A Deep Learning-Based Methodology for Precipitation Nowcasting With Radar

Nowcasting and early warning of severe convective weather play crucial roles in heavy rainfall warning, flood mitigation, and water resource management. However, achieving effective temporal-spatial resolution nowcasting is a very challenging task owing to the complex dynamics and chaos. Recently, an increasing amount of research has focused on utilizing deep learning approaches for this task because of their powerful abilities in learning spatiotemporal feature representation in an end-to-end manner. In this paper, we present convolutional long short-term memory with a layer called star-shape bridge to transfer features across time steps. We build an end-to-end trainable model for the nowcasting problem using the radar echo data set. Furthermore, we propose a raining-oriented loss function inspired by the critical success index and utilize the group normalization technique to refine the convergence performance in optimizing our deep network. Experiments indicate that our model outperforms convolutional long short-term memory with the cross entropy loss function and the conventional extrapolation method.