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

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https://doi.org/10.5194/npg-2016-44
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
04 Oct 2016
Review status
A revision of this discussion paper was accepted for the journal Nonlinear Processes in Geophysics (NPG) and is expected to appear here in due course.
An Estimate of Inflation Factor and Analysis Sensitivity in Ensemble Kalman Filter
Guocan Wu 1College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
2Joint Center for Global Change Studies, Beijing, China
Abstract. The estimation accuracy of forecast error matrix is crucial to the assimilation result. Ensemble Kalman filter (EnKF) is a widely used ensemble based assimilation method, which initially estimate the forecast error matrix using a Monte Carlo method with the short-term ensemble forecast states. However, this estimate needs to be further improved using inflation technique. In this study, the forecast error inflation factor is estimated based on cross validation and the analysis sensitivity is also investigated. The improved EnKF assimilation scheme is validated by assimilating spatially correlated observations to the atmosphere-like Lorenz-96 model. The experiment results show that, the analysis error is reduced and the analysis sensitivity to observations is improved.

Citation: Wu, G.: An Estimate of Inflation Factor and Analysis Sensitivity in Ensemble Kalman Filter, Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2016-44, in review, 2016.
Guocan Wu
Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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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 
Guocan Wu
Guocan Wu

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Short summary
The accuracy of the assimilation results crucially rely 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 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 rely on the estimate accuracy of forecast...
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