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

Abstract

Abstract. 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 85Kr during the spent nuclear fuel reprocessing process. In addition, a continuous measurement campaign of 85Kr 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. This paper presents a probabilistic approach to study the meteorological uncertainties in dispersion simulations at local and medium distances (2–20 km). First, the quality of AROME-EPS forecasts is confirmed by comparison with observations from both Météo-France and the IRSN. Then, the probabilistic performance of the atmospheric dispersion simulations was evaluated by comparison to the 85Kr measurements carried out during a period of 2 months, using two probabilistic scores: relative operating characteristic (ROC) curves and Peirce skill score (PSS). The sensitivity of dispersion results to the method used for the calculation of atmospheric stability and associated Gaussian dispersion standard deviations is also discussed. A desirable feature for a model used in emergency response is the ability to correctly predict exceedance of a given value (for instance, a dose guide level). When using an ensemble of simulations, the “decision threshold” is the number of members predicting an event above which this event should be considered probable. In the case of the 16-member dispersion ensemble used here, the optimal decision threshold was found to be 3 members, above which the ensemble better predicts the observed peaks than the deterministic simulation. 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.

Adaptive selection of members for convective-permitting regional ensemble prediction over the western Maritime Continent

“…These systems differ in terms of the number of ensemble members and the approach to capturing the various sources of uncertainties. Despite providing useful forecast guidance, many operational centers and studies report an issue with under-dispersiveness of the ensemble (Buizza et al, 2005;Raftery et al, 2005;Hohenegger et al, 2008;Gebhardt et al, 2011;El-Ouartassy et al, 2022;Lakatos et al, 2023;Manikanta et al, 2023), where the spread of the ensemble members is too small to fully capture the forecast uncertainty.…”

Using synthetic case studies to explore the spread and calibration of ensemble atmospheric dispersion forecasts

“…Le et al (2021) and Ulimoen et al (2022) consider similar aspects of dispersion ensemble predictions in the context of the Fukushima Daiichi nuclear power plant accident in 2011 but compare their model predictions against measurements recorded at monitoring stations and subsequent survey data. Meanwhile, El-Ouartassy et al (2022) evaluate the performance of short-range ensemble dispersion simulations using 85 Kr measurements recorded downwind of a nuclear fuel reprocessing plant during a 2month field campaign.…”