midas_obsSelection

link to source code

Dependency Diagrams:

Direct Dependency Diagram

program  midas_obsselection

Purpose

Main program for background check, and thinning of all observation types.

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Algorithm

For background check of each observation type, several quality control tests specific to that observation type are performed on each available observation to determine if the observation meets the standard to be assimilated later. Observation flags are modified for rejected observations as well as for the assimilated observations of inferior quality.The thinning is performed afterwards to reduce the number of observation data for assimilation. This is to reduce the 1) computational cost, and 2) observation error correlation during assimilation stage. The background-checked thinned observations are written to new files, ready for assimilation.

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The computed bias correction values are applied to the observation before the background check step for certain observation types (e.g. radiances). One of quality control tests during background check is the rogue check which is the allowed distance of the observation from the background state. Innovation vector y-H(xb) is required to measure this distance. The innovations are computed at the beginning of the program before the background check starts.

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Input and Output Files	Description of file
flnml	In - Main namelist file with parameters that user may modify
flnml_static	In - The “static” namelist that should not be modified
trlm_$NN (e.g. trlm_01)	In - Background state (a.k.a. trial) files for each timestep
analysisgrid	In - File defining grid for computing the analysis increment
obsfiles_$FAM/obs$FAM_$NNNN_$NNNN	In - Observation file for each “family” and MPI task
obserr	In - Observation error statistics
obsfiles_$FAM.updated/obs$FAM_$NNNN_$NNNN	Out - Updated obs file for each “family” and MPI task
Remainder are files related to radiance obs:
stats_$SENSOR_assim	In - Satellite radiance observation errors of difference sensors
stats_tovs	In - Satellite radiance observation errors
stats_tovs_symmetricObsErr	In - User-defined symmetric TOVS errors for all sky
Cmat_$PLATFORM_$SENSOR.dat	In - Inter-channel observation-error correlations
dynbcor.coeffs.$SENSOR.*.coeffs_$SENSOR	In - Dynamic bias correction file
ceres_global.std	In - High-res surface type and water fraction for radiance obs
rtcoef_$PLATFORM_$SENSOR.dat	In - RTTOV coefficient files
rttov_h2o_limits.dat	In - Min/max humidity limits applied to analysis
ozoneclim98	In - Ozone climatology

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Synopsis

Below is a summary of the obsSelection program calling sequence:

• Initial setups:

  • Read the NAMOBSSELECTION namelist and check/modify some values.

  • Various modules are setup: obsFiles_mod, timeCoord_mod.

  • Setup horizontal and vertical grid objects for “analysis grid” from analysisgrid file.

  • Setup obsSpaceData object and read observations from files: inn_setupObs.

  • Compute and update the stored surface type for some satellite radiance instruments.

  • Setup columnData module (read list of analysis variables from namelist) and allocate column object for storing trial on analysis levels.

  • Setup the observation error statistics in obsSpaceData object: oer_setObsErrors

  • Setup gridStateVector module to initialize the gridstatevector objects.

  • Applying optional bias corrections to some observation types.

  • Setup horizontal and vertical grid objects for “trial grid” from first trial file: trlm_01.

  • Allocate a stateVector object on the trial grid and then read the trials: gio_readTrials.

• Computation

  • Compute columnTrlOnTrlLev and columnTrlOnAnlIncLev from background state: inn_setupColumnsOnTrlLev, inn_setupColumnsOnAnlIncLev.

  • Compute innovation from background state: inn_computeInnovation.

  • Do background check for conventional observation: bgck_bgCheck_conv.

  • Update radiance bias correction in obsSpaceData and apply the bias corrections to the observations and innovations for radiances: bcs_calcBias, bcs_applyBiasCorrection.

  • Perform background check for multiple observation types.

  • If thinning was requested:

    • add some cloud parameters and set missing observation flags in observation files (specific for radiances) and

    • perform thinning for different observation types.

