Fully Differentiable Lagrangian Convolutional Neural Network for Physics-Informed Precipitation Nowcasting
This work addresses precipitation nowcasting for meteorology and disaster management, presenting an incremental improvement by integrating Lagrangian methods into neural networks.
The paper tackled precipitation nowcasting by proposing LUPIN, a fully differentiable Lagrangian convolutional neural network that combines data-driven learning with physics-informed domain knowledge, achieving performance that matches or exceeds benchmarks in an extreme event case study.
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. It consists of a U-Net that dynamically produces mesoscale advection motion fields, a differentiable semi-Lagrangian extrapolation operator, and an advection-free U-Net capturing the growth and decay of precipitation over time. Using our approach, we successfully implement the Lagrangian convolutional neural network for precipitation nowcasting in a fully differentiable and GPU-accelerated manner. This allows for end-to-end training and inference, including the data-driven Lagrangian coordinate system transformation of the data at runtime. We evaluate the model and compare it with other related AI-based models both quantitatively and qualitatively in an extreme event case study. Based on our evaluation, LUPIN matches and even exceeds the performance of the chosen benchmarks, opening the door for other Lagrangian machine learning models.