Empirical Mode Decomposition (EMD) is a powerful signal processing technique with diverse applications, particularly in the analysis of non-stationary data. In this study, we assess the capabilities of EMD for wind data analysis, aiming to uncover its effectiveness in capturing intricate temporal patterns and decomposing data into Intrinsic Mode Functions (IMFs) to identify crucial frequency components. Various methods of sifting have been studied as the IMFs and therefore results may vary according to the type. It has been concluded that the Ensemble Empirical Mode Decomposition (EEMD) is the most suitable method for these data. A comparison with Fourier analysis is also conducted to elucidate the strengths and limitations of each method. Furthermore, this investigation examines the Average Diurnal Variation (ADV) and Average Seasonal Variation (ASV) patterns within the wind data. It is found that these patters have a physical significance and interpretation of the IMFs and that it is easier to use EMD than Fourier for wind signals
