AI 中文总结
该研究提出可解释低秩状态空间模型，以EOF为稳定大尺度特征，实现大尺度区域温度场多步概率模拟，兼具可解释性与计算效率，支持状态结构推断及预测。
AI 中文摘要
我们提出了一种可解释的低秩状态空间模型，用于区域日温度场的条件模拟。核心统计问题是：经验正交函数（EOF）是否可视为该场的稳定大尺度特征，而非仅作为依赖样本的降维基向量。该框架通过少量保留的EOF系数表征主导温度场，对其季节均值和方差结构建模，以稳定的多元动力学传播所得低维状态，并利用结构化创新项表征剩余不确定性。所得降秩模型具有可解释性、计算高效性，适用于迭代集合生成，旨在支持对保留温度状态结构的推断及通过多步概率场模拟进行预测。
英文摘要
We propose an interpretable low-rank state-space model for conditional simulation of daily regional temperature fields. The central statistical question is whether empirical orthogonal functions (EOFs) can be treated as stable large-scale features of the field rather than only as sample-dependent basis vectors for dimension reduction. The framework represents the dominant temperature field through a small number of retained EOF coefficients, models their seasonal mean and variance structure, propagates the resulting low-dimensional state with stable multivariate dynamics, and uses structured innovations to represent the remaining uncertainty. The resulting reduced-rank model is interpretable, computationally efficient, and suitable for iterative ensemble generation. It is designed to support both inference on the structure of the retained temperature state and prediction through multi-horizon probabilistic field simulation.
