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, Shiming Xu

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
49 Downloads (Pure)

Abstract

In this study, we investigate a strategy to accelerate the data assimilation (DA) algorithm. Based on evaluations of the computational time, the analysis step of the assimilation turns out to be the most expensive part. After a study of the characteristics of the ensemble ash state, we propose a mask-state algorithm which records the sparsity information of the full ensemble state matrix and transforms the full matrix into a relatively small one. This will reduce the computational cost in the analysis step. Experimental results show the mask-state algorithm significantly speeds up the analysis step. Subsequently, the total amount of computing time for volcanic ash DA is reduced to an acceptable level. The mask-state algorithm is generic and thus can be embedded in any ensemble-based DA framework. Moreover, ensemble-based DA with the mask-state algorithm is promising and flexible, because it implements exactly the standard DA without any approximation and it realizes the satisfying performance without any change in the full model.
Original languageEnglish
Article number10
Pages (from-to)1751-1766
Number of pages16
JournalGeoscientific Model Development
Volume10
DOIs
Publication statusPublished - 24 Apr 2017

Keywords

  • high performance computing
  • Data assimilation
  • Sparse matrices

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