Graphical Abstract

Ma, Y., V. Chandrasekar, and S. K. Biswas, 2020: A Bayesian correction approach for improving Dual-frequency Precipitation Radar rainfall rate estimates. J. Meteor. Soc. Japan, 98, Special Edition on Global Precipitation Measurement (GPM): 5th Anniversary, https://doi.org/10.2151/jmsj.2020-025.
Early Online ReleaseGraphical Abstract with highlights

Plain Language Summary: The accurate estimation of precipitation is an important objective for the Dual-frequency Precipitation Radar (DPR), which is located on board the Global Precipitation Measurement (GPM) satellite core observatory. This study proposes a Bayesian correction (BC) approach to improve the DPR’s instantaneous rainfall rate product, where ground dual-polarization radar (GR) observations are used as references. Rainfall intensities such as light, moderate, and heavy rain and their variable influences on the model’s performance are considered.

Highlights: