GeoRad-3D: Factorized Geometry Transport and Residual Radiometry for 3D Radar Nowcasting

Volumetric radar nowcasting asks a model to forecast where storm structures move and how reflectivity evolves through height. The recent \stcgs{} method makes this setting practical by tracking 3D radar volumes with spatiotemporally coherent Gaussian parcels, but its forecasting head is designed to evolve all future Gaussian attributes with a unified deterministic regressor. This is a poor match to convective evolution: geometry is dominated by coherent transport, while strong-echo radiometry is local, intermittent, and uncertain. We present GeoRad-3D, a factorized forecasting head for coherent Gaussian radar states. The key idea is simple: move geometry first, predict the stable radiometric continuation next, model only the residual uncertainty, and form the final nowcast after rendering. For geometry, a height-conditioned shared field captures volume-level advection and mild coherent deformation, while a parcel-wise correction restores local parcel drift, shape change, and cross-height motion. For radiometry, a deterministic base predictor outputs the stable continuation, and conditional flow matching samples residuals around this base to model the local uncertainty concentrated around strong echoes. At inference, multiple aligned futures are rendered and aggregated through a base-anchored quantile summary in radar-product space, where threshold skill is measured. Under the published NEXRAD protocol, GeoRad-3D improves Pool4 CSI over the strongest published baseline by about 45\%, 82\%, and 124\% at 20, 30, and 40 dBZ. It further reduces LPIPS-Radar by 31.0\%, indicating better perceptual fidelity of rendered storm structures.