Articles | Volume 24, issue 10
https://doi.org/10.5194/acp-24-6251-2024
https://doi.org/10.5194/acp-24-6251-2024
Technical note
 | 
28 May 2024
Technical note |  | 28 May 2024

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

James M. Salter, Helen N. Webster, and Cameron Saint

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Cited articles

Andrianakis, I. and Challenor, P. G.: The effect of the nugget on Gaussian process emulators of computer models, Comput. Stat. Data An., 56, 4215–4228, 2012. a
Andrianakis, I., Vernon, I. R., McCreesh, N., McKinley, T. J., Oakley, J. E., Nsubuga, R. N., Goldstein, M., and White, R. G.: Bayesian History Matching of Complex Infectious Disease Models Using Emulation: A Tutorial and a Case Study on HIV in Uganda, PLoS Comput. Biol., 11, e1003968, https://doi.org/10.1371/journal.pcbi.1003968, 2015. a
Beckett, F. M., Witham, C. S., Leadbetter, S. J., Crocker, R., Webster, H. N., Hort, M. C., Jones, A. R., Devenish, B. J., and Thomson, D. J.: Atmospheric dispersion modelling at the London VAAC: A review of developments since the 2010 Eyjafjallajökull volcano ash cloud, Atmosphere, 11, 352, https://doi.org/10.3390/atmos11040352, 2020. a
Binois, M., Gramacy, R. B., and Ludkovski, M.: Practical heteroscedastic gaussian process modeling for large simulation experiments, J. Comput. Graph. Stat., 27, 808–821, 2018. a
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Models are used to make forecasts of volcanic ash dispersion during eruptions. These models have unknown inputs relating to the eruption itself, physical processes, and meteorological conditions. We use statistical models to predict the output of the expensive physical model and show we can account for the effects of the different inputs. We compare the model to real-world observations and show that accounting for all sources of uncertainty may lead to different conclusions about the inputs.
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