1 / 21

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

MIIDAPS Application to GSI for QC and Dynamic Emissivity in Passive Microwave Data Assimilation. Kevin Garrett, Erin Jones, Deyong Xu , Krishna Kumar, and Eric Maddy Riverside Technology, Inc., JCSDA Sid Boukabara JCSDA 12 th Annual JCSDA Workshop and Technical Review College Park, MD

missy
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. MIIDAPSApplication to GSI for QC and Dynamic Emissivity in Passive Microwave Data Assimilation Kevin Garrett, Erin Jones, DeyongXu, Krishna Kumar, and Eric Maddy Riverside Technology, Inc., JCSDA Sid Boukabara JCSDA 12th Annual JCSDA Workshop and Technical Review College Park, MD May 22, 2014

  2. Motivation • 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

  3. Outline • Overview of the MIIDAPS 1DVAR • Integration of MIIDAPS in GSI • Application to NPP ATMS data assimilation • Future work

  4. Overview of the MIIDAPS 1DVAR

  5. 1DVAR PreprocessorMulti-Instrument Inversion and Data Assimilation Preprocessing System Megha-Tropiques SAPHIR/MADRAS S-NPP ATMS • 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 • 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 MetOp-A AMSU/MHS MetOp-B AMSU/MHS MIIDAPS NOAA-18 AMSU/MHS NOAA-19 AMSU/MHS DMSP F16 SSMI/S DMSP F17 SSMI/S DMSP F18 SSMI/S TRMM TMI GPM GMI GCOM-W1 AMSR2 Benefit of the 1DVAR preprocessor is to enhance QC, as well as increase the number and types of observations assimilated (e.g. imager data)

  6. 1DVAR Retrieval/Assimilation Process Solution [X] Reached Observed TBs (processed) Convergence Compare Bias Correction Covariance Matrix [B] No Convergence Compute DX Simulated TBs Obs Error [E] K Retrieval mode Climatology CRTM Update State Vector [X] Initial State Vector [X] Assimilation mode Forecast Iterative Processes

  7. Mathematical Basis • Cost Function to Minimize • To find the optimal solution, solve for: • Assuming Linearity • This leads to iterative solution: Jacobians & Radiance Simulation from Forward Operator: CRTM

  8. 1DVAR Preprocessor Outputs

  9. Integration of MIIDAPS in GSI

  10. MIIDAPS GSI Interface GSI 2nd loop 1st loop Setuprad Module PP1dvar Module Covariance Matrix [B] MiRS Library Brightness Temperatures/scan/geo info 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 Bias correction 1dvar fields (clw, emiss) Obs Error [E] QC fields (flags, geo) Guess fields T(p), q(p), pSfc, Windsp

  11. 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 Number of Attempts Results are shown for S-NPP ATMS 1DVAR outputs case day 2013-07-22 Number of Iterations

  12. MIIDAPS Example Output Chisq 23 GHz Surface Emissivity TPW Liquid Water Path

  13. Application to S-NPP ATMS Data Assimilation

  14. Test Setup • Use GSI r38044 with MIIDAPS integrated • Run GSI cycle for 2013-07-23 00z • 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 cntrl qconly qc+rain qc+emiss

  15. Results: qconly

  16. Results: qconly

  17. Results: qconly

  18. Results: qconly

  19. Results: qc+rain

  20. Results: qc+emiss

  21. 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

More Related