Ensemble propagation and continuous matrix factorization algorithms
Ensemble propagation and continuous matrix factorization algorithms
复制标题
集成传播和连续矩阵分解算法
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
S. Reich
中科院分区：
文献类型：
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作者：
K. Bergemann;G. Gottwald;S. Reich
我们考虑传播解集合的问题及其均值和协方差矩阵的表征。我们提出微分方程，将系综的连续矩阵分解为广义奇异值分解（SVD）。连续分解应用于周期性重新缩放（集成育种）和周期性卡尔曼分析步骤（集成卡尔曼滤波器）下的集成传播。我们还使用连续矩阵分解在每个时间步之后对系综进行重新正交化，并将所得的修改后的系综传播算法应用于系综卡尔曼滤波器。 Lorenz-96 模型的结果表明，系综的重新正交化可以提高滤波器性能。版权所有 © 2009 英国皇家气象学会
We consider the problem of propagating an ensemble of solutions and its characterization in terms of its mean and covariance matrix. We propose differential equations that lead to a continuous matrix factorization of the ensemble into a generalized singular value decomposition (SVD). The continuous factorization is applied to ensemble propagation under periodic rescaling (ensemble breeding) and under periodic Kalman analysis steps (ensemble Kalman filter). We also use the continuous matrix factorization to perform a re‐orthogonalization of the ensemble after each time‐step and apply the resulting modified ensemble propagation algorithm to the ensemble Kalman filter. Results from the Lorenz‐96 model indicate that the re‐orthogonalization of the ensembles leads to improved filter performance. Copyright © 2009 Royal Meteorological Society
影响因子：
4
作者：
Livings, David M.;Dance, Sarah L.;Nichols, Nancy K.
通讯作者：
Nichols, Nancy K.