利用 InCyc:全球高分辨率热带气旋模拟数据库

IF 2.8 3区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Nicolas Bruneau, T. Loridan, Nic Hannah, Eugene Dubossarsky, Mathis Joffrain, John Knaff
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引用次数: 0

摘要

虽然热带气旋(TC)风险是全球关注的问题,但可用数据的质量却存在很大的地区差异。本文介绍了 InCyc,这是一个全球一致的历史气旋高分辨率全物理模拟数据库。InCyc 旨在促进跨流域的热带气旋风风险分析,并提供给研究机构使用。我们通过一个案例研究说明了该数据库的价值,该案例研究侧重于关键的风风险参数,即北大西洋和北太平洋西部盆地的峰值风位置和强度。我们介绍了一种基于随机森林算法的新方法,用于预测这些热带风暴风力风险参数的完整分布。通过对预测结果的分析,我们可以看出这种创新方法与其他常用于风风险评估的参数模型的比较。最后,我们还讨论了为什么捕捉变异性的全面分布至关重要,以及在热带气旋风险评估系统(即 "灾难模型")中的更广泛应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modelling Variability in Tropical Cyclone Maximum Wind Location and Intensity using InCyc: A Global Database of High-Resolution Tropical Cyclone Simulations
While Tropical Cyclone (TC) risk is a global concern, high regional differences exist in the quality of available data. This paper introduces InCyc, a globally consistent database of high-resolution full-physics simulations of historical cyclones. InCyc is designed to facilitate analysis of TC wind risk across basins and is made available to research institutions. We illustrate the value of this database with a case study focused on key wind risk parameters, namely the location and intensity of peak winds for the North Atlantic and western North Pacific basins. A novel approach based on random forest algorithms is introduced to predict the full distribution of these TC wind risk parameters. Based on a leave-one-storm-out evaluation, the analysis of the predictions shows how this innovative approach compares to other parametric models commonly used for wind risk assessment. We finally discuss why capturing the full distribution of variability is crucial as well as the broader use in the context of TC risk assessment systems (i.e. “catastrophe models”).
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来源期刊
Monthly Weather Review
Monthly Weather Review 地学-气象与大气科学
CiteScore
6.40
自引率
12.50%
发文量
186
审稿时长
3-6 weeks
期刊介绍: Monthly Weather Review (MWR) (ISSN: 0027-0644; eISSN: 1520-0493) publishes research relevant to the analysis and prediction of observed atmospheric circulations and physics, including technique development, data assimilation, model validation, and relevant case studies. This research includes numerical and data assimilation techniques that apply to the atmosphere and/or ocean environments. MWR also addresses phenomena having seasonal and subseasonal time scales.
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