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

作　　者：方巍[1,2,3] 薛琼莹 陶恩屹 齐媚涵 FANG Wei;XUE Qiongying;TAO Enyi;QI Meihan(School of Computer,Engineering Research Center of Digital Forensics,Ministry of Education,Nanjing University of Information Science&Technology,Nanjing 210044,China;Key Laboratory of Transportation Meteorology of China Meteorological Administration,Nanjing Joint Institute for Atmospheric Sciences,Nanjing 210041,China;Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET),Nanjing University of Information Science&Technology,Nanjing 210044,China)

机构地区：[1]南京信息工程大学计算机学院/数字取证教育部工程研究中心,南京210044 [2]中国气象局交通气象重点开放实验室/南京气象科技创新研究院,南京210041 [3]南京信息工程大学江苏省大气环境与装备技术协同创新中心,南京210044

出　　处：《气象科学》2024年第3期487-497,共11页Journal of the Meteorological Sciences

基　　金：国家自然科学基金资助项目(42075007);苏州大学计算机信息处理技术重点实验室开放项目(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);江苏省研究生科研与实践创新计划项目(NO.KYCX23_1388)。

摘　　要：降水临近预报对于强对流天气的预报具有重要的支撑作用。气象业务中主要采用雷达回波外推方法解决此问题。然而,现有方法通常缺乏从序列雷达数据中有效学习的能力,导致预测精度不佳。为了解决这一问题,本文提出了一种改进的时空图卷积模型ASTGCN(A Spatio-Temporal Graph Convolution Neural Network)用于强对流降水的临近预报。利用时空图卷积网络,有效地捕获相邻雷达帧之间的时间依赖性。此外,利用注意力机制和自动编码器来增强模型捕获时空相关性的能力。结果表明,该模型可以从数据中发现隐藏的图结构,从而捕获隐藏的空间关系。与现有模型(Transformer)相比,该模型的临界成功指数(CSI)提高了28%,表明其在强对流降水临近预报方面具有优越的性能。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 

分 类 号：P412.25[天文地球—大气科学及气象学]