海洋预报2025，Vol.42Issue(2)：89-98,10.DOI:10.11737/j.issn.1003-0239.2025.02.010
基于多尺度特征融合和时空注意力LSTM的台风云图预测研究
Typhoon cloud image prediction using Multi-Scale Feature Fusion and Spatiotemporal Attention LSTM
摘要
Abstract
Existing deep learning methodologies for typhoon prediction overlook the issue of internal feature loss,hindering a comprehensive capture of the intricate structural changes within typhoons.To address this problem,this paper introduces a Multiscale Feature Fusion Spatiotemporal Attention Long Short-Term Memory Network(MSTA-LSTM).Initially,a feature enhancement module is incorporated to strengthen typhoon feature information.Then,the loss of typhoon-specific details is mitigated through skip connections during the encoding and decoding processes.Simultaneously,the Convolutional Block Attention Module within the Spatiotemporal Long Short-Term Memory(ST-LSTM)units is used to optimize information transmission.Finally,decoded outputs from different scales are adjusted through deconvolution and fused to produce the final output.Validation and ablation experiments are conducted using a dataset of typhoon cloud maps obtained by Himawari-8 for the Pacific coastal regions from East Asia to Southeast Asia,which contains a training set of about 16 typhoon processes and a test set of 3 typhoon processes.Compared to other networks,the MSTA-LSTM network demonstrates improvements in the accuracy of typhoon cloud map prediction,with root mean square error,peak signal-to-noise ratio,and structural similarity index metric reaching 42.76,16.38,and 0.481 7,respectively.关键词
时间序列预测/多尺度特征/时空长短期记忆网络/注意力机制Key words
time series prediction/multiscale features/Spatiotemporal Long Short-Term Memory/attention mechanism分类
大气科学引用本文复制引用
程勇,钱坤,王军,渠海峰,李伟,杨玲,韩晓东,刘敏..基于多尺度特征融合和时空注意力LSTM的台风云图预测研究[J].海洋预报,2025,42(2):89-98,10.基金项目
国家自然科学基金(41975183、41875184). （41975183、41875184）
