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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 22
Atmos. Chem. Phys., 17, 13521–13543, 2017
https://doi.org/10.5194/acp-17-13521-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 17, 13521–13543, 2017
https://doi.org/10.5194/acp-17-13521-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 15 Nov 2017

Research article | 15 Nov 2017

Bayesian inverse modeling of the atmospheric transport and emissions of a controlled tracer release from a nuclear power plant

Donald D. Lucas et al.
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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Donald Lucas on behalf of the Authors (12 Sep 2017)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (12 Sep 2017) by Manvendra K. Dubey
RR by Anonymous Referee #2 (16 Sep 2017)
ED: Publish as is (22 Sep 2017) by Manvendra K. Dubey
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Short summary
Monte Carlo ensemble simulations, Bayesian inversion, and machine learning are used to quantify uncertainty in the atmospheric transport and emissions of a controlled tracer released from a nuclear power plant. Uncertainty of different settings in a weather model and source terms in a dispersion model are jointly estimated. The algorithm is validated using model-generated output and field observations and can benefit atmospheric researchers who need to estimate tracer transport uncertainty.
Monte Carlo ensemble simulations, Bayesian inversion, and machine learning are used to quantify...
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