GSI Fundamentals (3): Diagnostics

Transcription

1 24 GSI Community Tutorial July 4-6, 24. Boulder, CO GSI Fundamentals (3): Diagnostics Kathryn Newman Ming Hu & Chunhua Zhou Developmental Testbed Center (DTC)

2 Outline GSI fundamentals (): Setup and Compilation GSI fundamentals (2): Run and Namelist GSI fundamentals (3): Diagnostics Standard output Observation innovation statistics and diagnostic files Convergence information Conventional Observation Errors Background Error Covariance (BE) Analysis increments GSI fundamentals (4): Applications ü This talk is tailored to Chapter 4 of the GSI User s Guide for Community Release v3.3

3 Standard Output (stdout) Details in User s Guide Section 4. ü Critical information about the GSI analysis can be obtained ü Users can check:. Did GSI successfully complete? 2. Do optimal iterations look correct? 3. Are the background and analysis fields reasonable? ü Helpful in understanding where and why GSI may have failed

4 stdout: structure The structure of stdout is as follows:. Read in data and prepare analysis: Read in configuration (namelist) Read in background Read in observations Partition domain and data for parallel analysis Read in constant fields (fixed files) 2. Optimal iteration (analysis) 3. Save analysis result *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. PROGRAM GSI_ANL HAS BEGUN. COMPILED ORG: NP23 STARTING DATE-TIME JUN 4,24 6:58: WEN Indicates the start of the GSI analysis

5 Stdout: read in configuration state_vectors*init_anasv: 2D-STATE VARIABLES ps sst state_vectors*init_anasv: 3D-STATE VARIABLES u v tv tsen q oz cw p3d control_vectors*init_anacv: 2D-CONTROL VARIABLES ARE ps sst control_vectors*init_anacv: 3D-CONTROL VARIABLES ARE sf vp t q oz cw Read in: General set-up for GSI 3D-var analysis ü anavinfo ü State and control variables ü 4D-var setup (for 4dvar interface) ü namelist &SETUP GENCODE = 78., FACTQMIN =.E+, FACTQMAX =.E+, FACTV =., DELTIM = 2., DTPHYS = 36., BIASCOR = -., BCOPTION =, DIURNALBC =.E+, NDAT = 87, NITER =, 2*, 48*, NITER_NO_QC = 5*, MITER = 2, QOPTION = 2, NHR_ASSIMILATION = 3, MIN_OFFSET = 8, IOUT_ITER = 22, / &GRIDOPTS &BKGERR &ANBKGERR &JCOPTS &STRONGOPTS &OBSQC &SUPEROB_RADAR &LAG_DATA &HYBRID_ENSEMBLE &RAPIDREFRESH_CLDSURF &CHEM Details covered in GSI fundamentals (2)

6 stdout: check background input dh = 3 iy,m,d,h,m,s= 2 nlon,lat,sig_regional= rmse_var=t ordering=xyz WrfType,WRF_REAL= 4 4 ndim= 3 Check that bk fields have been staggering= N/A read in successfully start_index= end_index= k,max,min,mid T= k,max,min,mid T= k,max,min,mid T= k,max,min,mid T= k,max,min,mid T= k,max,min,mid T= K Maximum Minimum Repeats for all fields at each vertical level: P_TOP, ation m r o f n i e Tim field sion of bk Dimen Variable name in netcdf file Central grid ZNU, ZNW, RDX, RDY, MAPFAC_M, XLAT, XLONG, MUB, MU, PHB, QVAPOR, U, V, LANDMASK, SEAICE, SST,IVGTYP, ISLTYP, VEGFRA, SNOW, U, V, SMOIS, TSLB, TSK

