20.500.12556/RUL-183935 FlowCast advancing precipitation nowcasting with conditional flow matching 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. conditional flow matching precipitation nowcasting generative models spatiotemporal forecasting machine learning machine learning in environmental science pogojno ujemanje tokov zelo kratkoročno napovedovanje padavin prostorsko-časovno napovedovanje strojno učenje in znanosti o okolju true false true Angleški jezik Slovenski jezik Drugo 2026-06-22 12:55:58 2026-06-22 12:56:00 2026-06-23 04:00:59 0000-00-00 00:00:00 2026 0 0 21 str. 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.85:551.578.1 275895299 275890435 RAZ_Perrone_Ribeiro_Bernardo_2026.pdf RAZ_Perrone_Ribeiro_Bernardo_2026.pdf 1 692C2D0382A4E039BE38AFD4AF0DB412 994a09eeb611381058d181a3da9b35e393d49f7ae1708b08e68139b7b5a59277 3efbe7bd-6e29-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=236257 https://openreview.net/forum?id=47ToW7T1iU 1 e32a6227-6e28-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=236256 Fakulteta za računalništvo in informatiko 0 0 0