successfully. In addition to the information outlined for the conventional run, the radiance BUFR files should have been read in and distributed to each sub domain:
OBS_PARA: ps 1429 3190 4655 6774
OBS_PARA: t 2564 5200 7057 11128
OBS_PARA: q 2346 4626 6148 8128
OBS_PARA: pw 65 80 63 49
OBS_PARA: uv 3358 6453 8091 11998
OBS_PARA: hirs4 metop-a 0 0 1146 1661
OBS_PARA: hirs4 n19 213 1020 0 0
OBS_PARA: hirs4 metop-b 0 0 85 555
OBS_PARA: amsua n15 1458 2026 830 234
OBS_PARA: amsua n18 2223 2318 108 0
OBS_PARA: amsua n19 176 960 0 0
OBS_PARA: amsua metop-a 0 0 1077 1559
OBS_PARA: amsua metop-b 0 0 265 1829
When comparing this output to the content in step three of section 1.1.3, it can be seen that there are eight new radiance data types that have been read in: HIRS4 from METOP-A, METOP-B and NOAA-19, AMSU-A from NOAA-15, NOAA-18, NOAA-19, METOP-A, and METOP-B. The table above shows that most of the radiance data read in for this case are AMSU-A from NOAA satellite information.
Diagnose GSI Analysis Results¶
Check File fort.207¶
The file fort.207 contains the statistics for the radiance data, similar to file fort.203 for temperature. This file contains important details about the radiance data analysis. Section [sec4.5.2] explains this file in detail. Below are some values from the file fort.207 to provide a quick look at the radiance assimilation for this example.
The fort.207 file contains the following lines:
For O-B, before the first outer loop:
it satellite instrument # read # keep # assim penalty qcpnlty cpen qccpen
o-g 01 rad n15 amsua 83190 58236 25226 10356. 10356. 0.41053 0.41053
o-g 01 rad n18 amsua 83595 69147 27677 11067. 11067. 0.39988 0.39988
For O-A, after the second outer loop:
o-g 03 rad n15 amsua 83190 58236 30136 4672.4 4672.4 0.15504 0.15504
o-g 03 rad n18 amsua 83595 69147 32253 8546.8 8546.8 0.26499 0.26499
From the above information, it can be seen that AMSU-A data from NOAA-15 provides 83190 observations within the analysis time window and domain. After thinning, 58236 observations remained, and only 25226 passed the quality check and were used in the analysis. The penalty for this data decreased from 10356 to 4672.4 after two outer loops. It is important to note that the number of AMSU-A observations assimilated in the O-A calculation increased to 30136 from 25226 as more data passed the quality check in the 2nd outer loop.
The statistics for each channel can be viewed in the fort.207 file as well. Here, channels from AMSU-A NOAA-15 are listed as an example:
For O-B, before the first outer loop:
1 1 amsua_n15 1903 24 3.000 1.6543287 -0.3164878 0.1411234 1.7151923 1.6857402
2 2 amsua_n15 1927 0 2.200 1.0105548 -0.2430249 0.0385590 1.0284683 0.9993427
3 3 amsua_n15 1927 0 2.000 1.7941589 -0.1480894 0.0575956 0.7909800 0.7769935
4 4 amsua_n15 1927 0 0.600 -0.1848763 -0.0460476 0.0856369 0.2497797 0.2454985
5 5 amsua_n15 1927 4 0.300 0.0314288 -0.0292998 0.3025052 0.2008865 0.1987383
6 6 amsua_n15 4126 10 -0.230 -1.8448526 -0.0778284 0.8969513 0.2463875 0.2337725
7 7 amsua_n15 4468 13 0.250 -0.1497841 -0.0810899 0.5245399 0.2042760 0.1874916
8 8 amsua_n15 4468 13 0.275 -0.0251081 -0.0869918 0.6195120 0.2420568 0.2258847
9 9 amsua_n15 4463 18 0.340 0.1824903 0.2108491 0.6987405 0.3453174 0.2734717
10 10 amsua_n15 294 4187 0.400 0.7946961 0.7908103 2.5281681 0.7982470 0.1087077
15 15 amsua_n15 1922 5 3.500 1.6785936 -0.1471413 0.0940713 1.6461928 1.6396037
For O-A, after the second outer loop:
