用物理约束提升卫星图像降水预报精度，兼顾准确与稳定性。
PIANO: Physics-informed Dual Neural Operator for Precipitation Nowcasting
- 引入物理信息双神经算子，通过输运-扩散方程约束训练过程。
- 对4mm/h中等雨量和8mm/h短时强降雨预测均显著优于基线模型。
- 全年季节变化小，适合全球推广的实时降水预警系统。
降水临近预报对灾害早期预警至关重要，但现有方法计算成本高且适用范围有限。为此，我们提出一种基于卫星影像并融合物理约束的降水预报新方法。采用新型物理信息双神经算子（PIANO）结构，在训练中通过物理信息损失（PINN loss）强制施加输运-扩散基本方程，以提升预测的物理一致性。随后利用生成模型将卫星图像转换为雷达图像，用于降水预报。相较于基线模型，本方法在4mm/h中等强度降水事件及8mm/h短时强降水事件的预测上均有显著提升，且预测结果季节性波动小，表现出良好的泛化能力。研究验证了PIANO在物理信息引导降水预报中的潜力，并可作为该领域的基准方法。
原文摘要 · Abstract (English)
Precipitation nowcasting, key for early warning of disasters, currently relies on computationally expensive and restrictive methods that limit access to many countries. To overcome this challenge, we propose precipitation nowcasting using satellite imagery with physics constraints for improved accuracy and physical consistency. We use a novel physics-informed dual neural operator (PIANO) structure to enforce the fundamental equation of advection-diffusion during training to predict satellite imagery using a PINN loss. Then, we use a generative model to convert satellite images to radar images, which are used for precipitation nowcasting. Compared to baseline models, our proposed model shows a notable improvement in moderate (4mm/h) precipitation event prediction alongside short-term heavy (8mm/h) precipitation event prediction. It also demonstrates low seasonal variability in predictions, indicating robustness for generalization. This study suggests the potential of the PIANO and serves as a good baseline for physics-informed precipitation nowcasting.
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