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Title: Impact of meteorological inflow uncertainty on tracer transport and source estimation in urban atmospheres

Here, a computational Bayesian inverse technique is used to quantify the effects of meteorological inflow uncertainty on tracer transport and source estimation in a complex urban environment. We estimate a probability distribution of meteorological inflow by comparing wind observations to Monte Carlo simulations from the Aeolus model. Aeolus is a computational fluid dynamics model that simulates atmospheric and tracer flow around buildings and structures at meter-scale resolution. Uncertainty in the inflow is propagated through forward and backward Lagrangian dispersion calculations to determine the impact on tracer transport and the ability to estimate the release location of an unknown source. Our uncertainty methods are compared against measurements from an intensive observation period during the Joint Urban 2003 tracer release experiment conducted in Oklahoma City.
Authors:
 [1] ;  [1] ;  [1] ;  [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Report Number(s):
LLNL-JRNL-676479
Journal ID: ISSN 1352-2310
Grant/Contract Number:
AC52-07NA27344; PLS-14ERD006
Type:
Published Article
Journal Name:
Atmospheric Environment (1994)
Additional Journal Information:
Journal Name: Atmospheric Environment (1994); Journal Volume: 143; Journal ID: ISSN 1352-2310
Publisher:
Elsevier
Research Org:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; meteorological inflow uncertainty; source estimation; Bayesian inversion
OSTI Identifier:
1302508
Alternate Identifier(s):
OSTI ID: 1324521