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

• 成果类型: Article
• 署名作者: Wang, Chong; Yang, Nan; Li, Xiaofeng
• 署名单位: Chinese Academy of Sciences; Institute of Oceanology, CAS; Chinese Academy of Sciences; Institute of Oceanology, CAS
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-8529
• DOI: 10.1073/pnas.2415501122
• 发表日期: 2025-01-28
• 关键词: north-atlantic; intensity; improvements

摘要

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.