Nowcast3D: Reliable precipitation nowcasting via gray-box learning

Abstract

Extreme-precipitation nowcasting requires high spatial and temporal resolution together with extended lead times, yet current approaches remain constrained. Numerical weather prediction systems and their deep-learning emulators operate at relatively coarse space-time resolution and struggle to capture rapidly evolving convective systems. Radar extrapolation methods, which advect recent fields using estimated motion, have difficulty capturing the complex evolution of precipitation. Purely data-driven models often produce overly smoothed reflectivity fields and underestimate intensity. Hybrid 2D radar-based methods discard crucial vertical information, preventing accurate reconstruction of height-dependent dynamics. We introduce Nowcast3D, a gray-box, fully three-dimensional nowcasting framework that operates directly on volumetric radar reflectivity and couples physically constrained neural operators with data-driven learning. The model learns three fields that govern reflectivity evolution: a three-dimensional flow field for advective transport, a spatially varying diffusion field for local dispersive spreading, and a residual source term for unresolved microphysical effects. These learned operators advance the forecast in time under explicit physical constraints, while a conditional diffusion model, conditioned on both the observations and the physics-based forecast, generates ensembles of future radar volumes that quantify forecast uncertainty. In a blind evaluation by 160 meteorologists, Nowcast3D is preferred in 57% of post-hoc and 51% of prior assessments. By explicitly embedding three-dimensional dynamics and uncertainty into a single framework, Nowcast3D offers a scalable and robust approach for reliable nowcasting of extreme precipitation.