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Wind energy is rapidly expanding worldwide, and it is common practice to maximize production by selecting sites with higher wind potential. To perform critical operations such as wind flow modeling, wind turbine micro placement, annual energy yield calculation, and cost of energy estimation, a thorough understanding of a site's wind resource is required. The present study examines data from three independent wind measurement systems to see how measured data depends on the choice of the measurement system and how this might forecast the wind resource and, consequently, the energy output of a potential wind farm. 

The present analysis uses three measurement units, one meteorological mast (met mast), and ground-based AQ510 Sound Detection And Ranging (SoDAR) & SoDAR and ZX 300 Light Detection And Ranging (LiDAR) devices to capture wind data for nearly a year. This study describes the operating concept of remote sensing devices such as AQ510 SoDAR and ZX 300 LiDAR, the linear regression relationship between wind speed measured on the Met Mast versus SoDAR, Met Mast versus LiDAR, and SoDAR versus LiDAR. Additionally, an understanding of stratification for this potential wind farm’s site is explored for specific days during spring, summer, and winter. 

The results of the intercomparison study among Met Mast, SoDAR & LiDAR show quite a good relationship between the different measurement systems, being the correlation coefficient between the mast and the LiDAR measurements being slightly larger than between the mast and the SoDAR measurements. Comparison during the stability and instability regimes show a larger difference in some cases. Python and MS Excel are used to build data filtering procedures, the Richardson number, and comparison computations.