Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
doi:10.5194/acp-2017-336
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
19 Apr 2017
Review status
This discussion paper is under review for the journal Atmospheric Chemistry and Physics (ACP).
Bayesian inverse modeling of the atmospheric transport and emissions of a controlled tracer release from a nuclear power plant
Donald D. Lucas, Matthew D. Simpson, Philip Cameron-Smith, and Ronald L. Baskett Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
Abstract. Probability distribution functions (PDFs) of model inputs that affect the transport and dispersion of a trace gas released from a coastal California nuclear power plant are quantified using ensemble simulations, machine learning algorithms, and Bayesian inversion. The PDFs are constrained by observations of tracer concentrations and account for uncertainty in meteorology, transport, diffusion, and emissions. Meteorological uncertainty is calculated using an ensemble of simulations of the Weather Research and Forecasting (WRF) model that samples five categories of model inputs (initialization time, boundary layer physics, land surface model, nudging options, and reanalysis data). The WRF output is used to drive tens of thousands of FLEXPART dispersion simulations that sample a uniform distribution of six emissions inputs. Machine learning algorithms are trained on the ensemble data, and used to quantify the sources of ensemble variability and to infer, via inverse modeling, the values of the 11 model inputs most consistent with tracer measurements. We find a substantial ensemble spread in tracer concentrations (factors of 10 to 103), most of which is due to changing emissions inputs (about 80 %), though the cumulative effects of meteorological variations are not negligible. The performance of the inverse method is verified using synthetic observations generated from arbitrarily selected simulations. When applied to measurements from a controlled tracer release experiment, the most likely inversion results are within about 200 meters of the known release location, 5 and 50 minutes of the release start and duration times, respectively, and 22 % of the release amount. The inversion also estimates probabilities of different combinations of WRF inputs of matching the tracer observations.

Citation: Lucas, D. D., Simpson, M. D., Cameron-Smith, P., and Baskett, R. L.: Bayesian inverse modeling of the atmospheric transport and emissions of a controlled tracer release from a nuclear power plant, Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2017-336, in review, 2017.
Donald D. Lucas et al.
Donald D. Lucas et al.
Donald D. Lucas et al.

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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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