1 / 14

Generic Microwave 1DVAR Pre-processor Package

Generic Microwave 1DVAR Pre-processor Package. Kevin Garrett 1 , Sid-Ahmed Boukabara 2 Leslie Moy 1 , Flavio Iturbide-Sanchez 1 , Christopher Grassotti 1 and Wanchun Chen 3 JCSDA 9 th Workshop on Satellite Data Assimilation College Park, MD May 25, 2011

melora
Download Presentation

Generic Microwave 1DVAR Pre-processor Package

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. Generic Microwave 1DVAR Pre-processor Package Kevin Garrett1, Sid-Ahmed Boukabara2 Leslie Moy1, Flavio Iturbide-Sanchez1, Christopher Grassotti1 and Wanchun Chen3 JCSDA 9th Workshop on Satellite Data Assimilation College Park, MD May 25, 2011 1. I. M. Systems Group 2. NOAA/NESDIS/STAR, JCSDA 3. Dell, Inc

  2. Agenda • Overview of the Microwave Integrated Retrieval System (MiRS) • MiRS as a Quality Control Tool • MiRS-Based Rain and Ice detection • MiRS Dynamic Emissivity • The MiRS Software Package

  3. Overview • MiRS is a 1DVAR retrieval algorithm which has been developed by NOAA/NESDIS/STAR • Applied to microwave data from a variety of platforms/sensors, easily extendable • The 1DVAR uses CRTM as forward and jacobian operators • Independent of NWP (forecast not used as first-guess) • Valid in all-weather conditions and over all surfaces • MiRS can provide added information to NWP Data Assimilation applications • Quality control – detection of cloudy/precipitating scenes • Dynamic Surface emissivity • Retrievals in cloudy and Rainy Conditions

  4. DMSP SSMIS F16/F18 AQUA AMSR-E   TRMM/GPM/ M-T TMI, GMI proxy, SAPHIR/MADRAS NPP/JPSS ATMS MiRS Supported Sensors  • MiRS is applied to a number of microwave sensors, • each time gaining robustness and improving validation • for Future New Sensors • The exact same executable, forward operator, • covariance matrix used for all sensors • Modular design • Cumulative validation and consolidation of MiRS  POESN18/N19 Metop-A  : Applied Operationally : Applied occasionally : Tested in Simulation

  5. Algorithm Description CRTM as forward operator, validity-> clear, cloudy and precip conditions Covariances and Mean Background for each surface Variational Assimilation Retrieval (1DVAR) Algorithm Algorithm valid in all-weather conditions, over all-surface types EOF decomposition All parameters are retrieved simultaneously Sensor-independent (all sensor-dependent info is passed in through external files) Highly Modular Design Flexibility and Robustness Modeling & Instrumental Errors are input to algorithm Selection of Channels to use, parameters to retrieve

  6. Applications to NWP Data Assimilation

  7. Quality Control Metrics DMSP F18 4/11/2011 Metop-A 4/11/2011 NOAA-18 4/11/2011 Rainfall Rate mm/hr QC Flags (Blue = Precip) Chi-Sq

  8. Dynamic Surface Emissivity N18 04/10 N18 04/11 N18 04/12 37 GHz TB 190 GHz TB Rain Rate 37 GHz Emiss

  9. MiRS Software Package • MiRS software is rigorously tested for functionality and must meet operational coding standards • Use Forcheck and Valgrind utilities • Use Subversion repository (read-only external access now available) • JAVA-based GUI for high level user interaction • Signed license agreement required for software access (free of charge) either through downloadable tar file or from Subversion.

  10. MiRS Software Package Hardware Requirements Software Requirements *Science libraries needed for certain sensor Level 1b data readers (AMSR-E, ATMS, TMI)

  11. More Information • Publications S.-A. Boukabara, K. Garrett, and W. Chen, “Global Coverage of Total Precipitable Water using a Microwave Variational Algorithm,” IEEE TGARS, vol. 48, Sept. 2010 F. Iturbide-Sanchez, S.-A. Boukabara, R. Chen, K. Garrett, C. Grassotti, W. Chen, and F. Weng, “Assessment of a Variational Inversion System for Rainfall Rate over Land and Water Surfaces,” IEEE TGARS, Accepted March 2011 S.-A. Boukabara et al. “MiRS: An All-Weather 1DVAR Satellite Data Assimilation and Retrieval System,” IEEE TGARS, In Press. • Website http://mirs.nedsis.noaa.gov

  12. BACKUP SLIDES

  13. Algorithm Description VIPP Satellite Tbs Calibration NEDT Resolution Bias Correction Preclassifier Temp. Profile Preprocessing Humidity Profile TPW Liq. Amount Prof CLW 1DVAR Ice Amount Prof IWP Corrected TbsYm Solution Rain Amount Prof RWP YES Ym-Y fit within NEDT? Emissivity Spectrum SIC/SWE Skin Temperature NO Simulated TbsY Update X Rainfall Rate CRTM RR = c0 + c1RWP+c2IWP+c3CLW State Vector X • Rainfall rate based on relationship to integrated hydrometeor values First Guess /Background

  14. Rain And Ice Detection CPC Rain Gauge 12Z 4/11 – 12Z 4/12 MiRS Composite RR 12Z 4/11 – 12Z 4/12 Rain event as capture by MiRS from N18, Metop-A and F16 observations (Right) The following slides demonstrate utility of MiRS retrievals and metrics from this case and single satellite observations (N18).

More Related