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A Bayesian correction approach for improving dual-frequency precipitation radar rainfall rate estimates

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dc.contributor.authorMa, Yingzhao, author
dc.contributor.authorChandrasekar, V., author
dc.contributor.authorBiswas, Sounak K., author
dc.contributor.authorJournal of Meteorological Society of Japan, publisher
dc.date.accessioned2020-06-12T18:25:59Z
dc.date.available2020-06-12T18:25:59Z
dc.date.issued2020-01-27
dc.description.abstractThe 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. In this study, a Bayesian correction (BC) approach is proposed to improve the DPR’s instantaneous rainfall rate product. Ground dual-polarization radar (GR) observations are used as references, and a log-transformed Gaussian distribution is assumed as the instantaneous rainfall process. Additionally, a generalized regression model is adopted in the BC algorithm. Rainfall intensities such as light, moderate, and heavy rain and their variable influences on the model’s performance are considered. The BC approach quantifies the predictive uncertainties associated with the Bayesiancorrected DPR (DPR_BC) rainfall rate estimates. To demonstrate the concepts developed in this study, data from the GPM overpasses of the Weather Service Surveillance Radar (WSR-88D), KHGX, in Houston, Texas, between April 2014 and June 2018 are used. Observation errors in the DPR instantaneous rainfall rate estimates are analyzed as a function of rainfall intensity. Moreover, the best-performing BC model is implemented in three GPM-overpass cases with heavy rainfall records across the southeastern United States. The results show 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. This study demonstrates the potential of the proposed BC algorithm for enhancing the instantaneous rainfall rate product from spaceborne radar equipment.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationMa, 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, 000–000, doi:10.2151/jmsj. 2020-025.
dc.identifier.urihttps://hdl.handle.net/10217/208227
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights.licenseThis article is open access and distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0).
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectdual-frequency precipitation radar
dc.subjectBayesian analysis
dc.subjectglobal precipitation measurement
dc.subjectdualpolarization radar
dc.subjectextreme rainfall event
dc.titleA Bayesian correction approach for improving dual-frequency precipitation radar rainfall rate estimates
dc.typeText

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