Modeling Variability in Tropical Cyclone Maximum Wind Location and Intensity Using InCyc: A Global Database of High-Resolution Tropical Cyclone Simulations

Nicolas Bruneau,Thomas Loridan, Nic Hannah, Eugene Dubossarsky, Mathis Joffrain,John Knaff

MONTHLY WEATHER REVIEW(2024)

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摘要
While tropical cyclone (TC) risk is a global concern, high regional differences exist in the quality of avail-able 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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关键词
Tropical cyclones,Parameterization,Risk assessment,Machine learning
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