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 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.more » 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:

Lucas, Donald D.  ^[1];

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• Search DOE PAGES for ORCID "0000-0002-4649-6967"

Simpson, Matthew ^[1]; Cameron-Smith, Philip  ^[1];

• Search DOE PAGES for author "Cameron-Smith, Philip"
• Search DOE PAGES for ORCID "0000-0002-8802-8627"

Baskett, Ronald L. ^[1]

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+ Show Author Affiliations

1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Publication Date:

Wed Nov 15 00:00:00 EST 2017

Research Org.:

Lawrence Livermore National Laboratory (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