GSI Fundamentals (5): Review and Applications

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1 27 GSI/EnKF Community Tutorial July -4, 27, College Park, MD GSI Fundamentals (5): Review and Applications Jeff Beck, Ming Hu, Hui Shao2, Chunhua Zhou2, and Kathryn Newman2 Developmental Testbed Center (DTC) NOAA/ESRL/GSD NCAR2

2 Outline! GSI Fundamentals (): Setup and Compilation! GSI Fundamentals (2): Run and Namelist! GSI Fundamentals (3): Diagnostics! GSI Fundamentals (4): BE and Observation Error! GSI Fundamentals (5): PrepBUFR and BUFR Format! GSI Fundamentals (6): Review and Applications! Review steps for regional GSI Refresher based on previous talks! Conventional observations! Radiance data! Successfully set up global GSI! Learn to check run status! Learn to understand diagnostics in the context of a particular data source and/or application! Ensure the run was successful! This talk is tailored to Chapter 5 of the GSI User s Guide Introduction Case Study Conventional Assimilation Radiance Assimilation GPS RO Assimilation Global GSI Application Summary 2

3 Introduction! Steps to running a successful GSI Analysis: Obtain background field 2. Grab desired observational data 3. Choose the appropriate anavinfo file (and modify it) to have the same vertical levels as the GSI background 4. Modify run script to suit specific application and properly link observational data.! Additional steps specific to observational data (e.g: thinning and bias correction for radiance) Run GSI 6. Check run status and completion of each step of the GSI analysis (stdout) 7. Diagnose analysis results (fit files) 8. Check analysis increment, cost function/norm of gradient (DTC graphics utilities available) 5. Introduction Case Study Conventional Assimilation Radiance Assimilation GPS RO Assimilation Global GSI Application Summary 3

4 Step and 2: Background and observational data! Regional WRF-ARW used for following DA examples Landmask of case study background! Horizontal resolution 3-km & 5 vertical sigma levels! Using archived observational data for this case, but real-time and other archived observational data are available (NOMADS, NCEP FTP, NCAR CISL archive)! Case Study data available at: Case Study :. background 2. obs data 3. anavinfo 4. run script/namelist 5. Run 6. stdout 7.statistics 8. plotting 4

5 Step 3: anavinfo file! ncdump h wrfinput_d more netcdf wrfinput_d {dimensions: Time = UNLIMITED ; // ( currently) DateStrLen = 9 ; west_east = 332 ; south_north = 25 ; bottom_top = 5 ; bottom_top_stag = 5 ; soil_layers_stag = 4 ; west_east_stag = 333 ; south_north_stag = 26 ; DIM9 = 5 ; land_cat_stag = 24 ; soil_cat_stag = 6 ; num_ext_model_couple_dom_stag = ; fix/anavinfo_arw_netcdf state_derivatives ::!var level src ps met_guess u 4 met_guess v 4 met_guess tv 4 met_guess q 4 met_guess oz 4 met_guess cw 4 met_guess Prse 4 met_guess :: * New function as of v3.5 can do this automatically in the run script (only works for WRF-ARW netcdf data) Case Study :. background 2. obs data 3. anavinfo 4. run script/namelist 5. Run 6. stdout 7.statistics 8. plotting 5

6 Step 3: anavinfo file! ncdump h wrfinput_d more netcdf wrfinput_d {dimensions: Time = UNLIMITED ; // ( currently) DateStrLen = 9 ; west_east = 332 ; south_north = 25 ; bottom_top = 5 ; bottom_top_stag = 5 ; soil_layers_stag = 4 ; west_east_stag = 333 ; south_north_stag = 26 ; DIM9 = 5 ; land_cat_stag = 24 ; soil_cat_stag = 6 ; num_ext_model_couple_dom_stag = ; fix/anavinfo_arw_netcdf state_derivatives ::!var level src ps met_guess u 5 met_guess v 5 met_guess tv 5 met_guess q 5 met_guess oz 5 met_guess cw 5 met_guess Prse 5 met_guess :: * New function as of v3.5 can do this automatically in the run script (only works for WRF-ARW netcdf data) Case Study :. background 2. obs data 3. anavinfo 4. run script/namelist 5. Run 6. stdout 7.statistics 8. plotting 6

7 Conventional Observation Data Assimilation " Run Script " Run Status & Completion " Analysis Fit to Observations " Minimization " Analysis Increment " This case study available at: (Practice Case #) Introduction Case Study Conventional Assimilation Radiance Assimilation GPS RO Assimilation Global GSI Application Summary

