Evaluation of Radar Precipitation Products and Assessment of the Gauge-Radar Merging Methods in Southeast Texas for Extreme Precipitation Events

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dc.citation.articleNumber2033en_US
dc.citation.issueNumber8en_US
dc.citation.journalTitleRemote Sensingen_US
dc.citation.volumeNumber15en_US
dc.contributor.authorLi, Wenzhaoen_US
dc.contributor.authorJiang, Hanen_US
dc.contributor.authorLi, Dongfengen_US
dc.contributor.authorBedient, Philip B.en_US
dc.contributor.authorFang, Zheng N.en_US
dc.date.accessioned2023-07-21T16:14:02Zen_US
dc.date.available2023-07-21T16:14:02Zen_US
dc.date.issued2023en_US
dc.description.abstractMany radar-gauge merging methods have been developed to produce improved rainfall data by leveraging the advantages of gauge and radar observations. Two popular merging methods, Regression Kriging and Bayesian Regression Kriging were utilized and compared in this study to produce hourly rainfall data from gauge networks and multi-source radar datasets. The authors collected, processed, and modeled the gauge and radar rainfall data (Stage IV, MRMS and RTMA radar data) of the two extreme storm events (i.e., Hurricane Harvey in 2017 and Tropical Storm Imelda in 2019) occurring in the coastal area in Southeast Texas with devastating flooding. The analysis of the modeled data on consideration of statistical metrics, physical rationality, and computational expenses, implies that while both methods can effectively improve the radar rainfall data, the Regression Kriging model demonstrates its superior performance over that of the Bayesian Regression Kriging model since the latter is found to be prone to overfitting issues due to the clustered gauge distributions. Moreover, the spatial resolution of rainfall data is found to affect the merging results significantly, where the Bayesian Regression Kriging model works unskillfully when radar rainfall data with a coarser resolution is used. The study recommends the use of high-quality radar data with properly spatial-interpolated gauge data to improve the radar-gauge merging methods. The authors believe that the findings of the study are critical for assisting hazard mitigation and future design improvement.en_US
dc.identifier.citationLi, Wenzhao, Jiang, Han, Li, Dongfeng, et al.. "Evaluation of Radar Precipitation Products and Assessment of the Gauge-Radar Merging Methods in Southeast Texas for Extreme Precipitation Events." <i>Remote Sensing,</i> 15, no. 8 (2023) MDPI: https://doi.org/10.3390/rs15082033.en_US
dc.identifier.digitalremotesensing-15-02033en_US
dc.identifier.doihttps://doi.org/10.3390/rs15082033en_US
dc.identifier.urihttps://hdl.handle.net/1911/115009en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of Fair Use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleEvaluation of Radar Precipitation Products and Assessment of the Gauge-Radar Merging Methods in Southeast Texas for Extreme Precipitation Eventsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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