globalchange  > 气候变化事实与影响
DOI: 10.1016/j.atmosenv.2015.09.016
Scopus ID: 2-s2.0-84942524342
Automated source term and wind parameter estimation for atmospheric transport and dispersion applications
Author: Bieringer P; E; , Rodriguez L; M; , Vandenberghe F; , Hurst J; G; , Bieberbach G; , Jr; , Sykes I; , Hannan J; R; , Zaragoza J; , Fry R; N
Source Publication: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
Publishing Year: 2015
Volume: 122
pages begin: 206
pages end: 219
Language: 英语
Keyword: Adjoint methods ; Atmospheric ; Biological ; Chemical ; Source term estimation ; Transport and dispersion ; Variational data assimilation ; VIRSA
Scopus Keyword: Algorithms ; Atmospheric chemistry ; Atmospheric movements ; Atmospherics ; Chemicals ; Digital storage ; Hazards ; Iterative methods ; Adjoint methods ; Biological ; Source term estimation ; Transport and dispersions ; Variational data assimilation ; VIRSA ; Parameter estimation ; algorithm ; atmospheric modeling ; atmospheric plume ; atmospheric pollution ; atmospheric transport ; dispersion ; hazard assessment ; meteorological hazard ; remote sensing ; terrorism ; wind velocity ; algorithm ; Article ; atmospheric dispersion ; atmospheric transport ; hazard assessment ; meteorology ; methodology ; priority journal ; source term estimation method ; statistical analysis ; wind ; Neptunia
Subject of Scopus: Environmental Science: Water Science and Technology ; Earth and Planetary Sciences: Earth-Surface Processes ; Environmental Science: Environmental Chemistry
English Abstract: Accurate simulations of the atmospheric transport and dispersion (AT&D) of hazardous airborne materials rely heavily on the source term parameters necessary to characterize the initial release and meteorological conditions that drive the downwind dispersion. In many cases the source parameters are not known and consequently based on rudimentary assumptions. This is particularly true of accidental releases and the intentional releases associated with terrorist incidents. When available, meteorological observations are often not representative of the conditions at the location of the release and the use of these non-representative meteorological conditions can result in significant errors in the hazard assessments downwind of the sensors, even when the other source parameters are accurately characterized. Here, we describe a computationally efficient methodology to characterize both the release source parameters and the low-level winds (eg. winds near the surface) required to produce a refined downwind hazard. This methodology, known as the Variational Iterative Refinement Source Term Estimation (STE) Algorithm (VIRSA), consists of a combination of modeling systems. These systems include a back-trajectory based source inversion method, a forward Gaussian puff dispersion model, a variational refinement algorithm that uses both a simple forward AT&D model that is a surrogate for the more complex Gaussian puff model and a formal adjoint of this surrogate model. The back-trajectory based method is used to calculate a "first guess" source estimate based on the available observations of the airborne contaminant plume and atmospheric conditions. The variational refinement algorithm is then used to iteratively refine the first guess STE parameters and meteorological variables. The algorithm has been evaluated across a wide range of scenarios of varying complexity. It has been shown to improve the source parameters for location by several hundred percent (normalized by the distance from source to the closest sampler), and improve mass estimates by several orders of magnitude. Furthermore, it also has the ability to operate in scenarios with inconsistencies between the wind and airborne contaminant sensor observations and adjust the wind to provide a better match between the hazard prediction and the observations. © 2015 .Published by Elsevier Ltd.
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Affiliation: Aeris LLC, 1723 Madison CT, Louisville, CO, United States; Research Applications Laboratory, National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, CO, United States; Sage Management Enterprise, LLC, 6731 Columbia Gateway Drive, Suite 150, Columbia, MD, United States; Defense Threat Reduction Agency, 8725 John J. Kingman Rd., Ft. Belvoir, VA, United States; Colorado State University, Department of Atmospheric Science, 200 West Lake Street, Fort Collins, CO, United States

Recommended Citation:
Bieringer P,E,, Rodriguez L,et al. Automated source term and wind parameter estimation for atmospheric transport and dispersion applications[J]. Atmospheric Environment,2015-01-01,122
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