Multi-fractal analysis of nocturnal boundary layer time series from the Boulder Atmospheric Observatory.
DeCaria, Alex Joseph
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Time series from a nocturnal boundary layer are analyzed using fractal techniques. The behavior of the self-affine fractal dimension, D A , is found to drop during a gravity wave train and rise with turbulence. D A is proposed as a time series conditional sampling criterion for distinguishing waves from turbulence. Only weak correlations are found between DA and bulk turbulence measures such as Brunt-Vaisala frequency, Richardson number, and buoyancy length. The advantages of DA analysis over turbulent kinetic energy (TKE), its component variances, FFT spectra, and self-similar fractals are also discussed in terms of local versus global basis functions, dimensional suitability, noise, algorithmic complexity, and other factors. DA was found to be the only measure capable of reliably distinguishing the wave from turbulence.
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