Combining short-range dispersion simulations with fine-scale meteorological ensembles: probabilistic indicators and evaluation during a .sup.85Kr field campaign

Numerical atmospheric dispersion models (ADMs) are used for predicting the health and environmental consequences of nuclear accidents in order to anticipate countermeasures necessary to protect the populations. However, these simulations suffer from significant uncertainties, arising in particular f...

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Veröffentlicht in:Atmospheric chemistry and physics 2022-12, Vol.22 (24), p.15793
Hauptverfasser: El-Ouartassy, Youness, Korsakissok, Irène, Plu, Matthieu, Connan, Olivier, Descamps, Laurent, Raynaud, Laure
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Sprache:eng
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Zusammenfassung:Numerical atmospheric dispersion models (ADMs) are used for predicting the health and environmental consequences of nuclear accidents in order to anticipate countermeasures necessary to protect the populations. However, these simulations suffer from significant uncertainties, arising in particular from input data: weather conditions and source term. Meteorological ensembles are already used operationally to characterize uncertainties in weather predictions. Combined with dispersion models, these ensembles produce different scenarios of radionuclide dispersion, called "members", representative of the variety of possible forecasts. In this study, the fine-scale operational weather ensemble AROME-EPS (Applications of Research to Operations at Mesoscale-Ensemble Prediction System) from Météo-France is coupled with the Gaussian puff model pX developed by the IRSN (French Institute for Radiation Protection and Nuclear Safety). The source term data are provided at 10 min resolution by the Orano La Hague reprocessing plant (RP) that regularly discharges .sup.85 Kr during the spent nuclear fuel reprocessing process. In addition, a continuous measurement campaign of .sup.85 Kr air concentration was recently conducted by the Laboratory of Radioecology in Cherbourg (LRC) of the IRSN, within 20 km of the RP in the North-Cotentin peninsula, and is used for model evaluation.
ISSN:1680-7316
1680-7324