基于自注意力和稠密卷积改进ConvLSTM的雷达回波外推方法

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关键词雷达回波外推;ConvLSTM网络；自注意力机制；稠密卷积

中图分类号：P412.25；TP183 文献标志码：A 文章编号：2096-3599（2025）03-0107-10

DOI：10.19513/j.cnki.hyqxxb.20240514001

AbstractTo 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-atention 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 2O16. 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.

Keywordsradar echo extrapolation； ConvLSTM network；self-atention mechanism； dense convolution

0 引言

短临预报是指 0～2h 的临近预报和 0～12h 的短时预报，集中关注强降水、大风、冰雹等灾害性天气[1]。（剩余12046字）