1 1 amsua_n15 2050 10 3.000 2.0842622 0.1363398 0.0414276 1.0332965 1.0242622
2 2 amsua_n15 2060 0 2.200 1.0926873 -0.0731534 0.0177924 0.8048238 0.8014923
3 3 amsua_n15 2060 0 2.000 1.8733559 -0.0257656 0.0406385 0.6841159 0.6836305
4 4 amsua_n15 2060 0 0.600 -0.1278763 0.0136412 0.0547146 0.2103718 0.2099291
5 5 amsua_n15 2060 4 0.300 0.0730860 0.0118626 0.1759475 0.1624923 0.1620587
6 6 amsua_n15 4234 0 -0.230 -1.7911421 -0.0262042 0.5247873 0.1975867 0.1958414
7 7 amsua_n15 4470 11 0.250 -0.0766482 -0.0094307 0.1918646 0.1288789 0.1285334
8 8 amsua_n15 4475 6 0.275 0.0675371 -0.0047226 0.1715888 0.1341703 0.1340871
9 9 amsua_n15 4481 0 0.340 -0.0508290 -0.0396425 0.1722334 0.1711928 0.1665396
10 10 amsua_n15 4362 119 0.400 0.2373520 0.1943598 0.3425567 0.3140220 0.2466457
15 15 amsua_n15 2058 2 3.500 1.8261335 0.0617429 0.0487809 1.2317674 1.2302190
The second column is the channel number for AMSU-A and the last column is the standard deviation for each channel. It can be seen that most of the channels fit better to the observations after the second outer loop.
Check the Analysis Increment¶
The same methods for checking the optimal minimization as demonstrated in section 1.1.4.2 can be used for radiance assimilation. Similar features to the conventional assimilation should be seen with the minimization. The figures below show detailed information on how the radiance data impact the analysis results on top of the conventional data. Using the same NCL script as in section 1.1.4.3, analysis increment fields are plotted comparing the analysis results with radiance and conventional data to the analysis results with conventional data assimilation only. Figure [fig:increments_rad2] is for vertical level 49 and Figure [fig:increments_rad] is for vertical level six, representing the maximum temperature increment level (49) and maximum moisture increment level (6), respectively.
Analysis increment fields of the prepBUFR and radiance data analysis compared to the analysis with prepBUFR only at vertical level six
[fig:increments_rad]
Analysis increment fields of the prepBUFR and radiance data analysis compared to the analysis with prepBUFR only at vertical level 49
[fig:increments_rad2]
In order to fully understand the analysis results, the following topics should be reviewed:
The weighting functions of each channel and the data coverage at the analysis time. There are several sources on the internet that show the weighting functions of the AMSU-A channels. Channel one is the moisture channel, while the others are mainly temperature channels (Channels two, there, and 15 also have large moisture signals). Because a model top of 20 mb was specified for this case study, the actual impact should come from channels with peak weighting below 20 hPa.
The usage of each channel is located in the file named ’satinfo’ in the run directory. The first two columns show the observation type and platform of the channels, and the third column indicates if the channel is used in the analysis. Because many amsua_n15 and amsua_n18 data were used, they should be checked in detail. In this case, Channels six, 11, and 14 from amsua_n15 and channels nine and 14 from amsua_n18 were turned off.
Thinning information, including a quick look at the namelist in the run directory. The file “gsiparm.anl” shows that both amsua_n15 and amsu_n18 use thinning grid two, which is 60 km. In this case, the grid spacing is 30 km, which indicates to use the satellite observations every four grid-spaces, which might be a little dense.
