FlowCast: Advancing Precipitation Nowcasting with Conditional Flow Matching

• URL: http://arxiv.org/abs/2511.09731v1
• Date: Fri, 14 Nov 2025 01:06:29 GMT
• Title: FlowCast: Advancing Precipitation Nowcasting with Conditional Flow Matching
• Authors: Bernardo Perrone Ribeiro, Jana Faganeli Pucer,
• Abstract summary: We introduce FlowCast, the first model to apply Conditional Flow Matching (CFM) to precipitation nowcasting.<n>Unlike diffusion, CFM learns a direct noise-to-data mapping, enabling rapid, high-fidelity sample generation with drastically fewer function evaluations.<n>Our experiments demonstrate that FlowCast establishes a new state-of-the-art in predictive accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 model to apply Conditional Flow Matching (CFM) to precipitation nowcasting. Unlike diffusion, CFM learns a direct noise-to-data mapping, enabling rapid, high-fidelity sample generation with drastically fewer function evaluations. Our experiments demonstrate that FlowCast establishes a new state-of-the-art 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.

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