Radar-based precipitation nowcasting, the task of forecasting short-term precipitation fields from previous radar images, is a critical problem for flood risk management and decision-making. While deep learning has substantially advanced this field, two challenges remain fundamental: the uncertainty of atmospheric dynamics and the efficient modeling of high-dimensional data. Diffusion models have shown strong promise by producing sharp, reliable forecasts, but their iterative sampling process is computationally prohibitive for time-critical applications. We introduce FlowCast, the first end-to-end probabilistic model leveraging Conditional Flow Matching (CFM) as a direct noise-to-data generative framework for precipitation nowcasting. Unlike hybrid approaches, FlowCast learns a direct noise-to-data mapping in a compressed latent space, enabling rapid, high-fidelity sample generation. Our experiments demonstrate that FlowCast establishes a new state-of-the-art in probabilistic performance while also exceeding deterministic baselines in predictive accuracy. A direct comparison further reveals the CFM objective is both more accurate and significantly more efficient than a diffusion objective on the same architecture, maintaining high performance with significantly fewer sampling steps. This work positions CFM as a powerful and practical alternative for high-dimensional spatiotemporal forecasting.

翻译：基于雷达的降水临近预报，即利用历史雷达图像预测短期降水场，是洪水风险管理和决策支持中的关键问题。尽管深度学习已显著推动该领域发展，但仍存在两个基本挑战：大气动力学的不确定性以及高维数据的高效建模。扩散模型通过生成清晰可靠的预报展现出巨大潜力，但其迭代采样过程在计算上对时间敏感的应用而言过于昂贵。我们提出了FlowCast，首个利用条件流匹配（CFM）作为直接噪声到数据生成框架的端到端概率模型，用于降水临近预报。与混合方法不同，FlowCast在压缩的潜在空间中学习从噪声到数据的直接映射，从而实现快速、高保真度的样本生成。实验表明，FlowCast在概率性能上确立了新的最优水平，同时在预测精度上超越了确定性基线。直接比较进一步揭示，在相同架构下，CFM目标比扩散目标更准确且显著更高效，在采样步骤大幅减少的情况下仍保持高性能。这项工作将CFM定位为高维时空预报中强大且实用的替代方案。