A mask-state algorithm to accelerate volcanic ash data assimilation
Other literature type
(issn: 1991-9603, eissn: 1991-9603)
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.