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HU Jiahui, LU Chuhan, JIANG Youshan, et al. 2022. Application of Deep Learning Model TAGAN in Nowcasting of Strong Convective Echo [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(4): 805−818. doi: 10.3878/j.issn.1006-9895.2104.20225
Citation: HU Jiahui, LU Chuhan, JIANG Youshan, et al. 2022. Application of Deep Learning Model TAGAN in Nowcasting of Strong Convective Echo [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(4): 805−818. doi: 10.3878/j.issn.1006-9895.2104.20225

Application of Deep Learning Model TAGAN in Nowcasting of Strong Convective Echo

  • In recent years, deep learning models have been increasingly used in solving nowcasting problems that largely affect disaster prevention and mitigation. In this study, we take nowcasting as a spatio-temporal sequence prediction task and use the radar reflectivity factor as the test object. We use the two-stream attention generative adversarial network (TAGAN) deep learning model based on the Generative Adversarial Network frame to predict the radar echo image of the future 1 h and compare it with the Rover optical flow method and the 3D U-Net model based on the convolutional neural network. The radar echo data set of the 2018 Global Weather AI Challenge is selected for training and testing. The test results show that the TAGAN model has advances by multiple scores, including hit rate, false alarm rate, critical success index, and correlation coefficient. The TAGAN model performs well in these test scores and increases with the prediction time compared to the traditional optical flow method and the comparative deep learning model. Moreover, compared with that of the traditional optical flow model, the improvement effect of the TAGAN model is more significant. The results may shed some light on the expansion and improvement of the application of deep learning models in near-weather forecasting.
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