蒙特卡罗大气扩散模型与示踪剂实验结果的比较

G. Brusasca , G. Tinarelli , D. Anfossi
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引用次数: 47

摘要

提出了一种空气污染物扩散的拉格朗日统计(蒙特卡罗)模型。通过与解析解的比较以及与Willis和Deardorff水箱实验的比较,它能够模拟均匀和非均匀湍流中的大气弥散,这在以前的论文中已经说明了。本文用该模型模拟了真实大气PBL中的色散。数值计算结果与卡尔斯鲁厄核研究中心示踪实验数据进行了对比验证。该模型应用于卡尔斯鲁厄气象塔两个高度(160和195 m)同时释放的两种不同示踪剂的地面浓度预测问题。在模拟的两次示踪实验中,对流不稳定和中性条件普遍存在。通过相对平均偏差和归一化均方误差两个统计指标来评价模型的性能。还计算了观测到的和预测的地面浓度(glcs)逐点比值的累积频率分布。模拟的浓度与观测值吻合得很好。示踪剂数据还与10种高斯模型的模拟结果进行了比较。他们在色散曲线的选择和风速和风向的插入方式上存在差异。在所有的练习中,它们都没有证明比我们的粒子模型表现得更好。
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Comparison between the results of a Monte Carlo atmospheric diffusion model and tracer experiments

A Lagrangian statistical (Monte Carlo) model for airborne pollutant dispersion is presented. Its ability to simulate the atmospheric dispersion both in homogeneous and inhomogeneous turbulence by comparison with an analytical solution and with the Willis and Deardorff water tank experiments, respectively, has been stated in previous papers. In the present paper the model is used to simulate dispersion in the real atmospheric PBL. The numerical results obtained are verified against experimental data from the Karlsruhe Nuclear Research Center tracer experiments. The model is applied to the problem of predicting the ground level concentration of two different tracers simultaneously released from two heights (160 and 195 m) at the Karlsruhe meteorological tower. Convectively unstable and neutral conditions were prevailing during the two tracer experiments which have been simulated. Model performance was evaluated through two statistical indexes: relative mean bias and normalized mean square error. The cumulative frequency distribution of the point-by-point ratio between observed and predicted ground level concentrations (glcs) was also computed. The simulated concentrations agree very well with observations. The tracer data were also compared to the simulations of 10 Gaussian models. They differed one another for the choice of dispersion sigma curves and for the way to insert the wind speed and direction. None of them proved to perform better than our particle model in all the exercises.

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