物理辅助与拓扑信息深度融合的天气预测方法

Physics-Assisted and Topology-Informed Deep Learning for Weather Prediction

May 8, 2025

作者: Jiaqi Zheng, Qing Ling, Yerong Feng

cs.AI

摘要

尽管深度学习模型在天气预报中展现出显著潜力，但大多数模型要么忽视了天气演变背后的物理机制，要么忽略了地球表面的拓扑结构。针对这些不足，我们开发了PASSAT，一种新颖的物理辅助与拓扑信息融合的深度学习模型，专为天气预报设计。PASSAT将天气演变归因于两个关键因素：(i) 可由平流方程和纳维-斯托克斯方程描述的平流过程；(ii) 难以建模和计算的地球-大气相互作用。此外，PASSAT不仅将地球表面视为平面，还充分考虑了其拓扑结构。基于这些考量，PASSAT在球面流形上数值求解平流方程和纳维-斯托克斯方程，利用球面图神经网络捕捉地球-大气相互作用，并由此生成对求解平流方程至关重要的初始速度场。在5.625度分辨率的ERA5数据集上，PASSAT不仅超越了当前最先进的深度学习天气预报模型，还超越了业务数值天气预报模型IFS T42。代码及模型检查点可在https://github.com/Yumenomae/PASSAT_5p625获取。

English

Although deep learning models have demonstrated remarkable potential in weather prediction, most of them overlook either the physics of the underlying weather evolution or the topology of the Earth's surface. In light of these disadvantages, we develop PASSAT, a novel Physics-ASSisted And Topology-informed deep learning model for weather prediction. PASSAT attributes the weather evolution to two key factors: (i) the advection process that can be characterized by the advection equation and the Navier-Stokes equation; (ii) the Earth-atmosphere interaction that is difficult to both model and calculate. PASSAT also takes the topology of the Earth's surface into consideration, other than simply treating it as a plane. With these considerations, PASSAT numerically solves the advection equation and the Navier-Stokes equation on the spherical manifold, utilizes a spherical graph neural network to capture the Earth-atmosphere interaction, and generates the initial velocity fields that are critical to solving the advection equation from the same spherical graph neural network. In the 5.625^circ-resolution ERA5 data set, PASSAT outperforms both the state-of-the-art deep learning-based weather prediction models and the operational numerical weather prediction model IFS T42. Code and checkpoint are available at https://github.com/Yumenomae/PASSAT_5p625.