Extreme Events and Probability Analysis Along the United States East Coast Based on High Spatial‐Coverage Reconstructed Storm Surges

作者：Lianjun Yang, Taoyong Jin, Mingyu Xiao, Xianwen Gao, Weiping Jiang, Jiancheng Li · 发表于：Geophysical Research Letters · 年份：2023 · DOI：10.1029/2023gl103492 · 被引用次数：9 · 研究领域：Tropical and Extratropical Cyclones Research、Climate variability and models、Meteorological Phenomena and Simulations

Abstract Analyzing the features of extreme events and estimating their probabilities robustly require high spatial coverage, high temporal resolution, and sufficiently long storm surge (SS) records. However, in situ observations cannot always meet these demands due to spatiotemporal sparseness. Here, we proposed a novel regional all‐site modeling framework based on a machine learning method, the extreme gradient boosting tree. This framework can reconstruct long SS records simply and quickly and can estimate storm surges simultaneously at both gauged and ungauged locations. Compared to in situ observations, the distribution patterns of SS variations during extreme events can be recognized easily from the reconstructed hourly SS data set. Since its available record is longer than 60 years (1959–2020), the estimation uncertainties of extreme event probabilities are significantly decreased. Noticeably high extreme SS return levels were found along the coast of the northern Gulf of Mexico, which should be given great attention.