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The present paper provides a method to identify the appropriate time-scales at which turbulence components behave quasi-isotropically. In particular, the scales are identified on the basis of an analysis of anisotropic turbulence in the spectral domain. The definition of the spectral anisotropic tensor in terms of ogive functions, rather than of (co)spectra, allows for the evaluation of the overall degree of isotropy associated with time-scales smaller than a given one. In this way, the time-scale separating isotropic from anisotropic turbulence is related to the frequency at which the degree of isotropy meets a threshold value, representative of quasi-isotropic turbulence. The procedure is tested on a dataset of wind speed components and sonic temperature collected on the floor of the Adige Valley (northeastern Italian Alps), which is adopted as a case-study. Finally, the suitability of the estimated time-scales is assessed in the framework of the Monin-Obukhov Similarity Theory by evaluating the agreement of the dimensionless standard deviations with the similarity functions.

We present a refinement of the recursive digital filter proposed by McMillen (Boundary-Layer Meteorol 43:231-245, 1988), for separating surface-layer turbulence from low-frequency fluctuations affecting the mean flow, especially over complex terrain. In fact, a straightforward application of the filter causes both an amplitude attenuation and a forward phase shift in the filtered signal. As a consequence turbulence fluctuations, evaluated as the difference between the original series and the filtered one, as well as higher-order moments calculated from them, may be affected by serious inaccuracies. The new algorithm (i) produces a rigorous zero-phase filter, (ii) restores the amplitude of the low-frequency signal, and (iii) corrects all filter-induced signal distortions.

Evapotranspiration (ET) represents one of the essential processes controlling the exchange of energy by terrestrial vegetation, providing a strong connection between energy and water fluxes. Different methodologies have been developed in order to measure it at different spatial scales, ranging from individual plants to an entire watershed. In the last few years, several methods and approaches based on remotely sensed data have been developed over different ecosystems for the estimation of ET. In the present work, we outline the correlation between ET measured at four eddy covariance (EC) sites in Italy (situated either in forest or in grassland ecosystems) and (1) the emissivity contrast index (ECI) based on emissivity data from thermal infrared spectral channels of the MODIS and ASTER satellite sensors (CAMEL data-set); (2) the water deficit index (WDI), defined as the difference between the surface and dew point temperature modeled by the ECMWF (European Centre for Medium-Range Weather Forecasts) data. The analysis covers a time-series of 1 to 7 years depending on the site. The results showed that both the ECI and WDI correlate to the ET calculated through EC. In the relationship WDI-ET, the coefficient of determination ranges, depending on the study area, between 0.5 and 0.9, whereas it ranges between 0.5 and 0.7 when ET was correlated to the ECI. The slope and the sign of the latter relationship is influenced by the vegetation habitat, the snow cover (particularly in winter months) and the environmental heterogeneity of the area (calculated in this study through the concept of the spectral variation hypothesis using Rao’s Q heterogeneity index).