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Scaling Anisotropy and Complexity in Near‐Surface Atmospheric Turbulence

机译:近地表大气湍流的尺度各向异性和复杂性

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摘要

The development of a unified similarity scaling has so far failed over complex surfaces, as scaling studies show large deviations from the empirical formulations developed over flat and horizontally homogeneous terrain as well as large deviations between the different complex terrain data sets. However, a recent study of turbulence anisotropy for flat and horizontally homogeneous terrain has shown that separating the data according to the limiting states of anisotropy (isotropic, two‐component axisymmetric and one‐component turbulence) improves near‐surface scaling. In this paper we explore whether this finding can be extended to turbulence over inclined and horizontally heterogeneous surfaces by examining near‐surface scaling for 12 different data sets obtained over terrain ranging from flat to mountainous. Although these data sets show large deviations in scaling when all anisotropy types are examined together, the separation according to the limiting states of anisotropy significantly improves the collapse of data onto common scaling relations, indicating the possibility of a unified framework for turbulence scaling. A measure of turbulence complexity is developed, and the causes for the breakdown of scaling and the physical mechanisms behind the turbulence complexity encountered over complex terrain are identified and shown to be related to the distance to the isotropic state, prevalence of directional shear with height in mountainous terrain, and the deviations from isotropy in the inertial subrange.
机译:到目前为止,统一相似度标度的开发在复杂的曲面上失败了,因为标度研究显示,与在平坦和水平的均质地形上开发的经验公式存在较大偏差,并且在不同的复杂地形数据集之间也存在较大偏差。但是,最近对平坦和水平均匀地形的湍流各向异性的研究表明,根据各向异性的极限状态(各向同性,两分量轴对称和单分量湍流)分离数据可以改善近地表比例。在本文中,我们通过检查在从平坦到山区的地形上获得的12种不同数据集的近地表缩放比例,来探索这一发现是否可以扩展到倾斜和水平异质表面上的湍流。尽管当一起检查所有各向异性类型时这些数据集在缩放方面显示出较大的偏差,但是根据各向异性的极限状态进行的分离会显着改善将数据崩溃到常见缩放关系上的可能性,这表明有可能采用统一框架进行湍流缩放。研究了湍流复杂度的度量,并确定了比例分解的原因和在复杂地形上遇到的湍流复杂性背后的物理机制,并证明与到各向同性状态的距离,方向剪切的普遍性和高度有关。山区,以及惯性子范围内各向同性的偏差。

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