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Impact of meteorological inflow uncertainty on tracer transport and source estimation in urban atmospheres

机译：气象流入不确定性对城市大气中示踪物运输和来源估算的影响

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A computational Bayesian inverse technique is used to quantify the effects of meteorological inflow uncertainty on tracer transport and source estimation in a complex urban environment. We estimate a probability distribution of meteorological inflow by comparing wind observations to Monte Carlo simulations from the Aeolus model. Aeolus is a computational fluid dynamics model that simulates atmospheric and tracer flow around buildings and structures at meter-scale resolution. Uncertainty in the inflow is propagated through forward and backward Lagrangian dispersion calculations to determine the impact on tracer transport and the ability to estimate the release location of an unknown source. Our uncertainty methods are compared against measurements from an intensive observation period during the Joint Urban 2003 tracer release experiment conducted in Oklahoma City. The best estimate of the inflow at 50 m above ground for the selected period has a wind speed and direction of 4.6(-2.5)(+2.0) m s(-1) and 158.0(-23)(+16), where the uncertainty is a 95% confidence range. The wind speed values prescribed in previous studies differ from our best estimate by two or more standard deviations. Inflow probabilities are also used to weight backward dispersion plumes and produce a spatial map of likely tracer release locations. For the Oklahoma City case, this map pinpoints the location of the known release to within 20 m. By evaluating the dispersion patterns associated with other likely release locations, we further show that inflow uncertainty can explain the differences between simulated and measured tracer concentrations. (C) 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.
机译：在复杂的城市环境中，使用计算贝叶斯逆技术来量化气象流入不确定性对示踪剂运输和源估算的影响。通过将风的观测结果与风神模型的蒙特卡罗模拟进行比较，我们估计了气象流入的概率分布。 Aeolus是一个计算流体动力学模型，它以米级分辨率模拟建筑物和结构周围的大气和示踪剂流动。流入量的不确定性通过向前和向后的拉格朗日色散计算进行传播，以确定对示踪剂传输的影响以及估算未知源释放位置的能力。我们的不确定性方法与在俄克拉荷马城进行的2003年联合城市示踪剂释放实验中密集观察期的测量结果进行了比较。在选定的时间段内，距地面50 m处的流入量的最佳估计值是风速和风向分别为4.6（-2.5）（+ 2.0）ms（-1）和158.0（-23）（+ 16），其中不确定性是95％的置信度范围。先前研究中规定的风速值与我们的最佳估计值相差两个或多个标准偏差。流入概率还用于加权向后扩散羽流并生成可能的示踪剂释放位置的空间图。对于俄克拉荷马城的情况，此地图将已知释放的位置精确定位在20 m以内。通过评估与其他可能的释放位置相关的分散模式，我们进一步表明，流入不确定性可以解释模拟和测量的示踪剂浓度之间的差异。 （C）2016作者。由Elsevier Ltd.发行。这是CC BY-NC-ND许可下的开放获取文章。

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