Does Aurora Encode Atmospheric Structure? Latent Regime Analysis and Attribution
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Computer Science > Machine Learning
Title:Does Aurora Encode Atmospheric Structure? Latent Regime Analysis and Attribution
Abstract: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.
| Comments: | Accepted at the FM4Science and Sci4DL workshops at ICLR 2026 |
| Subjects: | Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph) |
| Cite as: | arXiv:2606.26361 [cs.LG] |
| (or arXiv:2606.26361v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.26361
arXiv-issued DOI via DataCite (pending registration)
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