Journal cover Journal topic
Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union

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  • IF value: 1.329 IF 1.329
  • IF 5-year<br/> value: 1.394 IF 5-year
    1.394
  • CiteScore<br/> value: 1.27 CiteScore
    1.27
  • SNIP value: 0.903 SNIP 0.903
  • SJR value: 0.709 SJR 0.709
  • IPP value: 1.455 IPP 1.455
  • h5-index value: 20 h5-index 20
Nonlin. Processes Geophys., 24, 329-341, 2017
https://doi.org/10.5194/npg-24-329-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
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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Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version      Supplement - Supplement
 
RC1: 'Referee comments', Anonymous Referee #1, 13 Nov 2016 Printer-friendly Version Supplement 
AC1: 'Reply to reviewer1', Guocan Wu, 30 Nov 2016 Printer-friendly Version Supplement 
 
RC2: 'Referee Report', Anonymous Referee #2, 22 Nov 2016 Printer-friendly Version 
AC2: 'Reply to reviewer2', Guocan Wu, 30 Nov 2016 Printer-friendly Version Supplement 
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  
CC BY 4.0
Publications Copernicus
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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.
The accuracy of the assimilation results crucially relies on the estimate accuracy of forecast...
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