CloudFlow: Physical Evolution-Guided Flow Matching for Multispectral Geostationary Meteorological Satellite Data Nowcasting

The increasing frequency of extreme weather events necessitates precise real-time nowcasting for effective disaster prevention and mitigation, a task that relies heavily on high spatiotemporal resolution observations from geostationary meteorological satellites (GMS). However, existing deep learning models face a persistent dilemma where many deterministic methods suffer from severe blurring, while many generative approaches are plagued by severe structural inconsistency and radiance intensity deviations. To overcome this dilemma, we propose CloudFlow, a two-stage framework that constructs a physics-inspired evolution prior and then performs physics-anchored conditional-flow refinement. In the first stage, the proposed Evolution Network is motivated by the continuity equation and implemented with a differentiable semi-Lagrangian transport operator, together with motion and source-sink parameterization. In the second stage, the proposed Physics-Anchored Flow Matching utilizes the coarse-grained prediction of the Evolution Network as a structural anchor to guide the reconstruction of fine-grained details. Extensive experiments on the Himawari-8/9 and GOES-16 datasets show that CloudFlow unifies predictive accuracy, visual fidelity, inference efficiency, robustness and evolution interpretability.