7 stdout: Check domain and convinfo Domain partition information: Task ID general_deter_subdomain: general_deter_subdomain: general_deter_subdomain: general_deter_subdomain: task,istart,jstart,ilat,jlon= task,istart,jstart,ilat,jlon= task,istart,jstart,ilat,jlon= task,istart,jstart,ilat,jlon= 2 3 Ob type Is Example using 4 processors (j-num) Js Read in convinfo: Js j-num i-num Is (i-num) usage READ_CONVINFO: tcp READ_CONVINFO: ps E-3.. READ_CONVINFO: ps E-3.. READ_CONVINFO: t 2 3..E-5.. READ_CONVINFO: gps assimilate monitor

8 stdout: observation ingest GSI resets file status depending on observation time & checks for consistency with usage in satinfo files read_obs_check: read_obs_check: read_obs_check: read_obs_check: read_obs_check: read_obs_check: bufr bufr bufr bufr bufr bufr file file file file file file date rw date date date is is is is data type hirs2_n4 data type hirs3_n prepbufr ps Not available at ob time not available satwnd not available radarbufr prepbufr t Ob time match prepbufr q hirs3bufr hirs3 n7 Not used in satinfo file not used in info file -- do not read file hirs2bufr not used in info file -- do not read file hirs3bufr List of observations to be read in and processors used to read them: READ_OBS: READ_OBS: READ_OBS: READ_OBS: read read read read hirs3 mhs ps t hirs3_n7 mhs_n8 ps t using using using using ntasks= ntasks= ntasks= ntasks= Loop over all data files to be read in observations, show GPS obs outside time window: READ_GPS: time outside window skip this report READ_PREPBUFR: file=prepbufr type= ithin= rmesh=2. isfcalc= ndata= READ_BUFRTOVS: file=amsuabufr type=amsua ithin= 2 rmesh= 6. isfcalc= ndata= sis= ntask= sis=amsua_n ntask= nread= 993 nread= Data file to read Number of observations in file GPS observation outside time window Grid for data thinning Data in analysis domain and time-window

9 stdout: Check Observations Input Observation distribution in an analysis using 4 processors 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: OBS_PARA: OBS_PARA: OBS_PARA: OBS_PARA: OBS_PARA: ps t q pw sst gps_ref hirs3 hirs4 amsua amsua amsua amsub mhs mhs hirs4 amsua mhs n7 metop-a n5 n8 metop-a n7 n8 metop-a n9 n9 n Conventional obs fairly evenly distributed Task 2 3

10 stdout: observation innovation Set up RHS of analysis equation GLBSOI: jiter,jiterstart,jiterlast,jiterend= 2 First outer analysis loop Calculate observation innovation for each data type in first outer loop: SETUPALL:,obstype,isis,nreal,nchanl=ps ps 22 Following SETUPALL:,obstype,isis,nreal,nchanl=t t 24 output will repeat multiple SETUPALL:,obstype,isis,nreal,nchanl=gps_ref gps 6 times in SETUPALL:,obstype,isis,nreal,nchanl=amsua amsua_n5 33 stdout 5 INIT_CRTM: crtm_init() on path "./" ACCoeff ReadFile (Binary)(INFORMATION) : FILE:./amsua_n5.SpcCoeff.bin; ^M ACCoeff RELEASE.VERSION:.4 N_FOVS=3 N_CHANNELS=5 Read_ODPS_Binary(INFORMATION) : FILE:./amsua_n5.TauCoeff.bin; ^M ODPS RELEASE.VERSION: 2. N_LAYERS= N_COMPONENTS=2 N_ABSORBERS= N_CHANNELS=5 N_COEFFS=26 IRwaterCoeff_ReadFile(INFORMATION) : FILE:./Nalli.IRwater.EmisCoeff.bin; ^M IRwaterCoeff RELEASE.VERSION: 3.2 N_ANGLES= 76 N_FREQUENCIES= 2223 N_WIND_SPEEDS= SETUPRAD: write header record for amsua_n to file pe.amsua_n5_ radiance obs innovation computation followed by CRTM coefficients info (only for st outer loop)

