热带气象学报2023，Vol.39Issue(5)：653-663,11.DOI:10.16032/j.issn.1004-4965.2023.057
一种雷达回波外推的注意力融合和信息回忆的LSTM方法
AN ATTENTION FUSION AND INFORMATION RECALL LSTM METHOD FOR RADAR ECHO EXTRAPOLATION
摘要
Abstract
Nowcasting is a prominent area of research in meteorology,and radar echo extrapolation is an effective technique for generating nowcasts.In recent years,deep learning technology has been applied to this task,but improving the accuracy of radar echo extrapolation forecasting remains a challenge.Based on the ST-LSTM network,this paper proposes an AFR-LSTM network to enhance the accuracy of radar echo extrapolation forecasting.Firstly,an attention fusion method for a spatiotemporal long-short-term memory network is proposed to integrate more historical information,ensuring that information can be fully integrated during the transmission process and reducing information loss.Moreover,we address the issue of information loss in the encoding process by incorporating an information reminiscence module between the encoder and decoder,which helps preserve the details of radar echo prediction.Through ablation experiments conducted on a real radar echo dataset(2019-2021 Jiangsu Meteorological Radar Data),AFR-LSTM demonstrates strong overall performance.Comparative experiments on this radar echo dataset also reveal that AFR-LSTM achieves a critical success index(CSI)value of 0.520 9 and a Heidke skill score(HSS)value of 0.532 4 in radar echo prediction,effectively preserving strong echoes and ensuring accurate location prediction.These results outperform existing methods,demonstrating that our proposed method can achieve more accurate image prediction.关键词
雷达回波外推/深度学习/注意力机制/时空长短期记忆网络Key words
radar echo extrapolation/deep learning/attention mechanism/space-time long short-term memory network分类
天文与地球科学引用本文复制引用
程勇,钱坤,康志明,何光鑫,王军,庄潇然..一种雷达回波外推的注意力融合和信息回忆的LSTM方法[J].热带气象学报,2023,39(5):653-663,11.基金项目
国家自然科学基金项目(41975183、41875184) （41975183、41875184）
广东省"珠江人才计划"引进创新创业团队项目(2019ZT08G669)共同资助 （2019ZT08G669）
