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.

翻译：临近降水预报是灾害早期预警的关键，目前依赖于计算成本高昂且具有限制性的方法，这限制了许多国家的使用。为克服这一挑战，我们提出利用卫星图像并施加物理约束进行临近降水预报，以提高精度和物理一致性。我们采用一种新颖的基于物理信息的双神经算子（PIANO）结构，在训练过程中通过PINN损失强制满足平流-扩散基本方程，以预测卫星图像。随后，我们使用生成模型将卫星图像转换为雷达图像，用于临近降水预报。与基线模型相比，我们提出的模型在中度（4毫米/小时）降水事件预测以及短期强（8毫米/小时）降水事件预测方面均显示出显著改进。该模型还表现出预测的季节性变异较低，表明其具有稳健的泛化能力。本研究揭示了PIANO的潜力，并为基于物理信息的临近降水预报提供了一个良好的基准。