深度学习模型TAGAN在强对流回波临近预报中的应用

近年来深度学习模型在解决对防灾减灾影响巨大且极具挑战性的临近预报问题的应用中日益增多。本文中，我们把临近预报作为一个时空序列预测的任务，将雷达反射率因子作为试验对象，使用基于对抗神经网络（GAN）优化构建的TAGAN深度学习模型预测未来1小时的雷达回波图像，并且与Rover光流法、基于卷积神经网络的3D U-Net模型进行对比试验。选取2018年全球气象AI挑战赛雷达回波数据集进行训练与测试，检验结果表明TAGAN模型在命中率（POD），虚警率（FAR），临界成功指数（CSI）以及相关系数等多种评分上要优于传统的光流法和对比的3D U-Net深度学习模型，TAGAN模型在以上的检验评分表现出色，并且随预测时间的增加较之传统光流模型效果更优，这为拓展和提升深度学习模型在临近天气预报中的应用提供了参考依据。

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.