11 stdout: Check outer loop and inner iteration Print Jo components at beginning of the inner loop: Begin Jo table outer loop Observation Type surface pressure temperature wind moisture gps radiance Jo Global End Jo table outer loop Nobs Nobs Jo E E E E E E+5 Jo E+5 Jo/n Jo/n.999 Print cost function values for each inner iteration: (st) outer loop Inner iteration GLBSOI: START pcgsoi jiter= Initial cost function = E+5 Initial gradient norm = E+3 cost,grad,step,b,step? = E E-4.E+ good cost,grad,step,b,step? = E E E- good cost,grad,step,b,step? = E E E- good cost,grad,step,b,step? = E E E- good E E E E+3

12 stdout: Check outer loop and inner iteration Print diagnostics about guess field after adding analysis increment: ================================================================================ Status Var Mean Min Max analysis U E E E+ analysis V E E E+ analysis TV E E E+2 analysis Q E-3.E E-2 analysis TSEN E E E+2 analysis OZ.E+.E+.E+ analysis DUMY.E+.E+.E+ analysis DIV.E+.E+.E+ analysis VOR.E+.E+.E+ analysis PRSL E E E+2 analysis PS E E E+2 analysis SST E E E+2 analysis radb E E E+ analysis pcpb.e+.e+.e+ analysis aftb.e+.e+.e+ ================================================================================ wwww u E E E+ wwww v E E E+ wwww tv E E E+ wwww tsen E E E+ wwww q E E E-3 wwww oz.e+.e+.e+ wwww cw.e+.e+.e+ wwww p3d E E E- wwww ps E E E- wwww sst E E E- Print diagnostics about analysis increment: GLBSOI: jiter,jiterstart,jiterlast,jiterend= 2 2 Repeat for 2nd outer loop

13 stdout: check Jo components Before st outer loop Observation Type surface pressure temperature wind moisture gps radiance Jo Global Nobs Nobs Jo Global Jo/n Jo/n.999 Jo After st outer loop surface pressure temperature wind moisture gps radiance Jo E E E E E E+5 Jo E Nobs E E E E E E+4 Jo E Jo/n Nobs E E E E E E+4 Jo E Jo/n.46 Shows fit of the analysis results to the data After 2nd outer loop surface pressure temperature wind moisture gps radiance Jo Global Outer loop set to 2: completion of outer loop is end of analysis

14 stdout: Check Analysis Result Output Save analysis result ordering=xy WrfType,WRF_REAL= 4 4 ndim= 2 Check that analysis fields look staggering= N/A reasonable start_index= end_index= k,max,min,mid T= k,max,min,mid T= k,max,min,mid T= k,max,min,mid T= rmse_var=t K Maximum Minimum Central grid QVAPOR, U, V, SEAICE, SST, TSK Variable name in netcdf file

15 Stdout: Final normal exit information ENDING DATE-TIME JUN 4,24 7:: WEN PROGRAM GSI_ANL HAS ENDED. *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. : *****************RESOURCE STATISTICS******************************* : : The total amount of wall time = : ( ) : *****************END OF RESOURCE STATISTICS*************************

16 Observation Fitting Statistics and Diagnostic Files Details in User s Guide Section 4.5 and A.2

17 Why need to check fitting statistics Data Analysis: adjust background fields based on observation data so that analysis fields fit the observation better. GSI has a series of text files to provide statistic information on how background or analysis fields fit to the observations in each outer loop (fort.2*) GSI also has a series of binary files to save diagnostic information for each observation (diag*)

18 Observation Innovation Statistics GSI User s Guide Table 4.4 Conventional observations File names are from fort.2 to fort.25 (or fit_variable.analysis_time) Each file is for one observation variable File name generated in run_gsi.ksh

