LAUSR

Precipitation nowcasting is an important task, which can be used in numerous applications. The key challenge of the task lies in radar echo map prediction. Previous studies leverage Convolutional Recurrent Neural Network (ConvRNN) to address the problem. However, the approaches are built upon mean square losses and the results tend to have inaccurate appearances, shapes and positions for predictions. To alleviate this problem, we explore the idea of adversarial regularization, and systematically compare four types of Generative Adversarial Networks (GANs), which are the combinations of GAN/Wasserstein GAN and its multi-scale version. Extensive experiments on a real-world radar data set and four typical meteorological examples are conducted. The results validate the effectiveness of adversarial regularization. The developed models show superior performances over the existing prediction approaches in the majority circumstances. Moreover, we find that the Wasserstein GAN regularization often delivers better results than the GAN regularization due to its robustness, and the Multi-scale Wasserstein GAN, in general, performs the best among all the methods. To reproduce the results, we release the source code at: https://github.com/luochuyao/MultiScaleGAN and the test system at: http://39.97.217.145:80/.