Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 17, issue 22
Articles | Volume 17, issue 22
https://doi.org/10.5194/acp-17-13521-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-17-13521-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Articles | Volume 17, issue 22
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, Matthew Simpson, Philip Cameron-Smith, and Ronald L. Baskett

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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.
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