气象2026，Vol.52Issue(5)：552-565,14.DOI:10.7519/j.issn.1000-0526.2025.080801
利用生成对抗网络提升江淮地区强降水临近预报性能
Using Generative Adversarial Network to Improve Heavy Precipitation Nowcasting in the Jianghuai Area
摘要
Abstract
This paper proposes two deep learning-based short-term heavy precipitation nowcasting methods for the Jianghuai Area by optimizing generative adversarial network(GAN),namely PhySGAN(combi-ning PhyDNet and SGAN)and PhyMGAN(combining PhyDNet and MGAN),so as to provide precipitation forecasts in the next 3 hours for the Jianghuai Area.Based on the forecast skill score,the performance assessment in"complex scenarios"and the analysis of typical application examples,this paper analyzes the forecast performance of the two methods in the short-time heavy precipitation(≥20 mm·h-1)forecasts in Jiangsu Province during the flood season of 2024.The results show that the TS scores of short-term heavy precipitation within 3 hours in different verification periods of PhySGAN and PhyMGAN are significantly improved compared with those of the basic experiment PhyDNet and the general GAN experiment PhyGAN.The two new methods can correct the low frequency problem of short-term heavy precipitation forecasts by PhyDNet and PhyGAN,so that the TS score increases with the increase of the forecast lead time,thereby effectively extending the nowcasting lead time of short-term heavy precipitation.Judged from the forecast performance shown by each method in"complex scenarios",deep learning can reflect the evolution of the generation and dissipation of heavy precipitation relative to traditional extrapolation methods.PhySGAN and PhyMGAN show better forecast performance than PhyDNet and PhyGAN.The former has a better ability to depict local details such as the shape and intensity of heavy precipitation,while the latter has a better representation of the overall contour and position of the heavy precipitation rain band.Combined with the application of typical heavy precipitation cases during the flood season,both PhySGAN and PhyMGAN can forecast the precipitation enhancement process in advance in both systematic heavy precipi-tation and local heavy precipitation cases,effectively guiding the early warning of disasters.In addition,PhyMGAN has a certain indicative effect on extreme rainfall intensities above 50 mm·h-1,while PhySGAN can better reflect the changes in the shape and position of the rain band.关键词
生成对抗网络/江淮地区/强降水临近预报/典型案例Key words
generative adversarial network(GAN)/Jianghuai Area/heavy precipitation nowcasting/rep-resentative cases分类
天文与地球科学引用本文复制引用
庄潇然,代刊,曾康,徐珺,王啸华,刘梅..利用生成对抗网络提升江淮地区强降水临近预报性能[J].气象,2026,52(5):552-565,14.基金项目
中国气象局能力提升联合研究专项(22NLTSZ001、24NLTSQ015)、江苏省气象局科研项目(KM202520)、中国气象局揭榜挂帅项目(CMAJBGS202512)、江苏省社会发展重大科技示范项目(BE2023766)和中国气象局重点创新团队(CMA2022ZD04)共同资助 （22NLTSZ001、24NLTSQ015）
