This paper presents a convolutional neural network model for precipitation nowcasting that combines data-driven learning with physics-informed domain knowledge. We propose LUPIN, a Lagrangian Double U-Net for Physics-Informed Nowcasting, that draws from existing extrapolation-based nowcasting methods and implements the Lagrangian coordinate system transformation of the data in a fully differentiable and GPU-accelerated manner to allow for real-time end-to-end training and inference. Based on our evaluation, LUPIN matches and exceeds the performance of the chosen benchmark, opening the door for other Lagrangian machine learning models.

翻译：本文提出了一种结合数据驱动学习与物理信息领域知识的卷积神经网络模型，用于降水临近预报。我们提出了LUPIN（拉格朗日双U-Net物理信息临近预报模型），该模型借鉴了现有基于外推的临近预报方法，以完全可微且GPU加速的方式实现数据的拉格朗日坐标系变换，从而支持实时端到端训练与推理。基于评估结果，LUPIN在性能上达到甚至超越了所选基准模型，为其他拉格朗日机器学习模型的应用开辟了道路。