# Measurement and Prediction of Wind Fields at an Offshore Site by Scanning Doppler LiDAR and WRF
## 3 Results and Discussion
### Measurement and Prediction of Wind Turbine Wake  
In this section, the flow field around the wind turbine is measured in order to characterize the wind turbine wake. Measuring the wind turbine wake using scanning Doppler LiDAR poses several challenges. In addition to a low sampling rate, PPI scans conducted using LiDAR can only provide measurements in conical planes. An alternative can be to conduct multiple RHI scans, but this method can only be applied when LiDAR is located in the wake of the turbine, and the wind direction does not vary too much. In this study, multiple PPI scans are performed for a sequence of elevation angles. Figure 18 shows a schematic of this scan approach. From the measured LOS velocities, only those at the hub height level are extracted (the grey plane in Figure 18). Wind speed is then constructed from the LOS velocity using Equation (5). Figure 17: Comparison of vertical profiles of WRF simulations against the LiDAR measurements at three different locations from the coast (**a**–**c**). Horizontal dashed lines indicate the hub height level, and the dark profiles indicate the percentage error in the WRF profiles compared to the LiDAR measurement. Data is from 18 November 2016 to 25 December 2016. Figure 19 visualizes the mean flow field around the wind turbine reconstructed from the PPI scans. A velocity deficit, which is a common characteristic of a turbine wake, is clearly visible in the figure. Furthermore, a dual peak is also observed in the near-wake region up to \(x/D=2\). It can be appreciated that the wake region persists approximately 7D downstream from the wind turbine. Note that, for \(x/D\approx 2\), the azimuth angles are approximately perpendicular to the incoming flow and, therefore, the measurement error is high in this region. Several other studies have performed scanning Doppler LiDAR-based measurements of wind turbine wakes. However, reconstruction of velocity fields from LiDAR-measured LOS velocities poses a great challenge. Therefore, most of the studies simply tried to analyze and interpret LOS velocities, and did not perform any further processing of the measured data [26; 27; 28]. As is also mentioned in the Introduction, the assumption of constant wind direction assumption during the measurement period and the application of multiple LiDARs are also receiving a lot of interest in the wind energy community [29; 30]. The method of wind turbine wake measurement with a single Scanning Doppler LiDAR for arbitrary directions proposed in this study is therefore different from those in the literature. Finally, the measurement data is compared against four analytical wake models proposed by [PERSON] [31], [PERSON] et al. [32], [PERSON] and [PERSON] [33], and [PERSON] and [PERSON] [34]. As with most other wake models, the wake models validated in this study require one or more of the following three input parameters: mean inflow velocity, inflow turbulence intensity, and thrust coefficient (\(C_{T}\)) of the turbine. The inflow wind speed and turbulence intensity measured by the met mast at the hub  
Figure 19: Visualization of horizontal mean wind field at hub height obtained from PPI scans collected on 28 September 2016 from 16:00 to 22:45. Vertical dashed lines indicate axial positions where profiles are plotted in Figure 21.  
Figure 18: Schematic of the multiple PPI scans for the measurement and reconstruction of flow field around wind turbine. Wind speed data is extracted in the grey horizontal plane at the hub height level. height levels are used. Figure 20a,b show the time series of the 10-min-averaged wind speed and turbulence intensity. The average inflow velocity during the comparison period was 8.2 m/s, and the turbulence intensity was 3.5%. From the \(C_{T}-u\) characteristic of the turbine, a thrust coefficient value of 0.8 was used [35]. Figure 20c shows one-minute-averaged wind directions and yaw directions of the turbine during the period when the wind turbine wake data was collected. Figure 20d shows the yaw misalignment.