  • Write the final background-checked bias corrected results (either thinned or not thinned) into the observation file.

  • If thinning was requested, remove observations which were flagged not to be assimilated from the observation file.

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Options

List of namelist blocks that can affect the obsSelection program.

• The use of obsSelection program is controlled by the namelist block

&NAMOBSSELECTION read by the obsSelection program.

• Some of the other relevant namelist blocks used to configure the obsSelection are listed in the following table:

Module	Namelist	Description of what is controlled
midas_obsSelection	NAMOBSSELECTION	whether thinning is performed or not.
biasCorrectionConv_mod	NAMBIASCONV	variables to perform bias correction for conventional observations.
biasCorrectionConv_mod	NAMSONDETYPES	additional variables to perform bias correction for radiosondes conventional observations.
backgroundCheck_mod	NAMBGCKCONV	variables to perform background check for conventional observations.
SSTbias_mod	NAMSSTBIASESTIMATE	variables to perform bias estimation and bias correction for satellite SST.
biasCorrectionSat_mod	NAMBIASSAT	variables to perform bias correction for satellite radiances.
multi_ir_bgck_mod	NAMBGCKIR	Variables to perform background check for hyperspectral infrared radiances.
bgckmicrowave_mod	NAMBGCK	Variables to perform background check for microwave radiances.
bgckcsr_mod	NAMCSR	Variables To perform background check for CSR radiances.
bgckssmis_mod	NAMBGCK	Variables to perform background check for SSMIS radiances.
bgckOcean_mod	NAMOCEANBGCHECK	Variables to perform background check for ocean data.
bgckOcean_mod	NAMICEBGCHECK	Variables to perform background check for SST data.
burpread_mod	NAMADDTOBURP	element IDs to add to the BURP file
thinning_mod	THIN_HYPER	variables to perform thinning on hyperspectral infrared radiances.
thinning_mod	THIN_TOVS	variables to perform thinning on microwave radiances.
thinning_mod	thin_csr	variables to perform thinning on CSR radiances.
thinning_mod	thin_raobs	variables to perform thinning on radiosonde observations.
thinning_mod	thin_scat	variables to perform thinning on scatterometer wind observations.
thinning_mod	thin_aircraft	variables to perform thinning on aircraft observations.
thinning_mod	thin_surface	variables to perform thinning on surface observations.
thinning_mod	thin_gbgps	variables to perform thinning on ground-based GPS observations.
thinning_mod	thin_gpsro	variables to perform thinning on GPS radio-occultation observations.
thinning_mod	thin_aladin	variables to perform thinning on aladin wind observations.
thinning_mod	thin_aladin	variables to perform thinning on aladin wind observations.
timeCoord_mod	NAMTIME	assimilation time window length, temporal resolution of the background state.

Needed modules

• version_mod: MODULE version_mod (prefix=’ver’ category=’8. Low-level utilities and constants’)

• codeprecision_mod: MODULE codePrecision_mod (prefix=’pre’ category=’8. Low-level utilities and constants’)

• ramdisk_mod: MODULE ramDisk_mod (prefix=’ram’ category=’8. Low-level utilities and constants’)

• midasmpi_mod: MODULE midasMpi_mod (prefix=’mmpi’ category=’8. Low-level utilities and constants’)

• utilities_mod: MODULE utilities_mod (prefix=’utl’ category=’8. Low-level utilities and constants’)

• mathphysconstants_mod: MODULE mathPhysConstants_mod (prefix=’mpc’ category=’8. Low-level utilities and constants’)

• horizontalcoord_mod: MODULE horizontalCoord_mod (prefix=’hco’ category=’7. Low-level data objects’)

• verticalcoord_mod: MODULE verticalCoord_mod (prefix=’vco’ category=’7. Low-level data objects’)

• timecoord_mod: MODULE timeCoord_mod (prefix=’tim’ category=’7. Low-level data objects’)

