通过分解高低频成分提升降水短时预报精度
DuoCast: Duo-Probabilistic Diffusion for Precipitation Nowcasting
- 将降水预测拆分为高低频分量，分别建模于正交潜空间
- 在四个雷达数据集上均优于现有模型，时空精度双提升
- 适合气象预报、灾害预警等需要高精度短时预测场景
精准的短时降水预报对农业、交通和防灾决策至关重要。现有深度学习方法常难以兼顾全局结构一致性和局部细节保留，尤其在复杂气象条件下。本文提出DuoCast，一种双扩散框架，将降水预报分解为低频与高频分量，分别在正交潜空间中建模。理论上证明该频率分解可降低预测误差。低频部分通过条件卷积编码器捕捉大尺度趋势，依赖天气锋面动力；高频部分采用自注意力架构细化微尺度变化。在四个基准雷达数据集上的实验表明，DuoCast持续优于当前最优基线，在空间细节和时间演化上均表现更优。
原文摘要 · Abstract (English)
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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