Abstract
Turbulence measurements performed at high frequencies yield data revealing intermittent and multi-scale processes. Analysing time series of turbulent variables thus requires extensive numerical treatment capable, for instance, of performing pattern recognition. This is particularly important in the case of the atmospheric surface layer and specifically in the vicinity of plant canopies, where largescale coherent motions play a major role in the dynamics of turbulent transport processes. In this paper, we examine the ability of the recently developedwavelet transform to extract information on turbulence structure from time series of wind velocities and scalars. It is introduced as a local transform performing a time-frequency representation of a given signal by a specific wavelet function; unlike the Fourier transform, it is well adapted to studying non-stationary signals. After the principles and the most relevant mathematical properties of wavelet functions and transform are given, we present various applications of relevance for our purpose: determination of time-scales, data reconstruction and filtering, and jump detection. Several wavelet functions are inter-compared, using simple artificially generated data presenting large-scale features similar to those observed over plant canopies. Their respective behaviour in the time-frequency domain leads us to assign a specific range of applications for each.
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Collineau, S., Brunet, Y. Detection of turbulent coherent motions in a forest canopy part I: Wavelet analysis. Boundary-Layer Meteorol 65, 357–379 (1993). https://doi.org/10.1007/BF00707033
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DOI: https://doi.org/10.1007/BF00707033