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
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.