Articles | Volume 22, issue 6
https://doi.org/10.5194/npg-22-723-2015
https://doi.org/10.5194/npg-22-723-2015
Research article
 | 
03 Dec 2015
Research article |  | 03 Dec 2015

Multivariate localization methods for ensemble Kalman filtering

S. Roh, M. Jun, I. Szunyogh, and M. G. Genton

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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 Anna Wenzel on behalf of the Authors (18 Aug 2015)  Author's response
ED: Reconsider after major revisions (further review by Editor and Referees) (24 Aug 2015) by Zoltan Toth
ED: Referee Nomination & Report Request started (25 Aug 2015) by Zoltan Toth
RR by Anonymous Referee #2 (10 Sep 2015)
RR by Anonymous Referee #1 (12 Sep 2015)
ED: Reconsider after major revisions (further review by Editor and Referees) (25 Sep 2015) by Zoltan Toth
AR by Mikyoung Jun on behalf of the Authors (29 Oct 2015)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (06 Nov 2015) by Zoltan Toth
RR by Anonymous Referee #2 (17 Nov 2015)
ED: Publish subject to technical corrections (18 Nov 2015) by Zoltan Toth
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Short summary
This paper shows how statistical methods can be applied to the ensemble Kalman filtering technique, a widely used method in atmospheric science and other related fields. Traditional methods for covariance localization, commonly done in the field with multiple variables, are compared to the newly proposed method with the use of a parametric localization function (proposed in statistics as a class of multivariate covariance functions) in the ensemble Kalman filter system with multiple variables.