Risk Mitigation for the Precipitation Processing System (PPS) Related to the Global Precipitation Mission (GPM)

ABSTRACT
The success of NASA's TRMM mission and the transition to the Global Precipitation Mission (GPM) has generated an increased demand for homogeneous rainfall products across a variety of satellite platforms and sensors that are to be processed at GSFC's Precipitation Processing System (PPS). One of the greatest external risks to the PPS stems from immature or incomplete science data processing algorithms for both level 1 brightness temperature as well as level 2 rainfall products. In previous work, we worked with the PPS to develop data format standards, satellite quality control (QC) standards, and intercalibration standards that are consistent with practices at diverse space agencies. What remains to be done now is to continuously take the best work of the broader scientific community and incorporate that into an executable algorithm for the PPS to apply in order to create the standard Level 1C data product. This will be done as part of the proposal. The level 2 rainfall algorithm work being proposed recognizes that land and ocean radiometer algorithms are fundamentally different due to differences in surface emissivity. While ocean algorithms are quite mature for use in the PPS, the same cannot be said for land algorithms. Little has changed over the last decade and few research activities are ongoing except for snowfall algorithm development. Leaving the rest of the global land areas unattended may cause great harm to the PPS as algorithms will have to be adjusted frequently and massively right after launch. As such, this proposal will perform some initial research over land in order to mature this portion of the algorithm sufficiently to lead to a robust, if perhaps not final algorithm to be used by the PPS for processing land rainfall. Unlike the GPM science proposals, this work will include any work being carried out within other groups so long as a robust algorithm results from the work. Our past experience in combining the land portion of the land algorithm with the ocean algorithm developed here at CSU will be useful in carrying out this work.