A GPM-DOE Midlatitude Continental Convective Clouds Experiment (MC3E)

Mathew R. Schwaller
Walter A. Petersen
Steven A. Rutledge
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To improve the fidelity of radiometer-based rainfall estimates over land at short temporal and spatial scales, the Global Precipitation Measurement mission (GPM) requires development of physically-based passive microwave (PMW) precipitation retrieval algorithms anchored by dual-frequency precipitation radar (DPR) drop size distribution (DSD), hydrometeor profile and rain rate retrievals. Emphasizing this need, the 2nd GPM Ground Validation White Paper (Kummerow and Petersen, 2006; hereafter GVWP) outlined the many significant challenges involved with the development and validation of these algorithms. To broadly paraphrase the GVWP, PMW algorithm development/validation over land requires not only an improved understanding of cloud and precipitation microphysics (particularly in the ice and mixed phases), but an improved representation of microphysical processes/properties (at the bulk and particle scales) in relevant cloud and/or empirical models- to include improved formulation of the radiative transfer occurring in a variable background of land-surface emissivity. Considering that 1) precipitation estimates made by the GPM satellite constellation will rely most heavily on PMW and combined DPR/PMW retrieval algorithms; 2) there are currently no robust physically-based PMW precipitation retrieval algorithms available for use over land1; and 3) GPM objectives ascribe considerable importance to making accurate measurements over land where people live, water resources are managed, and flooding occurs; the ability to accurately retrieve precipitation over land using combined DPR/PMW and or PMW-only algorithms, especially those areas not covered by radar and/or rain gauge networks, is critical to the overall success of GPM. The proposed GPM GV effort thus devotes significant effort and resources to improving the basic understanding required for developing and validating physically based PMW algorithms over land.

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