Rainfall estimates from the Special Sensor Microwave/Imager (SSM/I) form a critical component of NOAA's climate monitoring program [ http://www.ncdc.noaa.gov/oa/satellite.html] Compared to infrared and most other satellite retrieval techniques, rainfall estimates from SSM/I and other passive microwave radiometers are based on a more physically direct and thus more accurate relationship between the observed brightness temperatures and rainfall. Combining this with the availability of 19 years of data extending back to July of 1987 from multiple satellites makes SSM/I the best long-term source of near-global rainfall observations. Through NCDC, NOAA makes monthly rainfall products available to the community. In addition to direct product distribution, both the NOAA Climate Prediction Center's (CPC) Merged Analysis of Precipitation (CMAP) [Xie and Arkin, 1997] and the Global Precipitation Climatology Project (GPCP) [Huffman et al., 1997] rely heavily on rainfall estimates from SSM/I with GPCP using SSM/I rainfall estimates as the calibration standard for infrared and other satellite estimates over ocean regions. The close relationship between SSM/I rainfall estimates from a simple emission-based retrieval algorithm and the GPCP merged rainfall product is shown in Figure 1. Despite the widespread use of SSM/I rainfall products, these products have not kept pace with advances in rainfall algorithm development driven primarily by the combination of the radar and radiometer on the TRMM satellite. Instead, the distributed SSM/I rainfall products still rely primarily upon simple emission and/or scattering signatures whose biases have been documented by a number of authors [e.g., Janowiak et al., 1995, Berg et al., 2002].
As part of a NASA funded research effort, the P.I.s have developed the foundation for a state-of-the-art parametric retrieval algorithm for a GPM constellation of passive microwave sensors. This retrieval uses information from both the TRMM precipitation radar and passive microwave to produce an a-priori rainfall database, which is then sampled in a physically consistent manner to derive rainfall from radiometers such as SSM/I that do not have coincident radar data. The additional information provided by the TRMM precipitation radar significantly improves rainfall estimates from radiometers such as SSM/I that cannot be achieved from a stand-alone perspective. Building on previous experience related to the development of operational retrieval algorithms, the analysis of biases in climate rainfall products, and the development of a calibrated multi-satellite brightness temperature dataset, we propose to develop a high quality, consistent archive of long-term rainfall products suitable for a variety of weather and climate applications. The work being proposed will include implementation of the new GPROF rainfall algorithm for SSM/I, an investigation of the sensitivity of the retrieval to satellite intercalibration issues, an examination of the impact of diurnal variations on the monthly climate estimates, and finally the production, documentation, and distribution of the resulting climate rainfall products to the community. While it is premature to consider rainfall for NOAA's climate parameter stewardship program, the tenets of that program will be kept in mind for the algorithm, the uncertainty model, and the documentation.