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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 10, issue 4
Geosci. Model Dev., 10, 1751–1766, 2017
https://doi.org/10.5194/gmd-10-1751-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 10, 1751–1766, 2017
https://doi.org/10.5194/gmd-10-1751-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 24 Apr 2017

Development and technical paper | 24 Apr 2017

Accelerating volcanic ash data assimilation using a mask-state algorithm based on an ensemble Kalman filter: a case study with the LOTOS-EUROS model (version 1.10)

Guangliang Fu et al.

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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 Guangliang Fu on behalf of the Authors (07 Feb 2017)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (09 Feb 2017) by Rolf Sander
RR by Anonymous Referee #4 (01 Apr 2017)
ED: Publish as is (03 Apr 2017) by Rolf Sander
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Short summary
We propose a mask-state algorithm (MS) which records the sparsity information of the full ensemble state matrix and transforms the full matrix into a relatively small one. It will reduce the computational cost in the analysis step for plume assimilation applications. Ensemble-based DA with the mask-state algorithm is generic and flexible, because it implements exactly the standard DA without any approximation and it realizes the satisfying performance without any change of the full model.
We propose a mask-state algorithm (MS) which records the sparsity information of the full...
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