AN ATTENTION FUSION AND INFORMATION RECALL LSTM METHOD FOR RADAR ECHO EXTRAPOLATION
CHENG Yong
QIAN Kun
KANG Zhiming
HE Guangxin
WANG Jun
ZHUANG Xiaoran
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.
Keywords：radar echo extrapolationdeep learningattention mechanismspace-time long short-term memory network
Publication Date：2023-10-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 653-663 )