We studied the turbulent interactions among vertical wind velocity and temperature time-series measured in the Amazonian forest, during the wet season campaign of Large Biophere-Atmosphere Experiment in Amazonia (LBA) in 1999. The approach is based on the estimation of the correlation coefficient between the different scales in turbulent fields and Cross Wavelet Power (XWP). The results suggest that the correlations among scales of the vertical wind velocity are due to the Coherent Structures (CS), a large scale signature in the thermal profile. These coherent structures, kind of ramps, promoted an increase in the interaction among both variables, vertical wind velocity and temperature, and also depends on the atmospheric stability conditions. Furthermore, these coherent structures may explain the higher values of the correlation coefficient found in the large scales during the diurnal period compared with the nocturnal period, for the vertical wind velocity.

Nowadays, wavelet analysis of turbulent flows have become increasingly popular. However, the study of geometric characteristics from wavelet functions is still poorly explored. In this work we compare the performance of two wavelet functions in extracting the coherent structures from solar wind velocity time series. The data series are from years 1996 to 2002 (except 1998 and 1999). The wavelet algorithm decomposes the annual time-series in two components: the coherent part and non-coherent one, using the daubechies-4 and haar wavelet function. The threshold assumed is based on a percentage of maximum variance found in each dyadic scale. After the extracting procedure, we applied the power spectral density on the original time series and coherent time series to obtain spectral indices. The results from spectral indices show higher values for the coherent part obtained by daubechies-4 than those obtained by the haar wavelet function. Using the kurtosis statistical parameter, on coherent and non-coherent time series, it was possible to conjecture that the differences found between two wavelet functions may be associated with their geometric forms.

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Mandelbrot and Van Ness' fructionul Browniun motion (fBm) has been considered a suitable model to describe the long-range correlations exhibited by a wide variety of physiological signals, such as the heart rate. The fBm is a temporal fractal whose properties of self-affinity and infinite inter-dependency of its increments are characterized by a parameter H , referred to as the Hurst exponent. This parameter is quite useful in analysing biomedical signals and determining certain pathologies or physiological states these signals may exhibit. Over the yews, several algorithms for the computation of H have been published. In this study we compme the performance of the well-known Higuchi's method, with two other relatively recent ones. DePetrillo's and Chang's methods. The results reveal that the last one seems to he the most accurate in analysing these sequences.

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