气象2025，Vol.51Issue(4)：400-416,17.DOI:10.7519/j.issn.1000-0526.2025.012001
CastNet:深度学习定量降水临近预报模型
CastNet:Deep-Learning-Based Model for Quantitative Precipitation Nowcasting
摘要
Abstract
To improve the accuracy of precipitation nowcasting,this paper proposes an adversarial neural network model named CastNet that combines deep neural networks.This model utilizes the recurrent neur-al network to capture the spatio-temporal features of radar echo data,employs the adversarial neural net-work to simulate the generation and dissipation changes of cloud clusters,and then integrates the optical flow constraint into the neural network to guide the model training.This accelerates the learning process of the neural network and enhances the spatio-temporal consistency of the model,effectively solving the problem of forecast ambiguity and significantly improving the accuracy of precipitation intensity and loca-tion.Tests are conducted on 9 major precipitation processes in Guangxi and its surrounding areas from May to October 2023.The results show that under various precipita-tion intensities(≥0.1,≥2,≥7,≥15,≥25,≥40 mm·h-1),the average TS scores of SWAN 2.0 are 0.458,0.270,0.085,0.034,0.014 and 0.003,respectively;the average TS scores of SWAN 3.0 are 0.452,0.402,0.225,0.129,0.085 and 0.048,respectively;and the average TS scores of the CastNet model are 0.439,0.397,0.225,0.139,0.104 and 0.073,respectively.It can be seen clearly that the TS scores by the CastNet are higher than those of SWAN 2.0 and SWAN 3.0 under high-intensity precipita-tion of ≥7 mm·h-1 and above,except for few data points that are flat.In addition,as the forecast lead time extends,the relative advantage of CastNet becomes more obvious.关键词
短时强降水/定量降水预报/深度学习模型/循环神经网络/对抗神经网络Key words
short-time heavy precipitation/quantitative precipitation forecast/deep learning model/recur-rent neural network/adversarial neural network分类
天文与地球科学引用本文复制引用
曾小团,谭肇,沈玉伟,范娇,黄荣成,周弘媛,梁潇,黄大剑..CastNet:深度学习定量降水临近预报模型[J].气象,2025,51(4):400-416,17.基金项目
广西智能网格预报服务创新团队专项、广西自然科学基金项目(2022GXNSFAA035482)和广西气象科研计划指令性项目(桂气科ZL01)共同资助 （2022GXNSFAA035482）
