Using ensemble meteorological datasets to treat meteorological uncertainties in a Bayesian volcanic ash inverse modelling system: a case study, Grímsvötn 2011

Abstract

Atmospheric dispersion models are employed in forecasting the atmospheric transport of ash clouds following a volcanic eruption. Errors in input meteorological data can, however, lead to discrepancies between the modeled and observed ash cloud locations. Furthermore, meteorological errors can affect the performance of inversion techniques used to estimate ash emissions. This study explores using ensemble meteorological data sets to overcome issues with meteorological errors in a Bayesian inversion system, Inversion Technique for Emissions Modelling (InTEM) for volcanic ash. Two measures (the evidence value and a cost function) are obtained from the Bayesian framework, and each measure is used to select a “best” meteorological data set from within the ensemble. These two best meteorological data sets are constructed in an iterative manner which is ideally suited for operational use when ash cloud forecasts are updated at regular intervals. The method is applied to the 2011 eruption of the Icelandic volcano Grímsvötn and improvements to ash cloud forecasts using the best meteorological data sets assessed. In this case study, errors in the deterministic meteorological data are significant, resulting in a reduction of ash emissions estimated by InTEM for volcanic ash. We also illustrate how the best meteorological data sets lead to more accurate modeling of the ash cloud.