A key challenge for satellite-based precipitation algorithms is the improvement of detection and quantification of light precipitation (under 2 mm/hr), especially for land backgrounds, higher latitudes, cold seasons and over complex terrain. Land-based precipitation techniques designed for passive microwave imagers such as the Advanced Microwave Scanning Radiometer onboard EOS-Aqua (AMSR-E) are relatively insensitive to precipitation types of clouds associated with these precipitation intensities. The 94-GHz CloudSat Cloud Profiling Radar (CPR) orbiting in the A-Train series of environmental satellite platforms has been gathering data since June 2006. Its minimum detectable reflectivity of -26 dB coupled with a 70 dB dynamic range allows sensitivity for many cloud profiles extending to near the surface and subsequent light precipitation.

Present work focuses on developing, testing, and implementing the CloudSat's light precipitation algorithm in the NRL's near real-time processing system (NRTPS). Given the limited amount of information that such an observing system provides, several assumptions regarding cloud microphysical structure must be made. As such, the drop size distribution (DSD) is considered to be only a function of precipitation rate while the phase is set according to temperature profile. In addition, bright band (BB) and multiple scatter (MS) effects are also accounted for. Path integrated attenuation (PIA) obtained from surface return is used as an additional constraint in the retrieval algorithm, which is based on the optimal estimation method.