613 Development of a Combined Precipitation Estimation Algorithm for the GOES-R/GPM era

Wednesday, 26 January 2011
Washington State Convention Center
Ali Behrangi, JPL and California Institute of Technology, Pasadena, CA; and K. L. Hsu, S. Sorooshian, and B. H. Lambrigtsen

The increasing number of spectral bands in recently launched and future geostationary platforms (e.g., Meteosat Second Generation and GOES-R) and the anticipated launch and operation of the Global Precipitation Measurement (GPM) mission provide an unprecedented opportunity to improve consistency, accuracy, coverage, and timeliness of high resolution precipitation data. A new near real-time high-resolution precipitation estimation algorithm is introduced. The algorithm incorporates precipitation information from microwave imagers/sounders aboard low earth orbiting satellites as well as multi-spectral (visible/infrared) data from the geostationary satellites. The proposed algorithm uses successive infrared images to calculate cloud motion streamlines from a 2D cloud tracking algorithm followed by a cloud classification technique to classify clouds into predetermined number of classes using several infrared-extracted cloud features. These features represent radiative, textural and dynamic properties of clouds that can be obtained from high resolution multi-spectral data such as that will be obtained from the future Advanced Baseline Imager (ABI) on GOES-R. Precipitation estimates from microwave imagers/sounders are then advected and at the same time adjusted using the calculated cloud motion streamlines and precipitation characteristics in each cloud class. The algorithm has been developed and tested at 0.08-degree latitude/longitude resolution every 30 minutes and the evaluations over the conterminous United States compared favorably with the existing operational precipitation products.
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