# Measurement and Prediction of Wind Fields at an Offshore Site by Scanning Doppler LiDAR and WRF  
[PERSON]  
1 Department of Civil Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan; [EMAIL_ADDRESS] (J.P.G. ); [EMAIL_ADDRESS] (A.Y.) 1  
[PERSON] and [PERSON]  
1 Department of Civil Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan; [EMAIL_ADDRESS] (J.P.G. ); [EMAIL_ADDRESS] (A.Y.) 1  
Footnote 1: email: {[EMAIL_ADDRESS]} 1  
Footnote 2: email: {[EMAIL_ADDRESS]  
Footnote 3: email: {jay.prakash.goit}@g  
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mail.com  
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for the retrieval of the velocity vector from LiDAR-measured LOS velocity. Two approaches which have found widespread use in the retrieval of vertical profiles of wind speed are velocity azimuth display (VAD) and Doppler beam swinging (DBS). The VAD method employs a complete conical scan to collect closely spaced radial wind speed data [1]. The vertical profile of the wind speed are then deduced by fitting the \(u_{r}\) at each height to the Fourier series using statistical methods, such as least-square analysis [2; 3]. Unlike VAD, the DBS method only requires 4 or 5 beams to analytically deduce the wind speed at each height (cf. Section 2.2). An increasing number of studies have been conducting measurement campaigns to verify LiDAR measurements against tower-mounted cup or sonic anemometers or to compare mean wind speeds measured using different types of LiDARs. [PERSON] et al. evaluated a scanning Doppler LiDAR positioned adjacent to a met mast with calibrated anemometers [4]. They reported excellent correlation between the LiDAR and cup anemometers with a coefficient of determination (\(R^{2}\)) of 0.98 at 100m height. In a measurement campaign by [PERSON] and [PERSON] [5], at a complex terrain, they showed that both the slope and \(R^{2}\) for a LiDAR-cup anemometer comparison was approximately 0.97 at a height of 100 m. [PERSON] et al. investigated the accuracy of LiDAR by comparing long-term wind data against the tower mounted anemometers, and further evaluated the power and load distributions for the NREL 5-MW wind turbine obtained from the LiDAR-measured wind speeds [6]. Several other studies have also conducted similar inter-comparisons between LiDAR-measured wind speeds and wind speeds measured by tower-mounted anemometers (see, e.g., Ref. [7; 8; 9]). However, most of the validation studies were conducted for the altitudes lower than 150 m, and none of these studies investigated the effect of data availability. This indicates that the quality of measurements for several availability limits and for several carrier-to-noise-ratio (CNR) threshold values need to be investigated further. Both DBS and VAD techniques have limitations in that they cannot account for horizontal variations and are therefore unable to generate velocity fields from the volume scan. To address this issue, [PERSON] [10] and [PERSON] and [PERSON] [11] proposed the velocity volume processing (VVP) method, in which volumetric scan data is divided into several small analysis volumes, and the mean wind speed for each volume is estimated using multivariate regression analysis. This method assumes that the actual wind field is spatially linear and time invariant (see Ref. [3; 12] for details). However, the method strongly depends on the size of the analysis volume and the number of measured \(u_{r}\) data points in the volume. This will result in a very coarse resolution of the retrieved wind field in the region further away from the LiDAR. Therefore, the method is not popular in wind energy applications. A practical approach which is more common in wind energy is to perform scans along the mean wind direction, so that no further processing is required [13; 14]. This method, however, has not been validated and it cannot be applied to sites where the wind direction changes with time.