A Generative Adversarial and Spatiotemporal Differential Fusion Method in Radar Echo Extrapolation

Abstract

As an important part of remote sensing data, weather radar plays an important role in convective weather forecasts to reduce extreme precipitation disasters. The existing radar echo extrapolation methods do not utilize the local natural characteristics of the radar echo effectively but only roughly extract the whole characteristics of the radar echo. To address these challenges, we design a spatiotemporal difference and generative adversarial fusion model (STDGAN). Specifically, a spatiotemporal difference module (STD) is designed to extract local weather patterns and model them in detail. In our model, spatiotemporal difference information and spatiotemporal features captured by the model itself are fused together. In addition, our model is trained in a generative adversarial network (GAN) framework; it helps to generate a clearer map of future radar echoes at the image level. The discriminator consists of multi-scale feature extractors, which can simulate weather models of various scales more completely. Finally, extrapolation experiments were conducted using actual radar echo data from Shijiazhuang and Nanjing. The experiments have shown that our model has a more accurate prediction performance for predicting local weather patterns and overall echo change trajectories compared with previous research models. Our model achieved MSE, PSNE, and SSIM values of 132.22, 37.87, and 0.796, respectively, on the Shijiazhuang radar echo dataset. In addition, our model also showed better performance results on the Nanjing radar echo dataset. The results show that the MSE was 49.570, the PSNR was 0.714, and the SSIM was 30.633. The CC value was 0.855.