Journal cover Journal topic
Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/nhess-2016-288
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
05 Sep 2016
Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Natural Hazards and Earth System Sciences (NHESS) and is expected to appear here in due course.
Multi-level emulation of a volcanic ash transport and dispersion model to quantify sensitivity to uncertain parameters
Natalie J. Harvey1, Nathan Huntley2, Helen Dacre1, Michael Goldstein2, David Thomson3, and Helen Webster3 1Department of Meteorology, University of Reading, Reading, RG6 6BB, UK
2Department of Mathematical Sciences, Durham University, Durham, DH1 3LE, UK
3Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
Abstract. Following the disruption to European airspace caused by the eruption of Eyjafjallajokull in 2010 there has been a move towards producing quantitative predictions of volcanic ash concentration using volcanic ash transport and dispersion simulators. However, there is no formal framework for determining the uncertainties on these predictions and performing many simulations using these complex models is computationally expensive. In this paper a Bayes linear emulation approach is applied to the Numerical Atmospheric-dispersion Modelling Environment (NAME) to better understand the influence of source and internal model parameters on the simulator output. Emulation is a statistical method for predicting the output of a computer simulator at new parameter choices without actually running the simulator. A multi-level emulation approach is applied to combine information from many evaluations of a computationally fast version of NAME with relatively few evaluations of a slower, more accurate, version. This approach is effective when it is not possible to run the accurate simulator many times and when there is also little prior knowledge about the influence of parameters. The approach is applied to the mean ash column loading in 75 geographical regions on 14 May 2010. Through this analysis it has been found that the parameters that contribute the most to the output uncertainty are initial plume rise height, mass eruption rate, free tropospheric turbulence levels and precipitation threshold for wet deposition. This information can be used to inform future model development and observational campaigns and routine monitoring. The analysis presented here suggests the need for further observational and theoretical research into parameterisation of atmospheric turbulence. Furthermore it can also be used to inform the most important parameter perturbations for a small operational ensemble of simulations. The use of an emulator also identifies the input and internal parameters that do not contribute significantly to simulator uncertainty. Finally, the analysis highlights that the fast, less accurate, version of NAME can provide useful information without needing the accurate version at all. This approach can easily be extended to other case studies, simulators or hazards.

Citation: Harvey, N. J., Huntley, N., Dacre, H., Goldstein, M., Thomson, D., and Webster, H.: Multi-level emulation of a volcanic ash transport and dispersion model to quantify sensitivity to uncertain parameters, Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2016-288, in review, 2016.
Natalie J. Harvey et al.
Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version      Supplement - Supplement
 
RC1: 'Review of "Multilevel emulation of a volcanic ash transport and dispersion model to quantify sensitivity to uncertain parameters', Anonymous Referee #1, 15 Oct 2016 Printer-friendly Version 
 
RC2: 'Reviewer comment', Francesca Pianosi, 18 Oct 2016 Printer-friendly Version 
 
AC1: 'Response from Authors', Natalie Harvey, 17 Jan 2017 Printer-friendly Version Supplement 
Natalie J. Harvey et al.
Natalie J. Harvey et al.

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