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

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...

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Bibliographic Details
Published in:Advances in Meteorology 2013-01, Vol.2013 (2013), p.213-218-043
Main Authors: Leng, Hongze, Song, Junqiang, Lu, Fengshun, Cao, Xiaoqun
Format: Article
Language:English
Subjects:
Analysis of covariance
Kalman filtering
Meteorological research
Methods
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Summary: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.
ISSN:1687-9309
1687-9317
DOI:10.1155/2013/410812