Submitted as: development and technical paper 23 Sep 2020
Submitted as: development and technical paper | 23 Sep 2020
On the model uncertainties in Bayesian source reconstruction using the emission inverse modelling system FREARtool v1.0 and the Lagrangian transport and dispersion model Flexpart v9.0.2
- 1Radiation Protection Bureau, Health Canada, 775 Brookfield Road, Ottawa, Canada
- 2Belgian Nuclear Research Institute, Boeretang 200, Mol, Belgium
- 3Royal Meteorological Institute of Belgium, Ringlaan 3, Brussels, Belgium
- 1Radiation Protection Bureau, Health Canada, 775 Brookfield Road, Ottawa, Canada
- 2Belgian Nuclear Research Institute, Boeretang 200, Mol, Belgium
- 3Royal Meteorological Institute of Belgium, Ringlaan 3, Brussels, Belgium
Abstract. Bayesian source reconstruction is a powerful tool for determining atmospheric releases. It can be used, amongst other applications, to identify a point source releasing radioactive particles into the atmosphere. This is relevant for applications such as emergency response in case of a nuclear accident, or Comprehensive Nuclear-Test-Ban treaty verification. The method involves solving an inverse problem using environmental radioactivity observations and atmospheric transport models. The Bayesian approach has the advantage of providing credible intervals on the inferred source parameters in a natural way. However, it requires the specification of the inference input errors, such as the observation error and model error. The latter is particularly hard to provide as there is no straightforward way to determine the atmospheric transport and dispersion model error. Here, the importance of model error is illustrated for Bayesian source reconstruction using a recent and unique case where radionuclides were detected on several continents. A numerical weather prediction ensemble is used to create an ensemble of atmospheric transport and dispersion simulations, and a method is proposed to determine the model error.
Pieter De Meutter et al.


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RC1: 'review comment', Anonymous Referee #1, 11 Oct 2020
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AC1: 'Reply to Referee 1', Pieter De Meutter, 06 Nov 2020
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AC1: 'Reply to Referee 1', Pieter De Meutter, 06 Nov 2020
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RC2: 'Source term estimate assorted with uncertainties using backward atmospheric transport modelling, Bayesian reconstruction and an ensemble of weather predictions', P. Armand, 18 Oct 2020
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AC2: 'Reply to Referee 2', Pieter De Meutter, 06 Nov 2020
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AC2: 'Reply to Referee 2', Pieter De Meutter, 06 Nov 2020
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RC3: 'Review Comments for "On tuning of atmospheric inverse methods: Comparison on ETEX and Chernobyl datasets using FLEXPART v8.1 and v10.3"', Anonymous Referee #3, 27 Oct 2020
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AC3: 'Reply to Referee 3', Pieter De Meutter, 06 Nov 2020
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AC3: 'Reply to Referee 3', Pieter De Meutter, 06 Nov 2020
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SC1: 'executive editor comment on gmd-2020-162', Astrid Kerkweg, 27 Oct 2020


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RC1: 'review comment', Anonymous Referee #1, 11 Oct 2020
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AC1: 'Reply to Referee 1', Pieter De Meutter, 06 Nov 2020
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AC1: 'Reply to Referee 1', Pieter De Meutter, 06 Nov 2020
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RC2: 'Source term estimate assorted with uncertainties using backward atmospheric transport modelling, Bayesian reconstruction and an ensemble of weather predictions', P. Armand, 18 Oct 2020
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AC2: 'Reply to Referee 2', Pieter De Meutter, 06 Nov 2020
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AC2: 'Reply to Referee 2', Pieter De Meutter, 06 Nov 2020
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RC3: 'Review Comments for "On tuning of atmospheric inverse methods: Comparison on ETEX and Chernobyl datasets using FLEXPART v8.1 and v10.3"', Anonymous Referee #3, 27 Oct 2020
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AC3: 'Reply to Referee 3', Pieter De Meutter, 06 Nov 2020
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AC3: 'Reply to Referee 3', Pieter De Meutter, 06 Nov 2020
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SC1: 'executive editor comment on gmd-2020-162', Astrid Kerkweg, 27 Oct 2020
Pieter De Meutter et al.
Data sets
SRS data Pieter De Meutter and Andy Delcloo https://doi.org/10.5281/zenodo.4003640
Pieter De Meutter et al.
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