19 Observation Innovation Statistics (Cont ) File name Variables in file fit of satellite radiance data, such as: fort.27 or fit_rad.analysis_time amsua_n5(, n6, n7, n8, metop-a, aqua, n9), amsub_n7, fort.28 fort.29 fort.2 fort.2 fort.22 fort.23 fort.24 fort.25 Ranges/units hirs3_n7, hirs4_n9 (, metop-a), mhs_n8(, metop-a, n9), Satellite atms_npp, cris_npp, iasi66_metop-a, airs28subset_aqua, radiance avhrr3_n8(, n9, metop-b), amsre_aqua, ssmi_f5, ssmis_f6(, f7,f8,f9,f2), sndrd?_g(, g2, g3, g4, g5), imgr_g(, g2, g3, g4, g5), seviri_m8(,m9 m) fit of pcp_ssmi, pcp_tmi fit of radar radial wind (rw) Radar radial wind fit of lidar wind (dw) fit of super wind data (srw) fit of GPS data fractional difference GPS fit of conventional sst data C Tropical cyclone central pressure Lagrangian data

20 Example: fit_p (fort.2) current fit of surface pressure data, ranges in mb pressure levels (hpa)=. 2. it obs type stype count bias ps ps ps ps all rms cpen qcpen rms cpen qcpen O-B current fit of surface pressure data, ranges in mb pressure levels (hpa)=. 2. it obs type stype count bias 3 ps ps ps ps all O-A Results from test case using 2 outer loops with inner iterations in each outer loop

21 Monitoring Rejected Data Used in Analysis Example: fit_w (fort.22) Vertical levels ptop it obs type styp pbot count bias rms cpen qcpen all count all bias All prepbufr types.93 all rms all cpen rej rej rej rej rej rej rej rej rej all all all all count bias rms cpen qcpen count bias rms cpen mon mon mon mon mon mon mon mon mon all all all all count bias rms cpen qcpen count bias rms cpen

22 Example: fit_w (fort.22) O-B O-A After st outer loop O-A After 2st outer loop ptop it obs type styp pbot count bias rms cpen ptop it obs type styp pbot count bias rms cpen ptop it obs type styp pbot count bias rms cpen

23 Example: fit_rad (fort.27) Channels in satinfo file RADINFO_READ: amsua_n5 2 amsua_n5 3 amsua_n5... Content of satinfo file jpch_rad= 268 chan= var= 3. varch_cld= 9. use= ermax= chan= 2 var= 2. varch_cld= 3.5 use= ermax= chan= 3 var= 2. varch_cld= 7. use= ermax= 4.5 b_rad= 4.5 b_rad= 4.5 b_rad=. pg_rad=. pg_rad=. pg_rad=. icld_det=. icld_det=. icld_det= RADINFO_READ: guess air mass bias correction amsua_n amsua_n it satellite instrument # read # keep # rad n8 amsua rad metop-a amsua rad aqua amsua coefficients below coefficients for all channels assim penalty qcpnlty penalty qcpnlty cpen qccpen O-B it satellite instrument 3 rad n8 amsua 3 rad metop-a amsua 3 rad aqua amsua O-A # read # keep # assim cpen Results from test case using 2 outer loops with inner iterations in each outer loop qccpen

24 Example: fit_rad (fort.27) Summary as a function of observation type sat n5... type amsua penalty qcpenalty nobs 6523 qc 2447 iland isnoice icoast ireduce qc2 qc3 qc4 qc ivarl nlgross qc6 qc7 6 6 radiance ob may include mult. channels, not all channels are used in analysis Summary as a function of channel: amsua_n5 2 2 amsua_n5 3 3 amsua_n5 4 4 amsua_n5 channel nobs nobs tossed... it satellite instrument rad n4 hirs2 rad n6 hirs3 rad n7 hirs3 rad n8 hirs4 rad metop-a hirs variance Bias (before BC) Bias (after BC) penalty O-B (w/bc) Standard dev Final summary for each observation type: # read # keep # assim 478 penalty qcpnlty cpen qccpen

25 Diagnostic files (User s Guide A.2) Files include observation departure for each obs: diag_amsua_metop-a_anl diag_amsua_metop-a_ges diag_amsub_n7_anl diag_amsub_n7_ges diag_amsua_n5_anl diag_amsua_n5_ges diag_conv_anl diag_conv_ges To get these files, turn write_diag on: write_diag()=.true.,write_diag(2)=.false.,write_diag(3)=.true., To read this binary information: Code to read these files (util/analysis_utilities/read_diag) read_diag_conv.f9 (diag_conv*) read_diag_rad.f9 (diag_amsub_n7* ) Compile:./make two executables: read_diag_conv.exe read_diag_rad.exe

