# Coastal Wind Measurements Using a Single Scanning LiDAR
## 4 Discussion  
For assessing offshore wind conditions, in addition to the mean wind speeds and wind directions, the accuracy of the turbulence intensity (TI) measurements using a scanning LiDAR is also crucial. This parameter is tightly associated with wind turbine design. We discussed the applicability of a single scanning LiDAR for TI measurements by comparing the observations from the 100s with those from the SA at a height of 10 m ASL. Figure 19 shows the time series of the 10-minute mean and standard deviation of wind speed values from the SA and 100s for the period of one week from 1 to 7 May 2019. This corresponds to Case 9 in Table 2. As the SA measurements were strongly influenced by the presence of the neighboring observational hut, observations with wind directions between 49\({}^{\circ}\) and 89\({}^{\circ}\) were excluded from the analysis. The match between the time series of the mean wind speeds recorded by the 100s and the time series of the mean wind speeds recorded by the SA was not quite as good as it was for the 100s-V1 comparisons. In comparing the mean 100s wind speeds to the mean SA wind speeds, we found that the 100s wind speeds compared to the SA wind speeds had a mean deviation of \(-0.17\) m/s (\(-3.4\%\)) and a standard deviation of 0.55 m/s (10.8\(\%\)), and the linear regression equation was y = 0.959x, with a determination coefficient of 0.963. The comparatively poor match may be due to the non-homogeneous nature of the wind field near the surface. Figure 19 indicates that measuring the standard deviations of wind speeds with scanning LiDARs was more challenging than measuring the 10-minute mean wind speeds. We found that, for a time scale of 2 h, the standard deviation of the 100s values followed a trend similar to that of the standard deviations obtained from the SA. However, large differences were observed for the shorter time scales. The sonic anemometer used in this study had a 0.25-second temporal interval (= 4 Hz), while the 100s required approximately 15 s (= 0.067 Hz) to measure horizontal wind speeds and directions with the VVP method. As a result, the 100s collected only 40 samples in a 10-minute duration, while the SA collected 2400 samples during the same time. This difference may explain why the 100s time series of standard deviation values shows greater fluctuations than that the SA time series, as well as the difference in the measurement volumes between the 100s and SA. Figure 18: Distribution of the RAE as a function of the 10-minute mean R\({}^{2}\) from the VVP method. Results are presented for Case 8. The accuracy of the standard deviations obtained from the 100s was worse than that of the mean wind speeds. However, the instantaneous values of the standard deviation of wind speeds are not directly used for wind turbine design. In practice, TI, which is the 10-minute standard deviation of wind speed, normalized by the mean wind speed, is first calculated. The 90% quantile values of TI with a bin width of 1 or 2 m/s are then plotted as a function of the mean wind speed to evaluate the turbulence condition. Figure 20a shows the relationships between the 10-minute mean wind speed and TI of the SA and 100s for Case 9. The dark lines indicate the IEC standards [30] for classifying wind turbines based on site-specific turbulence conditions. The number of samples for each bin is shown in Figure 20b. The TI values produced by the 100s were slightly larger than those by the SA for mean wind speeds in the range of 5-8 m/s; for a mean wind speed of 9-11 m/s, the 100s values were lower. We found that the difference between the SA and 100s values was comparable to or less than the difference between the IEC categories for most ranges. Given that the comparisons here were with an SA installed near the surface, the accuracy of the standard deviation measurements for the 100s at the height of a wind turbine hub might well improve, as might the accuracy of the mean wind speeds and directions.