Articles | Volume 24, issue 3
https://doi.org/10.5194/npg-24-329-2017
https://doi.org/10.5194/npg-24-329-2017
Research article
 | 
03 Jul 2017
Research article |  | 03 Jul 2017

An estimate of the inflation factor and analysis sensitivity in the ensemble Kalman filter

Guocan Wu and Xiaogu Zheng

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Guocan Wu on behalf of the Authors (19 Dec 2016)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (23 Dec 2016) by Amit Apte
RR by Anonymous Referee #2 (06 Jan 2017)
RR by Anonymous Referee #1 (06 Jan 2017)
ED: Reconsider after major revisions (further review by Editor and Referees) (09 Mar 2017) by Amit Apte
AR by Guocan Wu on behalf of the Authors (22 Mar 2017)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (11 Apr 2017) by Amit Apte
RR by Anonymous Referee #2 (24 Apr 2017)
ED: Publish subject to minor revisions (further review by Editor) (13 May 2017) by Amit Apte
AR by Guocan Wu on behalf of the Authors (17 May 2017)  Author's response   Manuscript 
ED: Publish as is (26 May 2017) by Amit Apte
AR by Guocan Wu on behalf of the Authors (31 May 2017)
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Short summary
The accuracy of the assimilation results crucially relies on the estimate accuracy of forecast error covariance matrix in data assimilation. Ensemble Kalman filter estimates the forecast error covariance matrix as the sampling covariance matrix of the ensemble forecast states, which need to be further inflated. The experiment results on the Lorenz-96 model show that the analysis error is reduced and the analysis sensitivity to observations is improved using the proposed inflation technique.