发表机构
National University of Singapore（新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出流集成滤波器（FlowEF），利用条件流匹配将预报集成从经典滤波器传输到分析集成，学习非线性更新，在稀疏观测动力学系统中优于四种经典集成滤波器及最先进的生成式数据同化模型。
AI 中文摘要
数据同化从部分且有噪声的观测中估计动力学状态。经典的集成滤波器效率高，但通过有限样本协方差和仿射高斯分布限制了分析更新。我们引入了流集成滤波器（FlowEF），它利用条件流匹配将预报集成从经典的基线滤波器传输到分析集成。FlowEF在训练期间使用局部化高斯源，在部署时从基线滤波器传输预报集成成员，并将其速度场以来自该基线滤波器的集成和观测为条件。因此，所提出的模型在学习非线性更新的同时，独立地映射每个基线集成成员。对于稀疏观测的动力学系统，FlowEF在所有四种经典集成滤波器上均改善了确定性和概率性指标。它还在最先进的生成式数据同化模型中取得了最佳性能。
英文摘要
Data assimilation estimates a dynamical state from partial and noisy observations. Classical ensemble filters are efficient but restrict analysis updates through finite sample covariance and affine Gaussian distribution. We introduce the Flow Ensemble Filter (FlowEF), which uses conditional flow matching to transport the forecast ensemble from a classical baseline filter to an analysis ensemble. FlowEF uses a localized Gaussian source during training, transports forecast ensemble members from a baseline filter at deployment, and conditions its velocity field on ensembles from that baseline filter and the observation. The proposed model therefore learns a nonlinear update while mapping each baseline ensemble independently. For sparsely observed dynamical systems, FlowEF improves both deterministic and probabilistic metrics over all four classical ensemble filters. It also achieves the best performance among the state-of-the-art generative data assimilation models.
Comments57 pages, 6 figures, 12 tables