Bias correction: Radiance bias correction was previously discussed. It is very important for a successful radiance data analysis. The run script can only link to the GDAS bias correction coefficients that are provided as an example in ./fix:
cp ${FIX_ROOT}/gdas1.t00z.abias.20150617 ./satbias_in cp ${FIX_ROOT}/gdas1.t00z.abias_pc.20150617 ./satbias_pcUsers can download the operational bias correction coefficients during their experiment period as a starting point to calculate the coefficients suitable for their experiments.Radiance bias correction for regional analyses is a difficult issue because of the limited coverage of radiance data. This topic is out of the scope of this document, but this issue should be considered and understood when using GSI with radiance applications.
Assimilating GPS Radio Occultation Data with Regional GSI¶
Run Script¶
The addition of GPS Radio Occultation (RO) data into the GSI analysis is similar to that of adding radiance data. In the example below, the RO data is used as refractivity. There is also an option to use the data as bending angles. The same run scripts in sections 1.1.1 and 1.2.1 can be used with the addition of the following link to RO observations:
ln -s ${OBS_ROOT}/gdas1.t${HH}z.gpsro.tm00.bufr_d gpsrobufr
For this case study, the GPS RO BUFR file was downloaded and saved in
the OBS_ROOT directory. The file is linked to the name gpsrobufr,
following the namelist section &OBS_INPUT:
! dfile dtype dplat dsis dval dthin dsfcalc
gpsrobufr gps_ref null gps 1.0 0 0
This indicates that GSI is expecting a GPS refractivity BUFR file named gpsrobufr. In the following example, GPS RO and conventional observations are both assimilated. Change the run directory name in the run scripts to reflect this test:
WORK_ROOT=/scratch1/gsiprd_${ANAL_TIME}_gps_prepbufr
Run GSI and Check the Run Status¶
The process of running GSI is the same as described in section 1.1.2. Once run_gsi_regional.ksh has been submitted, move into the working directory, gsiprd_2014061700_gps_prepbufr, to check the GSI analysis results. The run directory will look exactly the same as with the conventional data, with the exception of the link to the GPS RO BUFR files used in this case. Following the same steps as in section 1.1.3, check the standard output file to see if GSI has run through each part of the analysis process successfully. In addition to the information outlined for the conventional run, the GPS RO BUFR files should have been read in and distributed to each sub domain:
OBS_PARA: ps 1429 3190 4655 6774
OBS_PARA: t 2564 5200 7057 11128
OBS_PARA: q 2346 4626 6148 8128
OBS_PARA: pw 65 80 63 49
OBS_PARA: uv 3358 6453 8091 11998
OBS_PARA: gps_ref 1799 1368 2664 3520
Comparing the output to the content in section 1.1.3, it can be seen that the GPS RO refractivity data have been read in and distributed to four sub-domains successfully.
Diagnose GSI Analysis Results¶
Check File fort.212¶
The file fort.212 shows the fit of the analysis/background to the GPS/RO data as fractional differences. It has the same structure as the fit files for conventional data. Below is a quick look to be sure the GPS RO data were used:
Observation - Background (O-B)
ptop 1000.0 900.0 800.0 600.0 400.0 300.0 250.0 200.0 150.0 100.0 50.0 0.0
it obs type styp pbot 1200.0 1000.0 900.0 800.0 600.0 400.0 300.0 250.0 200.0 150.0 100.0 2000.0
----------------------------------------------------------------------------------------------------------------------------------------------
o-g 01 all count 0 13 58 223 355 342 232 261 326 440 729 3740
o-g 01 all bias 0.00 -0.76 -0.03 -0.06 -0.04 0.01 -0.03 0.04 -0.04 -0.16 -0.18 -0.14
o-g 01 all RMS error 0.00 1.41 0.75 0.96 0.79 0.35 0.32 0.42 0.54 0.57 0.55 0.59
Observation - Analysis (O-A)
ptop 1000.0 900.0 800.0 600.0 400.0 300.0 250.0 200.0 150.0 100.0 50.0 0.0
it obs type styp pbot 1200.0 1000.0 900.0 800.0 600.0 400.0 300.0 250.0 200.0 150.0 100.0 2000.0
----------------------------------------------------------------------------------------------------------------------------------------------
o-g 03 all count 1 18 65 229 355 342 231 266 330 440 731 3776
o-g 03 all bias -0.40 -0.43 0.03 0.02 -0.02 -0.01 -0.02 0.00 0.01 -0.01 -0.02 0.00
o-g 03 all RMS error 0.40 1.03 0.54 0.59 0.70 0.26 0.14 0.20 0.24 0.28 0.39 0.41
It can be seen that most of the GPS RO data are located in the upper levels, with a total of 3740 observations used in the analysis during the 1st outer loop, and 3776 used to calculate O-A. After the analysis, the data bias reduced from -0.14 to 0.00, and the RMS error was reduced from 0.59 to 0.41. It can be concluded that the analysis with GPS RO data looks reasonable from these statistics.
