基于生成对抗网络和卫星数据的云图临近预报北大核心CSCD

利用风云四号气象卫星A星(FY-4A)红外云图,基于生成对抗网络方法,提出了红外云图外推预报模型,实现了华东区域未来3 h的云图预报,预报的时空分辨率分别为1 h和4 km。结果表明:该外推模型预报的云图可较好描述云团移动、发展和减弱趋势,对研究区域内云团的强度、位置和形态得到较为理想的预报效果。为了验证提出的云图外推模型的有效性,将其与光流法和轨迹门控循环单元模型进行比较。对比试验结果表明:该云图外推模型具有最优的预报效果,说明使用生成对抗网络进行云图外推具有较好的可行性,能有效应用于气象业务中监测云团的生消和移动并提前预警灾害性天气的发生,为天气预报提供重要的参考依据。

Nowcasting of Cloud Images Based on Generative Adversarial Network and Satellite Data

Satellite cloud images contain abundant information, which can reflect daily weather conditions. Nowcasting based on cloud images can strengthen the application of satellite data in the early warning and forecasting of severe weather. At present, the cloud images predicted by most nowcasting methods based on artificial intelligence are not accurate enough and the lead time is limited. Thus, it's necessary to improve the accuracy and period validity of cloud images in nowcasting. Using the infrared cloud image data of Fengyun-4A (FY-4A) and the generative adversarial network (GAN) method, an infrared cloud image extrapolation nowcasting model is proposed. The cloud images in the next 3 hours in East China are predicted by the proposed model, and the spatial resolution of predicted cloud images is 4 km and the temporal resolution is 1 hour. The results show that the evaluation values of SSIM (structural similarity), PSNR (peak signal to noise ratio) and RMSE (root mean square error) predicted by the proposed GAN-based cloud images extrapolation model are 0.75, 20.92 and 10.00 K, respectively. In addition, the MAE (mean absolute error), MSE (mean squared error), and SSIM are chosen as loss function and analyzed, aiming to verify the rationality of the loss function in the generator. Comparative experiments of different loss functions show that it is reasonable and effective to choose SSIM combined with MAE as the loss function. Furthermore, to verify the effectiveness of the proposed GAN-based model, the prediction results are compared with those of the optical flow method and the TrajGRU model with the GAN-based model. The experimental results show that the cloud image extrapolation model based on GAN has the superior prediction performance, with the highest SSIM and PSNR, and the lowest RMSE within 1-3 h of cloud images nowcasting. The observational examples show that the cloud images predicted by the proposed model can well describe the movement, development and dissipation trend of clouds. Meanwhile, the experiments obtain accurate prediction performance on the intensity, location and shape of clouds in the study region. It indicates that the cloud image extrapolation model based on GAN is rational and feasible, which can be effectively applied to the meteorological business to monitor the occurrence and movement of clouds and warn the occurrence of severe weather in advance, and can provide an important reference for weather forecasting.