ADNM-UNet: An Asymmetric Dual-Branch Noncausal Mamba U-Net With Multiscale Attention Enhancement for Cloud Mask Nowcasting

Cloud mask underpins accurate precipitation nowcasting, which in turn is vital for understanding the hydrological cycle, supporting disaster prevention, solar energy forecasting and transportation. However, cloud mask nowcasting remains challenging because meteorological data exhibit irregular temporal and spatial variations, including fine-scale structures, and often suffer from highly skewed precipitation intensity distributions. Existing methods struggle to capture complex spatiotemporal dynamics and preserve fine-scale structures due to limitations in handling sparse data from numerical weather prediction (NWP) model. To address these issues, we propose an asymmetric dual-branch non-causal mamba U-Net (ADNM-UNet) featuring three key components: (1) The Asymmetric Dual-branch Non-causal Mamba (ADNM) implements a novel asymmetric bidirectional modeling framework that resolves directional bias in conventional Mamba architectures. This design preserves precise cloud boundary delineation while capturing long-range spatiotemporal dependencies in sparse data from NWP. (2) The Multi-scale Attention Enhancement Module (MAEM) enhances discriminative feature representation and suppresses spectral redundancy through anisotropic convolution kernels and hybrid pooling. This mechanism significantly improves edge retention in precipitation systems while attenuating atmospheric noise interference. (3) Complementing these advancements, the Wavelet Decomposition and Fusion Module (WDFM) maintains cloud contour integrity across scales through multiresolution decomposition. Extensive experiments demonstrate that ADNM-UNet outperforms existing methods across all metrics, achieving 27.96% improvement in CSI and 22.89% in HSS for metrics over the best performing baseline models at high intensity scenarios. Our project is open source and available on GitHub at: https://github.com/kanyu369/ADNM-UNet.