A Generative Adversarial Network with an Attention Spatiotemporal Mechanism for Tropical Cyclone Forecasts  

作　　者：Xiaohui LI Xinhai HAN Jingsong YANG Jiuke WANG Guoqi HAN Jun DING Hui SHEN Jun YAN 

机构地区：[1]Satellite Ocean Environment Dynamics,Second Institute of Oceanography,Ministry of Natural Resources,Hangzhou 310012,China [2]School of Oceanography,Shanghai Jiao Tong University,Shanghai 200240,China [3]Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai),Zhuhai 519082,China [4]School of Artificial Intelligence,Sun Yat-sen University,Zhuhai 519082,China [5]Fisheries and Oceans Canada,Institute of Ocean Sciences,Sidney V8L 4B2,Canada [6]Zhejiang Marine Monitoring and Forecasting Center,Hangzhou 310007,China

出　　处：《Advances in Atmospheric Sciences》2025年第1期67-78,共12页大气科学进展(英文版)

基　　金：supported by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(NO.SML2021SP201);the National Natural Science Foundation of China(Grant No.42306200 and 42306216);the National Key Research and Development Program of China(Grant No.2023YFC3008100);the Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(Grant No.311021004);the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University(Project No.SL2021ZD203)。

摘　　要：Tropical cyclones(TCs)are complex and powerful weather systems,and accurately forecasting their path,structure,and intensity remains a critical focus and challenge in meteorological research.In this paper,we propose an Attention Spatio-Temporal predictive Generative Adversarial Network(AST-GAN)model for predicting the temporal and spatial distribution of TCs.The model forecasts the spatial distribution of TC wind speeds for the next 15 hours at 3-hour intervals,emphasizing the cyclone's center,high wind-speed areas,and its asymmetric structure.To effectively capture spatiotemporal feature transfer at different time steps,we employ a channel attention mechanism for feature selection,enhancing model performance and reducing parameter redundancy.We utilized High-Resolution Weather Research and Forecasting(HWRF)data to train our model,allowing it to assimilate a wide range of TC motion patterns.The model is versatile and can be applied to various complex scenarios,such as multiple TCs moving simultaneously or TCs approaching landfall.Our proposed model demonstrates superior forecasting performance,achieving a root-mean-square error(RMSE)of 0.71 m s^(-1)for overall wind speed and 2.74 m s^(-1)for maximum wind speed when benchmarked against ground truth data from HWRF.Furthermore,the model underwent optimization and independent testing using ERA5reanalysis data,showcasing its stability and scalability.After fine-tuning on the ERA5 dataset,the model achieved an RMSE of 1.33 m s^(-1)for wind speed and 1.75 m s^(-1)for maximum wind speed.The AST-GAN model outperforms other state-of-the-art models in RMSE on both the HWRF and ERA5 datasets,maintaining its superior performance and demonstrating its effectiveness for spatiotemporal prediction of TCs.

关 键 词：tropical cyclones spatiotemporal prediction generative adversarial network attention spatiotemporal mechanism deep learning 

分 类 号：P457.8[天文地球—大气科学及气象学] TP183[自动化与计算机技术—控制理论与控制工程]