发表机构
Arizona State University（亚利桑那州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文探索以微软13亿参数天气模型Aurora为测试平台，验证“天气柔术”控制范式可行性，发现扰动响应、幅度合理性及潜在空间方向性结构等支持性证据，但仅为可行性诊断。
AI 中文摘要
天气柔术是一种受混沌理论启发的极端气候事件控制范式。作为一种命题，微小、精确、有针对性且成本低廉的扰动可以改变大型动力系统的轨迹。这一策略已在Lorenz-63系统中得到分析性证明，在该系统中，一个在两个吸引子之间切换的自然混沌轨迹可以通过任意小的扰动被无限期地限制在单一吸引子上。本文考察了微软的Aurora——一个拥有13亿参数的全球大气模型——作为该策略测试平台的可行性。本文探讨了三个问题：（1）Aurora是否是一个足够可靠的模拟环境，能够作为一个有意义的测试平台？（2）重定向其轨迹所需的扰动是否足够小，在物理上具有合理性？（3）Aurora学习到的潜在空间（对气候属性的参数估计器）是否产生任何明显的、结构化的和/或可解释的特征，这些特征能够传达对初始条件的地理/大气响应？我们发现了与这三个问题一致的证据：Aurora建模的轨迹对超出测量漂移的扰动做出响应，所需扰动幅度相对于模型自身的预测不确定性而言较小，并且其潜在表示表现出对柔术式干预做出响应的方向性结构，尽管该结构并未将极端状态与正常状态完全分开。这些结果应被视为可行性诊断，而非控制的演示：我们未在Aurora上实施或测试实际的转向干预，且我们的若干发现，特别是关于模型潜在空间几何结构的发现，属于探索性的。
英文摘要
Weather Jiu-Jitsu is a control paradigm for extreme climatological events, inspired by chaos theory. As a proposition, small, precise, targeted, and cost-inexpensive perturbations can redirect trajectories of a large dynamical system. This strategy has been demonstrated analytically in the Lorenz-63 system, where a naturally chaotic trajectory switching between two attractors can be confined to a single attractor, indefinitely, via arbitrarily small perturbations. This paper examines the feasibility of Microsoft's Aurora -- a 1.3 billion parameter global atmospheric model -- as a test bed for this strategy. This paper explores three questions: (1) Is Aurora a reliable enough simulation environment to serve as a meaningful testbed? (2) Are the perturbations required to redirect its trajectories small enough to be physically plausible? (3) Does Aurora's learned latent space (the parametric estimators on climatological attributes) yield any apparent, structured, and/or perhaps interpretable features that can convey a geo/atmospheric response to initial conditions? We find evidence consistent with all three: Aurora's modeled trajectories respond to perturbations beyond measurement drift, the perturbation magnitudes required are small relative to the model's own forecast uncertainty, and its latent representations exhibit directional structure that responds to Jiu-Jitsu-type interventions, even though that structure does not separate extreme from normal states outright. These results should be read as feasibility diagnostics rather than a demonstration of control: we do not implement or test an actual steering intervention on Aurora, and several of our findings, particularly around the model's latent-space geometry, are exploratory.
Comments33 pages, 34 figures
