Articles | Volume 10, issue 4
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, Hai Xiang Lin, Arnold Heemink, Sha Lu, Arjo Segers, Nils van Velzen, Tongchao Lu, and Shiming Xu


Interactive discussion

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
AR by Guangliang Fu on behalf of the Authors (04 Apr 2017)
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.