FlowCast : advancing precipitation nowcasting with conditional flow matching

Perrone Ribeiro, Bernardo (author), Faganeli Pucer, Jana (author)

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.

• Language: English
• Keywords: conditional flow matching, precipitation nowcasting, generative models, spatiotemporal forecasting, machine learning, machine learning in environmental science
• Work type: Other
• Typology: 1.08 - Published Scientific Conference Contribution
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2026
• Number of pages: 21 str.
• PID: 20.500.12556/RUL-183935
• UDC: 004.85:551.578.1
• COBISS.SI-ID: 275895299
• Publication date in RUL: 22.06.2026

Record is a part of a monograph

• Title: The Fourteenth International Conference on Learning Representations : ICLR 2026, Rio de Janeiro, Brazil, Apr. 23, 2026
• Place of publishing: [S. l.
• Publisher: s. n.]
• Year: 2026
• COBISS.SI-ID: 275890435

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: pogojno ujemanje tokov, zelo kratkoročno napovedovanje padavin, prostorsko-časovno napovedovanje, strojno učenje in znanosti o okolju

Projects

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: P2-0209-2022
• Name: Umetna inteligenca in inteligentni sistemi