Probabilistic 3D Radar Nowcasting via Sparse Volumetric Transport

Probabilistic 3D radar nowcasting forecasts how storms may move, develop vertically, and change in intensity. At high resolution, current methods reconstruct radar volumes from compressed representations such as 3D Gaussians and autoencoder latents, which can degrade high-intensity echoes. Diffusion and flow-matching models also repeat iterative sampling for every ensemble member, making high-resolution ensembles costly for time-critical nowcasting. We introduce Probabilistic Sparse Volumetric Transport (PSVT), which transports observed echoes along a small set of \(K\) 3D transport paths and estimates their growth and decay. Each path carries the full-resolution observed reflectivity through the native voxel space, so high-intensity echoes come directly from the observation rather than from reconstruction. The \(K\) path-wise predictions describe alternative evolutions of the same storm. Their deviations serve as \(K\) shared factors that define the spatiotemporal dependence of echo variations. PSVT models the local uncertainty of reflectivity at each voxel with a Gaussian distribution. Each member takes its variations from one linear combination of the \(K\) factors. This couples echo variations across space and time while avoiding iterative sampling and repeated network evaluations. On NEXRAD-3Y, PSVT improves single-forecast CSI at 40 dBZ by 114.0% over FlowCast and generates 16-member ensembles about \(30\times\) faster.