Assessment of the Sensitivity to the Input Conditions with a Lagrangian Particle Dispersion Model in the UDINEE Project

When modelling the dispersion of pollutants in the atmosphere, uncertainty in the simulation results that depend on the available data used as input conditions is a critical issue, particularly in the context of emergency response to the accidental release of harmful substances. In the framework of...

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Bibliographic Details
Published in:Boundary-layer meteorology 2019-06, Vol.171 (3), p.491-512
Main Authors: Tinarelli, Gianni L., Trini Castelli, Silvia
Format: Article
Language:English
Subjects:
Accidental release
Air pollution
Atmospheric models
Atmospheric Protection/Air Quality Control/Air Pollution
Atmospheric Sciences
Bias
Computer simulation
Data
Dispersion
Earth and Environmental Science
Earth Sciences
Emergency preparedness
Emergency response
Frameworks
Ground level
Meteorology
Modelling
Pollutants
Pollution dispersion
Research Article
Sensitivity analysis
Statistical analysis
Statistical methods
Uncertainty
Wind speed
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Summary:When modelling the dispersion of pollutants in the atmosphere, uncertainty in the simulation results that depend on the available data used as input conditions is a critical issue, particularly in the context of emergency response to the accidental release of harmful substances. In the framework of the UDINEE Project, a Lagrangian particle dispersion model is used to simulate puff emissions in four different test cases, using input wind velocity data from two different datasets, both representative of the flow at the time of the release. The effect of the choice of the input data on the final concentration distribution at ground level is discussed and compared with observations. A statistical analysis is applied to estimate the deviation between the results of the two runs. The bias between the two concentration fields, connected to the variability and the uncertainty in the input flow, is found to be of similar magnitude to the typical bias between model predictions and observations.
ISSN:0006-8314
1573-1472
DOI:10.1007/s10546-018-0413-z