Norwegian Meteorological Institute, N-0371 Oslo, Norway; Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences (NMBU), N-1433 Aas, Norway; Centre of Excellence for Environmental Radioactivity (CERAD), P. O. BOX 5003, NMBU, N-1433 Aas, Norway.
Norwegian Meteorological Institute, N-0371 Oslo, Norway; Centre of Excellence for Environmental Radioactivity (CERAD), P. O. BOX 5003, NMBU, N-1433 Aas, Norway.
Sci Total Environ. 2022 Feb 1;806(Pt 1):150128. doi: 10.1016/j.scitotenv.2021.150128. Epub 2021 Sep 15.
Atmospheric dispersion models are crucial for nuclear risk assessment and emergency response systems since they rapidly predict air concentrations and deposition of released radionuclides, providing a basis for dose estimations and countermeasure strategies. Atmospheric dispersion models are associated with relatively large and often unknown uncertainties that are mostly attributed to meteorology, source terms and parametrisation of the dispersion model. By developing methods that can provide reliable uncertainty ranges for model outputs, decision makers have an improved basis for handling nuclear emergency situations. In the present work, model skill of the Severe Nuclear Accident Programme (SNAP) model was quantified by employing an ensemble method in which 51 meteorological realisations from a numerical weather prediction model were combined with 9 source term descriptions for the accidental Cs releases from Fukushima Daiichi Nuclear Power Plant during 14th-17th March 2011. The meteorological forecast was compared to observations of wind speed from 30 meteorological stations. The 459 dispersion realisations were compared with hourly observations of activity concentrations from 100 air filter stations. Exclusive use of deterministic meteorology resulted in most members of the dispersion ensemble showing too low concentration values, however this was mitigated by applying ensemble meteorology. Ensemble predictions, including both the meteorological and source term ensemble, show an overall higher prediction skill compared to individual meteorology and source term runs, with true predictive rate accuracy increasing from 30%-50% to 70%-90%, with a decrease in positive predictive rate accuracy from 75%-80% to 65%-75%. Skill scores and other ensemble indicators also showed improvements in using ensembles of source terms and meteorology. From the present study on the Fukushima accident there are strong indications that ensemble predictions improve the basis for decision making in the early phase after a nuclear accident, which emphasises the importance of including ensemble prediction in nuclear preparedness tools of the future.
大气扩散模型对于核风险评估和应急响应系统至关重要，因为它们可以快速预测释放的放射性核素在空气中的浓度和沉积，为剂量估算和对策策略提供基础。大气扩散模型与相对较大且通常未知的不确定性相关联，这些不确定性主要归因于气象、源项和扩散模型的参数化。通过开发能够为模型输出提供可靠的不确定性范围的方法，决策者在处理核紧急情况时有了更好的依据。在本工作中，通过采用集合方法来量化严重核事故计划（SNAP）模型的模型技能，其中 51 个气象实例是从数值天气预报模型中组合而成的，9 个源项描述是用于模拟 2011 年 3 月 14 日至 17 日福岛第一核电站意外 Cs 释放。气象预报与 30 个气象站的风速观测值进行了比较。459 次扩散实例与 100 个空气过滤器站的每小时活性浓度观测值进行了比较。仅使用确定性气象学导致扩散集合中的大多数成员显示出过低的浓度值，但通过应用集合气象学可以减轻这种情况。与单个气象学和源项运行相比，包括气象学和源项集合的集合预测显示出整体更高的预测技能，真实预测率准确性从 30%-50%增加到 70%-90%，而正预测率准确性从 75%-80%降低到 65%-75%。使用源项和气象学集合的技能评分和其他集合指标也显示出改进。从本研究对福岛事故的分析来看，有强烈迹象表明集合预测可以改善核事故后早期阶段的决策基础，这强调了在未来的核准备工具中纳入集合预测的重要性。
