AI天气模型虽不照搬传统方程，却隐含物理规律，可能通过粒子运动模拟大气。
The physics of AI weather models
- AI模型以隐空间粒子运动模拟大气，遵循学习到的能量最低化路径。
- 不同架构的AI模型在预测能力与核对齐上高度相关，表明共享相似物理表征。
- 模型从大尺度到小尺度逐层细化，支持梯度流机制，适合研究气候建模原理者阅读。
AI天气模型是否在求解物理方程？尽管其架构与传统数值天气预报（NWP）模型不同，但通过计算预测技能与中心核对齐（Centered Kernel Alignment）的相关性，我们发现不同AI模型以相似方式表征大气。我们认为，模型的结构与训练过程限制了其可模拟的物理规律形式。具体而言，我们提出这些模型实现了一种大气的粒子描述：每个网格点的潜在变量对应高维隐空间中一个粒子的位置。我们假设粒子在隐空间沿梯度流向学习到的自由能泛函最小值移动。对GraphCast和Aurora模型的分析显示，它们在早期处理器层对大尺度变化进行修改，并随层数加深逐渐转向小尺度，这与梯度流假说一致。
原文摘要 · Abstract (English)
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.
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