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.

中文翻译：

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将短程弥散模拟与精细气象集合相结合：85Kr 野外活动期间的概率指标和评估

摘要。大气扩散的数值模型用于预测核事故的健康和环境后果，以预测保护民众所需的对策。然而，这些模型的模拟存在很大的不确定性，尤其是来自输入数据：天气条件和源项。为了表征天气不确定性，必须将众所周知的源项数据和气象集合相结合，以生成集合扩散模拟，该模拟有可能在紧急情况下产生不同可能的放射性核素扩散情景。在这项研究中，来自法国气象局的精细操作天气集合 AROME-EPS 与法国辐射防护和核安全研究所 (IRSN) 开发的高斯烟团模型 pX 耦合。源项数据由定期排放的 Orano La Hague 后处理厂 (RP) 提供⁸⁵ Kr 在乏核燃料后处理过程中。然后，为了评估分散结果，连续测量⁸⁵IRSN 瑟堡放射生态学实验室 (LRC) 最近在北科唐坦半岛的 RP 附近进行了 Kr 空气浓度检测。本文提出了一种概率方法来研究局部和中等距离（2-20 公里）扩散模拟中的气象不确定性。作为第一步，通过与 Météo-France 和 IRSN 的观测结果进行比较来确认 AROME-EPS 预测的质量。下一步是评估色散集合模拟的概率性能，以及色散结果对用于计算大气稳定场及其相关色散高斯标准偏差的方法的敏感性。使用了两个概率分数：相对操作特征 (ROC) 曲线和皮尔斯技能分数 (PSS)。结果表明，Pasquill 的稳定性诊断提供了更好的分散模拟。此外，整体分散性能优于确定性分散，最佳决策阈值（PSS 最大值）为 3 个成员。这些结果突出了集合预测与单一确定性预测相比的附加价值，以及它们在危机情况下对决策过程的潜在兴趣。