# Can LiDARs Replace Meteorological Masts in Wind Energy?
## 3 Comparison of LiDAR Measurement with Sonic and Cup Anemometer
### Effect of Terrain  
LiDAR measurements in complex terrain are considered to be prone to larger error and higher uncertainties. The horizontal homogeneity assumption of the DBS technique is not valid in complex terrain which induce inhomogeneity to the local atmospheric boundary layer flow. Past studies used models and computational fluid dynamics (CFD) simulations to correct the error due to complex terrain in the LiDAR measurement [11; 33]. However, these and other models are calibrated for the topography of specific sites. As stated earlier, use of multiple LiDARs is also being explored to measure wind speed in complex terrain [34]. This section discusses the accuracy of the classical DBS mode for moderately complex terrain using a long-term measurement campaign.  
Figure 12: Probability density function (PDF) of the maximum wind speed in every 10-min time slot at 57 m height. Bin size for PDF is 1 m/s of peak wind speed.  
Figure 13: Gust factor (\(G=\widehat{V}/\overline{V}\)) as a function of mean wind speed at 57 m height. (**a**) Sonic anemometer; (**b**) V2 profiling LiDAR. The lines indicate the 95 th percentile of the measured data.  
As shown in Figure 2, the ruggedness index of the FREA test site for sector 60\({}^{\circ}\) to 180\({}^{\circ}\) is almost 0%, i.e., the site is homogeneous for this sector. On the other hand, from 270\({}^{\circ}\) through 330\({}^{\circ}\), the ruggedness index is 38 to 65%, and thus the wind blowing from this sector should be influenced by moderately complex terrain. Figure 14 shows wind roses from the sonic anemometer measurement and the V2 LiDAR measurement at 57 m height. It can be appreciated that both the sonic anemometer and the LiDAR show a similar distribution of wind speeds and directions. As can be seen in the figures, the dominant wind directions are WNW and NW. All wind speeds above 12 m/s blow from these two directions. However, these directions lie in the sector with moderately complex terrain. The fraction of wind speeds blowing from any other direction is less than 10%.  
Figure 15 compares the LiDAR measurement for homogeneous terrain (wind direction range 60\({}^{\circ}\) to 180\({}^{\circ}\)) and moderately complex terrain (wind direction range 270\({}^{\circ}\) to 330\({}^{\circ}\)) against the corresponding sonic anemometer data at 57 m height. Note that the number of data for the homogeneous terrain comparison is 4743, while that for the moderately complex terrain comparison is 17,232. For the mean horizontal wind speed, both the homogeneous and complex terrain data sets show good agreement with the sonic anemometer measurement, although the root-mean-square error (RMSE) is higher for the latter. Since the mean wind speeds and number of data sets are lower for the homogeneous case, it is not possible to accurately quantify the comparison. Nevertheless, one can conclude that the LiDAR-measured mean wind speed is accurate for moderately complex terrain. Regarding the standard deviation, \(R^{2}\) is 0.916 for the homogeneous terrain case and 0.975 for the complex terrain case, while the RMSE is 0.112 and 0.144 respectively for the homogeneous and complex terrain cases. The LiDAR performance is roughly the same for both cases. The long-term measurements employed in the current study must have been affected by changing atmospheric stability. For example, even when the wind is blowing from the direction of homogeneous terrain, if the ABL is thermally unstable (i.e., convective ABL), the flow will be non-uniform. Therefore, measured data should be further separated based on the ABL stability. This will be considered in future studies.  
Figure 14: Wind rose showing wind speed and wind direction as measured at 57 m height. (**a**) Sonic anemometer; (**b**) V2 LiDAR.