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.articleNumber2033
dc.citation.issueNumber8
dc.citation.journalTitleRemote Sensing
dc.citation.volumeNumber15
dc.contributor.authorLi, Wenzhao
dc.contributor.authorJiang, Han
dc.contributor.authorLi, Dongfeng
dc.contributor.authorBedient, Philip B.
dc.contributor.authorFang, Zheng N.
dc.date.accessioned2023-07-21T16:14:02Z
dc.date.available2023-07-21T16:14:02Z
dc.date.issued2023
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.
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.
dc.identifier.digitalremotesensing-15-02033
dc.identifier.doihttps://doi.org/10.3390/rs15082033
dc.identifier.urihttps://hdl.handle.net/1911/115009
dc.language.isoeng
dc.publisherMDPI
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.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleEvaluation of Radar Precipitation Products and Assessment of the Gauge-Radar Merging Methods in Southeast Texas for Extreme Precipitation Events
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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