The primary cause of intermittency in turbulent flows is believed to be the multifractal nature of turbulence. This study investigates the multifractal framework of turbulent variables, including velocity, temperature, and passive scalars \(\hbox {H}_2\) O and \(\hbox {CO}_2\) , in the atmospheric surface layer. The multifractal framework involves the estimation of the multifractal spectrum, D(h), using the scaling exponents, \(\xi _q\) , and the Hölder exponents, h. The turbulent series is initially classified into two distinct categories: large scale, also known as coherent scale, and small scales referred to as incoherent scale. Subsequently, the scaling exponents \(\xi _q\) , derived from wavelet leaders, are employed to estimate the multifractal spectrum, D(h), using the Legendre transform. The collected results are compared with existing multifractal models such as log-normal and log-Poisson in order to determine which model best fits our data. Parameters like log-cumulants, intensity of multifractality ( \(\alpha\) ), and asymmetry ( \(A_h\) ) are calculated to better comprehend turbulent data. The statistical properties of coherent scales or large scales demonstrate a non-Gaussian universality that seems to be unaffected by the criteria for stratification and topography.
