Radar echo extrapolation method based on self-attention and dense convolution improved ConvLSTM
YANG Xiaoyu
NIU Xuemei
QI Kai
Abstract：To address the problems of fuzzy distortion in long-term echoes and low accuracy in predicting strong echoes in existing radar echo extrapolation models,this paper designs a radar echo extrapolation method based on self-attention and dense convolution improved convolutional long short-term memory(ConvLSTM)network by using the composite reflectivity mosaic image of Doppler radar data in Anhui from May to September 2016.Based on ConvLSTM,the model incorporates self-attention mechanism into each cell and Encoder-Decoder to enhance the ability of extracting features with long-term spatial dependence.Meanwhile,the model uses dense convolution instead of common convolution to improve the feature reuse ability.The experiment uses the past 1-h radar echo image to predict the future 2-h radar echo image,and compares the resluts with the ConvLSTM before the improvement,proving that the proposed model can improve the accuracy of radar echo extrapolation.
Keywords：radar echo extrapolationConvLSTM networkself-attention mechanismdense convolution
Publication Date：2025-06-30
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：10( 107-116 )