| Summary: | In this study, we evaluated the performance of rain-retrieval algorithms for the Version 6 Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR) products, against disdrometer observations and improved their retrieval algorithms by using a revised shape parameter <i>µ</i> derived from long-term Particle Size Velocity (Parsivel) disdrometer observations in Jianghuai region from 2014 to 2018. To obtain the optimized shape parameter, raindrop size distribution (DSD) characteristics of summer and winter seasons over Jianghuai region are analyzed, in terms of six rain rate classes and two rain categories (convective and stratiform). The results suggest that the GPM DPR may have better performance for winter rain than summer rain over Jianghuai region with biases of 40% (80%) in winter (summer). The retrieval errors of rain category-based <i>µ</i> (3−5%) were proved to be the smallest in comparison with rain rate-based <i>µ</i> (11−13%) or a constant <i>µ</i> (20−22%) in rain-retrieval algorithms, with a possible application to rainfall estimations over Jianghuai region. Empirical <i>D<sub>m</sub></i>−<i>Z<sub>e</sub></i> and <i>N<sub>w</sub></i>−<i>D<sub>m</sub></i> relationships were also derived preliminarily to improve the GPM rainfall estimates over Jianghuai region.
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