Technical note: Exploring parameter and meteorological uncertainty via emulation in volcanic ash atmospheric dispersion modelling
Salter, JM; Webster, HN; Saint, C
Date: 28 May 2024
Article
Journal
Atmospheric Chemistry and Physics
Publisher
European Geosciences Union / Copernicus Publications
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Abstract
Consideration of uncertainty in volcanic ash cloud forecasts is increasingly of interest, with an industry goal to
provide probabilistic forecasts alongside deterministic forecasts. Simulations of volcanic clouds via dispersion modelling are
subject to a number of uncertainties, relating to the eruption itself (mass of ash emitted, ...
Consideration of uncertainty in volcanic ash cloud forecasts is increasingly of interest, with an industry goal to
provide probabilistic forecasts alongside deterministic forecasts. Simulations of volcanic clouds via dispersion modelling are
subject to a number of uncertainties, relating to the eruption itself (mass of ash emitted, and when), parametrisations of physical processes, and the meteorological conditions. To fully explore these uncertainties through atmospheric dispersion model simulations alone may be expensive, and instead an emulator can be used to increase understanding of uncertainties in the
model inputs and outputs, going beyond combinations of source, physical and meteorological inputs that were simulated by
the dispersion model. We emulate the NAME dispersion model for simulations of the Raikoke 2019 eruption, and use these
emulators to compare simulated ash clouds to observations derived from satellites, constraining NAME source and internal
parameters via history matching. We demonstrate that the effect of varying both meteorological scenarios and model parameters can be captured in this way, with accurate emulation using only a small number of runs per meteorological scenario. We
show that accounting for meteorological uncertainty simultaneously with other uncertainties may lead to the identification of
different sensitive model parameters, and may lead to less constrained source and internal NAME parameters, however through
idealised experiments we argue that this is a reasonable result and is properly accounting for all sources of uncertainty in the
model inputs.
Mathematics and Statistics
Faculty of Environment, Science and Economy
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Except where otherwise noted, this item's licence is described as © Author(s) 2024. Open access. This work is distributed under the Creative Commons Attribution 4.0 License.