Preprints
https://doi.org/10.5194/egusphere-2022-646
https://doi.org/10.5194/egusphere-2022-646
 
04 Aug 2022
04 Aug 2022

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

Youness El-Ouartassy1,2, Irène Korsakissok2, Matthieu Plu1, Olivier Connan3, Laurent Descamps1, and Laure Raynaud1 Youness El-Ouartassy et al.
  • 1CNRM, University of Toulouse, Météo-France, CNRS, 31057, Toulouse, France
  • 2Institut de Radioprotection et de Sûreté Nucléaire (IRSN), PSE-SANTE/SESUC/BMCA, F-92260, Fontenay-aux-Roses, France
  • 3Institut de Radioprotection et de Sûreté Nucléaire (IRSN), PSE-ENV/SRTE/LRC, F-50130, Cherbourg-En-Cotentin, France

Abstract. Numerical models of atmospheric dispersion are used for predicting the health and environmental consequences of nuclear accidents, in order to anticipate the countermeasures necessary to protect the populations. However, the simulations of these models suffer from significant uncertainties, arising in particular from input data: weather conditions and source term. To characterize weather uncertainties, it is essential to combine a well-known source term data and meteorological ensembles to generate ensemble dispersion simulations which has the potential to produce different possible scenarios of radionuclides dispersion during emergency situations. In this study, the fine-scale operational weather ensemble AROME-EPS from Météo-France is coupled to the Gaussian puff model pX developed by French Institute for Radiation Protection and Nuclear Safety (IRSN). The source term data is provided by Orano La Hague reprocessing plant (RP) that regularly discharges 85Kr during the spent nuclear fuel reprocessing process. Then, to evaluate the dispersion results, a continuous measurement campaign of 85Kr air concentration was recently conducted by the Laboratory of Radioecology in Cherbourg (LRC) of IRSN, around RP in the North-Cotentin peninsula.

This paper presents a probabilistic approach to study the meteorological uncertainties in dispersion simulations at local and medium distances (2–20 km). As first step, the quality of AROME-EPS forecasts is confirmed by comparison with observations from both Météo-France and IRSN. The following step is to assess the probabilistic performance of the dispersion ensemble simulation, as well as the sensitivity of dispersion results to the method used to calculate atmospheric stability fields and their associated dispersion Gaussian standard deviations. Two probabilistic scores are used: Relative Operating Characteristic (ROC) curves and Peirce Skill Score (PSS).

The results show that the stability diagnostics of Pasquill provides better dispersion simulations. In addition, the ensemble dispersion performs better than deterministic one, and the optimum decision threshold (PSS maximum) is 3 members. These results highlight the added value of ensemble forecasts compared to a single deterministic one, and their potential interest in the decision process during crisis situations.

Youness El-Ouartassy et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-646', Anonymous Referee #1, 22 Aug 2022
    • AC1: 'Reply on RC1', Youness El-Ouartassy, 28 Oct 2022
  • RC2: 'Comment on egusphere-2022-646', Anonymous Referee #2, 02 Sep 2022
    • AC2: 'Reply on RC2', Youness El-Ouartassy, 28 Oct 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-646', Anonymous Referee #1, 22 Aug 2022
    • AC1: 'Reply on RC1', Youness El-Ouartassy, 28 Oct 2022
  • RC2: 'Comment on egusphere-2022-646', Anonymous Referee #2, 02 Sep 2022
    • AC2: 'Reply on RC2', Youness El-Ouartassy, 28 Oct 2022

Youness El-Ouartassy et al.

Youness El-Ouartassy et al.

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Short summary
This work investigates the potential value of using fine-scale meteorological ensembles to represent the inherent meteorological uncertainties in atmospheric dispersion models outputs. Probabilistic scores were used to evaluate the probabilistic performance of dispersion ensembles, using an original data set of new continuous Krypton-85 air concentration measurements and a well-known source term. The results show that the ensemble dispersion simulations performs better than deterministic ones.