ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 52 Issue 5 > 2026,52(5):552-565. DOI:10.7519/j.issn.1000-0526.2025.080801 Prev Next
Using Generative Adversarial Network to Improve Heavy Precipitation Nowcasting in the Jianghuai Area
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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 (combining 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 the 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 precipitation 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.
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