Check the Analysis Increment¶
The same methods for checking the minimization in section 1.1.4.2 can be used for the GPS RO assimilation.
The following figures provide detailed information about how the new data impacts the analysis. Using the NCL script from section [sec5.1.4], analysis increment fields are plotted comparing the analysis results with GPS RO and conventional data to the analysis results with conventional data assimilation only for vertical level 48, which represents the maximum temperature increment.
Analysis increment fields comparing the use of GPS RO and conventional observations to only prepBUFR at vertical level 48.
[fig:increments_bufr]
Introduction to GSI Hybrid 3DEnVar Analysis¶
The three-dimensional hybrid ensemble-variational (hybrid 3DEnVar) analysis is an important option in the GSI system that has been used operationally. It provides the ability to bring the flow dependent background error covariance into the analysis based on ensemble forecasts. If ensemble forecasts have been generated, setting up GSI to do a hybrid analysis is straightforward and only requires two changes in the run script in addition to the current 3DVAR run script:
- Change 1: Link the ensemble members to the GSI run directoryThis change is required to link the ensemble members to the GSI run directory and assign each ensemble member a name that GSI recognizes. GSI can accept four kinds of ensemble forecasts, controlled by the namelist variable regional_ensemble_option. Table [tab51] provides a list of options for regional_ensemble_option and the naming convention for linking the ensemble files to GSI recognized names.
List of ensemble forecasts that can be read by GSI¶ regional_ ensemble_ option explanation Function called GSI recognized ensemble file names 1 GFS ensemble internally interpolated to hybrid grid get_gefs_for_regional filelist : a text file including the path and name of ensemble files 2 Ensemble is in WRF-NMM (HWRF) format get_wrf_nmm_ensperts d01_en001,d01_en002,… 3 Ensemble is in ARW netcdf format get_wrf_mass_ensperts_netcdf wrf_en001, wrf_en002, … 4 Ensemble is in NMMB format get_nmmb_ensperts nmmb_ens_mem001, nmmb_ens_mem002,… [tab51]
Users have to change the GSI run script to add the links to the ensemble forecasts if they want to use the GSI hybrid function. Below is an example of using an ensemble in ARW netcdf format, assuming that all the ensemble members are located in a directory defined by the parameter $mempath and the ensemble members have a name such as: wrfout_d01_$iiimem, where $iiimem is an integer indicating the ensemble member ID. The following lines should be added to the run script with loop iiimem from one to the total number of ensemble members:
if [ -r ${mempath}/wrfout_d01_${iiimem} ]; then ln -sf ${mempath}/wrfout_d01_${iiimem} ./wrf_en${iiimem} else echo "member ${mempath}/wrfout_d01_${iiimem} does not exit" fi - Change 2: Set up the namelist options in section HYBRID_ENSEMBLEUsers need to set
l_hyb_ens=.true.to turn on the hybrid ensemble analysis. Commonly used namelist options for the hybrid analysis are listed in table [tab52]:The list of namelist options for GSI hybrid¶ Options explanation l_hyb_ens if true, turn on hybrid ensemble option uv_hyb_ens if true, ensemble perturbation wind variables are u and v; otherwise, ensemble perturbation wind variables are stream function and velocity potential generate_ens if true, generate an internal ensemble based on the existing background error; recommended = false n_ens number of ensemble members beta1_inv (1/beta1), the weight given to the static background error covariance. 