Advancing forecasting capabilities: A contrastive learning model for forecasting tropical cyclone rapid intensification

作者：Chong Wang, Nan Yang, Xiaofeng Li · 发表于：Proceedings of the National Academy of Sciences · 年份：2025 · DOI：10.1073/pnas.2415501122 · 被引用次数：15 · 研究领域：Tropical and Extratropical Cyclones Research、Climate variability and models、Ocean Waves and Remote Sensing

Tropical cyclones (TCs), particularly those that rapidly intensify (RI), pose a significant threat due to the uncertainty in forecasting them. RI TC periods, which intensify by at least 13 m/s within 24 h, remain challenging to forecast accurately. Existing models achieve a probability of detection (POD) of 82.6% and a false alarm rate (FARate) of 27.2%. To address this, we developed a contrastive-based RI TC forecasting (RITCF-contrastive) model, utilizing satellite infrared imagery alongside atmospheric and oceanic data. The RITCF-contrastive model was tested on 1,149 TC periods in the Northwest Pacific from 2020 to 2021, achieving a POD of 92.3% and a FARate of 8.9%. RITCF-contrastive improves on previous models by addressing sample imbalance and incorporating TC structural features, leading to a 11.7% improvement in POD and a 3 times reduction in FARate compared to existing deep learning methods. The RITCF-contrastive model not only enhances RI TC forecasting but also offers a unique approach to forecasting these dangerous weather events.