MIIDAPS Application to GSI for QC and Dynamic Emissivity in Passive Microwave Data Assimilation

Multi-Instrument Inversion and Data Assimilation Preprocessing System – 12 th Annual JCSDA Workshop, College Park, MD Increase number and types of radiance observations assimilated including those traditionally difficult to assimilate – Optimize filtering – Improve assimilation of surface sensitive channels – Explore application to cloudy radiance assimilation Accomplished by… Providing a streamlined preprocessing algorithm for satellite radiance data – Consistent algorithm for all satellite data – Provides quality control flags for various application (e.g. clear-sky DA) – Surface characterization through dynamic emissivity – Atmospheric characterization (clear, cloudy, precipitating) Develop a generalized QC algorithm for all satellite radiances Motivation 2

Multi-Instrument Inversion and Data Assimilation Preprocessing System – 12 th Annual JCSDA Workshop, College Park, MD Outline Overview of the MIIDAPS 1DVAR Integration of MIIDAPS in GSI Application to NPP ATMS data assimilation Future work 3

Multi-Instrument Inversion and Data Assimilation Preprocessing System – 12 th Annual JCSDA Workshop, College Park, MD Overview of the MIIDAPS 1DVAR 4

Multi-Instrument Inversion and Data Assimilation Preprocessing System – 12 th 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 Preprocessor Multi-Instrument Inversion and Data Assimilation Preprocessing System 5 MIIDAPS S-NPP ATMS DMSP F16 SSMI/S DMSP F17 SSMI/S DMSP F18 SSMI/S GPM GMI MetOp-A AMSU/MHS MetOp-B AMSU/MHS GCOM-W1 AMSR2 Megha-Tropiques SAPHIR/MADRAS TRMM TMI NOAA-18 AMSU/MHS NOAA-19 AMSU/MHS Inversion Process  Inversion/algorithm consistent across all sensors  Uses CRTM for forward and Jacobian operators  Use forecast, fast regression or climatology as first guess/background Benefit 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 – 12 th Annual JCSDA Workshop, College Park, MD Obs Error [E] No Convergence 1DVAR Retrieval/Assimilation Process 6 Initial State Vector [X] Climatology Forecast Retrieval mode Assimilation mode CRTM Simulated TBs Observed TBs (processed) Compare Convergence Solution [X] Reached Compute  X K Update State Vector [X] Iterative Processes Covariance Matrix [B] Bias Correction

Multi-Instrument Inversion and Data Assimilation Preprocessing System – 12 th Annual JCSDA Workshop, College Park, MD Cost Function to Minimize To find the optimal solution, solve for: Assuming Linearity This leads to iterative solution: 7 Mathematical Basis Jacobians & Radiance Simulation from Forward Operator: CRTM

Multi-Instrument Inversion and Data Assimilation Preprocessing System – 12 th Annual JCSDA Workshop, College Park, MD 1DVAR Preprocessor Outputs 8

Multi-Instrument Inversion and Data Assimilation Preprocessing System – 12 th Annual JCSDA Workshop, College Park, MD Integration of MIIDAPS in GSI 9

Multi-Instrument Inversion and Data Assimilation Preprocessing System – 12 th Annual JCSDA Workshop, College Park, MD MIIDAPS GSI Interface 10 GSI 2 nd loop 1 st loop Setuprad ModulePP1dvar Module Initialize CRTM structures Collocate guess to obs Call pp1dvar Call CRTM for background calc Call quality control subroutines Bias correction Gross error check Diagnostic file output Guess fields T(p), q(p), pSfc, Windsp Brightness Temperatures/ scan/geo info MiRS Library Obs Error [E] Covariance Matrix [B] Bias correction QC fields (flags, geo) 1dvar fields (clw, emiss)

Multi-Instrument Inversion and Data Assimilation Preprocessing System – 12 th Annual JCSDA Workshop, College Park, MD MIIDAPS Flexibility Multiple aspects of the 1dvar analysis are tunable: – Use of guess fields – State vector params – Number of EOFs – Channel selection – Obs error scaling – Bias correction – Number of attempts (loops) – Number of iterations/loop 11 Number of Attempts Number of Iterations Results are shown for S-NPP ATMS 1DVAR outputs case day

Multi-Instrument Inversion and Data Assimilation Preprocessing System – 12 th Annual JCSDA Workshop, College Park, MD MIIDAPS Example Output GHz Surface Emissivity Liquid Water Path Chisq TPW

Multi-Instrument Inversion and Data Assimilation Preprocessing System – 12 th Annual JCSDA Workshop, College Park, MD Application to S-NPP ATMS Data Assimilation 13

Multi-Instrument Inversion and Data Assimilation Preprocessing System – 12 th Annual JCSDA Workshop, College Park, MD Test Setup Use GSI r38044 with MIIDAPS integrated Run GSI cycle for z – Control run (no MIIDAPS, special QC, etc) – Run with 1DVAR, new (generalized) QC subroutine Apply to ATMS only QC subroutine checks 1DVAR QC flag only (good/bad) Gross error check still implemented – Run with 1DVAR, new QC subroutine Same as previous but add check on precipitation – Run with 1DVAR, new QC subroutine Same as qc+rain Replace physical emissivity in CRTM call with 1DVAR dynamic emissivity 14 cntrl qconly qc+rain qc+emiss

Multi-Instrument Inversion and Data Assimilation Preprocessing System – 12 th Annual JCSDA Workshop, College Park, MD Results: qconly 15

Multi-Instrument Inversion and Data Assimilation Preprocessing System – 12 th Annual JCSDA Workshop, College Park, MD Results: qconly 16

Multi-Instrument Inversion and Data Assimilation Preprocessing System – 12 th Annual JCSDA Workshop, College Park, MD Results: qconly 17

Multi-Instrument Inversion and Data Assimilation Preprocessing System – 12 th Annual JCSDA Workshop, College Park, MD Results: qconly 18

Multi-Instrument Inversion and Data Assimilation Preprocessing System – 12 th Annual JCSDA Workshop, College Park, MD Results: qc+rain 19

Multi-Instrument Inversion and Data Assimilation Preprocessing System – 12 th Annual JCSDA Workshop, College Park, MD Results: qc+emiss 20

Multi-Instrument Inversion and Data Assimilation Preprocessing System – 12 th Annual JCSDA Workshop, College Park, MD Future Work Tune 1DVAR assimilation for GSI implementation – Bias correction, background, covariances etc. Continue development of generalize QC – Apply to all sensors (incl. AMSR2, GMI) Apply to optimally thinned data/explore use outside GSI Assess impact on analysis fields Assess impact on the forecast 21