LAUSR

Wind field simulation in the surface layer is often used to manage natural resources in terms of air quality, gene flow (through pollen drift), and plant disease transmission (spore dispersion). Although Lagrangian stochastic (LS) models describe stochastic wind behaviors, such models assume that wind velocities follow Gaussian distributions. However, measured surface-layer wind velocities show a strong skewness and kurtosis. This paper presents an improved model, a non-Gaussian LS model, which incorporates controllable non-Gaussian random variables to simulate the targeted non-Gaussian velocity distribution with more accurate skewness and kurtosis. Wind velocity statistics generated by the non-Gaussian model are evaluated by using the field data from the Cooperative Atmospheric Surface Exchange Study, October 1999 experimental dataset and comparing the data with statistics from the original Gaussian model. Results show that the non-Gaussian model improves the wind trajectory simulation by stably producing precise skewness and kurtosis in simulated wind velocities without sacrificing other features of the traditional Gaussian LS model, such as the accuracy in the mean and variance of simulated velocities. This improvement also leads to better accuracy in friction velocity (i.e., a coupling of three-dimensional velocities). The model can also accommodate various non-Gaussian wind fields and a wide range of skewness–kurtosis combinations. Moreover, improved skewness and kurtosis in the simulated velocity will result in a significantly different dispersion for wind/particle simulations. Thus, the non-Gaussian model is worth applying to wind field simulation in the surface layer. 准确模拟大气表层的风场能够有效帮助管理自然资源,例如空气质量,花粉基因流向,以及植物病毒传播等。拉格朗日随机模型是一种风场模拟模型,能够有效模拟风的随机行为。该模型假设风速服从高斯分布,然而实测大气表层风速分布具有很强的偏度和峰度。为了更加准确地模拟实测风场,本文提出了一种改进模型,称作非高斯拉格朗日随机模型。该模型以非高斯风速分布为目标,结合可控的非高斯随机变量,生成符合预期偏度和峰度的随机数序列。为了验证模型的正确性,本文利用CASES(Cooperative Atmospheric Surface Exchange Study)于1999年10月实测的大气表层风场数据,评估模型的模拟效果,并与传统的高斯模型进行对比。结果表明,非高斯模型能够显著提高风速偏度和峰度的模拟准确性,并具有较好的稳定性。于此同时,非高斯模型够维持传统模型的其它所有优点,例如在风速平均值和方差上的模拟准确性。此外,提高风速偏度和峰度准确性还使得非高斯模型能够生成更好的摩擦速度。非高斯模型还能够应用于不同类型的非高斯风场,广泛适应不同的偏度和峰度组合。再者,虽然非高斯模型与传统高斯模型仅仅在风速偏度和峰度模拟上有所差别,但两个模型模拟出的风/微粒传播轨迹却有显著差异,这意味着非高斯模型在大气表层风场模拟上可能具有良好的应用价值。