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.
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.
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
Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
Matthew Simpson
Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
Philip Cameron-Smith
Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
Ronald L. Baskett
Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
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Saved (final revised paper)
Latest update: 02 Apr 2025
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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