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,
Early Online Release 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.
- 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.