Extracting Hidden Features of an Atmospheric Turbulent Flow Through Implementing Discrete Wavelet Analysis on Sodar’s Signal

Understanding dynamic nature of atmospheric turbulences is an important issue in different topics of environmental researches. The first step of this understanding is subjected to collect wind data in an adequate way and later analyzing them with suitable mathematical methods. Nowadays due to significant limitations of employing traditional wind sensors, utilizing remote sensing equipment like Sodar is widespread in practical wind data collection. During Samar storm occurrence in September 2015 in south of Iran, simultaneous operation of a Sodar device and wind sensors mounted on a Met-Tower provided a unique situation to investigate dynamic nature of the turbulent flow induced by the storm. However, data provided by Met-Tower’s sensors suffered from less accuracy due to wake effect caused by tower’s structure. To probe the essence of turbulence, one conventional way is to calculate power spectral densities of observed wind signals. Although this calculation provides some fundamental information about turbulence structure of the flow, revealing hidden physical aspects of the phenomenon needs more innovative methods. One of partly new method in the field is to implement discrete wavelet analysis on observed wind signals which sheds light on latent properties of the flow. In this study turbulence essence of Samar storm is investigated through calculating Welch power spectral density and implementing discrete wavelet analysis on wind signal observed by Sodar. The outcomes of current research bring out hidden features of turbulent flow from shadow to light. Furthermore superiority of Sodar for investigating atmospheric flows is emphasized.