A Physically-based Rainfall Rate Algorithm for the Global Precipitation Mission Kevin Garrett 1, Leslie Moy 1, Flavio Iturbide-Sanchez 1, and Sid-Ahmed.

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Presentation transcript:

A Physically-based Rainfall Rate Algorithm for the Global Precipitation Mission Kevin Garrett 1, Leslie Moy 1, Flavio Iturbide-Sanchez 1, and Sid-Ahmed Boukabara 2 5 th IPWG Workshop Hamburg, Germany October 12, I. M. Systems Group 2. NOAA/NESDIS/STAR, JCSDA

Agenda Overview of the Microwave Integrated Retrieval System (MiRS) The MiRS Testbed MiRS for TRMM and GPM Next Steps and Summary

Overview NOAA/NESDIS/STAR has developed a flexible physical algorithm: the Microwave Integrated Retrieval System (MiRS) – Can be applied to any microwave sounder/imager – 1DVAR approach using CRTM as forward and jacobian operators – Retrieves sounding and surface parameters simultaneously, including hydrometeor profiles and rainfall rate – Applicable over all surfaces and in all-weather conditions – Run operationally at NOAA OSDPD (and integrated at NDE for NPP/JPSS future processing) – MiRS is currently being extended to support GPM

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 POES N18/N19  DMSP SSMIS F16/F18  AQUA AMSR-E  NPP/NPOESS ATMS   : Applied Operationally  : Applied occasionally  : Tested in Simulation Metop-A  TRMM/GPM/ M-T TMI, GMI proxy, SAPHIR/MADRAS  MiRS in Context

Algorithm Description Satellite Tbs Preprocessing Corrected Tbs Ym First Guess /Background State Vector X Ym-Y fit within NEDT? Simulated Tbs Y CRTM Update X Solution YES NO Temp. Profile Humidity Profile Emissivity Spectrum Skin Temperature Liq. Amount Prof Ice Amount Prof Rain Amount Prof VIPP RR = c 0 + c 1 RWP+c 2 IWP+c 3 CLW 1DVAR TPW CLW IWP RWP SIC/SWE Rainfall Rate Rainfall rate based on relationship to integrated hydrometeor values Coefficients derived from MM5 simulations Daily Global Precipitation Estimate (PE) computed from MiRS POES, Metop and DMSP instantaneous RR Calibration NEDT Resolution Bias Correction Preclassifier

Independent Validation through IPWG Provide MiRS PE over CONUS, S.A. and Australia Independent assessment versus radars/gauges Comparison to other precipitation algorithms Images courtesy of John Janowiak, UMD; Daniel Vila, CPTEC; and Elizabeth Ebert, Australian Bureau of Meteorology

Independent Validation through IPWG Monitor a running time series of statistics relative to rain gauges Intercomparison with other PE algorithms and radar

MiRS Testbed Comparison to 24 hr. PE to CPC Rain Gauge Analysis (top) and time series of PoD and Correlation from May 2009 – Aug Time series comparison of MiRS RR to NCEP Stage IV PoD and Correlation, Sept 2009 – Sept

MiRS Testbed MiRS Hydrometeor Profiles TRMM 2A12 Hydrometeor Profiles Hydrometeor Profiles/Vertical Cross Sections Rainfall Climatologies Monthly Averaged MiRS NOAA-18 Rainfall Rate for 2009, 1° grid Monthly Averaged MSPPS NOAA-18 Rainfall Rate for , 1° grid

MiRS for TRMM/GPM MiRS will be extended to the Global Precipitation Mission Microwave Imager – GMI is similar to TRMM Microwave Imager with additional high frequency channels (166 and 183 GHz) MiRS has been extended to TMI and GMI simulated data MiRS will also be extended to Megha-Tropiques SAPHIR/MADRAS

MiRS for TRMM/GPM Example of retrieved rainfall rate from MiRS on TMI data at ~5 km resolution (left) compared to TRMM 2A12 (right) for

MiRS for GPM Next Steps Mid-latitude Profiles Tropical Profiles Rain Ice WRF Model Simulation Enhanced hydrometeor profile and rainfall rate retrievals – Profiles constrained by latitude and season – Rainfall rate relationships to integrated hydrometeors by latitude and season

Summary MiRS is an operational algorithm at NOAA/NESDIS and provides important hydrological information MiRS will be applied to GMI observations in the future Current enhancements to the algorithm expected to improve performances of hydrometeor retrievals for all sensors

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 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,” Submitted IEEE TGARS, July S.-A. Boukabara et al. “MiRS: An All-Weather 1DVAR Satellite Data Assimilation and Retrieval System,” Submitted IEEE TGARS, May Website

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