8 Step 4: Run Script run_gsi_regional.ksh! Set up GSI run script following GSI Run and Namelist talk! Set paths to data, exe, fix files, etc: Experimental Setup ANAL_TIME=2467 WORK_ROOT=gsiprd_${ANAL_TIME}_prepbufr BK_ROOT=data/${ANAL_TIME}/arw BK_FILE=${BK_ROOT}/wrfinput_d.2467 OBS_ROOT=/data/${ANAL_TIME}/obs PREPBUFR=${OBS_ROOT}/nam.tz.prepbufr.tm.nr CRTM_ROOT=CRTM_Version GSI_ROOT=GSI_Release_Version/ FIX_ROOT=${GSI_ROOT}/fix GSI_EXE=${GSI_ROOT}/run/gsi.exe GSI_NAMELIST=${GSI_ROOT}/run/comgsi_namelist.sh bk_core=arw bkcv_option=nam if_clean=clean Location of PREPBUFR data Location of GSI namelist script! Namelist using default options in the sample script (see: Chapter 3 of the GSI User s Guide) Conventional :. background 2. obs data 3. anavinfo 4. run script 5. Run 6. stdout 7. statistics 8. plotting 8

9 Step 5: Run Status While GSI is still running! In ${ WORK_ROOT} contents should include: imgr_g2.taucoeff.bin ssmi_f5.spccoeff.bin imgr_g3.spccoeff.bin ssmi_f5.taucoeff.bin imgr_g3.taucoeff.bin ssmis_f6.spccoeff.bin " Indicates CTRM coefficients linked to this run directory stdout: standard out file wrf_inout: background file gsiparm.anl: GSI namelist prepbufr: PREPBUFR file for conventional observation convinfo: data usage control for conventional data berror_stats: background error file errtable: observation error file Indicates run scripts have successfully setup a run environment for GSI and the.exe is running " Conventional :. background 2. obs data 3. anavinfo 4. run script 5. Run 6. stdout 7. statistics 8. plotting 9

10 Step 5: Run Status While GSI is still running! Check the content of the standard out file to monitor the stage of the GSI analysis! tail -f stdout st outer loop GLBSOI: START pcgsoi jiter= Inner iteration pcgsoi: gnorm(:2),b= e+5.e+ Initial cost function = E+4 Initial gradient norm = E E+5 cost,grad,step,b,step? = E E E-2.E+ good pcgsoi: gnorm(:2),b= E E E+4 cost,grad,step,b,step? = E E E E- good pcgsoi: gnorm(:2),b= E E- " E+4 Check for descending cost function value with each inner iteration Conventional :. background 2. obs data 3. anvinfo 4. run script 5. Run 6. stdout 7. statistics 8. plotting

11 Step 5: Run Completion! Upon successful completion the run directory should look like: anavinfo berror_stats convinfo diag_conv_anl.2467 diag_conv_ges.2467 errtable fit_p.2467 fit_q.2467 fit_rad.2467 fit_t.2467 fit_w.2467 fort.2 fort.22 fort.23 fort.24 fort.25 fort.26 fort.27 fort.28 fort.29 fort.2 fort.2 fort.22 fort.23 fort.24 fort.25 fort.27 fort.28 fort.29 fort.22 fort.22 fort.223 fort.224 fort.225 fort.226 fort.227 fort.228 satbias_angle fort.229 satbias_in fort.23 satbias_out gsi.exe satinfo gsiparm.an stdout l2rwbufr prepbufr list_run_directory wrfanl.2467 ozinfo wrf_inout pcpbias_out pcpinfo stdout.anl.2467 prepobs_prep.bufrtable! Number of files will be greatly reduced from the run stage due to the clean' option in the run script.! Completion of GSI without crashing does not guarantee a successful analysis, need to assess output further Conventional :. background 2. obs data 3. anavinfo 4. run script 5. Run 6. stdout 7. statistics 8. plotting

12 Step 6: Run Completion stdout: Reading in namelist! Verify GSI started normally and has read in the namelist: SETUP_4DVAR: SETUP_4DVAR: SETUP_4DVAR: &SETUP GENCODE = FACTQMIN l4dvar= F l4densvar= F winlen= , =.E+,! Verify GSI has read in background field: stdout: Reading in background field! Check the range of the minimum and maximum values to indicate if background fields are normal rmse_var = U ndim= 3 WrfType = 4 WRF_REAL= 4 ierr = ordering = XYZ staggering = N/A start_index = end_index = 333 k,max,min,mid U= k,max,min,mid U= 2 k,max,min,mid U= 3 k,max,min,mid U= 4 k,max,min,mid U= K Maximum Minimum Central grid Conventional :. background 2. obs data 3. anavinfo 4. run script 5. Run 6. stdout 7. statistics 8. plotting 2

