Abstract
We describe a wavelet-based technique to determine the spectral turbulentcontribution to the vertical flux of sensible heat in a position-wavelength representation. This technique combines awavelet transform (Morlet wavelet) with conditional sampling. We apply this methodto aircraft datacollected during a sea-breeze circulation (BEMA97 experiment) with heterogeneousturbulence conditions horizontally and vertically as well. The turbulent fluxes are analysed with the conditional wavelet techniqueas a function of the wavelength and the horizontal distance.The turbulent processes within the thermal internal boundary layer associatedwith the sea breeze are clearly identified. The results exhibit the wavelength bands through which the upward flux (originating fromthe surface) and the downward flux (originating from the top of theboundary layer) are important.
Highlights
The analysis of turbulent signals in the atmospheric boundary layer requires various mathematical tools in order to translate the erratic behaviour of the time-series into statistical parameters
We focus on the vertical flux of sensible heat, which is critical to the distinction of the thermal internal boundary layer (TIBL), the entrainment zone (EZ) and the atmosphere above (Galmarini and Attié, 2000; hereafter GA2000)
We used data collected with an instrumented aircraft during the BEMA97 campaign on the Spanish coast in June 1997
Summary
The analysis of turbulent signals in the atmospheric boundary layer requires various mathematical tools in order to translate the erratic behaviour of the time-series into statistical parameters. Auto covariance, cross-covariance and energy spectra analyses have been widely used, mainly since the rapid improvement of computer performance This scale analysis obtains characteristics valid only for the whole sample. As most atmospheric signals are inhomogeneous, studying the local characteristics of samples is crucial for many problems Recent tools such as a wavelet transform are applied to turbulence signals allowing a local spectral analysis.
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