Aurora模型虽无显式指令，却能捕捉大气垂直结构与季节规律。
Does Aurora Encode Atmospheric Structure? Latent Regime Analysis and Attribution
- 通过主成分分析与归因技术解析模型内部表征
- 风暴事件未形成独立聚类，但垂直结构特征显著
- 对关键区域遮蔽使预测误差提升3.31倍，适合气象建模研究者
大规模机器学习基础模型能够高效准确地模拟大气动力学，但其运行过程如同黑箱。本文利用空间池化主成分分析（PCA）与逐层相关性传播（LRP）方法，研究Aurora模型的内部表示。结果表明，Aurora的潜在空间主要按季节周期组织，极端风暴事件并未形成线性可分的聚类。LRP显示，模型关注的特征与1987年大风暴的三维垂直结构一致。扰动实验表明，遮蔽相关区域导致预测误差比随机遮蔽高出3.31倍。这些发现表明，Aurora在无显式指导的情况下，仍能学习到气象学上的连贯性与垂直结构。
原文摘要 · Abstract (English)
ML foundation models are able to emulate atmospheric dynamics accurately and efficiently but operate as opaque ``black boxes''. We investigate the internal representations of the Aurora model using spatially pooled PCA and layer-wise relevance propagation (LRP). We find evidence that Aurora's latent space is primarily organized by seasonal cycles, whereas extreme storm events do not form a linearly separable cluster. LRP indicates that the model attends to features consistent with the 3D vertical structure of the Great Storm of 1987. Perturbation tests show masking relevant regions degrades forecasts $3.31\times$ more than random masking. These findings suggest that Aurora learns meteorological coherence and vertical structure without explicit instruction.
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