RainOps: Multi-Source Availability-Aware Nowcasting with Residual Probabilistic Refinement

Precipitation nowcasting is the task of learning to predict future precipitation fields at high spatial and temporal resolution, typically over the next few hours, from heterogeneous observations. Existing approaches struggle to leverage diverse observation sources, to remain robust when sources are unavailable, and to efficiently generate ensemble forecasts. RainOps addresses these challenges with an efficient framework that separates deterministic forecasting from stochastic refinement. An availability-conditioned backbone produces a deterministic forecast from the available observations, while lightweight stochastic residual refinement generates diverse ensemble members without rerunning the backbone. A post-hoc calibration stage further refines precipitation intensities. Thorough empirical evaluation on benchmark data demonstrates that RainOps outperforms state-of-the-art baselines, in particular for challenging rare high-intensity precipitation. Without any retraining, RainOps successfully maintains high performance when auxiliary observations are missing, while exploiting additional data sources where available.