The statistical design of assessments of impacts from explosive volcanic eruptions

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Explosive volcanic eruptions generate far-reaching impacts, in particular the dispersion of volcanic ash in the atmosphere and its deposition on the ground which can have significant effects on agriculture, infrastructure, and human health. The dispersion of ash in the atmosphere is dependent on the magnitude of the eruption, and is driven by wind fields which are highly variable and difficult to forecast. Prediction of atmospheric ash dispersion is further complicated by the rarity of such eruption events, as well as the lack of direct observations of ash concentration at sufficiently high spatial and temporal resolution during such eruptions. This project will focus on the statistical design of operational assessments of volcanic ash dispersion, including:1. Statistically-coherent design of probabilistic volcanic ash hazard assessments. Current practice in producing a probabilistic ash hazard assessment uses an ensemble of simulations with sizes set by time or computing constraints, with the result that the variance of individual impact thresholds vary within and between assessments. This component of the research will use fundamental statistical approaches to standardise the design of probabilistic ash hazard assessments, and explore (i) variance reduction through stratification via the use of weather patterns as forcing data for ensembles, and (ii) appropriate statistical methods for larger (and consequently rarer) eruptions;2. Bayesian statistical methods to characterise uncertainty in estimation of the intensity of explosive eruptions and ensemble forecast design. Current practice in operational response to volcanic eruptions and in eruption forecast design uses observations of the eruption plume height to estimate the mass eruption rate through a semi-empirical relationship. This component of the research will use applied Bayesian regression methods to characterise the uncertainty in the height-eruption rate semi-empirical relationship, and thus in the design of ensemble forecasts and operational ash dispersion modelling.