Articles | Volume 21, issue 17
https://doi.org/10.5194/acp-21-13247-2021
https://doi.org/10.5194/acp-21-13247-2021
Research article
 | 
07 Sep 2021
Research article |  | 07 Sep 2021

Quantification of uncertainties in the assessment of an atmospheric release source applied to the autumn 2017 106Ru event

Joffrey Dumont Le Brazidec, Marc Bocquet, Olivier Saunier, and Yelva Roustan

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Cited articles

Abida, R. and Bocquet, M.: Targeting of observations for accidental atmospheric release monitoring, Atmos. Environ., 43, 6312–6327, https://doi.org/10.1016/j.atmosenv.2009.09.029, 2009. a
Anderson, J. L.: A Method for Producing and Evaluating Probabilistic Forecasts from Ensemble Model Integrations, J. Climate, 9, 1518–1530, https://doi.org/10.1175/1520-0442(1996)009<1518:AMFPAE>2.0.CO;2, 1996. a
Atchadé, Y. F., Roberts, G. O., and Rosenthal, J. S.: Towards optimal scaling of metropolis-coupled Markov chain Monte Carlo, Stat. Comput., 21, 555–568, https://doi.org/10.1007/s11222-010-9192-1, 2011. a
Baklanov, A. and Sørensen, J. H.: Parameterisation of radionuclide deposition in atmospheric long-range transport modelling, Phys. Chem. Earth Pt. B, 26, 787–799, https://doi.org/10.1016/S1464-1909(01)00087-9, 2001. a
Baragatti, M.: Sélection bayésienne de variables et méthodes de type Parallel Tempering avec et sans vraisemblance, Thèse de doctorat, Aix-Marseille 2, 2011. a
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The assessment of the environmental consequences of a radionuclide release depends on the estimation of its source. This paper aims to develop inverse Bayesian methods which combine transport models with measurements, in order to reconstruct the ensemble of possible sources. Three methods to quantify uncertainties based on the definition of probability distributions and the physical models are proposed and evaluated for the case of 106Ru releases over Europe in 2017.
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