26 Observation departure for each ob Run read_diag_conv.exe: (namelist.conv needed) &iosetup infilename='./diag_conv_anl.23222', outfilename='./results_conv_anl', / GSI diagnostic file Text file to save the content of diagnostic file Example content of results_conv_anl station ID 2997 : PADK : obs type obs time obs latitude obs longitude obs pressure usage obs value O-B Run read_diag_rad.exe: (namelist.rad needed) &iosetup infilename='./diag_amsua_n8_ges.23222', outfilename='./results_amsua_n5_anl', / Same as conventional

27 Convergence information Details in User s Guide Section 4.6 and A.3

28 Find convergence information From stdout (see example in stdout of this talk) From fort.22 Includes many details Cost function and gradient Contribution from background and each data type DTC provides a ksh script to filter this file util/analysis_utilities/plot_cost_grad/filter_fort22.ksh grep 'cost,grad,step,b' fort.22 sed -e 's/cost,grad,step,b,step? = //g' sed -e 's/good//g' > cost_gradient.txt outer loop e E E-3.E+ Inner iteration

29 Convergence information cost_gradient.txt outer loop Inner iteration e e e-2.e e e e e E E E E E E E E E E E E E E E E E E E E+ Penalty (cost function) Norm of gradient

30 Plot convergence information DTC provides a NCL script to make plot util/analysis_utilities/plot_cost_grad/gsi_cost_gradient.ncl load "$NCARG_ROOT/lib/ncarg/nclex/gsun/gsn_code.ncl" load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/contributed.ncl" begin # of iterations in st outer loop nloop=5 # of iterations in 2nd outer loop nloop2=5 step = stringtofloat(systemfunc("cut c7-9./cost_gradient.txt")) cost = stringtofloat(systemfunc("cut c-35./cost_gradient.txt")) gradient = stringtofloat(systemfunc("cut c36-6./cost_gradient.txt")) titles = new(4,string) titles()="cost outer " titles()="gradient outer " titles(2)="cost outer 2" titles(3)="gradient outer 2" plot = new(4,graphic) figure file name and format xwks = gsn_open_wks("pdf","gsi_cost_gradient )

31 Check convergence: example figure Cost function Norm of gradient st outer loop with 5 inter iteration 2nd outer loop with 5 inter iteration Iteration step Iteration step

32 Conventional observation errors Details in User s Guide Section 4.7

33 Conventional Observation errors ü Each observation has its own observation errors Ø Global: oberrflg=true: obs errors generated from external obs error table oberrflg=false: read from PrepBUFR file Ø Regional: forced use of external obs error table regardless of oberrflg Observation error files in release (~/comgsi_v3.3/fix ): nam_errtable.r3dv (default) prepobs_errtable.global rtma/new_rtma_nam_errtable.r3dv Prepbufr Ob 2 OBSERVATION TYPE.E+4.267E+.5E+4.332E+.E+4.47E+.95E E+.9E E+.85E E+ P T.563E+.6326E E+.8635E E+.2E+ q.e+.e+.e+.e+.e+.e+.685e+.685e+.685e+.737e e e+.e+.e+.e+.e+.e+.e+ UV Ps Pw Observation error file easy to edit to tune error values

34 Gross check Gross check is a QC check to exclude questionable obs that degrade the analysis Users can adjust thresholds for data types in convinfo: otype ps ps t t type sub iuse twindow numgrp ngroup nmiter gross ermax ermin var_b var_pg ithin Gross check controlled by these columns ratio = (Observation-Background)/max(ermin,min(ermax,obserror)) If ratio > gross: observation rejected Rejected observations will show up in fit files under rej

