How dependent are quantitative volcanic ash concentration and along-flight dosage forecasts to model structural choices?

Producing quantitative volcanic ash forecasts is challenging due to multiple sources of uncertainty. Careful consideration of this uncertainty is required to produce timely and robust hazard warnings. Structural uncertainty occurs when a model fails to produce accurate forecasts, despite good knowledge of the eruption source parameters, meteorological conditions and suitable parameterizations of transport and deposition processes. This uncertainty is frequently overlooked in forecasting practices. Using a Lagrangian particle dispersion model, simulations with varied output spatial resolution, temporal averaging period and particle release rate are performed to quantify the impact of these structural choices. This experiment reveals that, for the 2019 Raikoke eruption, structural choices give measurements of peak ash concentration spanning an order of magnitude, significantly impacting decision-relevant thresholds used in aviation flight planning. Conversely, along-flight dosage estimates exhibit less sensitivity to structural choices, suggesting it is a more robust metric to use in flight planning. Uncertainty can be reduced by eliminating structural choices that do not result in a favourable level of agreement with a high-resolution reference simulation. Reliable forecasts require output spatial resolution $\le$ 80 km, temporal averaging periods $\le$ 3 h and particle release rates $\ge$ 5000 particles/h. This suggests that simulations with relatively small numbers of particles could be used to produce a large ensemble of simulations without significant loss of accuracy. Comparison with previous Raikoke simulations indicates that the uncertainty associated with these constrained structural choices is smaller than those associated with satellite constrained eruption source parameter and internal model parameter uncertainties. Thus, given suitable structural choices, other epistemic sources of uncertainty are likely to dominate. This insight is useful for the design of ensemble methodologies which are required to enable a shift from deterministic to probabilistic forecasting. The results are applicable to other long-range dispersion problems and to Eulerian dispersion models.