Dispersion Model Development for Open Burn/Open Detonation Sources

1996 ◽  
Author(s):  
J. C. Weil ◽  
B. Templeman ◽  
R. Banta ◽  
R. Weber ◽  
W. Mitchell
Keyword(s):  
Dispersion Model ◽  
Model Development ◽  
Open Detonation

scholarly journals Dense gas dispersion model development and testing for the Jack Rabbit II phase 1 chlorine release experiments

2018 ◽  
Vol 192 ◽  
pp. 218-240 ◽  
Author(s):  
Simon Gant ◽  
Jeffrey Weil ◽  
Luca Delle Monache ◽  
Bryan McKenna ◽  
Maria M. Garcia ◽  
...  
Keyword(s):  
Dispersion Model ◽  
Phase 1 ◽  
Model Development ◽  
Dense Gas ◽  
Gas Dispersion

Open Burn/Open Detonation Dispersion Model (OBODM) User's Guide. Volume I. User's Instructions

1998 ◽  
Author(s):  
Jay R. Bjorklund ◽  
James F. Bowers ◽  
Gregory C. Dodd ◽  
John M. White
Keyword(s):  
Dispersion Model ◽  
Open Detonation

Integration of an atmospheric dispersion model with a dynamic multimedia fate model: Development and illustration

2012 ◽  
Vol 164 ◽  
pp. 182-187 ◽  
Author(s):  
Melissa Morselli ◽  
Davide Ghirardello ◽  
Matteo Semplice ◽  
Giuseppe Raspa ◽  
Antonio Di Guardo
Keyword(s):  
Dispersion Model ◽  
Model Development ◽  
Atmospheric Dispersion ◽  
Atmospheric Dispersion Model ◽  
Fate Model

scholarly journals Multi-level emulation of a volcanic ash transport and dispersion model to quantify sensitivity to uncertain parameters

2016 ◽  
Author(s):  
Natalie J. Harvey ◽  
Nathan Huntley ◽  
Helen Dacre ◽  
Michael Goldstein ◽  
David Thomson ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Dispersion Model ◽  
Model Development ◽  
Atmospheric Dispersion ◽  
Theoretical Research ◽  
Model Parameters ◽  
Formal Framework ◽  
Geographical Regions ◽  
Multi Level ◽  
Ash Transport

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.

scholarly journals Multi-level emulation of a volcanic ash transport and dispersion model to quantify sensitivity to uncertain parameters

2018 ◽  
Vol 18 (1) ◽  
pp. 41-63 ◽  
Author(s):  
Natalie J. Harvey ◽  
Nathan Huntley ◽  
Helen F. Dacre ◽  
Michael Goldstein ◽  
David Thomson ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Dispersion Model ◽  
Model Development ◽  
Atmospheric Dispersion ◽  
Theoretical Research ◽  
Model Parameters ◽  
Formal Framework ◽  
Geographical Regions ◽  
Multi Level ◽  
Ash Transport

Abstract. Following the disruption to European airspace caused by the eruption of Eyjafjallajökull 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 of these predictions and performing many simulations using these complex models is computationally expensive. In this paper a Bayesian 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 using two configurations of NAME with different numbers of model particles. Information from many evaluations of the computationally faster configuration is combined with results from relatively few evaluations of the slower, more accurate, configuration. 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 faster, less accurate, configuration of NAME can, on its own, provide useful information for the problem of predicting average column load over large areas.

Antecedents of learners' mental model development

2008 ◽  
Author(s):  
Nicole Kohari ◽  
Robert Lord ◽  
Joelle Elicker ◽  
Steven Ash ◽  
Bryce Hruska
Keyword(s):  
Mental Model ◽  
Model Development
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