Technical note: Exploring parameter and meteorological uncertainty via emulation in volcanic ash atmospheric dispersion modelling

• Published in: Atmospheric chemistry and physics Vol. 24; no. 10; pp. 6251 - 6274
• Main Authors: Salter, James M, Webster, Helen N, Saint, Cameron
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 28.05.2024; Copernicus Publications
• Subjects: Analysis; Atmospheric diffusion; Atmospheric dispersion; Cloud forecasting; Clouds; Dispersion; Emulators; Environment models; Eruptions; Meteorological conditions; Meteorology; Modelling; Parameter identification; Parameter sensitivity; Parameter uncertainty; Parameters; Pollution dispersion; Satellite observation; Simulation; Uncertainty; Volcanic ash; Volcanic eruptions; Volcanoes; Weather; Weather forecasting
• ISSN: 1680-7324; 1680-7316; 1680-7324
• DOI: 10.5194/acp-24-6251-2024

​​​​​​​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), parameterisations 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 (Numerical Atmospheric-dispersion Modelling Environment) 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 and 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.