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Composite Analysis-Based Machine Learning for Prediction of Tropical Cyclone-Induced Sea Surface Height Anomaly

Cui, Hongxing, Tang, Danling, Liu, Huizeng, Sui, Yi, Gu, Xiaowei (2023) Composite Analysis-Based Machine Learning for Prediction of Tropical Cyclone-Induced Sea Surface Height Anomaly. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16 . pp. 2644-2653. ISSN 1939-1404. (doi:10.1109/jstars.2023.3247881) (KAR id:100189)

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Sea surface height anomaly (SSHA) induced by tropical cyclones (TCs) is closely associated with oscillations and is a crucial proxy for thermocline structure and ocean heat content in the upper ocean. The prediction of TC-induced SSHA, however, has been rarely investigated. This study presents a new composite analysis-based random forest (RF) approach to predict daily TC-induced SSHA. The proposed method utilizes TC’s characteristics and pre-storm upper oceanic parameters as input features to predict TC-induced SSHA up to 30 days after TC passage. Simulation results suggest that the proposed method is skillful at inferring both the amplitude and temporal evolution of SSHA induced by TCs of different intensity groups. Using a TC-centered 5°×5° box, the proposed method achieves highly accurate prediction of TC-induced SSHA over the Western North Pacific with root mean square error of 0.024m, outperforming alternative machine learning methods and the numerical model. Moreover, the proposed method also demonstrated good prediction performance in different geographical regions, i.e., the South China Sea and the Western North Pacific subtropical ocean. The study provides insight into the application of machine learning in improving the prediction of SSHA influenced by extreme weather conditions. Accurate prediction of TC-induced SSHA allows for better preparedness and response, reducing the impact of extreme events (e.g., storm surge) on people and property.

Item Type: Article
DOI/Identification number: 10.1109/jstars.2023.3247881
Uncontrolled keywords: Sea surface height anomaly; tropical cyclones; machine learning; random forest; composite analysis.
Subjects: Q Science > Q Science (General)
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: 22 Feb 2023 22:17 UTC
Last Modified: 12 Jan 2024 06:40 UTC
Resource URI: (The current URI for this page, for reference purposes)

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