0 <= beta1_inv <= 1, should be tuned for optimal performance; beta2_inv = 1 - beta1_inv is the weight given to the ensemble derived covariance =1, ensemble information turned off =0, static background errors turned off s_ens_h homogeneous isotropic horizontal ensemble localization scale (km) s_ens_v vertical localization scale If positive, in grid units; if negative, in lnp unit regional_ensemble _option integer, used to select the type of ensemble to read in for regional applications. Currently takes values from one to four: =1: use GEFS internally interpolated to ensemble grid; =2: ensembles are in WRF-NMM format; =3: ensembles are in ARW netcdf format; =4: ensembles are in NMMB format. grid_ratio_ens for regional runs, the ratio of ensemble to analysis grid resolution. If turned on and specified with an appropriate value, this could increase the computational efficiency. [tab52]
Please note: the parameterss_ens_h,s_ens_v, andbeta1_invare tunable parameters. They should be tuned for best performance.After setting up the namelist parameters and the path/name of the ensemble members, GSI can be run in the same manner as the other 3DVAR cases introduced in this chapter. The same procedures could be followed as in the previous sections to check the run status and diagnose the GSI analysis.
Introduction to GSI Hybrid 4DEnVar Analysis¶
The GSI hybrid 4DEnVAR analysis is similar to the hybrid 3DEnVar except that the hybrid 4DEnVar will use multiple background files and GFS ensemble forecasts. As an example, the following shows how to conduct a hybrid 4DEnVar analysis using three time levels of background files and ensembles.
Before creating a hybrid 4DEnVar analysis, be sure to read the previous section about how to run hybrid 3DEnVar first. The following steps are additional procudures beyond hybrid 3DEnVar and assume that all hybrid 3DEnVar settings have already been set.
(1). Set if_4DEnVar=Yes in run_gsi_regional.ksh.
(2). Set the correct background files and ensemble files at different time levels in run_gsi_regional.ksh. See the following example:
if [ ${if_4DEnVar} = Yes ] ; then
BK_FILE_P1=${BK_ROOT}/wrfout_d01_2017-05-13_19:00:00
BK_FILE_M1=${BK_ROOT}/wrfout_d01_2017-05-13_17:00:00
ENSEMBLE_FILE_mem_p1=${ENS_ROOT}/sfg_2017051312_fhr09s
ENSEMBLE_FILE_mem_m1=${ENS_ROOT}/sfg_2017051312_fhr03s
fi
Note that the background file at the analysis time (201705131800 for the above example) is set by BK_FILE and the ensemble files at the analysis time are set by ENSEMBLE_FILE_mem as introduced in previous sections. See the following example:
BK_FILE=${BK_ROOT}/wrfout_d01_2017-05-13_18:00:00
...
if [ ${if_hybrid} = Yes ] ; then
...
ENSEMBLE_FILE_mem=${ENS_ROOT}/sfg_2017051312_fhr06s
...
Now GSI can be run following the hybrid 3DEnVar case introducted in the prevous section. Similar procedures can be conducted to check the GSI run status and results.
Summary¶
This chapter applied previous information outlined in the users guide to demonstrate how to set up, run, and analyze GSI for various regional applications. It is important to always check for a successful GSI analysis, as running to completion does not always indicate a successful analysis was generated. Using the tools and methods described in this chapter, a complete picture of the GSI analysis can be obtained.
It is important to realize that GSI applications are not limited to regional analyses with WRF. Other GSI applications, including global analyses for GFS, chemical analyses, and others will be introduced in the next chapter.