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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- Automated release rate inversion and plume bias correction for atmospheric radionuclide leaks: A robust and general remediation to imperfect radionuclide transport modeling S. Fang et al. 10.1016/j.scitotenv.2020.142140
- Comparative study on gradient-free optimization methods for inverse source-term estimation of radioactive dispersion from nuclear accidents S. Jang et al. 10.1016/j.jhazmat.2023.132519
- Examining the potential for detecting simultaneous noble gas and aerosol samples in the international monitoring system radionuclide network P. Eslinger et al. 10.1016/j.jenvrad.2023.107349
- MCMC methods applied to the reconstruction of the autumn 2017 Ruthenium-106 atmospheric contamination source J. Dumont Le Brazidec et al. 10.1016/j.aeaoa.2020.100071
- A machine learning emulator for Lagrangian particle dispersion model footprints: a case study using NAME E. Fillola et al. 10.5194/gmd-16-1997-2023
- A spatiotemporally separated framework for reconstructing the sources of atmospheric radionuclide releases Y. Xu et al. 10.5194/gmd-17-4961-2024
- Source term inversion of nuclear accident based on deep feedforward neural network W. Cui et al. 10.1016/j.anucene.2022.109257
- Inferring Atmospheric Release Characteristics in a Large Computer Experiment Using Bayesian Adaptive Splines D. Francom et al. 10.1080/01621459.2018.1562933
- Machine Learning Emulation of Spatial Deposition from a Multi-Physics Ensemble of Weather and Atmospheric Transport Models N. Gunawardena et al. 10.3390/atmos12080953
- Source localisation and its uncertainty quantification after the third DPRK nuclear test P. De Meutter et al. 10.1038/s41598-018-28403-z
- Atmospheric modeling and source reconstruction of radioactive ruthenium from an undeclared major release in 2017 O. Saunier et al. 10.1073/pnas.1907823116
- A baseline for source localisation using the inverse modelling tool FREAR P. De Meutter et al. 10.1016/j.jenvrad.2024.107372
- Surrogate tree ensemble model representing 2D population doses over complex terrain in the event of a radiological release into the air N. Hvala et al. 10.1016/j.pnucene.2023.104594
- Bayesian atmospheric tomography for detection and quantification of methane emissions: application to data from the 2015 Ginninderra release experiment L. Cartwright et al. 10.5194/amt-12-4659-2019
- Multi-scenario validation of the robust inversion method with biased plume range and values X. Dong et al. 10.1016/j.jenvrad.2023.107363
- Can Delhi's Pollution be Affected by Crop Fires in the Punjab Region? M. Takigawa et al. 10.2151/sola.2020-015
- Source estimation of an unexpected release of Ruthenium-106 in 2017 using an inverse modelling approach L. Western et al. 10.1016/j.jenvrad.2020.106304
- Emulation of greenhouse‐gas sensitivities using variational autoencoders L. Cartwright et al. 10.1002/env.2754
- The Application of an Evolutionary Programming Process to a Simulation of the ETEX Large-Scale Airborne Dispersion Experiment D. Werth et al. 10.1175/JAMC-D-18-0098.1
- Methodology for the investigation of undeclared atmospheric releases of radionuclides: Application to recent radionuclide detections in Northern Europe from 2019 to 2022 O. Saunier et al. 10.1016/j.anucene.2023.109907
Saved (final revised paper)
Latest update: 23 Nov 2024
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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