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Atmospheric dispersion model output is frequently used to provide advice to decision makers, for example, about the likely location of volcanic ash erupted from a volcano or the location of deposits of radioactive material released during a nuclear accident. Increasingly scientists and decision makers are requesting information on the uncertainty of these dispersion model predictions. One source of uncertainty is in the meteorology used to drive the dispersion model and in this study ensemble meteorology from the Met Office ensemble prediction system is used to provide meteorological uncertainty to dispersion model predictions. Two hypothetical scenarios, one volcanological and one radiological, are repeated every 12 hours over a period of 4 months. The scenarios are simulated using ensemble meteorology and deterministic forecast meteorology and compared to output from simulations using analysis meteorology using the Brier skill score. Adopting the practice commonly used in evaluating numerical weather prediction models (NWP) where observations are sparse or non-existent we consider output from simulations using analysis NWP data to be truth. The results show that on average the ensemble simulations per- form better than the deterministic simulations although not all individual ensemble simulations outperform their deterministic counterpart. The results also show that greater skill scores are achieved by the ensemble simulation for later time steps rather than earlier time steps and at those later time steps for deposition than for air concentration. For the volcanic ash scenarios it is shown that the performance of the ensemble at one flight level can be different to that at a different flight level, e.g. a negative skill score might be obtained for FL350-550 and a positive skill score for FL200-350. This study does not take into account any source term uncertainty but it does take the first steps towards demonstrating the value of ensemble dispersion model predictions. [ABSTRACT FROM AUTHOR]

We present a Bayesian inversion method for estimating volcanic ash emissions using satellite retrievals of ash column load and an atmospheric dispersion model. An a priori description of the emissions is used based on observations of the rise height of the volcanic plume and a stochastic model of the possible emissions. Satellite data are processed to give column loads where ash is detected and to give information on where we have high confidence that there is negligible ash. An atmospheric dispersion model is used to relate emissions and column loads. Gaussian distributions are assumed for the a priori emissions and for the errors in the satellite retrievals. The optimal emissions estimate is obtained by finding the peak of the a posteriori probability density under the constraint that the emissions are non-negative. We apply this inversion method within a framework designed for use during an eruption with the emission estimates (for any given emission time) being revised over time as more information becomes available. We demonstrate the approach for the 2010 Eyjafjallajökull and 2011 Grímsvötn eruptions. We apply the approach in two ways, using only the ash retrievals and using both the ash and clear sky retrievals. For Eyjafjallajökull we have compared with an independent dataset not used in the inversion and have found that the inversion-derived emissions lead to improved predictions. [ABSTRACT FROM AUTHOR]