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Article

Forecasting Sea Surface Cooling During Typhoons Based on Machine Learning

1
College of Marine Science and Technology, Zhejiang Ocean University, Zhoushan 316022, China
2
Shengsi Haichuang Science and Technology Development Research Institute, Zhoushan 202452, China
3
Institute of Physical Oceanography and Remote Sensing, Ocean College, Zhejiang University, Zhoushan 316021, China
4
State Key Laboratory of Ocean Sensing & Ocean College, Zhejiang University, Zhoushan 316021, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2026, 18(9), 1296; https://doi.org/10.3390/rs18091296
Submission received: 13 March 2026 / Revised: 18 April 2026 / Accepted: 22 April 2026 / Published: 24 April 2026

Abstract

Sea surface cooling (SSC) induced by typhoons has a significant impact on typhoon intensity and regional air–sea interaction. This study develops a machine learning model based on a multilayer perceptron (MLP) to predict SSC during typhoon passage over the western North Pacific. The model uses pre-typhoon ocean background conditions and ocean states at the typhoon peak moment as inputs, including wind field, sea level anomaly (SLA), mixed layer depth (MLD), and 100 m water temperature. Trained on historical typhoon data and multi-source ocean observations from 2002 to 2018, the model directly predicts SSC during typhoon events from 2019 to 2020. Results show that the model achieves a mean absolute error (MAE) of 0.379 °C, a root mean square error (RMSE) of 0.488 °C, and a bias of 0.087 °C. The model reproduces the typical rightward bias in SSC spatial distribution. Under normal ocean conditions, such as open deep-water areas with moderate stratification and no strong eddy interference, the model performs well, with errors below 0.1 °C at some points. Although some biases exist under complex ocean environments and abrupt changes in typhoon dynamics, the model still captures the overall cooling trend. This study demonstrates the feasibility of machine learning for typhoon–ocean interaction forecasting. The proposed framework can provide technical support for typhoon intensity forecasting, marine disaster warning, and aquaculture risk prevention.
Keywords: typhoon; sea surface cooling; machine-learning-based prediction model typhoon; sea surface cooling; machine-learning-based prediction model

Share and Cite

MDPI and ACS Style

Zhang, Y.; Cai, H.; Song, D. Forecasting Sea Surface Cooling During Typhoons Based on Machine Learning. Remote Sens. 2026, 18, 1296. https://doi.org/10.3390/rs18091296

AMA Style

Zhang Y, Cai H, Song D. Forecasting Sea Surface Cooling During Typhoons Based on Machine Learning. Remote Sensing. 2026; 18(9):1296. https://doi.org/10.3390/rs18091296

Chicago/Turabian Style

Zhang, Ye, Huiwen Cai, and Dan Song. 2026. "Forecasting Sea Surface Cooling During Typhoons Based on Machine Learning" Remote Sensing 18, no. 9: 1296. https://doi.org/10.3390/rs18091296

APA Style

Zhang, Y., Cai, H., & Song, D. (2026). Forecasting Sea Surface Cooling During Typhoons Based on Machine Learning. Remote Sensing, 18(9), 1296. https://doi.org/10.3390/rs18091296

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