Application of Spatiotemporal Convolution Network for Precipitation Nowcasting

Different from traditional methods, this paper uses machine learning methods to solve the nowcasting problem of severe convective weather. Therefore, we propose a spatiotemporal convolutional block for nowcasting. The network adopts the encoder-decoder structure and proposes a new spatiotemporal causal convolution for timing feature extraction. Our network inputs 5 frames of images to predict the weather conditions for the next 30 minutes. The experimental results show that the results of our network structure are better and the training time is shorter. Compared with other networks, it can better capture the temporal and spatial correlation.