Work

Sea surface temperature forecasting during extreme weather using a multi-variable patch-based deep learning framework

Year: 2026

Type: article

Abstract: Accurate sea surface temperature (SST) forcasting is essential for ocean monitoring, especially during extreme weather events when air-sea interactions intensify. This study introduces TCN-PatchTST, an enhanced variant of PachTST, utilizing observations from four buoys around Taiwan for 72-hour SST forecasting. The results indicate that TCN-PatchTST consistently outperforms physical and data-driven baselines, including HYCOM, XGBoost, LSTM and the standard PatchTST in terms of prediction accuracy and demonstrates greater stability during extreme weather conditions. Additionally, SHAP analysis interprets the main factors influencing SST variability. Findings suggest that this multivariable approach significantly improves the reliability of SST forecasts across varying environmental conditions.

Source: ICT Express

DOI: 10.1016/j.icte.2026.08.007

Authors: Dat Nguyen Khanh, Kun-Che Ho, Chun-Yi Lin, Feng-Chun Su, Chung-Ru Ho

Institutions: National Formosa University, National Taiwan Ocean University

Language: English

Topic: Oceanographic and Atmospheric Processes

Subfield: Oceanography

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $1,500