检验大模型天气预报器的物理一致性，发现其隐空间有合理气象特征。
Physical Consistency of Aurora's Encoder: A Quantitative Study
- 用线性分类器检测隐表示是否匹配陆海边界等物理概念。
- 验证了模型能学习到极端温度与大气不稳定的物理特征。
- 适合关注模型可信度与可解释性的气象AI研究者参考。
像Aurora这样的大规模天气预报模型虽然精度高，但其内部表征大多不透明，存在‘黑箱’问题，阻碍其在高风险业务场景中的应用。本文通过探测Aurora编码器的物理一致性，考察其潜在表示是否与已知的物理和气象概念一致。利用大规模嵌入数据集，训练线性分类器识别三个关键概念：基本的陆海边界、高影响极端温度事件以及大气不稳定性。结果提供了量化证据，表明Aurora能够学习到具有物理一致性的特征，同时揭示其在捕捉最罕见事件方面的局限性。该工作强调了可解释性方法在验证和建立下一代人工智能驱动天气模型信任中的关键作用。
原文摘要 · Abstract (English)
The high accuracy of large-scale weather forecasting models like Aurora is often accompanied by a lack of transparency, as their internal representations remain largely opaque. This "black box" nature hinders their adoption in high-stakes operational settings. In this work, we probe the physical consistency of Aurora's encoder by investigating whether its latent representations align with known physical and meteorological concepts. Using a large-scale dataset of embeddings, we train linear classifiers to identify three distinct concepts: the fundamental land-sea boundary, high-impact extreme temperature events, and atmospheric instability. Our findings provide quantitative evidence that Aurora learns physically consistent features, while also highlighting its limitations in capturing the rarest events. This work underscores the critical need for interpretability methods to validate and build trust in the next generation of Al-driven weather models.
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