Understanding atmospheric turbulence is essential for evaluation of weather forecasting and atmospheric models (Lee et al., 2020) and the study of pollutant dispersion (Cohan et al., 2011) in the Atmospheric Boundary Layer (ABL). According the Monin-Obukhov Similarity Theory (MOST), the Integral Turbulence Characteristics (ITC), are used to characterize the state of turbulence at all frequencies (Foken, 2017). These ITC are useful to assess the quality of eddy covariance flux measurements (Foken et al., 2012), to estimate the fluxes by the flux-variance method (Hsieh et al., 2008). However, due to the non-universality of ITC models, more investigations are necessary, especially in tropical regions where low wind conditions frequently occur. In this study realized above a forest site in Benin, we have (1) analyzed the dependence of the ITC for different seasons (dry and wet) and transitional phases (drying, moistening); (2) examined whether these relationships follow or not the MOST and build data-driven ITC models and (3) investigated the efficiency of turbulent transfer at the study site.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.