13 Step 6: Run Completion stdout: Reading in observational data! In the middle of the stdout file: OBS_PARA: ps OBS_PARA: t OBS_PARA: q OBS_PARA: pw OBS_PARA: uv Observation Type Distribution of observations in each sub-domain! This table is important to see if the observations have been read in properly.! But the data have to go through QC process to be finally used in the analysis, more detailed information in fit files Conventional :. background 2. obs data 3.anavinfo 4. run script 5. Run 6. stdout 7. statistics 8. plotting 3

14 Step 6: Run Completion stdout: Optimal Iteration The namelist specified 2 outer loops with 5 inner loops! The minimization step will look like: GLBSOI: START pcgsoi jiter= pcgsoi: gnorm(:2),b= e+5 Initial cost function = E+4 Initial gradient norm = E+2 cost,grad,step,b,step? = E E E+4.E+ good.e E+2! last iteration: cost,grad,step,b,step? = E E E- good pcgsoi: gnorm(:2),b= E E E E-6 cost,grad,step,b,step? = E E E- good E-3 PCGSOI: WARNING **** Stopping inner iteration *** gnorm e- less than.4e-9 update_guess: successfully complete The iteration met stopped before meeting the maximum iteration (based on J function) Iteration check: The J function value should descend through iterations Conventional :. background 2. obs data 3. anavinfo 4. run script 5. Run 6. stdout 7. statistics 8. plotting 4

15 Step 6: Run Completion stdout: Write out analysis results! Analysis variable information (very similar to section where background data are read in): max,min MU= rmse_var=mu ordering=xy WrfType,WRF_REAL= ndim= staggering= N/A start_index= end_index= k,max,min,mid T= k,max,min,mid T= k,max,min,mid T= k,max,min,mid T= The following lines will appear at the end of the file if GSI succesfully completed: ENDING DATE-TIME JUN 6,24 6:47: PROGRAM GSI_ANL HAS ENDED. FRI !GSI successfully ran through every step with no run issues, but uncertain whether it produced a successful analysis until more diagnosis has been completed Conventional :. background 2. obs data 3. anavinfo 4. run script 5. Run 6. stdout 7. statistics 8. plotting 5

16 Step 7: Analysis Fit to Observations The analysis uses the observations to correct the background fields to push the analysis results to fit the observations under certain constraints. " Easiest way to confirm the GSI analysis fit the observations better than the background:!check fort/fit files Example: fort.23 (temperature) Is the bias and RMS within a reasonable range? " it obs type styp ptop pbot o-g t 2 count o-g t 2 bias o-g t 2 rms o-g o-g t 8 count t 8 bias o-g t 8 rms o-g all count o-g all bias o-g all rms O-B Data types used: 2: rawinsonde 8: surface marine 53 rawinsonde obs exist between mb, mean bias=-.5, rms =.69 Whole atmosphere/all data: OB: 9482 obs, mean bias =.8, rms =.45 Conventional :. background 2. obs data 3. anavinfo 4. run script 5. Run 6. stdout 7. statistics 8. plotting 6

17 Step 7: Analysis fit to Observations! fort.23 (temperature) continued! Looking at the whole atmosphere and every observation type provides a quick analysis of fit o-g o-g o-g all all all count bias rms all all all count bias rms O-B o-g 3 o-g 3 o-g 3 O-A " " " fort.22 (w) fort.2 (p) fort.24 (q) Check other parameters! 9482 total observations from the background to 9483 for the analysis; the bias reduced from.8 to.5 & rms reduced from.45 to.22. This kind of reduction is reasonable for a large scale analysis "! Statistics show analysis results fit to observations closer than background how close analysis fit is to observation is based on ratio of background error variance and observation error. Conventional :. background 2. obs data 3. anavinfo 4. run script 5. Run 6. stdout 7. statistics 8. plotting 7

