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首页> 外文期刊>Atmospheric Chemistry and Physics Discussions >Bayesian inverse modeling of the atmospheric transport and emissions of a??controlled tracer release from a??nuclear power plant
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Bayesian inverse modeling of the atmospheric transport and emissions of a??controlled tracer release from a??nuclear power plant

机译：贝叶斯逆向建模与核电厂核示踪物释放的大气传输和排放

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pstrongAbstract./strong Probability distribution functions (PDFs) of model inputs that affect the transport and dispersion of a??trace gas released from a??coastal California nuclear power plant are quantified using ensemble simulations, machine-learning algorithms, and Bayesian inversion. The PDFs are constrained by observations of tracer concentrations and account for uncertainty in meteorology, transport, diffusion, and emissions. Meteorological uncertainty is calculated using an ensemble of simulations of the Weather Research and Forecasting (WRF) model that samples five categories of model inputs (initialization time, boundary layer physics, land surface model, nudging options, and reanalysis data). The WRF output is used to drive tens of thousands of FLEXPART dispersion simulations that sample a??uniform distribution of six emissions inputs. Machine-learning algorithms are trained on the ensemble data and used to quantify the sources of ensemble variability and to infer, via inverse modeling, the values of the 11 model inputs most consistent with tracer measurements. We find a??substantial ensemble spread in tracer concentrations (factors of 10 to 10sup3/sup), most of which is due to changing emissions inputs (about 80span class="thinspace"/span%), though the cumulative effects of meteorological variations are not negligible. The performance of the inverse method is verified using synthetic observations generated from arbitrarily selected simulations. When applied to measurements from a??controlled tracer release experiment, the inverse method satisfactorily determines the location, start time, duration and amount. In a 2a??kma????a??2a??km area of possible locations, the actual location is determined to within 200span class="thinspace"/spanm. The start time is determined to within 5span class="thinspace"/spanmin out of 2span class="thinspace"/spanh, and the duration to within 50span class="thinspace"/spanmin out of 4span class="thinspace"/spanh. Over a??range of release amounts of 10 to 1000span class="thinspace"/spankg, the estimated amount exceeds the actual amount of 146span class="thinspace"/spankg by only 32span class="thinspace"/spankg. The inversion also estimates probabilities of different WRF configurations. To best match the tracer observations, the highest-probability cases in WRF are associated with using a??late initialization time and specific reanalysis data products./p.
机译：> >摘要。使用集成模拟，机器对影响从加利福尼亚沿海核电厂释放的微量气体的传输和扩散的模型输入的概率分布函数（PDF）进行量化。学习算法和贝叶斯反演。 PDF受到示踪剂浓度观测的限制，并解释了气象，运输，扩散和排放方面的不确定性。气象不确定性是通过使用天气研究和预报（WRF）模型的仿真集合来计算的，该模型对五种类型的模型输入（初始化时间，边界层物理，地表模型，微动选项和重新分析数据）进行采样。 WRF输出用于驱动数以万计的FLEXPART色散模拟，该模拟对六个排放输入的均匀分布进行采样。机器学习算法在集合数据上进行训练，用于量化集合变异性的来源，并通过逆向建模推断出与示踪剂测量最一致的11个模型输入值。我们发现示踪剂浓度中有很大的整体分布（因子为10到10 3 ），其中大部分是由于排放输入的变化（大约80 class =“ thinspace”> ％），尽管气象变化的累积影响不可忽略。使用从任意选择的模拟生成的综合观测值，可以验证逆方法的性能。当应用于控制示踪剂释放实验的测量结果时，逆方法可以令人满意地确定位置，开始时间，持续时间和数量。在可能位置的2a ?? kma ???? a ?? 2a ?? km区域中，实际位置确定在200 class =“ thinspace”> m之内。开始时间确定为在2 class =“ thinspace”> h中的5 class =“ thinspace”> min之内，持续时间在50 class =“ thinspace”> “> min（小于4 class =” thinspace“> h）。在10到1000 class =“ thinspace”> kg的释放量范围内，估计量仅超过146 class =“ thinspace”> kg的实际量32 class =“ thinspace”> kg。反演也估计不同WRF配置的概率。为了与示踪剂观测值最佳匹配，WRF中最高概率的情况与使用后期初始化时间和特定的重新分析数据产物相关。

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