Precipitation nowcasting is a vital spatio-temporal prediction task for meteorological applications but faces challenges due to the chaotic property of precipitation systems. Existing methods predominantly rely on single-source radar data to build either deterministic or probabilistic models for extrapolation. However, the single deterministic model suffers from blurring due to MSE convergence. The single probabilistic model, typically represented by diffusion models, can generate fine details but suffers from spurious artifacts that compromise accuracy and computational inefficiency. To address these challenges, this paper proposes a novel coarse-to-fine Vision Mamba Unet and residual Diffusion (VMU-Diff) based precipitation nowcasting framework. It realizes precipitation nowcasting through a two-stage process, i.e., a deterministic model-based coarse stage to predict global motion trends and a probabilistic model-based fine stage to generate fine prediction details. In the coarse prediction stage, rather than single-source radar data, both radar and multi-band satellite data are taken as input. A spatial-temporal attention block and several Vision mamba state-space blocks realize multi-source data fusion, and predict the future echo global dynamics. The fine-grained stage is realized by a spatio-temporal refine generator based on residual conditional diffusion models. It first obtains spatio-temporal residual features based on coarse prediction and ground truth, and further reconstructs the residual via conditional Mamba state-space module. Experiments on Jiangsu SWAN datasets demonstrate the improvements of our method over state-of-the-art methods, particularly in short-term forecasts.

翻译：降水临近预报是气象应用中一项关键的时空预测任务，但由于降水系统的混沌特性，该任务面临诸多挑战。现有方法主要依赖单源雷达数据构建确定性或概率性外推模型。然而，单一确定性模型因均方误差收敛而产生模糊效应；单一概率模型（以扩散模型为代表）虽能生成精细细节，但存在虚假伪影影响精度且计算效率低下的问题。为解决这些难题，本文提出一种新颖的粗到细Vision Mamba Unet与残差扩散模型（VMU-Diff）框架用于降水临近预报。该方法通过两阶段过程实现降水预测：基于确定性模型的粗阶段用于预测整体运动趋势，基于概率性模型的精细阶段用于生成精细预测细节。在粗预测阶段，模型不再仅依赖单源雷达数据，而是同时输入雷达与多波段卫星数据，通过时空注意力模块和多个Vision Mamba状态空间模块实现多源数据融合，并预测未来回波的整体动态。精细预测阶段由基于残差条件扩散模型的时空细化生成器实现，该模块首先基于粗预测结果与真实数据提取时空残差特征，进而通过条件Mamba状态空间模块重建残差。在江苏SWAN数据集上的实验表明，本方法在短期预测方面较现有最优方法具有显著提升。