Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Geosci. Model Dev., 10, 1751-1766, 2017
https://doi.org/10.5194/gmd-10-1751-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
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 Fu1, Hai Xiang Lin1, Arnold Heemink1, Sha Lu1, Arjo Segers2, Nils van Velzen1,3, Tongchao Lu4, and Shiming Xu5 1Delft University of Technology, Delft Institute of Applied Mathematics, Mekelweg 4, 2628 CD Delft, the Netherlands
2TNO, Department of Climate, Air and Sustainability, P.O. Box 80015, 3508 TA Utrecht, the Netherlands
3VORtech, P.O. Box 260, 2600 AG Delft, the Netherlands.
4School of Mathematics, Shandong University, Jinan, Shandong, China
5Department of Earth System Science, Tsinghua University, Beijing, China
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.

Citation: Fu, G., Lin, H. X., Heemink, A., Lu, S., Segers, A., van Velzen, N., Lu, T., and Xu, S.: 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), Geosci. Model Dev., 10, 1751-1766, https://doi.org/10.5194/gmd-10-1751-2017, 2017.
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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.
We propose a mask-state algorithm (MS) which records the sparsity information of the full...
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