GPM DPR Retrievals: Algorithm, Evaluation, and Validation

The primary goal of the dual-frequency precipitation radar (DPR) aboard the Global Precipitation Measurement (GPM) Core Observatory satellite is to infer precipitation rate and rain-drop/particle size distributions (DSD/PSD). The focus of this paper is threefold: (1) to describe the DPR retrieval algorithm that uses an adjustable relationship between rain rate (R) and the mass-weighted diameter (D-m) or an R-D-m relationship in solving for R and D-m simultaneously; (2) to evaluate the DPR algorithm based on the physical simulations that employ measured DSD/PSD to understand the mechanism and error characteristics of the retrieval method; (3) to review ground validation studies for DPR products as well as to analyze the strengths and weaknesses of ground radar and rain gauge/disdrometer validations. Overall, the DPR Version 6 algorithm provides reasonably accurate estimates of R and D-m in rain. Non-uniformity in the rain profile, however, tends to degrade the accuracy of the R and D-m estimates to some extent as the range-independent assumption of the adjustable parameter (epsilon) of the R-D-m relation is not able to fully account for natural variation of DSD in the vertical profile. The DPR snow rate is underestimated as compared with the independent dual-frequency ratio (DFR) technique. This is possibly the result of the constraint associated with the path integral attenuation (PIA)/differential PIA (delta PIA) used in the DPR algorithm to find the best epsilon and range-independent epsilon assumption. A range-variable epsilon model, proposed in the DPR Version 7 algorithm, is expected to improve rain and snow retrieval.