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MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD Quality Control-Consistent algorithm for all sensors to determine.

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Presentation on theme: "MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD Quality Control-Consistent algorithm for all sensors to determine."— Presentation transcript:

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2 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD Quality Control-Consistent algorithm for all sensors to determine data quality Data quality depends on DA system capability Quality control algorithm flexibility for variety of capabilities Preprocessing-Characterize satellite observations before assimilation Surface characterization: surface properties (ocean/non-ocean) Atmosphere characterization: clear, cloudy, precipitation Background update/state linearization Streamline quality control and preprocessing for all satellite radiance observations in data assimilation 2 Ultimate goal: Facilitate assimilation of all-sky/all- surfaces/full spectrum satellite radiance observations

3 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD 3 Overview of the MIIDAPS 1DVAR Integration Status Preliminary Forecast Impacts Next Steps

4 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD 4 Overview of the MIIDAPS 1DVAR Integration Status Preliminary Forecast Impacts Next Steps

5 MIIDAPS Status – 13 th JCSDA Technical Review and Science 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 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) **CrIS****IASI** **MIIDAPS extended to the hyperspectral Infrared for IR only or IR+MW 1DVAR analysis

6 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD Obs Error [E] No Convergence 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

7 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD 7 Extended state vector with hyperspectral IR covers trace gas absorption IR expansion CO CO2 O3 CH4 N2O Extended state vector to hydrometeor effective size General Expansion Cloud radius Rain radius Graupel radius

8 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD 8 MIIDAPS atmospheric state vector extended to trace gas profiles MIIDAPS emissivity state vector expanded to IR channels (AIRS, CrIS, IASI) ATMS/CrIS brightness temperatures simulated using ECMWF analysis (T, Q, CLW), trace gas climatologies, and various IR/MW emissivity models MIIDAPS applied to simulated data to retrieve state vector elements for MW only, IR only and combined IR+MW

9 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD 9 MW Only 94.5% Conv. MW+IR 94% Conv. TempWVTempWV MW OnlyIR OnlyECMWFIR+MW

10 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD 10 Overview of the MIIDAPS 1DVAR Integration Status Preliminary Forecast Impacts Next Steps

11 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD 11 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 MiRS Library Obs Error [E] Covariance Matrix [B] Bias correction Guess fields T(p), q(p), pSfc, Windsp Brightness Temperatures/ scan/geo info QC fields (flags, geo) 1dvar fields (clw, emiss) InputsOutputs

12 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD Integration updates – 1DVAR analysis and guess fields written to binaries and archived (tar) (1 st outer loop only) – 1DVAR controlled with GSI namelist variable Initialized to FALSE by default (EnKF) – Extended to SSMI/S (1DVAR, QC) 12

13 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD 13 1 GDAS Cycle T670 Resolution 200 CPU 1DVAR run on ATMS and F18 SSMI/S only (22,000 Observations)

14 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD 14 Overview of the MIIDAPS 1DVAR Integration Status Preliminary Forecast Impacts Next Steps

15 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD GSI r46725, T670/254 (GFS/GSI) – PRCN: GDAS/GFS Operational configuration – PR1D: PRCN + MIIDAPS applied to ATMS only Summer season – August 1, 2014-September 10, 2014 MIIDAPS Applied to ATMS only – ATMS QC based on MIIDAPS output – SSMI/S QC still being tuned – Use of 1DVAR Geophysical outputs still being explored 15

16 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD CLW > 0.2 Remove: 19-51 GHz 89-190 GHz 16 ChiSq > 5 Remove: All Channels GWP/RWP > 0.05 Remove : 19-53 Ghz, 89-190 GHz Heritage GSI QC Flags for LEFT: SNPP ATMS Channel 5 (52 GHz) and RIGHT: Channel 7 (54 GHz) MIIDAPS-based GSI QC Flags for LEFT: SNPP ATMS Channel 5 (52 GHz) and RIGHT: Channel 7 (54 GHz) MIIDAPS-based QC Scheme: Sounders With 1DVAR QC Operational QC Points Passing MIIDAPS QC but Failing Operational QC ATMS 52.8 GHz ATMS 54.5 GHz O-B (all observations) for LEFT: SNPP ATMS Channel 5 (52 GHz) and RIGHT: Channel 7 (54 GHz)

17 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD 17

18 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD 18

19 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD 19 1. PR1D 2. PRCN FCST: 00Z 8/7/2014 1. PR1D 2. PRCN FCST: 00Z 8/8/2014 FCST: 00Z 8/9/2014 FCST: 00Z 8/4/2014 FCST: 00Z 8/5/2014 FCST: 00Z 8/6/2014

20 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD 20 ChiSq > 5 Remove: All Channels GWP/RWP > 0.05 Remove : 19-53 Ghz, 89-190 GHz CLW > 0.15 Remove: 19-51 GHz 89-190 GHz About 1000 more obs per channel passing quality control

21 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD 21 Overview of the MIIDAPS 1DVAR Integration Status Preliminary Forecast Impacts Next Steps

22 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD Finalize QC implementation for current sensors and extend to new sensors – ATMS, SSMI/S, AMSU/MHS, GMI, AMSR2, SAPHIR, IR sensors Use of 1DVAR geophysical fields – Surface emissivity, hydrometeors Explore use of 1DVAR analysis as background and all-sky radiance assimilation – Resolve displacement errors – Linearization 22 Figure. MIIDAPS retrieved liquid water path and GFS 6hr forecast valid 12Z Jul 3, 2014, for Hurricane Arthur event off the U. S. Southeast coast. a) Displacement of MIIDAPS 1DVAR analysis and GFS forecast; b) 1DVAR liquid water path; c) GFS 6hr liquid water path; and d) MIIDAPS-GFS liquid water path. GFS forecast is collocated in space/time to GPM GMI observation points. b)c)d)a)

23 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD 23

24 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD 24 Number of AttemptsLiquid Water Path TPW Chisq Graupel Water PathNumber of Iter MIIDAPS Analsyis for 2014-08-01 00Z GDAS Cycle

25 MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD Cost Function to Minimize To find the optimal solution, solve for: Assuming Linearity This leads to iterative solution: 25 Jacobians & Radiance Simulation from Forward Operator: CRTM


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