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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 24, issue 3
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.
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

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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Cited articles

Allen, D. M.: The relationship between variable selection and data augmentation and a method for prediction, Technometrics, 16, 125–127, 1974.
Anderson, J. L.: An adaptive covariance inflation error correction algorithm for ensemble filters, Tellus A, 59, 210–224, 2007.
Anderson, J. L.: Spatially and temporally varying adaptive covariance inflation for ensemble filters, Tellus A, 61, 72–83, 2009.
Anderson, J. L. and Anderson, S. L.: A Monte Carlo implementation of the nonlinear fltering problem to produce ensemble assimilations and forecasts, Mon. Weather Rev., 127, 2741–2758, 1999.
Burgers, G., Leeuwen, P. J., and Evensen, G.: Analysis scheme in the ensemble kalman filter, Mon. Weather Rev., 126, 1719–1724, 1998.
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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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