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Advances in Meteorology
Volume 2013, Article ID 410812, 6 pages
Research Article

A New Data Assimilation Scheme: The Space-Expanded Ensemble Localization Kalman Filter

1College of Computer, National University of Defense Technology, Changsha 410073, China
2China Aerodynamics Research and Development Center, Mianyang, Sichuan 621000, China

Received 6 January 2013; Revised 23 February 2013; Accepted 9 March 2013

Academic Editor: Hann-Ming Henry Juang

Copyright © 2013 Hongze Leng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This study considers a new hybrid three-dimensional variational (3D-Var) and ensemble Kalman filter (EnKF) data assimilation (DA) method in a non-perfect-model framework, named space-expanded ensemble localization Kalman filter (SELKF). In this method, the localization operation is directly applied to the ensemble anomalies with a Schur Product, rather than to the full error covariance of the state in the EnKF. Meanwhile, the correction space of analysis increment is expanded to a space with larger dimension, and the rank of the forecast error covariance is significantly increased. This scheme can reduce the spurious correlations in the covariance and approximate the full-rank background error covariance well. Furthermore, a deterministic scheme is used to generate the analysis anomalies. The results show that the SELKF outperforms the perturbed EnKF given a relatively small ensemble size, especially when the length scale is relatively long or the observation error covariance is relatively small.