Incorporating Eruption Source Parameter and Meteorological Variability in the Generation of Probabilistic Volcanic Ash Hazard Forecasts

Abstract

Explosive volcanic eruptions can produce large masses of tephra that are transported over long distances, with potential impacts on the ground and to aircraft. Volcanic Ash Advisory Centers (VAACs) provide advice to aviation following an eruption, using atmospheric dispersion models, initialized with eruption source parameters (ESPs) and driven by forecast meteorological data. In this paper, we develop a framework for producing probabilistic forecasts incorporating uncertainty in these inputs. Meteorological uncertainty is typically provided as an ensemble of numerical weather prediction (NWP) data and ESPs include eruption plume height and mass eruption rate (MER); these are linked by atmospheric processes, and their relationship can be modeled by Bayesian regression to quantify their uncertainties. These uncertainties can be propagated through the model to compute probabilistic quantities of ash concentration. The linearity of the advection-diffusion-sedimentation (ADS) equation solved by dispersion models allows us to run a single simulation for each of the NWP ensemble members, and then rescale the results to any combination of MER (or height) and emission profile. This gives a computational speed-up compared to conventional approaches of computing every combination of ESPs and NWP. We demonstrate our method in the operational setting of the London VAAC, using the UK Met Office's NAME dispersion model, although it can be applied to any eruption scenario using any dispersion model that solves the ADS equation. The major source of uncertainty for this case study arises from the MER (due to limited variation in NWP data), although both sources of uncertainty are significant.