Nowcast3D: Reliable precipitation nowcasting via gray-box learning

• URL: http://arxiv.org/abs/2511.04659v2
• Date: Mon, 10 Nov 2025 13:55:46 GMT
• Title: Nowcast3D: Reliable precipitation nowcasting via gray-box learning
• Authors: Huaguan Chen, Wei Han, Haofei Sun, Ning Lin, Xingtao Song, Yunfan Yang, Jie Tian, Yang Liu, Ji-Rong Wen, Xiaoye Zhang, Xueshun Shen, Hao Sun,
• Abstract summary: Extreme precipitation nowcasting demands high-dimensional fidelity and extended lead times, yet existing approaches remain limited.<n>We introduce a gray-box, fully three-temporal nowcasting framework that directly processes radar motions and couples physically constrained neural operators with datadriven learning.<n>The framework achieves more accurate forecasts up to three-hour lead time in a blind evaluation by 160 meteorologists.
• Score: 43.74533335638689
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extreme precipitation nowcasting demands high spatiotemporal fidelity and extended lead times, yet existing approaches remain limited. Numerical Weather Prediction (NWP) and its deep-learning emulations are too slow and coarse for rapidly evolving convection, while extrapolation and purely data-driven models suffer from error accumulation and excessive smoothing. Hybrid 2D radar-based methods discard crucial vertical information, preventing accurate reconstruction of height-dependent dynamics. We introduce a gray-box, fully three-dimensional nowcasting framework that directly processes volumetric radar reflectivity and couples physically constrained neural operators with datadriven learning. The model learns vertically varying 3D advection fields under a conservative advection operator, parameterizes spatially varying diffusion, and introduces a Brownian-motion--inspired stochastic term to represent unresolved motions. A residual branch captures small-scale convective initiation and microphysical variability, while a diffusion-based stochastic module estimates uncertainty. The framework achieves more accurate forecasts up to three-hour lead time across precipitation regimes and ranked first in 57\% of cases in a blind evaluation by 160 meteorologists. By restoring full 3D dynamics with physical consistency, it offers a scalable and robust pathway for skillful and reliable nowcasting of extreme precipitation.

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