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Predicting Tropical Cyclone-Induced Sea Surface Temperature Responses Using Machine Learning

Cui, Hongxing, Tang, Danling, Mei, Wei, Liu, Hongbin, Sui, Yi, Gu, Xiaowei (2023) Predicting Tropical Cyclone-Induced Sea Surface Temperature Responses Using Machine Learning. Geophysical Research Letters, 50 (18). ISSN 0094-8276. (doi:10.1029/2023GL104171) (KAR id:102592)

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This study proposes to construct a model using random forest method, an efficient machine learning-based method, to predict the spatial structure and temporal evolution of the sea surface temperature (SST) cooling induced by northwest Pacific tropical cyclones (TCs), a process of the so-called wind pump. The predictors in use include 12 predictors related to TC characteristics and pre-storm ocean conditions. The model is shown to skillfully predict the spatiotemporal evolutions of the cold wake generated by TCs of different intensity groups, and capture the cross-case variance in the observed SST response. Another model is further built based on the same method to assess the relative importance of the 12 predictors in determining the magnitude of the maximum cooling. Computations of feature scores of those predictors show that TC intensity, translation speed and size, and pre-storm mixed layer depth and SST dominate, depending on the area where the cooling is considered.

Item Type: Article
DOI/Identification number: 10.1029/2023GL104171
Uncontrolled keywords: tropical cyclones, sea surface temperature response, machine learning, wind pump
Subjects: G Geography. Anthropology. Recreation
Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Funders: University of Kent (
Depositing User: Xiaowei Gu
Date Deposited: 29 Aug 2023 16:49 UTC
Last Modified: 08 Jan 2024 15:11 UTC
Resource URI: (The current URI for this page, for reference purposes)

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