In addition to its stated objective of making a global survey of vertical profiles of cloud microphysical properties, the recently launched CloudSat 94-GHz Cloud Profiling Radar (CPR) is also well-suited to the problem of determining the distribution of light precipitation including drizzle and light rainfall, which proved difficult to detect using current observing systems. This CPR sensor, combined with a suite of optical, passive microwave, and active sensors on board of the A-Train constellation, provide an extensive well-correlated dataset to examine, leading to a better understanding and characterization of our climate system.

Present work focuses on developing and implementing the CloudSat's light rainfall algorithm in the NRL's near real-time processing system (NRTPS), verifying all forms of input, evaluating the products leveraging off CloudSat and International Precipitation Working Group (IPWG) validation activities, and compiling and analyzing initial statistics of CPR data matched to passive microwave (PMW) footprints. Special attention is given to building up a global surface reflectivity (Zsfc) database, stratified as a function of surface type (ocean, or various classes of land type based on an IGBP database) and physical conditions (surface wind and temperature – ocean surfaces only), for use in specification of the path-integrated attenuation (PIA) constraint used in the light rainfall retrieval. These statistics are represented in terms of means and variances that can be introduced directly into the forward model and observations error covariance matrix under optimal estimation inversion theory constructs.