<title>Segmentation-independent estimates of turbulence parameters</title>

Abstract

We present a new approach for analyzing local power law processes and apply it to temperature measurements from the upper atmosphere. We segment the data and use the wavelet scale spectrum to estimate the parameters of the power law, the scale factor and the exponent. These parameters vary from segment to segment. Part of this variation is due to the non-stationarity of the data. Another part is due to estimation errors that depend on the segmentation. In this paper show h o w to remove e ectively these segmentation dependent v ariations.

Characterization of local turbulence in magnetic confinement devices

“…Important issues, relevant for interpretation of results, such as the choice of segmentation and filtering to smooth out the effects of segmentation, are described in detail in [22] and [25]. The intervals of stationarity are determined from a variogram (second order structure function) analysis of the wavelet coefficients.…”

“…The lower limit of the segment size is governed by the maximum temporal extent of the inertial range. No effects of partitioning on estimation of the inertial range or the turbulence parameters σ and H were detected due to the procedure for removal of segmentation dependent effects [22,25].…”