Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
doi:10.5194/gmd-2016-208
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Development and technical paper
24 Aug 2016
Review status
A revision of this discussion paper is under review for the journal Geoscientific Model Development (GMD).
A mask-state algorithm to accelerate volcanic ash data assimilation
Guangliang Fu1, Hai-Xiang Lin1, Arnold Heemink1, Arjo Segers2, Nils van Velzen1,3, Tongchao Lu4, Shiming Xu5, and Sha Lu1 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 250100, China
5Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science (CESS), Tsinghua University, Beijing, China
Abstract. In this study, we investigate strategies for accelerating data assimilation on volcanic ash forecasts. Based on evaluations of computational time, the analysis step of the assimilation is evaluated as the most expensive part. After a careful study on 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 expensive analysis step. Subsequently, the total amount of computing time for volcanic ash data assimilation is reduced to an acceptable level, which is important for providing timely and accurate aviation advices. The mask-state algorithm is generic and thus can be embedded in any ensemble-based data assimilation framework. Moreover, ensemble-based data assimilation with the mask-state algorithm is promising and flexible, because it implements exactly the standard data assimilation without any approximation and it realizes the satisfying performance without any change of the full model.

Citation: Fu, G., Lin, H.-X., Heemink, A., Segers, A., van Velzen, N., Lu, T., Xu, S., and Lu, S.: A mask-state algorithm to accelerate volcanic ash data assimilation, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-208, in review, 2016.
Guangliang Fu et al.
Guangliang Fu et al.
Guangliang Fu et al.

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