Abstract. This study demonstrates the ability of large-eddy simulation (LES) forced by a large-scale model to reproduce plume dispersion in an actual field campaign. Our aim is to bring together field observations taken under non-ideal conditions and LES to show that this combination can help to derive point source strengths from sparse observations. We prepared a one-day case study based on data collected near an oil well during the ROMEO campaign (ROmanian Methane Emissions from Oil and gas) that took place in October 2019. We set up our LES using boundary conditions derived from the meteorological reanalysis ERA5 and released a point source in line with the configuration in the field. The weather conditions produced by the LES show close agreement with field observations, although the observed wind field showed complex features due to the absence of synoptic forcing. In order to align the plume direction with field observations, we created a second simulation experiment with manipulated wind fields. The estimated source strengths using the LES plume agrees well with the emitted artificial tracer gas plume, indicating the suitability of LES to infer source strengths from observations under complex conditions. To further harvest the added value of LES, higher order statistical moments of the simulated plume were analysed. Here, we found good agreement with plumes from previous LES and laboratory experiments in channel flows. We derived a length scale of plume mixing from the boundary layer height, the mean wind speed and convective velocity scale. It was demonstrated that this length scale represents the distance from the source at which the predominant plume behaviour transfers from meandering dispersion to relative dispersion.

中文翻译：

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技术说明：使用观测驱动的大涡模拟解释点源甲烷羽流的现场观测

摘要。这项研究证明了由大型模型强制的大涡模拟 (LES) 在实际现场活动中重现羽流扩散的能力。我们的目标是将非理想条件下的现场观测与 LES 结合起来，以表明这种组合有助于从稀疏观测中推导出点源强度。我们根据 2019 年 10 月发生的 ROMEO 活动（石油和天然气中的罗马尼亚甲烷排放）期间在油井附近收集的数据准备了为期一天的案例研究。我们使用源自气象再分析 ERA5 的边界条件设置了 LES并发布了符合现场配置的点源。LES 产生的天气状况与实地观测非常吻合，尽管由于没有天气强迫，观测到的风场表现出复杂的特征。为了使羽流方向与现场观测一致，我们创建了第二个模拟实验，其中包含操纵风场。使用 LES 羽流估计的源强度与发射的人工示踪气体羽流非常吻合，表明 LES 适合从复杂条件下的观测中推断源强度。为了进一步收获 LES 的附加值，分析了模拟羽流的高阶统计矩。在这里，我们发现与之前 LES 和实验室通道流实验中的羽流非常吻合。我们从边界层高度、平均风速和对流速度尺度推导出羽流混合的长度尺度。