Mamba-UNet: Dual-Branch Mamba Fusion U-Net With Multiscale Spatio-Temporal Attention for Precipitation Nowcasting

Abstract

Precipitation nowcasting is a challenging task in the context of global climate variability. However, existing radar echo or numerical weather prediction data methods lack deep modeling between echograms at different time points and have difficulty in accurately capturing irregular variations and small-scale features of precipitable clouds. To address these challenges, we propose for the first time a U-Net short-term precipitation prediction network based on vision Mamba technology for the precipitation nowcasting mission, named Mamba-UNet. Specifically, Mamba-UNet includes two core modules: the dual-branch Mamba fusion module and the multiscale spatiotemporal attention module. Finally, we propose a loss function namely dynamic quantile weighted loss to address the problem of imbalanced precipitation intensity distribution. To validate the capacity of the proposed method, the experiments were conducted on an analysis dataset of the local analysis and prediction system model in a specific region of East China. The experimental results show that our proposed Mamba-UNet has the best overall performance.