TY - CHAP
T1 - Building an Uncertainty Modeling Framework for Real-Time VATD
AU - Webley, Peter
AU - Patra, Abani
AU - Bursik, Marcus
AU - Bruce Pitman, E.
AU - Dehn, Jonathan
AU - Singh, Tarung
AU - Singla, Puneet
AU - Jones, Matthew D.
AU - Madankan, Reza
AU - Ramona Stefanescu, E.
AU - Pouget, Solene
N1 - Publisher Copyright:
© 2017 by the American Geophysical Union.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2016/11/19
Y1 - 2016/11/19
N2 - When forecasting the future location of volcanic-ash clouds, uncertainties exist in the input parameters used in dispersion modeling and in the weather prediction data used for modeling the advection terms. Recent developments have shown that probabilistic modeling provides the tools to assess the variability in downwind ash concentrations. We show a probabilistic modeling approach where ensembles of forecasts are generated from a suite of simulations using a coupled one-dimensional plume model and a Lagrangian dispersion model. This approach produces charts of the probability of ash-cloud concentrations and mass loadings exceeding user-defined thresholds. We focus on the initial plume uncertainties and discuss how uncertainties in numerical weather prediction data could also be applied within our approach. Our results show how, by assigning the initial likelihoods of input parameters, the probabilistic approach can produce mean ash concentrations and mass loadings as well as probabilities of breaching a defined threshold. We show how, given the variability in the inputs, the probabilistic modeling can be used to assess the confidence in the ash-mass loadings. This is critical for real-time volcanic-hazard assessment and our approach illustrates how a new tool could be developed for those in decision support.
AB - When forecasting the future location of volcanic-ash clouds, uncertainties exist in the input parameters used in dispersion modeling and in the weather prediction data used for modeling the advection terms. Recent developments have shown that probabilistic modeling provides the tools to assess the variability in downwind ash concentrations. We show a probabilistic modeling approach where ensembles of forecasts are generated from a suite of simulations using a coupled one-dimensional plume model and a Lagrangian dispersion model. This approach produces charts of the probability of ash-cloud concentrations and mass loadings exceeding user-defined thresholds. We focus on the initial plume uncertainties and discuss how uncertainties in numerical weather prediction data could also be applied within our approach. Our results show how, by assigning the initial likelihoods of input parameters, the probabilistic approach can produce mean ash concentrations and mass loadings as well as probabilities of breaching a defined threshold. We show how, given the variability in the inputs, the probabilistic modeling can be used to assess the confidence in the ash-mass loadings. This is critical for real-time volcanic-hazard assessment and our approach illustrates how a new tool could be developed for those in decision support.
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U2 - 10.1002/9781119028116.ch6
DO - 10.1002/9781119028116.ch6
M3 - Chapter
AN - SCOPUS:85018337884
SN - 9781119027867
SP - 59
EP - 87
BT - Natural Hazard Uncertainty Assessment
PB - wiley
ER -