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Nowcasting of Cloud Images Based on Generative Adversarial Network and Satellite Data
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摘要: 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。Abstract: 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.
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图 2 不同外推模型不同预报时效云图预报能力对比
Fig. 2 Prediction performance of diverse cloud image extrapolation models at different lead times
图 3 2020年6月23日05:00—09:00(输入模型的过去5 h)观测云图
Fig. 3 Observed cloud images in the past 5 hours from 0500 UTC to 0900 UTC on 23 Jun 2020(inputs of models)
图 4 2020年6月23日10:00—12:00未来3 h观测与预报云图对比
Fig. 4 Comparison of the observed and the predicted cloud images in the next 3 hours from 1000 UTC to 1200 UTC on 23 Jun 2020
表 1 生成器中选取不同损失函数训练的云图外推模型的外推效果
Table 1 Performance of the cloud image extrapolation model trained with different loss functions in the generator
损失函数 评估指标 相对误差绝对值/% 均方根误差/K 结构相似性 峰值信噪比 均方误差 157.80 10.48 0.69 20.50 平均误差绝对值 155.75 10.11 0.74 20.87 结构相似性 158.43 10.36 0.74 20.63 结构相似性结合平均误差绝对值 151.64 10.00 0.75 20.92 
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表 2 使用不同损失函数时，生成对抗网络模型在不同时效上的云图预报能力
Table 2 Predictive performance of the generative adversarial network model for the cloud images at different lead times using different loss functions
损失函数 评估指标 结构相似性 峰值信噪比 1 h 2 h 3 h 1 h 2 h 3 h 平均误差绝对值 0.79 0.74 0.71 23.25 20.44 18.92 均方误差 0.75 0.69 0.61 22.99 19.53 18.98 结构相似性 0.79 0.74 0.71 22.92 20.17 18.82 结构相似性结合平均误差绝对值 0.80 0.75 0.72 23.21 20.51 19.03
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表 3 基于测试集的不同外推模型预报效果评估
Table 3 Prediction performance of different models based on testing set
模型 评估指标 相对误差绝对值% 均方根误差/K 结构相似性 峰值信噪比 生成对抗网络 151.64 10.00 0.75 20.92 轨迹门控循环单元模型 172.84 11.10 0.72 19.99 光流法 186.35 11.97 0.66 19.22
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