In this thesis, wavelet analysis was used to investigate atmospheric turbulence data in the surroundings of cumulus clouds. Airborne data collected within EUREC4A campaign were analysed. Haar and Morlet wavelets were employed to perform, respectively, discrete and continuous wavelet transforms on the vertical velocity component, temperature fluctuation and turbulent kinetic energy which are all key quantities for atmospheric turbulence description. Signal visualisations were prepared using wavelet coefficients to represent the data simultaneously in time and frequency domains. An attempt was made to define a criterion determining the cloud dynamical boundary using normalised absolute value of the coefficients. A threshold was chosen to determine the extent of the cloud’s dynamical influence. Frequencies contributing less to the signal were filtered out, and a reconstructed signal was compared to the original. The results where discussed qualitatively and compared with those obtained previously with recurrence quantification analysis method.