Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error
Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error
复制标题
具有模型误差的降阶卡尔曼滤波器的混沌动力学和协方差膨胀的作用
DOI：
10.5194/npg-25-633-2018
复制
发表时间：
2018
影响因子：
2.2
通讯作者：
M. Bocquet
中科院分区：
文献类型：
--
作者：
C. Grudzien;A. Carrassi;M. Bocquet
抽象。集合卡尔曼滤波器及其变种已被证明是强大的数据同化在高维地球物理模型，本地化，使用合奏的尺寸极小的模型尺寸。然而，估计的协方差的降低的秩表示留下未过滤的大维度互补子空间。利用后向李雅普诺夫向量滤波的动力学性质，本文探讨了一个以前无法解释的机制，提供了一个新的理论解释的协方差膨胀的作用，在基于集成卡尔曼滤波器。我们推导的预测误差演化描述了动态上涌的未过滤的误差从外部的跨度的异常到过滤的子空间。线性系统的分析结果明确地描述了机制的上升流，和相关的递归Riccati方程的预测误差，而非线性近似数值探讨。
Abstract. The ensemble Kalman filter and its variants have shown to be robust for data assimilation in high dimensional geophysical models, with localization, using ensembles of extremely small size relative to the model dimension. However, a reduced rank representation of the estimated covariance leaves a large dimensional complementary subspace unfiltered. Utilizing the dynamical properties of the filtration for the backward Lyapunov vectors, this paper explores a previously unexplained mechanism, providing a novel theoretical interpretation for the role of covariance inflation in ensemble-based Kalman filters. Our derivation of the forecast error evolution describes the dynamic upwelling of the unfiltered error from outside of the span of the anomalies into the filtered subspace. Analytical results for linear systems explicitly describe the mechanism for the upwelling, and the associated recursive Riccati equation for the forecast error, while nonlinear approximations are explored numerically.
基于投影阴影的数据同化
影响因子：
2.1
作者：
de Leeuw, Bart;Dubinkina, Svetlana;Frank, Jason;Steyer, Andrew;Tu, Xuemin;Vleck, Erik Van
通讯作者：
Vleck, Erik Van