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)
- 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.