解析混沌系统中预报不确定性的来源，提升气象模型的预测可信度。
Decomposing Ensemble Spread in Lorenz '96 With Learned Stochastic Parameterizations
- 分离初始扰动、内在波动与模型随机性三类不确定性
- 带时间持续性的随机参数化可加速早期预报发散
- 为气候模型设计提供可量化的评估框架
天气与气候预测因混沌动力学、初始条件不精确及物理过程表征不全而具有固有不确定性。业务集合预报通过预报发散来表征这些不确定性，但许多方法产生低估发散的估计，即发散速度慢于实际误差增长。本文以广泛使用的双尺度Lorenz '96系统为可控测试平台，系统地解耦内在变率、初始条件扰动与随机模型不确定性。比较多种集合配置与参数化策略，包括现有确定性与自回归方法，以及新型贝叶斯与流模型方法。结果表明：集合扰动并不增加系统的长期方差，而是调节轨迹退相关与不变测度探索的速度。带有时间持续结构的随机参数化能增强早期发散增长，并改善发散与误差的一致性。整体上，本文厘清了混沌系统中不同不确定性源的相互作用机制，为天气与气候模型中随机参数化的构建与评估提供指导。
原文摘要 · Abstract (English)
Weather and climate forecasts are inherently uncertain due to chaotic dynamics, imperfect initial conditions, and incomplete representation of the underlying physical processes. Operational ensemble forecasts aim to represent these uncertainties through forecast spread, yet many approaches yield underdispersive estimates, with spread that grows too slowly relative to forecast error. Using the two-scale Lorenz 1996 system as a widely used, controlled testbed, we design a systematic approach to disentangle intrinsic variability, initial-condition perturbations, and stochastic model uncertainty. We compare multiple ensemble configurations and parameterization strategies, including existing deterministic and autoregressive as well as novel Bayesian and flow-based approaches. Our results show that ensemble perturbations do not increase the system's long-term variance; rather, they regulate how rapidly trajectories decorrelate and explore the invariant measure. Stochastic parameterizations, particularly those with temporally persistent structure, enhance early spread growth and improve spread-error consistency. Overall, we bring clarity to how different sources of uncertainty interact in a chaotic system and provide guidance for the design and evaluation of stochastic parameterizations in weather and climate models.
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