Articles | Volume 10, issue 4
https://doi.org/10.5194/gmd-10-1751-2017
https://doi.org/10.5194/gmd-10-1751-2017
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

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Cited articles

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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.