18 Step 7: Checking Minimization! In addition to stdout, GSI writes fort.22 with more detailed information on minimization! Quick check of the trend of the cost function and norm of the gradient:! Dump information from fort.22 to an output file: " grep 'cost,grad,step,b' fort.22 sed -e 's/cost,grad,step,b,step? = 's/good//g' > cost_gradient.txt //g' sed -e cost_gradient.txt will have 6 columns (4 shown below) E E E E E E E E E E E E E E E E E E E E-3 Outer loop Inner iteration number Cost function! Both the cost function and the norm of gradient descended with each iteration: Cost function decreased from 3.24 E+4 to 2.83 E+4 Norm of gradient decreased from 3.36 E+2 to 2.75 E-3 Norm of the gradient Conventional :. background 2. obs data 3. anavinfo 4. run script 5. Run 6. stdout 7. statistics 8. plotting 8

19 Step 8: Checking Minimization! To gain a complete picture of the minimization process, it is possible to plot the cost function values and norm of the gradient versus iteration! Script available in v3.6 release:./util/analysis_utilities/ plot_ncl/ GSI_cost_gradient.ncl Cost function and norm of gradient descend in both outer loops Conventional :. background 2. obs data 3. anavinfo 4. run script 5. Run 6. stdout 7. statistics 8. plotting 9

20 Step 8: Checking Analysis Increment! Analysis increments provide an idea of where and how much the background fields have been modified by the observations! Graphic tool available in v3.4 release:./util/analysis_utilities/plot_ncl/analysis_increment.ncl Analysis increment at 665 mb Note: different scales for each plot! The U.S. CONUS domain has many upper level observations and the data availability over the ocean is sparse (only doing conventional observation assimilation in this case) Conventional :. background 2. obs data 3. anavinfo 4. run script 5. Run 6. stdout 7. statistics 8. plotting 2

21 Radiance Assimilation In addition to conventional observations: " Run script " Data thinning and bias correction " Run status & completion " Diagnosing analysis results Introduction Case Study Conventional Assimilation Radiance Assimilation GPS RO Assimilation Global GSI Application Summary

22 Step 4: Run Script run_gsi_regional.ksh! Key difference from conventional assimilation: properly link radiance BUFR files to the GSI run directory! Add the following radiance BUFR files: AMSU-A: gdas.2z.bamua.tm.bufr_d HIRS4: gdas.tz.bhrs4.tm.bufr_d! Insert the following data links after PREPBUFR data in run_gsi_regional.ksh: ln -s ${OBS_ROOT}/gdas.t2z.bamua.tm.bufr_d amsuabufr ln -s ${OBS_ROOT}/gdas.t2z.bhrs4.tm.bufr_d hirs4bufr! Keep link to prepbufr when assimilating both prepbufr and radiance: ln -s ${PREPBUFR}./prepbufr Conventional :. background 2. obs data 3. anavinfo 4. run script 5. Run 6. stdout 7. statistics 8. plotting 22

23 Step 4: Radiance Data Thinning! Radiance data thinning is set up in the namelist section &OBS_INPUT: dmesh()=2.,dmesh(2)=6.,dmesh(3)=3,time_window_max=.5,ext_sonde=.true.! dfile amsuabufr dtype dplat amsua n5 dsis amsua_n5 dval dthin. 2 dsfcalc! In this case, data thinning for NOAA-5 AMSU-A observation is 6 km Radiance :. background 2. obs data 3.anavinfo 4. run script/namelist 5. Run 6. stdout 7. statistics 8. plotting 23

24 Step 4: Radiance Bias Correction! Radiance angle dependent bias correction can also be done variationally within GSI, together with mass bias correction.! run_gsi_regional.ksh: cp ${FIX_ROOT}/gdas.tz.abias.new./satbias_in! comgsi_namelist.sh: $SETUP adp_anglebc=.true. The file gdas.tz.abias.new serves as an example of new bias correction coefficients. Users can download the satbias coefficient file (i.e., gdas.tz.abias) Radiance :. background 2. obs data 3.anavinfo 4. run script/namelist 5. Run 6. stdout 7. statistics 8. plotting 24

25 Step 6: Run Completion stdout: Reading in observational data Once GSI is running, the work directory will look similar to the conventional case, with additional links to the radiance BUFR files Check stdout file for successful completion of each step The radiance data should have been read in and distributed to each sub domain: 8 new radiance data types have been read in OBS_PARA: OBS_PARA: OBS_PARA: OBS_PARA: OBS_PARA: OBS_PARA: OBS_PARA: OBS_PARA: OBS_PARA: OBS_PARA: OBS_PARA: OBS_PARA: OBS_PARA: ps t q pw uv hirs4 hirs4 hirs4 amsua amsua amsua amsua amsua metop-a n9 metop-b n5 n8 n9 metop-a metop-b Radiance :. background 2. obs data 3.anavinfo 4. run script/namelist 5. Run 6. stdout 7. statistics 8. plotting 25