• gridstatevector_mod: MODULE gridStateVector_mod (prefix=’gsv’ category=’6. High-level data objects’)

• gridstatevectorfileio_mod: MODULE gridStateVectorFileIO_mod (prefix=’gio’ category=’4. Data Object transformations’)

• backgroundcheck_mod: MODULE backgroundCheck_mod (prefix=’bgck’ category=’1. High-level functionality’)

• multiirbgck_mod: MODULE multiIRbgck_mod (prefix=’irbg’ category=’1. High-level functionality’)

• innovation_mod: MODULE innovation_mod (prefix=’inn’ category=’1. High-level functionality’)

• obsspacedata_mod: MODULE obsSpaceData_mod (prefix=’obs’ category=’6. High-level data objects’)

• columndata_mod: MODULE columnData_mod (prefix=’col’ category=’6. High-level data objects’)

• obsfiles_mod: MODULE obsFiles_mod (prefix=’obsf’ category=’3. Observation input/output’)

• obserrors_mod: MODULE obsErrors_mod (prefix=’oer’ category=’2. B and R matrices’)

• biascorrectionsat_mod: MODULE biasCorrectionSat_mod (prefix=’bcs’ category=’1. High-level functionality’)

• biascorrectionconv_mod: MODULE biasCorrectionConv_mod (prefix=’bcc’ category=’1. High-level functionality’)

• thinning_mod: MODULE thinning_mod (prefix=’thn’ category=’1. High-level functionality’)

• bgckmicrowave_mod: MODULE bgckMicrowave_mod (prefix=’mwbg’ category=’1. High-level functionality’)

• bgckssmis_mod: MODULE bgckSSMIS_mod (prefix=’ssbg’ category=’1. High-level functionality’)

• bgckcsr_mod: MODULE bgckCSR_mod (prefix=’csrbg’ category=’1. High-level functionality’)

• bgckocean_mod: MODULE bgckOcean_mod (prefix=’ocebg’ category=’1. High-level functionality’)

• sstbias_mod: MODULE sstBias_mod (prefix=’sstb’ category=’1. High-level functionality’)

Routines called

ver_printnameandversion(), mmpi_initialize(), utl_tmg_start(), utl_isnamelistpresent(), utl_abort(), ram_setup(), obsf_setup(), tim_setup(), mpc_printconstants(), pre_printprecisions(), hco_setupfromfile(), vco_setupfromfile(), col_setvco(), inn_setupobs(), ssbg_computessmissurfacetype(), obs_famexist(), mwbg_computemwhs2surfacetype(), col_setup(), col_allocate(), obs_numheader(), oer_setobserrors(), gsv_setup(), sstb_applysatellitesstbiascorrection(), bcc_applyaibcor(), bcc_applygpbcor(), bcc_applyuabcor(), inn_gethcovcofromtrlmfile(), gsv_allocate(), tim_getdatestamp(), gsv_zero(), gio_readtrials(), inn_setupcolumnsontrllev(), inn_setupcolumnsonanlinclev(), inn_computeinnovation(), bgck_bgcheck_conv(), bcs_calcbias(), bcs_applybiascorrection(), irbg_bgcheckir(), mwbg_bgcheckmw(), csrbg_bgcheckcsr(), ssbg_bgcheckssmis(), ocebg_bgchecksst(), ocebg_bgcheckseaice(), obsf_copyobsdirectory(), obsf_writefiles(), obsf_updatemissingobsflags(), obsf_addcloudparametersandemissivity(), thn_thinhyper(), thn_thintovs(), thn_thincsr(), thn_thinraobs(), thn_thinscat(), thn_thinsatwinds(), thn_thinaircraft(), thn_thinsurface(), thn_thingbgps(), thn_thingpsro(), thn_thinaladin(), bcs_dumpbiastosqliteafterthinning(), obs_prnthdr(), obs_prntbdy(), obsf_cleanobsfiles(), obs_finalize(), utl_tmg_stop()