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MIIDAPSApplication to GSI for QC and Dynamic Emissivity in Passive Microwave Data AssimilationKevin Garrett, Erin Jones, Deyong Xu, Krishna Kumar, and Eric MaddyRiverside Technology, Inc., JCSDA

Sid BoukabaraJCSDA

12th Annual JCSDA Workshop and Technical ReviewCollege Park, MDMay 22, 2014Increase number and types of radiance observations assimilated including those traditionally difficult to assimilateOptimize filteringImprove assimilation of surface sensitive channelsExplore application to cloudy radiance assimilationAccomplished byProviding a streamlined preprocessing algorithm for satellite radiance dataConsistent algorithm for all satellite dataProvides quality control flags for various application (e.g. clear-sky DA)Surface characterization through dynamic emissivityAtmospheric characterization (clear, cloudy, precipitating)Develop a generalized QC algorithm for all satellite radiances

Motivation2Multi-Instrument Inversion and Data Assimilation Preprocessing System 12th Annual JCSDA Workshop, College Park, MD OutlineOverview of the MIIDAPS 1DVARIntegration of MIIDAPS in GSIApplication to NPP ATMS data assimilationFuture work3Multi-Instrument Inversion and Data Assimilation Preprocessing System 12th Annual JCSDA Workshop, College Park, MD Overview of the MIIDAPS 1DVAR4Multi-Instrument Inversion and Data Assimilation Preprocessing System 12th Annual JCSDA Workshop, College Park, MD

Assimilation/Retrieval All parameters retrieved simultaneously Valid globally over all surface types Valid in all weather conditions Retrieved parameters depend on information content from sensor frequencies

1DVAR PreprocessorMulti-Instrument Inversion and Data Assimilation Preprocessing System5MIIDAPS

S-NPP ATMSDMSP F16 SSMI/SDMSP F17 SSMI/SDMSP F18 SSMI/SGPM GMI

MetOp-A AMSU/MHSMetOp-B AMSU/MHSGCOM-W1 AMSR2

Megha-TropiquesSAPHIR/MADRASTRMM TMINOAA-18 AMSU/MHSNOAA-19 AMSU/MHSInversion Process Inversion/algorithm consistent across all sensors Uses CRTM for forward and Jacobian operators Use forecast, fast regression or climatology as first guess/backgroundBenefit of the 1DVAR preprocessor is to enhance QC, as well as increase the number and types of observations assimilated (e.g. imager data)Multi-Instrument Inversion and Data Assimilation Preprocessing System 12th Annual JCSDA Workshop, College Park, MD Obs Error [E]No Convergence1DVAR Retrieval/Assimilation Process6Initial State Vector [X]ClimatologyForecastRetrieval modeAssimilation modeCRTMSimulated TBsObserved TBs (processed)CompareConvergenceSolution [X]ReachedComputeDXKUpdate State Vector [X]Iterative ProcessesCovarianceMatrix [B]Bias CorrectionMulti-Instrument Inversion and Data Assimilation Preprocessing System 12th Annual JCSDA Workshop, College Park, MD Cost Function to Minimize

To find the optimal solution, solve for:

Assuming Linearity

This leads to iterative solution:

7Mathematical Basis

Jacobians & Radiance Simulation from Forward Operator: CRTMMulti-Instrument Inversion and Data Assimilation Preprocessing System 12th Annual JCSDA Workshop, College Park, MD 1DVAR Preprocessor Outputs

8Multi-Instrument Inversion and Data Assimilation Preprocessing System 12th Annual JCSDA Workshop, College Park, MD Integration of MIIDAPS in GSI9Multi-Instrument Inversion and Data Assimilation Preprocessing System 12th Annual JCSDA Workshop, College Park, MD MIIDAPS GSI Interface10GSI2nd loop1st loopSetuprad ModulePP1dvar ModuleInitialize CRTM structuresCollocate guess to obsCall pp1dvarCall CRTM for background calcCall quality control subroutinesBias correctionGross error checkDiagnostic file outputGuess fieldsT(p), q(p), pSfc, WindspBrightness Temperatures/scan/geo infoMiRS Library

Obs Error [E]CovarianceMatrix [B]Bias correctionQC fields(flags, geo)1dvar fields(clw, emiss)

Multi-Instrument Inversion and Data Assimilation Preprocessing System 12th Annual JCSDA Workshop, College Park, MD MIIDAPS FlexibilityMultiple aspects of the 1dvar analysis are tunable:Use of guess fieldsState vector paramsNumber of EOFsChannel selectionObs error scalingBias correctionNumber of attempts (loops)Number of iterations/loop11

Number of AttemptsNumber of IterationsResults are shown for S-NPP ATMS 1DVAR outputs case day 2013-07-22 Multi-Instrument Inversion and Data Assimilation Preprocessing System 12th Annual JCSDA Workshop, College Park, MD MIIDAPS Example Output12

23 GHz Surface EmissivityLiquid Water PathChisqTPWMulti-Instrument Inversion and Data Assimilation Preprocessing System 12th Annual JCSDA Workshop, College Park, MD Application to S-NPP ATMS Data Assimilation13Multi-Instrument Inversion and Data Assimilation Preprocessing System 12th Annual JCSDA Workshop, College Park, MD Test SetupUse GSI r38044 with MIIDAPS integratedRun GSI cycle for 2013-07-23 00zControl run (no MIIDAPS, special QC, etc)Run with 1DVAR, new (generalized) QC subroutineApply to ATMS onlyQC subroutine checks 1DVAR QC flag only (good/bad)Gross error check still implementedRun with 1DVAR, new QC subroutineSame as previous but add check on precipitationRun with 1DVAR, new QC subroutineSame as qc+rainReplace physical emissivity in CRTM call with 1DVAR dynamic emissivity14cntrlqconlyqc+rainqc+emissMulti-Instrument Inversion and Data Assimilation Preprocessing System 12th Annual JCSDA Workshop, College Park, MD Results: qconly15

Multi-Instrument Inversion and Data Assimilation Preprocessing System 12th Annual JCSDA Workshop, College Park, MD Results: qconly16

Multi-Instrument Inversion and Data Assimilation Preprocessing System 12th Annual JCSDA Workshop, College Park, MD Results: qconly17

Multi-Instrument Inversion and Data Assimilation Preprocessing System 12th Annual JCSDA Workshop, College Park, MD Results: qconly18

Multi-Instrument Inversion and Data Assimilation Preprocessing System 12th Annual JCSDA Workshop, College Park, MD Results: qc+rain19

Multi-Instrument Inversion and Data Assimilation Preprocessing System 12th Annual JCSDA Workshop, College Park, MD Results: qc+emiss20

Multi-Instrument Inversion and Data Assimilation Preprocessing System 12th Annual JCSDA Workshop, College Park, MD Future WorkTune 1DVAR assimilation for GSI implementationBias correction, background, covariances etc.Continue development of generalize QCApply to all sensors (incl. AMSR2, GMI)Apply to optimally thinned data/explore use outside GSIAssess impact on analysis fieldsAssess impact on the forecast21Multi-Instrument Inversion and Data Assimilation Preprocessing System 12th Annual JCSDA Workshop, College Park, MD