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

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 10 3), 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 selectedmore » simulations. When applied to measurements from a controlled tracer release experiment, the inverse method satisfactorily determines the location, start time, duration and amount. In a 2 km × 2 km area of possible locations, the actual location is determined to within 200m. The start time is determined to within 5min out of 2h, and the duration to within 50min out of 4h. Over a range of release amounts of 10 to 1000kg, the estimated amount exceeds the actual amount of 146kg by only 32kg. The inversion also estimates probabilities of different WRF configurations. In conclusion, to best match the tracer observations, the highest-probability cases in WRF are associated with using a late initialization time and specific reanalysis data products.« less

Authors:
ORCiD logo [1];  [1]; ORCiD logo [1];  [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1460937
Report Number(s):
[LLNL-JRNL-710162]
[Journal ID: ISSN 1680-7324; 845305; TRN: US1901931]
Grant/Contract Number:  
[AC52-07NA27344]
Resource Type:
Accepted Manuscript
Journal Name:
Atmospheric Chemistry and Physics (Online)
Additional Journal Information:
[Journal Name: Atmospheric Chemistry and Physics (Online); Journal Volume: 17; Journal Issue: 22]; Journal ID: ISSN 1680-7324
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 58 GEOSCIENCES; 97 MATHEMATICS AND COMPUTING

Citation Formats

Lucas, Donald D., Simpson, Matthew, Cameron-Smith, Philip, and Baskett, Ronald L. Bayesian inverse modeling of the atmospheric transport and emissions of a controlled tracer release from a nuclear power plant. United States: N. p., 2017. Web. doi:10.5194/acp-17-13521-2017.
Lucas, Donald D., Simpson, Matthew, Cameron-Smith, Philip, & Baskett, Ronald L. Bayesian inverse modeling of the atmospheric transport and emissions of a controlled tracer release from a nuclear power plant. United States. doi:10.5194/acp-17-13521-2017.
Lucas, Donald D., Simpson, Matthew, Cameron-Smith, Philip, and Baskett, Ronald L. Wed . "Bayesian inverse modeling of the atmospheric transport and emissions of a controlled tracer release from a nuclear power plant". United States. doi:10.5194/acp-17-13521-2017. https://www.osti.gov/servlets/purl/1460937.
@article{osti_1460937,
title = {Bayesian inverse modeling of the atmospheric transport and emissions of a controlled tracer release from a nuclear power plant},
author = {Lucas, Donald D. and Simpson, Matthew and Cameron-Smith, Philip and Baskett, Ronald L.},
abstractNote = {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 inverse method satisfactorily determines the location, start time, duration and amount. In a 2 km × 2 km area of possible locations, the actual location is determined to within 200m. The start time is determined to within 5min out of 2h, and the duration to within 50min out of 4h. Over a range of release amounts of 10 to 1000kg, the estimated amount exceeds the actual amount of 146kg by only 32kg. The inversion also estimates probabilities of different WRF configurations. In conclusion, to best match the tracer observations, the highest-probability cases in WRF are associated with using a late initialization time and specific reanalysis data products.},
doi = {10.5194/acp-17-13521-2017},
journal = {Atmospheric Chemistry and Physics (Online)},
number = [22],
volume = [17],
place = {United States},
year = {2017},
month = {11}
}

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