A Multi-scale Spatiotemporal Correlation Attention and State Space Modeling-based Approach for Precipitation Nowcasting

ObjectivePrecipitation nowcasting is a representative task in meteorological forecasting. It uses radar echoes or precipitation sequences to predict precipitation distribution in the next 0～2 hours. It supports disaster warning and key decision-making and protects lives and property. Current mainstream methods show loss of local details, limited representation of conditional information, and weak adaptability in complex regions. This study proposes a PredUMamba model based on a diffusion model. The model introduces a Mamba block with an adaptive zigzag scanning mechanism that extracts key local detail information and reduces computational complexity. A multi-scale spatiotemporal correlation attention module is also designed to enhance interactions across spatiotemporal hierarchies and to achieve a comprehensive representation of conditional information. In addition, a radar echo dataset tailored for complex regions is constructed for the southern Anhui mountainous area to evaluate the model's ability to predict sudden and extreme rainfall. This work provides an intelligent solution and theoretical support for precipitation nowcasting.MethodsThe PredUMamba model adopts a two-stage diffusion network. In the first stage, a frame-by-frame Variational AutoEncoder (VAE) is trained to map precipitation data from pixel space to a low-dimensional latent space. In the second stage, a diffusion network is built on the encoded latent space. An adaptive zigzag Mamba module with a spatiotemporal alternating scanning strategy is proposed. Sequential scanning is performed within rows and turn-back scanning is performed between rows. This design captures detailed precipitation-field features while maintaining low computational complexity. A multi-scale spatiotemporal correlation attention module is further introduced on temporal and spatial scales. On the temporal scale, adaptive convolution kernels and attention-based convolution layers extract local and global information. On the spatial scale, a lightweight correlation attention mechanism aggregates spatial information and strengthens historical conditional information representation. A radar dataset for the southern Anhui mountainous area is constructed to evaluate model adaptability in complex terrain.Results and DiscussionsThe adaptive zigzag Mamba module and multi-scale spatiotemporal correlation attention module strengthen the model’s ability to capture intrinsic spatiotemporal dependencies. They extract conditional information more accurately and yield prediction results closer to real conditions. Experiments show that PredUMamba achieves the best performance across all indicators on the Southern Anhui Mountain Area and Shanghai radar datasets. On the SEVIR dataset, FVD, ${\mathrm{CSI}}_{{-}}{\mathrm{pool4}} $, and ${\mathrm{CSI}}_{{-}}{\mathrm{pool6}} $ outperform other methods, and CSI and CRPS achieve competitive results. Visualization results further show that PredUMamba does not produce temporal blurring (Fig. 4). This indicates stronger stability and clear advantages in detail generation and motion-trend prediction. The model preserves edge details aligned with real precipitation fields and maintains accurate motion patterns.ConclusionsThis study proposes an innovative PredUMamba model based on a diffusion network architecture. Model performance is improved through a Mamba module with an adaptive zigzag scanning mechanism and a multi-scale spatiotemporal correlation attention module. The adaptive zigzag module captures fine-grained spatiotemporal features and reduces computational complexity. The multi-scale attention module strengthens historical conditional information extraction through temporal dual-branch processing and a lightweight spatial correlation mechanism, enabling joint representation of local and global features. A radar dataset for the southern Anhui mountainous area is also constructed to validate model applicability in complex terrain. The dataset covers precipitation under various terrain conditions and supports extreme rainfall prediction. Comparative experiments on the constructed dataset and on public datasets show that PredUMamba achieves the best results on the southern Anhui mountainous area and Shanghai datasets. On the SEVIR dataset, FVD, ${\mathrm{CSI}}_{{-}}{\mathrm{pool4}} $, and ${\mathrm{CSI}}_{{-}}{\mathrm{pool6}} $ outperform other methods, and CRPS and CSI achieve competitive results. As this work focuses on a data-driven forecasting approach, future research will integrate physical-condition constraints to improve interpretability and enhance prediction accuracy for small- and medium-scale convective systems.