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)

Abstract

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.

Atmospheric Dispersion Modelling at the London VAAC: A Review of Developments since the 2010 Eyjafjallajökull Volcano Ash Cloud

“…For these techniques to be used in an operational setting, they need to be computationally quick [141], and observations must be robust and have good spatial and time coverage. Validation of dispersion model output, and the use of inversion, DA and insertion techniques often rely on good satellite observations.…”

Ensemble-Based Data Assimilation of Volcanic Ash Clouds from Satellite Observations: Application to the 24 December 2018 Mt. Etna Explosive Eruption

“…DA is widely used and applied in atmospheric and oceanic contexts [44][45][46], while few studies have been dealing with volcanic applications so far. Improvements in ash forecasting by assimilating data from aircraft-and satellite-based measurements have been shown in Fu et al [47][48][49][50][51][52] and Osores et al [53].…”

Volcanic ash forecast using ensemble-based data assimilation: an ensemble transform Kalman filter coupled with the FALL3D-7.2 model (ETKF–FALL3D version 1.0)

“…For example, Wilkins et al (2015) implemented a data insertion methodology to improve the initial conditions of ash concentrations based on satellite estimations of ash mass loadings in a Lagrangian dispersion model. Fu et al (2015Fu et al ( , 2017a applied an ensemble Kalman filter technique to the estimation of ash concentrations in an Eulerian dispersion model based on flight concentration measurements and satellite estimations using idealized experiments and real observations. Their results showed that both observational sets (flight measurements and satellite mass loads) reduced forecast errors, which in their particular case were attributed to a wrong model representation of ash sedimentation processes.…”

“…One important issue when using satellite estimates of ash mass loadings is that observations only provide a 2-D distribution of ash mass, while models usually require the vertical profile of ash concentrations. Fu et al (2017b) presented a modified approach for comparison between models and observations in the context of the ensemble Kalman filter that tries to deal with this limitation.…”