机器学习揭示湍流极端事件可预测性存在层级差异
Hierarchy of extreme-event predictability in turbulence revealed by machine learning
- 用自回归条件扩散模型分析数值模拟数据，量化每类极端事件的可预测时限
- 不同事件的可预测时长从约1到超过4个李雅普诺夫时间不等，呈现明显层级
- 大尺度结构和应变核心区的持续存在是决定预测能力的关键机制
湍流中极端事件的可预测性强烈依赖于系统状态，但缺乏控制方程或高成本扰动集合时，难以量化单个事件的预测时限。本文在二维柯尔莫哥洛夫流动的直接数值模拟数据上训练自回归条件扩散模型，并基于CRPS技能得分定义事件级预测时限。结果显示，涡量极值表现出显著层级：预测技能持续时间为约1至超过4个李雅普诺夫时间。谱滤波表明，这些时限主要由大尺度结构控制。极端事件前常伴随强烈的应变核心，组织形成四极子涡旋包，其寿命能清晰区分长与短预测时限事件。结果表明，相干结构的持续存在是湍流极端事件可预测性的主导机制，为从观测数据中诊断可预测性极限提供了数据驱动路径。
原文摘要 · Abstract (English)
Extreme-event predictability in turbulence is strongly state dependent, yet event-by-event predictability horizons are difficult to quantify without access to governing equations or costly perturbation ensembles. Here we train an autoregressive conditional diffusion model on direct numerical simulations of the two-dimensional Kolmogorov flow and use a CRPS-based skill score to define an event-wise predictability horizon. Enstrophy extremes exhibit a pronounced hierarchy: forecast skill persists from $\approx 1$ to $> 4$ Lyapunov times across events. Spectral filtering shows that these horizons are controlled predominantly by large-scale structures. Extremes are preceded by intense strain cores organizing quadrupolar vortex packets, whose lifetime sharply separates long- from short-horizon events. These results identify coherent-structure persistence as a governing mechanism for the predictability of turbulence extremes and provide a data-driven route to diagnose predictability limits from observations.
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