## 3 Results
### Atmospheric Stability in Anholt OWF  
To find out how the error with actual power generation was related to changes in atmospheric stability in the atmospheric boundary layer, first, the atmospheric stability distribution characteristics in the vicinity of the Anholt offshore wind farm were analyzed. Figure 14: Monthly error rates of simulated power output based on REWS (Blue) and HHWS (Red) and the actual power output, and the distribution of wind speed differences between REWS and HHWS (Gray). These are the analysis data for turbines A01 and C01, respectively, and November, December, and January, which are close to winter, and May, June, and July, which are close to summer, are analyzed, respectively. As explained in Section 2.4, atmospheric stability in this study was analyzed based on wind shear, turbulence intensity, and Richardson number. All atmospheric stability indicators were classified into five states according to the criteria in Table 3 (strong unstable, unstable, neutral, stable, and strong stable). First, atmospheric stability analysis based on wind shear was performed. Figure 15 shows the result of air stability classification after calculating the wind shear coefficient from wind speed data at (40 and 116) m (the y-axis altitude in the figure is the corrected value offset by 25 m from the offshore platform). When the vertical wind speed profiles are classified by atmospheric stability, it is clear that the more unstable the atmosphere, the smaller the difference in wind speed between the upper and lower layers, and sometimes the wind speed in the upper layer is smaller than that in the lower layer. Based on offshore LiDAR observations, the wind shear standard atmospheric stability in the vicinity of the Anholt offshore wind farm can be said to be unstable or neutral (average wind shear coefficient of 0.11). In general, atmospheric stability is expressed as neutral when the wind shear coefficient is 0.143, according to the 1/7 rule in micrometeorology. Figure 16 confirms the changes in the daily wind shear coefficient and the distribution of the appearance frequency of atmospheric stability. The detailed wind shear changes in the rotor disk reveal why an error can be made by considering only the hub altitude and wind speed. It can be predicted that various wind shear coefficients and changes with time will affect the load and torque of the turbine, including the blades. This can be a major cause of the increase or decrease in the amount of power generation. Figure 15: Wind shear-based vertical wind speed profile types for each atmospheric stability using two-year observation data from Anholt offshore LiDAR. Red square, orange square, gray cross, green circle, and blue circle represent strongly unstable, unstable, near-neutral, stable, and strongly stable, respectively. Yellow diamond represents the all-averaged vertical wind speed profile. The second is the classification of atmospheric stability according to the intensity of turbulence. Figure 17 derives cross-section analysis results for wind speed and turbulence intensity at the Anholt offshore wind farm. Analysis was performed on the part marked with a white solid line in the domain of the offshore wind farm displayed in the upper right corner. It can be said that the longitude range corresponding to the horizontal axis of each figure coincides with the white solid line indicated in the domain. The upper part in  
Figure 16: (**a**) Daily change in wind shear coefficient, and (**b**) atmospheric stability frequency distribution ratio for each height. Wind shear coefficient between (85 and 140) m has a distribution of (0.08–0.12), and atmospheric stability is unstable or near-neutral. Figure 17 shows the wind speed and turbulence intensity in the northern part of the wind farm, where the distance between the offshore wind turbines is dense. On the other hand, the lower part of Figure 17 is the southern part of the offshore wind farm close to where the five wind turbines to be analyzed are located.