Evaluation of Radar Precipitation Products and Assessment of the Gauge-Radar Merging Methods in Southeast Texas for Extreme Precipitation Events
Evaluation of Radar Precipitation Products and Assessment of the Gauge-Radar Merging Methods in Southeast Texas for Extreme Precipitation Events
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DOI：
10.3390/rs15082033
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Wenzhao Li;Han Jiang;Dongfeng Li;P. Bedient;Zheng N. Fang
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文献类型：
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作者：
Wenzhao Li;Han Jiang;Dongfeng Li;P. Bedient;Zheng N. Fang
已经开发出许多雷达-测量仪合并方法，以利用测量仪和雷达观测的优势来生成改进的降雨数据。本研究利用并比较了两种流行的合并方法：回归克里金法和贝叶斯回归克里金法，从计量网络和多源雷达数据集中生成每小时降雨量数据。作者收集、处理并建模了德克萨斯州东南部沿海地区发生的两次极端风暴事件（即 2017 年飓风哈维和 2019 年热带风暴伊梅尔达）的测量和雷达降雨数据（第四阶段、MRMS 和 RTMA 雷达数据）。考虑到统计指标、物理合理性和计算费用对建模数据的分析表明，虽然两种方法都可以有效地改善雷达降雨数据，但回归克里金模型表现出其优于贝叶斯回归克里金模型的性能，因为后者被发现由于聚集的仪表分布而容易出现过拟合问题。此外，降雨数据的空间分辨率对合并结果影响很大，当使用分辨率较粗的雷达降雨数据时，贝叶斯回归克里金模型的工作效果不佳。该研究建议使用高质量雷达数据和适当空间插值的仪表数据来改进雷达仪表合并方法。作者认为，该研究的结果对于协助减轻危害和未来的设计改进至关重要。
Many 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.