MSLKNet: A Multi-Scale Large Kernel Convolutional Network for Radar Extrapolation

Abstract

Radar echo extrapolation provides important information for precipitation nowcasting. Existing mainstream radar echo extrapolation methods are based on the Single-Input-Single-Output (SISO) architecture. These approaches of recursively predicting the predictive echo image with the current echo image as input often results in error accumulation, leading to severe performance degradation. In addition, the echo motion variations are extremely complex. Different regions of strong or weak echoes should receive different degrees of attention. Previous methods have not been specifically designed for this aspect. This paper proposes a new radar echo extrapolation network based entirely on a convolutional neural network (CNN). The network uses a Multi-Input-Multi-Output (MIMO) architecture to mitigate cumulative errors. It incorporates a multi-scale, large kernel convolutional attention module that enhances the extraction of both local and global information. This design results in improved performance while significantly reducing training costs. Experiments on dual-polarization radar echo datasets from Shijiazhuang and Nanjing show that the proposed fully CNN-based model can achieve better performance while reducing computational cost.

Keywords

• radar echo extrapolation
• multi-scale large kernel convolution
• long-term prediction