The world’s energy consumption is rising, which will eventually result in the depletion of fossil fuels. A transition from fossil fuels to renewable energy sources as main energy supply has to be achieved to ensure the world’s energy supply on the long term [9]. To ensure the world’s energy supply, the European Commission adopted the climate and energy package with binding legislations, known as the 20-20-20 targets. To reach the targets set by the European Commission, renewable energy sources have to become less dependent on governmental subsidies and therefore the cost price of renewable energies has to be lowered. For wind turbines, this results in an reduction of the Levelized Cost of Electricity (LCoE) by reducing the CAPEX (Capital Expenditures) and OPEX (Operational Expenditures). One way to do this is to take the Atmospheric Stability into account. More accurate load and power predictions can be executed, which could eventually lead in reduction of CAPEX and OPEX by increasing the accuracy of wind profiles and description of Turbulence Intensity. • Load calculations: More accurate load calculations could result in cost reductions of production, material etc. • Power calculations: More accurate power calculations could result in decrease of uncertainty of the power predictions leading to reduction of the OPEX through decrease of the interest rate. For the estimation of the atmospheric stability multiple classifications systems and methodologies can be used. In this master thesis, the focus will be on the classification of the atmospheric stability with the Monin-Obukhov Similarity Theory (MOST) using multiple methodologies which are used in the industry and research. However, each methodology results to different values for the atmospheric stability. The goal of the project is to compare different methodologies, determine the best for each case and develop guidelines for atmospheric stability estimations.To achieve the goal, a comparison of multiple methodologies is executed using measurement campaigns on multiple levels at three locations. In the research significant differences in distribution, friction velocity and heat flux could be observed as a result of using different multiple methodologies and measurement heights. This is even noticed for the reference methodology, the Eddy Covariance Method, differences are found between different heights at the same site. Analysis of the atmospheric stability distributions, slope and correlation of both the friction velocity and heat flux showed that the Profile Method 2 gives the best approximation of the atmospheric stability conditions, due to the found high correlation and consistency using various measurements heights.The Profile Method 2 uses one wind speed measurement at the tower and two temperature measurements, at the surface and tower. A preference was found for tower measurements around a measurement height of 40-60 meters. At lower altitudes the sensory accuracy significantly influences the temperature difference and at higher altitudes it is often outside of the Surface Boundary Layer where MOST not any more is applicable. The research also showed that the methodologies based on the Richardson Number are strongly influenced by measurements outside the Surface Boundary Layer. Using the approximation of the Surface Boundary Layer height and filtering the timesteps where the measurement height is outside of the Surface Boundary Layer, results in a large data reduction. The resulting distribution can deviate with the local conditions, while equivalent load calculations require typical conditions of the Surface Boundary Layer. Translating various distributions found by different methodologies to the impact on the bending moment of the blade root of a wind turbine, by comparing the equivalent loads resulted in significant differences. The differences in equivalent load vary from 1-2% between the Eddy Covariance Methods at different heights up to around 15% for the Profile Method 1 and Gradient Richardson Number. Using the Profile Method 2, preferred methodology for onshore conditions, gave a difference of only 3% with the reference. Comparing this with the equivalent load found using the IEC guideline, where a 30% difference in equivalent load was found between the IEC guideline and the Eddy Covariance Method. This shows the impact and importance of the accurate determination of the atmospheric stability and possible reduction of LCoE which could be realized, by selecting the most suitable methodology. Finally resulting in the development of an appropriate guideline for atmospheric stability estimation.