Document Type
Article
Publication Date
2-1-2013
Department
Marine Science
Abstract
Improving the performance of ensemble filters applied to models with many state variables requires regularization of the covariance estimates by localizing the impact of observations on state variables. A covariance localization technique based on modeling of the sample covariance with polynomial functions of the diffusion operator (DL method) is presented. Performance of the technique is compared with the non-adaptive (NAL) and adaptive (AL) ensemble localization schemes in the framework of numerical experiments with synthetic covariance matrices in a realistically inhomogeneous setting. It is shown that the DL approach is comparable in accuracy with the AL method when the ensemble size is less than 100. With larger ensembles, the accuracy of the DL approach is limited by the local homogeneity assumption underlying the technique. Computationally, the DL method is comparable with the NAL technique if the ratio of the local decorrelation scale to the grid step is not too large.
Publication Title
Monthly Weather Review
Volume
141
Issue
2
First Page
848
Last Page
860
Recommended Citation
Yaremchuk, M.,
Nechaev, D. A.
(2013). Covariance Localization with the Diffusion-Based Correlation Models. Monthly Weather Review, 141(2), 848-860.
Available at: https://aquila.usm.edu/fac_pubs/7649
Comments
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