Wang Chong, Yang Nan, Li Xiaofeng
Key Laboratory of Ocean Observation and Forecasting, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266000, China.
Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266000, China.
Proc Natl Acad Sci U S A. 2025 Jan 28;122(4):e2415501122. doi: 10.1073/pnas.2415501122. Epub 2025 Jan 21.
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.
热带气旋（TCs），尤其是那些迅速增强（RI）的气旋，由于其预报的不确定性而构成重大威胁。在24小时内增强至少13米/秒的快速增强热带气旋时段，要准确预报仍然具有挑战性。现有模型的探测概率（POD）为82.6%，误报率（FARate）为27.2%。为了解决这个问题，我们开发了一种基于对比的快速增强热带气旋预报（RITCF-contrastive）模型，利用卫星红外图像以及大气和海洋数据。RITCF-contrastive模型在2020年至2021年西北太平洋的1149个热带气旋时段上进行了测试，探测概率达到92.3%，误报率为8.9%。RITCF-contrastive通过解决样本不平衡问题并纳入热带气旋结构特征，对先前的模型进行了改进，与现有的深度学习方法相比，探测概率提高了11.7%，误报率降低了3倍。RITCF-contrastive模型不仅增强了快速增强热带气旋的预报能力，还为预报这些危险天气事件提供了一种独特的方法。
