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arXiv · 2412.01091

DuoCast: Duo-Probabilistic Diffusion for Precipitation Nowcasting

Penghui Wen·Mengwei He·Patrick Filippi·Na Zhao·Feng Zhang·Thomas Francis Bishop·Zhiyong Wang·Kun Hu
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Abstract

Accurate short-term precipitation forecasting is critical for weather-sensitive decision-making in agriculture, transportation, and disaster response. Existing deep learning approaches often struggle to balance global structural consistency with local detail preservation, especially under complex meteorological conditions. We propose DuoCast, a dual-diffusion framework that decomposes precipitation forecasting into low- and high-frequency components modeled in orthogonal latent subspaces. We theoretically prove that this frequency decomposition reduces prediction error compared to conventional single branch U-Net diffusion models. In DuoCast, the low-frequency model captures large-scale trends via convolutional encoders conditioned on weather front dynamics, while the high-frequency model refines fine-scale variability using a self-attention-based architecture. Experiments on four benchmark radar datasets show that DuoCast consistently outperforms state-of-the-art baselines, achieving superior accuracy in both spatial detail and temporal evolution.

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Penghui Wen, Mengwei He, Patrick Filippi, Na Zhao, Feng Zhang, Thomas Francis Bishop, Zhiyong Wang, Kun Hu. 2024-12-02. DuoCast: Duo-Probabilistic Diffusion for Precipitation Nowcasting. https://arxiv.org/abs/2412.01091

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