26 Step 7: Diagnosing Analysis Results! The fort.27 file is for radiance data Penalty (contribution from this observation type to cost function)! Statistics for each outer loop: it satellite instrument # read # keep o-g rad n5 amsua o-g rad n8 amsua O-B # within the analysis time window and domain o-g 3 rad n5 amsua o-g 3 rad n8 amsua # assim penalty # after thinning qcpnlty cpen qccpen # used in analysis O-A! The penalty for n5 decreased and the amount of assimilated data increased after 2 outer loops 26

27 Step 8: Checking Analysis Impact! Analysis increments plotted for radiance & conventional vs. conventional only! Graphics tool available as of the v3.4 release:./util/analysis_utilities/plot_ncl/analysis_increment.ncl Analysis Increment at 7 mb Increment = (AMSU-A+HIRS4+ PREPBUFR) - PREPBUFR! Impact of radiance data compared to conventional alone most evident over data sparse oceans Radiance :. background 2. obs data 3. run script/namelist 4. Run 5. stdout 6. statistics 7. plotting 27

28 Global GSI Application 3DVar " Run Script " Run Status & Completion " Diagnosing analysis results Please download the fix files for global GSI application from the following link: Introduction Case Study Conventional Assimilation Radiance Assimilation GPS RO Assimilation Global GSI Application Summary

29 Step 4: Run Script! Set up GSI run script ANAL_TIME=24776 GFSCASE=T254 WORK_ROOT=gsiprd_${ANAL_TIME}_${GFSCASE} run_gsi_global.ksh # Set the JCAP resolution which you want. # All resolutions use LEVS=64 if [[ "$GFSCASE" = "T62" ]]; then JCAP=62 JCAP_B=62 elif [[ "$GFSCASE" = "T26" ]]; then BK_ROOT=data/gfs/${GFSCASE} JCAP=26 OBS_ROOT=/data/gfs/${GFSCASE} JCAP_B=26 PREPBUFR=${OBS_ROOT}/ gdas.t6z.prepbufr.nr CRTM_ROOT=CRTM_REL-2..3 elif [[ "$GFSCASE" = "enkf_glb_t254" ]]; then JCAP=254 JCAP_B=254 elif [[ "$GFSCASE" = "T254" ]]; then GSI_ROOT=GSI_Release_Version/ JCAP=254 FIX_ROOT=${GSI_ROOT}/fix/global JCAP_B=574 GSI_EXE=${GSI_ROOT}/run/gsi.exe GSI_NAMELIST=${GSI_ROOT}/run/ comgsi_namelist_gfs.sh elif [[ "$GFSCASE" = "T574" ]]; then JCAP=574 JCAP_B=534 else bk_core=arw echo "INVALID case = $GFSCASE" bkcv_option=nam exit if_clean=clean fi! See Chapter 5 of the GSI User s Guide 29

30 Step 4: Run Script run_gsi_global.ksh! Need GFS background at 3, 6, and 9 hours! Setup is for 3DVar (4DEnVar now uses seven background time levels) # Bring over background field (it's modified by GSI so we can't link to it) cp $BK_ROOT/sfcf3./sfcf3 GFS surface forecast files at 3 cp $BK_ROOT/sfcf6./sfcf6 time levels: 3, 6, 9 hours cp $BK_ROOT/sfcf9./sfcf9 cp $BK_ROOT/sigf3./sigf3 cp $BK_ROOT/sigf6./sigf6 cp $BK_ROOT/sigf9./sigf9 GFS atmosphere forecast files at 3 time levels: 3,6,9 hours 3

31 Step 5: Run Completion! Run directory after completion of GSI for a global run: 3

32 Step 5: Run Completion! Run directory after completion of GSI for a global run: sfcanl.gsi -- global analysis background for surface siganl -- global analysis background for the atmosphere 32

33 Summary! Steps to running a successful GSI Analysis:. Obtain background field 2. Collect desired observational data Choose the appropriate anavinfo file (and modify it) to have the same vertical levels as the GSI background Modify run script to suit specific application and properly link observational data 3. 4.! Additional steps specific to observational data (e.g: thinning and bias correction for radiance) Run GSI Check run status and completion of each step of the GSI analysis (stdout) Diagnose analysis results (fit files) Check analysis increment, cost function/norm of gradient (DTC graphics utilities available) 33

34 Questions? GSI Help Desk: GSI User s Guide: Thank You!

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