Articles | Volume 22, issue 2
https://doi.org/10.5194/npg-22-205-2015
https://doi.org/10.5194/npg-22-205-2015
Research article
 | 
07 Apr 2015
Research article |  | 07 Apr 2015

Improved variational methods in statistical data assimilation

J. Ye, N. Kadakia, P. J. Rozdeba, H. D. I. Abarbanel, and J. C. Quinn

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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 Nirag Kadakia on behalf of the Authors (18 Dec 2014)  Author's response
ED: Reconsider after major revisions (further review by Editor and Referees) (13 Jan 2015) by Zoltan Toth
AR by Nirag Kadakia on behalf of the Authors (09 Feb 2015)  Author's response    Manuscript
ED: Reconsider after major revisions (further review by Editor and Referees) (11 Feb 2015) by Zoltan Toth
ED: Referee Nomination & Report Request started (12 Feb 2015) by Zoltan Toth
RR by Anonymous Referee #2 (27 Feb 2015)
RR by Anonymous Referee #1 (02 Mar 2015)
ED: Publish subject to minor revisions (further review by Editor) (02 Mar 2015) by Zoltan Toth
AR by Nirag Kadakia on behalf of the Authors (03 Mar 2015)  Author's response    Manuscript
ED: Publish as is (23 Mar 2015) by Zoltan Toth
AR by Nirag Kadakia on behalf of the Authors (23 Mar 2015)  Author's response    Manuscript
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Short summary
We propose an improved method of data assimilation, in which measured data are incorporated into a physically based model. In data assimilation, one typically seeks to minimize some cost function; here, we discuss a variational approximation in which model and measurement errors are Gaussian, combined with an annealing method, to consistently identify a global minimum of this cost function. We illustrate this procedure with archetypal chaotic systems, and discuss higher-order corrections.