气象科学2024，Vol.44Issue(3)：487-497,11.DOI:10.12306/2024jms.0023
基于时空图卷积的强对流降水临近预报研究
A study on the proximity prediction of strong convective precipitation based on the spatio-temporal graph convolution
摘要
Abstract
Precipitation nowcasting plays an important supporting role in forecasting severe convective weather.In meteorological services,the radar echo extrapolation method is mainly used to solve precipitation nowcasting problems.However,existing methods often lack the ability to effectively learn from sequential radar data,resulting in poor prediction accuracy.In order to solve this problem,this paper proposed ASTGCN(A Spatio-Temporal Graph Convolution Neural Network)for nowcasting of severe convective precipitation.Efficiently capture the temporal dependence between adjacent radar frames using a spatio-temporal graph convolutional network.In addition,an attention mechanism and an autoencoder were utilized to enhance the model's ability to capture spatio-temporal correlations.Experimental results show that the model can discover hidden graph structures from data and thereby capture hidden spatial relationships.Compared with the existing model(Transformer),the Critical Success Index(CSI)of this model is improved by 28％,indicating its superior performance in the nowcasting of severe convective precipitation.关键词
强对流降水临近预报/深度学习/ASTGCN模型/注意力机制/雷达回波外推Key words
nowcasting of severe convective precipitation/deep learning/ASTGCN model/attention mechanism/radar echo extrapolation分类
天文与地球科学引用本文复制引用
方巍,薛琼莹,陶恩屹,齐媚涵..基于时空图卷积的强对流降水临近预报研究[J].气象科学,2024,44(3):487-497,11.基金项目
国家自然科学基金资助项目(42075007) （42075007）
苏州大学计算机信息处理技术重点实验室开放项目(KJS2275) （KJS2275）
南京气象科技创新研究院北极阁开放研究基金资助项目(BJG202306) （BJG202306）
江苏省研究生科研与实践创新计划项目(NO.KYCX23_1388) （NO.KYCX23_1388）
