Handling Nonlinearity in an Ensemble Kalman Filter: Experiments with the Three-Variable Lorenz Model
Handling Nonlinearity in an Ensemble Kalman Filter: Experiments with the Three-Variable Lorenz Model
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DOI：
10.1175/mwr-d-11-00313.1
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发表时间：
2012-08
影响因子：
3.2
通讯作者：
Shu‐Chih Yang;E. Kalnay;B. Hunt
中科院分区：
文献类型：
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作者：
Shu‐Chih Yang;E. Kalnay;B. Hunt
摘要集合卡尔曼滤波器（EnKF）仅对于线性模型是最佳的，因为它假设高斯分布。提出了一种不同于三维和四维变分资料同化（Var）的新型外环，以提高EnKF处理非线性动力学问题的能力，尤其是在长同化窗口的情况下。“原地运行”（RIP）算法的思想是在存在强非线性误差增长时通过重用观测值来增加观测值的影响，从而在局部集合变换卡尔曼滤波（LETKF）框架内改善集合均值和扰动。准外环（quasi-outer-loop，QOL）算法是RIP算法的一种简化形式，其目的是提高系综平均值，使系综扰动集中在一个更精确的状态，并利用三变量Lorenz模型对LETKF-RIP和LETKF-QOL算法在非线性情况下的性能进行了测试。结果表明，RIP和QOL允许LETKF使用更长的同化窗口与s…
AbstractAn ensemble Kalman filter (EnKF) is optimal only for linear models because it assumes Gaussian distributions. A new type of outer loop, different from the one used in 3D and 4D variational data assimilation (Var), is proposed for EnKF to improve its ability to handle nonlinear dynamics, especially for long assimilation windows. The idea of the “running in place” (RIP) algorithm is to increase the observation influence by reusing observations when there is strong nonlinear error growth, and thus improve the ensemble mean and perturbations within the local ensemble transform Kalman filter (LETKF) framework. The “quasi-outer-loop” (QOL) algorithm, proposed here as a simplified version of RIP, aims to improve the ensemble mean so that ensemble perturbations are centered at a more accurate state.The performances of LETKF–RIP and LETKF–QOL in the presence of nonlinearities are tested with the three-variable Lorenz model. Results show that RIP and QOL allow LETKF to use longer assimilation windows with s...