这篇论文揭示了AI天气模型可能隐含的物理机制，对气象AI研究者、数值天气预报开发者以及关注AI可解释性的学者来说，是理解模型内在工作原理的关键进展，值得深入阅读。
这篇论文探讨了AI天气模型是否隐含地求解了物理方程，尽管这些方程可能不同于传统数值天气预报（NWP）模型所用的方程。通过计算预报技能与中心核对齐的相关性，作者发现不同架构和容量的AI天气模型以相似方式表征大气。他们提出，AI模型的架构和训练约束了其可能模拟的物理定律形式，具体而言，模型实现了一种粒子描述，其中每个网格点的潜变量对应高维潜空间中粒子的位置，粒子运动遵循梯度流，趋向于学习到的自由能泛函的最小值。对GraphCast和Aurora模型的分析支持了这一假说：早期处理层在大空间尺度上做出改变，随着层数加深逐渐转向小尺度。
The physics of AI weather models
Could it be that AI weather models are solving physical equations, although they may not be the equations used by conventional NWP models? We compute correlations of forecast skill and Centered Kernel Alignment, providing evidence that different AI weather models represent the atmosphere in similar ways, despite differences in architecture and capacity. We argue that the architecture and training of the AI models constrains the form of the physical laws that they might simulate. In particular, we propose that the models implement a particle description of the atmosphere, where the latent variables at each mesh point correspond to the position of a particle in the high dimensional latent space. We hypothesize that the movement of the particles follows a gradient flow in the latent space towards a minimum of a learned free energy functional. Analysis of the GraphCast and Aurora models show that they make changes on large spatial scales in the early processor layers and move to smaller scale with increasing layer depth, consistent with the gradient flow hypothesis.