35 Background Error Covariance Details in User s Guide 4.8 and Advanced User s Guide

36 Background Error Lecture tomorrow morning on BE May also use domain-specific BEs rather than pre-computed Pre-computed BEs available in v3.3 release (~/comgsi_v3.3/fix/*_endian): : regional background error statistics, computed using forecasts from the NCEP s NAM model covering North America. nam_glb_berror.f77.gcv : contains the global background errors based on the NCEP s GFS model, a global forecast model. nam_nmmstat_na.gcv ü Also included in this release package is the background error matrix for RTMA GSI: new_rtma_regional_nmm_berror.f77.gcv Be sure to use appropriate BE for domain Regional BE is latitude (not longitude) dependent can be used on any NH domain

37 anavinfo The background error covariance matrix used in the GSI analysis is the fixed berror multiplied by several factors set by the namelist and anavinfo vs scale factor for vertical correlation lengths for background error hzscl(3) scale factor for three scales specified for horizontal smoothing hswgt(3) weights to apply to each horizontal scales ü as/tsfc_sdv in the control_vector section of anavinfo are factors for tuning the variance of each analysis control variable

38 Analysis Increment Details in User s Guide Section 4.9 and A.4

39 Analysis increment: plot for ARW case util/analysis_utilities/plots_ncl/analysis_increment.ncl util/analysis_utilities/plots_ncl/gsi_singleobs_arw.ncl load "$NCARG_ROOT/lib/ncarg/nclex/gsun/gsn_code.ncl" load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/contributed.ncl" GSI analysis begin cdf_analysis = addfile(./gsiprd_23222_prepbufr/wrf_inout.cdf","r") cdf_bk = addfile(./dtc/na3km/bk/wrfinput_d_2-3-22_2::.cdf","r") Ta = cdf_analysis->t(,:,:,:) Tb = cdf_bk->t(,:,:,:) DT = Ta Tb Analysis increment = GSI analysis First guess First guess

40 Analysis increment: plot for NMM case util/analysis_utilities/plots_ncl/gsi_singleobs_nmm.ncl util/analysis_utilities/plots_ncl/fill_nmm_grid2.ncl load "$NCARG_ROOT/lib/ncarg/nclex/gsun/gsn_code.ncl" load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/contributed.ncl" load "./fill_nmm_grid2.ncl" begin cdf_analysis = addfile(./nmm_2722/gsiprd/wrf_inout.cdf","r") cdf_bk = addfile(./nmm_netcdf/2722/wrfinput_d_nmm_netcdf.cdf","r") Ta = cdf_analysis->t(,:,:,:) Tb = cdf_bk->t(,:,:,:) DT = Ta Tb do k=, nz- fill_nmm_grid2(dt(k,:,:),nx,ny,f3d(k,:,:),) end do Convert from E grid to A grid In: E grid field Out: A grid field

41 Analysis increment: example ΔT ΔU ΔV Δq GSI analysis increment at the 5th level

42 usage GSI took 3 m.5 s to finish *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. PROGRAM GSI_ANL HAS BEGUN. COMPILED ORG: NP23 STARTING DATE-TIME JUN 4,24 6:58: WEN ENDING DATE-TIME JUN 4,24 7:: WEN PROGRAM GSI_ANL HAS ENDED. *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. *. Additional resource stats *****************RESOURCE STATISTICS******************************* The total amount of wall time = The total amount of time in user mode = The total amount of time in sys mode = The maximum resident set size (KB) = Number of page faults without I/O activity = Number of page faults with I/O activity = Number of times filesystem performed INPUT = Number of times filesystem performed OUTPUT = Number of Voluntary Context Switches = 7723 Number of InVoluntary Context Switches = 229 *****************END OF RESOURCE STATISTICS*************************

43 Summary

44 Many methods can be used: We can diagnose GSI analysis by checking: ü Standard output ü Observation fitting statistics and diagnosis files ü Convergence information ü Analysis increment There are more: Single observation test Plot observation innovation overlay analysis increment Forecast

45 Questions?