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Kate Sauter's summer project involved the verification of the Goddard Profiling Algorithm which detects precipitation. Her mentor was Dr. Christian Kummerow of Colorado State University.

The Goddard Profiling Algorithm (GPROF) is a Bayesian algorithm that came from NASA's Tropical Rainfall Measuring Mission (TRMM) program to retrieve surface rainfall rate and precipitation vertical structure. While the algorithm provides very robust results over oceans, the land portion is highly empirical, requiring a series of tests to separate cold brightness temperatures over land from actual precipitation. As GPROF 2014 is being readied for the upcoming GPM mission, one of the key objectives of the algorithm was to forego the empirical rain tests in favor of a more physical scheme to determine rainfall.

Kate's project analyzed the first set of systematic retrievals, focusing on nine days in 2011 from NMQ (National Mosaic and Multi–Sensor Quantitative Precipitation Estimation) to asses the ability of the algorithm to detect rain areas and assign rainfall rates with the new scheme. The results from the retrieval are being run on SSMIS (Special Sensor Microwave Imager/Sounder) on DMSP (Defense Meteorological Satellite Program) F16. Kate's work is demonstrated in this poster.

Kate is from Glen Rock, Pennsylvania and attends Virginia Tech where she is a junior studying Meteorology and Geospatial and Environmental Analysis. She is interested in satellite analysis of the atmosphere and large weather systems. In her spare time she enjoys field hockey, running, going to the beach, traveling and just being out of doors.

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