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)
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2017
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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.
| Reference Key |
fu2017geoscientificaccelerating
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|---|---|
| Authors | ;G. Fu;H. X. Lin;A. Heemink;S. Lu;A. Segers;N. van Velzen;T. Lu;S. Xu |
| Journal | international journal of quantum chemistry |
| Year | 2017 |
| DOI |
10.5194/gmd-10-1751-2017
|
| URL | |
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