Generoso Sylvia, Achim Pascal, Morin Mireille, Gross Philippe
CEA, DAM, DIF, F-91297, Arpajon, Cedex, France.
CEA, DAM, DIF, F-91297, Arpajon, Cedex, France.
J Environ Radioact. 2023 Dec;270:107263. doi: 10.1016/j.jenvrad.2023.107263. Epub 2023 Aug 28.
The French National Data Center (NDC) uses an automated simulation of the Xe worldwide atmospheric background as one of the means to categorize the radionuclide measurements of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) International Monitoring System (IMS). These simulations take into account Xe releases from the known or assumed major industrial emitters in the world and global-scale meteorological data. However, a quantification of the simulation uncertainties in this operational set up is yet to be addressed. This work discusses the benefits of meteorological ensemble data as available from National Centers for Environmental Prediction (NCEP) for that purpose. For this study, the daily dispersion of releases from the Institute for Radio Elements (IRE), a medical isotope production facility located in Fleurus (Belgium), was calculated over one year with emissions measured in-site and ensemble meteorological data. The ensemble contains 31 members, which resulted in as many predictions of activity concentration for any given time and place. The resulting distribution statistics (mean, median and spread), and the control run, were confronted to the deterministic run and to measurements at one IMS-like station near Paris (France) and one IMS station in Freiburg (Germany). Overall, the ensemble results have decreased the simulation performance, as expected given the use of meteorological analyses only. However, contrasting patterns were found with a detailed analysis of daily activity concentration over two one-month-and-a-half periods. Noticeably, outlier results were found to carry the best forecast in some significant detections, proving their relevance for the measurement categorization, despite their isolated character. Importantly, the ensemble has allowed the quantification of meteorological uncertainties, which was beneficial in all cases. It either has improved the confidence of IMS data categorization or has pointed to low confidence predictions. A criterion to identify the latter is suggested, based on information provided by the ensemble distributions. In addition, maps of probability of detections and of relative spread are suggested to show additional benefits of ensemble meteorology.
法国国家数据中心（NDC）利用氙全球大气本底的自动模拟作为对《全面禁止核试验条约》（CTBT）国际监测系统（IMS）放射性核素测量进行分类的手段之一。这些模拟考虑了全球已知或假定的主要工业排放源的氙排放以及全球尺度的气象数据。然而，在这种业务设置中对模拟不确定性的量化尚未得到解决。这项工作讨论了美国国家环境预测中心（NCEP）提供的气象集合数据在此方面的益处。在本研究中，利用位于比利时弗勒鲁斯的医用同位素生产设施——放射性元素研究所（IRE）的现场测量排放数据和集合气象数据，计算了该研究所一年的日排放扩散情况。该集合包含31个成员，这使得对于任何给定时间和地点都有同样多的活度浓度预测。将所得的分布统计数据（均值、中位数和离散度）以及控制运行结果与确定性运行结果以及法国巴黎附近一个类似IMS的站点和德国弗莱堡一个IMS站点的测量结果进行了对比。总体而言，正如仅使用气象分析所预期的那样，集合结果降低了模拟性能。然而，通过对两个一个半月时间段内的日活度浓度进行详细分析，发现了不同的模式。值得注意的是，在一些重大探测中，异常结果被发现具有最佳预测效果，尽管它们具有孤立性，但证明了它们与测量分类的相关性。重要的是，该集合使得能够对气象不确定性进行量化，这在所有情况下都是有益的。它要么提高了IMS数据分类的可信度，要么指出了低可信度的预测。基于集合分布提供的信息，提出了一个识别后者的标准。此外，还建议绘制探测概率图和相对离散度图，以展示集合气象学的其他益处。
