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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, 511-525.
Special Edition on Global Precipitation Measurement (GPM): 5th Anniversary
https://doi.org/10.2151/jmsj.2020-025   Graphical 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.

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Highlights:

• A Bayesian correction (BC) approach based on a generalized regression model is proposed.
• Observation errors in the DPR instantaneous rainfall rate estimates are analyzed as a function of rainfall intensity.
• The experiment demonstrates that the DPR_BC rainfall rate estimates have superior skill scores and are in better agreement with the GR references than with the DPR estimates.