Computation of probabilistic hazard maps and source parameter estimation for volcanic ash transport and dispersion

Madankan, Reza; Pouget, S.; Singla, Puneet; Bursik, Marcus; Dehn, J.; Jones, Matthew D.; Patra, Abani; Pavolonis, Michael J.; Pitman, E. Bruce; Singh, Tarunraj; Webley, P. W.

doi:10.1016/j.jcp.2013.11.032

Cited by 61 publications

(47 citation statements)

References 34 publications

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“…12 we compare the computational costs of the different model calculations. The simulation on the adaptive mesh needed only about 50 min, while the calculation on a uniform mesh with a fine-grid level of 17 (i.e., the same maximum local resolution as in our adaptive mesh calculation) required already around 9 h. Those significantly reduced computational cost would allow for ensemble runs with varying meteorological boundary conditions as well as different ash injection assumptions to better constrain and forecast probable dispersion patterns and directions (see e.g., Madankan et al, 2014).…”

Section: Performance Due To Adaptive Meshingmentioning

confidence: 94%

An adaptive semi-Lagrangian advection model for transport of volcanic emissions in the atmosphere

Gerwing

Hort

Behrens

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. The dispersion of volcanic emissions in the Earth atmosphere is of interest for climate research, air traffic control and human wellbeing. Current volcanic emission dispersion models rely on fixed-grid structures that often are not able to resolve the fine filamented structure of volcanic emissions being transported in the atmosphere. Here we extend an existing adaptive semi-Lagrangian advection model for volcanic emissions including the sedimentation of volcanic ash. The advection of volcanic emissions is driven by a precalculated wind field. For evaluation of the model, the explosive eruption of Mount Pinatubo in June 1991 is chosen, which was one of the largest eruptions in the 20th century. We compare our simulations of the climactic eruption on 15 June 1991 to satellite data of the Pinatubo ash cloud and evaluate different sets of input parameters. We could reproduce the general advection of the Pinatubo ash cloud and, owing to the adaptive mesh, simulations could be performed at a high local resolution while minimizing computational cost. Differences to the observed ash cloud are attributed to uncertainties in the input parameters and the course of Typhoon Yunya, which is probably not completely resolved in the wind data used to drive the model. The best results were achieved for simulations with multiple ash particle sizes.

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Section: Performance Due To Adaptive Meshingmentioning

confidence: 94%

An adaptive semi-Lagrangian advection model for transport of volcanic emissions in the atmosphere

Gerwing

Hort

Behrens

et al. 2018

Nat. Hazards Earth Syst. Sci.

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show abstract

“…Kennedy and O'Hagan, 2001;Craig et al, 2001), constructing posterior probability distributions by refining specified prior distributions using observations (see e.g. Denlinger et al, 2012;Anderson and Segall, 2013;Madankan et al, 2014). For a model with a large number of inputs, the calculation of the posterior probability distribution can be computationally demanding.…”

Section: Analyses Of Sensitivity and Uncertaintymentioning

confidence: 99%

A global sensitivity analysis of the PlumeRise model of volcanic plumes

Woodhouse

Hogg

Phillips

2016

Journal of Volcanology and Geothermal Research

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Integral models of volcanic plumes allow predictions of plume dynamics to be made and the rapid estimation of volcanic source conditions from observations of the plume height by model inversion. Here we introduce PlumeRise, an integral model of volcanic plumes that incorporates a description of the state of the atmosphere, includes the effects of wind and the phase change of water, and has been developed as a freely available web-based tool. The model can be used to estimate the height of a volcanic plume when the source conditions are specified, or to infer the strength of the source from an observed plume height through a model inversion. The predictions of the volcanic plume dynamics produced by the model are analysed in four case studies in which the atmospheric conditions and the strength of the source are varied. A global sensitivity analysis of the model to a selection of model inputs is performed and the results are analysed using parallel coordinate plots for visualisation and variance-based sensitivity indices to quantify the sensitivity of model outputs. We find that if the atmospheric conditions do not vary widely then there is a small set of model inputs that strongly influence the model predictions. When estimating the height of the plume, the source mass flux has a controlling influence on the model prediction, while variations in the plume height strongly effect the inferred value of the source mass flux when performing inversion studies. The values taken for the entrainment coefficients have a particularly important effect on the quantitative predictions. The dependencies of the model outputs to variations in the inputs are discussed and compared to simple algebraic expressions that relate source conditions to the height of the plume.

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“…Stefanescu et al (2014) and Madankan et al (2014) include both ensemble meteorology and an ensemble of ESPs in their study to quantify overall uncertainty in volcanic ash forecasts. They demonstrate that the range of predicted concentrations can be large at forecast lead times of 48 h. Similarly, Vogel et al (2014) performed time-lagged ensemble simulations of volcanic ash dispersion from the Eyjafjallajökull plume and found that for some times the spread in ensemble-met forecasts is small but at others it is large.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Atmospheric Conditions Leading to Large Error Growth in Volcanic Ash Cloud Forecasts

Dacre

Harvey

2018

Journal of Applied Meteorology and Climatology

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Volcanic ash poses an ongoing risk to safety in the airspace worldwide. The accuracy with which volcanic ash dispersion can be forecast depends on the conditions of the atmosphere into which it is emitted. In this study, meteorological ensemble forecasts are used to drive a volcanic ash transport and dispersion model for the 2010 Eyjafjallajökull eruption in Iceland. From analysis of these simulations, the authors determine why the skill of deterministic-meteorological forecasts decreases with increasing ash residence time and identify the atmospheric conditions in which this drop in skill occurs most rapidly. Large forecast errors are more likely when ash particles encounter regions of large horizontal flow separation in the atmosphere. Nearby ash particle trajectories can rapidly diverge, leading to a reduction in the forecast accuracy of deterministic forecasts that do not represent variability in wind fields at the synoptic scale. The flow-separation diagnostic identifies where and why large ensemble spread may occur. This diagnostic can be used to alert forecasters to situations in which the ensemble mean is not representative of the individual ensemble-member volcanic ash distributions. Knowledge of potential ensemble outliers can be used to assess confidence in the forecast and to avoid potentially dangerous situations in which forecasts fail to predict harmful levels of volcanic ash.

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Computation of probabilistic hazard maps and source parameter estimation for volcanic ash transport and dispersion

Cited by 61 publications

References 34 publications

An adaptive semi-Lagrangian advection model for transport of volcanic emissions in the atmosphere

An adaptive semi-Lagrangian advection model for transport of volcanic emissions in the atmosphere

A global sensitivity analysis of the PlumeRise model of volcanic plumes

Characterizing the Atmospheric Conditions Leading to Large Error Growth in Volcanic Ash Cloud Forecasts

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