A deep learning-based global tropical cyclogenesis prediction model and its interpretability analysis  

作　　者：Bin MU Xin WANG Shijin YUAN Yuxuan CHEN Guansong WANG Bo QIN Guanbo ZHOU 

机构地区：[1]School of Software Engineering,Tongji University,Shanghai 201804,China [2]Department of Atmospheric and Oceanic Sciences&Institute of Atmospheric Sciences,Fudan University,Shanghai 200438,China [3]National Meteorological Center,Beijing 100081,China [4]Shanghai Typhoon Institute,China Meteorological Administration,Shanghai 200030,China

出　　处：《Science China Earth Sciences》2024年第12期3671-3695,共25页中国科学（地球科学英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.U2142211,42075141&42341202);the National Key Research and Development Program of China(Grant No.2020YFA0608000);the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0100);the Fundamental Research Funds for the Central Universities。

摘　　要：Tropical cloud clusters(TCCs)can potentially develop into tropical cyclones(TCs),leading to significant casualties and economic losses.Accurate prediction of tropical cyclogenesis(TCG)is crucial for early warnings.Most traditional deep learning methods applied to TCG prediction rely on predictors from a single time point,neglect the ocean-atmosphere interactions,and exhibit low model interpretability.This study proposes the Tropical Cyclogenesis Prediction-Net(TCGP-Net)based on the Swin Transformer,which leverages convolutional operations and attention mechanisms to encode spatiotemporal features and capture the temporal evolution of predictors.This model incorporates the coupled ocean-atmosphere interactions,including multiple variables such as sea surface temperature.Additionally,causal inference and integrated gradients are employed to validate the effectiveness of the predictors and provide an interpretability analysis of the model's decision-making process.The model is trained using GridSat satellite data and ERA5 reanalysis datasets.Experimental results demonstrate that TCGP-Net achieves high accuracy and stability,with a detection rate of 97.9%and a false alarm rate of 2.2%for predicting TCG 24 hours in advance,significantly outperforming existing models.This indicates that TCGP-Net is a reliable tool for tropical cyclogenesis prediction.

关 键 词：Tropical cyclogenesis prediction Deep learning Feature fusion INTERPRETABILITY Causal inference 

分 类 号：P457.8[天文地球—大气科学及气象学]