NucleCast: Low-Cost Residual Flow-Matching Refinement for Radar Nowcasting

Radar nowcasting requires predicting rapidly evolving precipitation fields while accounting for uncertainty in their future development. Deterministic forecasts often smooth over multiple plausible outcomes, whereas generative models represent alternative futures through ensembles. Repeated network evaluations make diffusion and flow-based ensemble sampling computationally and infrastructure-intensive. We introduce NucleCast, a two-stage probabilistic nowcasting framework that refines a deterministic forecast anchor rather than generating the entire future field from noise. An efficient Mamba-3-based forecaster first predicts the large-scale evolution. A pixel-space conditional flow matching model then directly predicts clean residual corrections using a mask-conditioned compound-Poisson source that proposes localized precipitation variations. Each ensemble member requires only four flow matching model evaluations, with the same deterministic anchor shared across members. Evaluated on forecast skill, spatial and spectral structure, NucleCast achieves performance competitive with state-of-the-art generative baselines while drastically reducing computational cost. Furthermore, we investigate newly emerging precipitation by explicitly distinguishing recovered events from false off-support predictions. Our sampling analysis reveals that finer numerical integration generates additional fine-scale structures and off-support precipitation.