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Concentration Fluctuations of Single Particle Stochastic Lagrangian Model Assessment with Experimental Field Data
Atmosphere ( IF 2.9 ) Pub Date : 2021-05-01 , DOI: 10.3390/atmos12050589 Enrico Ferrero , Filippo Maccarini
Atmosphere ( IF 2.9 ) Pub Date : 2021-05-01 , DOI: 10.3390/atmos12050589 Enrico Ferrero , Filippo Maccarini
A single particle Lagrangian Stochastic model has been developed and applied with the purpose of simulating the concentration fluctuations dispersion. This model treats concentration variance as a quantity whose motion is driven by an advection-diffusion process so that it can be studied by a single particle model. A parameterization for both velocity standard deviations and Lagrangian time-scales is required as input to the model. The paper is focused on the estimation of the best parameterization needed to simulate both mean and standard deviation concentrations in a case study. We consider the FFT-07 field experiment. The trials took place at Dugway Proving Ground, UTAH (USA) and consist of a dispersion analysis of a gas emitted from a point-like source in different atmospheric conditions with a continuous emission technique. The very small spatial scales (a few hundred meters) and short duration (about 10 minutes) that characterize the trials make the comparison with model results very challenging, since traditional boundary layer parameterizations fail in correctly reproducing the turbulent field and, as a consequence, the dispersion simulation yields unsatisfactorily results. We vary the coefficients of the turbulence parameterization to match the small-scale turbulence. Furthermore, we show that the parameterization for the variance dissipation time-scale, already tested in neutral conditions, can be used also in stable and unstable conditions and in low-wind speed conditions. The model gives good results as far as mean concentration is concerned and rather satisfactory results for the concentration standard deviations. Comparison between model results and observation is shown through both statistical and graphical analyses.
中文翻译:
实验场数据对单粒子随机拉格朗日模型评估的浓度波动
为了模拟浓度波动分散,已经开发并应用了单粒子拉格朗日随机模型。该模型将浓度方差视为其量由对流扩散过程驱动的量,以便可以通过单个粒子模型对其进行研究。速度标准差和拉格朗日时间尺度的参数化都需要作为模型的输入。本文着重于对案例研究中模拟均值和标准差浓度所需的最佳参数化的估计。我们考虑FFT-07现场实验。该试验在美国犹他州达格威(Dugway)试验场进行,包括采用连续排放技术对点状气源在不同大气条件下排放的气体进行弥散分析。由于传统的边界层参数化无法正确地再现湍流场,因此,表征试验的非常小的空间尺度(几百米)和持续时间短(约10分钟)使得与模型结果的比较非常具有挑战性。色散模拟的结果令人满意。我们改变湍流参数化的系数以匹配小尺度湍流。此外,我们表明,已经在中性条件下进行测试的方差耗散时间尺度的参数化也可以在稳定和不稳定条件下以及低风速条件下使用。就平均浓度而言,该模型给出了良好的结果,而对于浓度标准偏差,则给出了令人满意的结果。
更新日期:2021-05-02
中文翻译:
实验场数据对单粒子随机拉格朗日模型评估的浓度波动
为了模拟浓度波动分散,已经开发并应用了单粒子拉格朗日随机模型。该模型将浓度方差视为其量由对流扩散过程驱动的量,以便可以通过单个粒子模型对其进行研究。速度标准差和拉格朗日时间尺度的参数化都需要作为模型的输入。本文着重于对案例研究中模拟均值和标准差浓度所需的最佳参数化的估计。我们考虑FFT-07现场实验。该试验在美国犹他州达格威(Dugway)试验场进行,包括采用连续排放技术对点状气源在不同大气条件下排放的气体进行弥散分析。由于传统的边界层参数化无法正确地再现湍流场,因此,表征试验的非常小的空间尺度(几百米)和持续时间短(约10分钟)使得与模型结果的比较非常具有挑战性。色散模拟的结果令人满意。我们改变湍流参数化的系数以匹配小尺度湍流。此外,我们表明,已经在中性条件下进行测试的方差耗散时间尺度的参数化也可以在稳定和不稳定条件下以及低风速条件下使用。就平均浓度而言,该模型给出了良好的结果,而对于浓度标准偏差,则给出了令人满意的结果。