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)

作者：Fu, Guangliang^*; Lin, Hai Xiang; Heemink, Arnold; Lu, Sha; Segers, Arjo; van Velzen, Nils; Lu, Tongchao; Xu, Shiming

来源：Geoscientific Model Development, 2017, 10(4): 1751-1766.

DOI：10.5194/gmd-10-1751-2017

• IMPLEMENTATION
• AIRCRAFT
• CLOUDS

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.

• 出版日期2017-4-24
• 单位山东大学; 清华大学