Convective precipitation nowcasting using U-Net Model

Abstract

Convective precipitation nowcasting remains challenging due to the fast change of convective weather. Radar images are the most important data source in nowcasting research area. This study proposes a radar data-based U-Net model for precipitation nowcasting. The input of the model is five consecutive radar images; the output is 30-min prediction of radar reflectivity image. The model consists of three parts: upsampling, downsampling and skip-connection. Two models, U-Net and TrajGRU, are used for comparison in the experiments. Different from U-Net, which is a CNN-based (Convolution Neural Network) model, TrajGRU is an RNN-based (Recurrent Neural Network) model, which is good at time-series processing and has been widely used in precipitation research community. The experimental results show that the CNN-based U-Net can achieve almost the same performance as TrajGRU. This result shows the great potential of CNN-based models in handling time-series applications.