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)
| Autor: | G. Fu, H. X. Lin, A. Heemink, S. Lu, A. Segers, N. van Velzen, T. Lu, S. Xu |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: | |
| Zdroj: | Geoscientific Model Development, Vol 10, Iss 4, Pp 1751-1766 (2017) |
| Druh dokumentu: | article |
| ISSN: | 1991-959X 1991-9603 |
| DOI: | 10.5194/gmd-10-1751-2017 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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