IDR/Collaborative Research: Characterizing Uncertainty in the Motion of Volcanic Plumes Advected by Wind Fields
E. Bruce Pitman Principal Investigator
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This grant provides support to develop a methodology for the quantitative assessment of confidence in predictions of the motion of ash clouds caused by volcanic eruptions. By combining statistical modeling, stochastic analysis, and the tools of computational science together with a widely-used volcanic particle dispersion code, this grant will provide tools to assess real time predictions of ash cloud motion that accounts for varying wind conditions and a range of model variables. More specifically, the PUFF computer code will be used as the ash cloud simulation tool. PUFF, which propagates an airborne ash plume, will be integrated with a volcanic eruption model called BENT. BENT outputs ash distribution as a function of time and height. This integration substitutes for the unknown PUFF input parameters several BENT inputs that are better (but not entirely) constrained by physics. This integrated model will be used to develop a framework for analyzing the effects of uncertain parameters and of differing windfield models, on the output distribution for the cloud location. Polynomical chaos quadrature and stochastic integration techniques will be used to provide a quantitative measure of the reliability (i.e. error) of those predictions. <br/><br/>Understanding the effects of uncertainty in dispersion model predictions are important to assess and mitigate potential hazards associated with volcanic ash clouds. There are economic and sociologic impacts both near the volcano and far downwind from the eruption. For example, the airline industry decides flight schedules, routes, and fuel consumption estimates, based in part on dispersion model forecasts. The results of this research will provide decision makers and civil protection authorities with a framework for evaluating hazard risk. Importantly, this methodological development is independent of the specific PUFF code, so the framework for prediction and reliability analysis can be directly applied to other ash cloud codes, and more generally to other hazard models -- such as radiological and chemical plume dispersion, and gas release from truck or rail accidents.