A New Data Assimilation Scheme: The Space-Expanded Ensemble Localization Kalman Filter. (31st March 2013)

• Record Type: Journal Article
• Authors: Leng, Hongze; Song, Junqiang; Lu, Fengshun; Cao, Xiaoqun
• Other Names: Juang Hann-Ming Henry Academic Editor.
• Is Part Of: Advances in meteorology. Volume 2013(2013)
• Publication Date: 2013-03-31
• Subjects: Meteorology -- Periodicals; Meteorology; Periodicals; 551.505
• DOI: 10.1155/2013/410812
• Languages: English
• ISSNs: 1687-9309

Abstract

Abstract : 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.