Weibull Wind-Speed Distribution Parameters Derived from a Combination of Wind-Lidar and Tall-Mast Measurements Over Land, Coastal and Marine Sites

Abstract

Wind-speed observations from tall towers are used in combination with observations up to 600 m in altitude from a Doppler wind lidar to study the long-term conditions over suburban (Hamburg), rural coastal (Høvsøre) and marine (FINO3) sites. The variability in the wind field among the sites is expressed in terms of mean wind speed and Weibull distribution shape-parameter profiles. The consequences of the carrier-to-noise-ratio (CNR) threshold-value choice on the wind-lidar observations are revealed as follows. When the wind-lidar CNR is lower than a prescribed threshold value, the observations are often filtered out as the uncertainty in the wind-speed measurements increases. For a pulsed heterodyne Doppler lidar, use of the traditional -22 dB CNR threshold value at all measuring levels up to 600 m results in a ≈7 % overestimation in the long-term mean wind speed over land, and a ≈12 % overestimation in coastal and marine environments. In addition, the height of the profile maximum of the shape parameter of the Weibull distribution (so-called reversal height) is found to depend on the applied CNR threshold; it is found to be lower at small CNR threshold values. The reversal height is greater in the suburban (high roughness) than in the rural (low roughness) area. In coastal areas the reversal height is lower than that over land and relates to the internal boundary layer that develops downwind from the coastline. Over the sea the shape parameter increases towards the sea surface. A parametrization of the vertical profile of the shape parameter fits well with observations over land, coastal regions and over the sea. An applied model for the dependence of the reversal height on the surface roughness is in good agreement with the observations over land.

Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements

“…Due to the simplicity and the establishment of common filtering methods, there have been very few studies dealing with the effects of LiDAR filtering to date. The first critical examination of the influence of CNR-filtering on wind speed distributions was presented by Gryning et al [15]. From Gryning et al [15] and Pal et al [10,11], we interpret that LiDAR data filtering based on a rigid CNR-threshold can lead to inaccurate velocity determination.…”

“…The first critical examination of the influence of CNR-filtering on wind speed distributions was presented by Gryning et al [15]. From Gryning et al [15] and Pal et al [10,11], we interpret that LiDAR data filtering based on a rigid CNR-threshold can lead to inaccurate velocity determination. For quality assurance of the measurement data, a variety of filters may be combined to obtain an outlier free data set [16,17].…”

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“…Doppler lidar measures the mean wind speed with a high and known accuracy; Floors (2013) and Peña et al (2013) found good agreement between wind lidar and cup-anemometer measurements at 100 m for a CNR > −22 dB, with agreement deteriorating as the CNR threshold is lowered (as expected from Figure 2). The relationship between the long-term wind speed and the CNR threshold value is further discussed in Gryning et al (2016). In other words, we can assume U S ≈ U L ≈ U.…”

“…Typically, a threshold of −22 or −23 dB is used as a limit for the accepted uncertainty in the lidar measurements (e.g. Gryning et al, 2016), which corresponds to an uncertainty of about 0.15 m s −1 . The uncertainty is calculated for each radial velocity component separately and propagated into the geographic wind components calculated using the formulations from Päschke et al (2015).…”

Wind Resource Assessment for High-Rise BIWT Using RS-NWP-CFD

“…Slight distortion of wind shear over 400 m above ground level is observed in LIDAR data between the raw data (lines with white circles) and the concurrent data (lines with black circles) which is about 50% of the raw data. In SODAR data, minor difference is shown between the raw and concurrent data below 500 m aboveground where DRR is over 50% [12]. Figure 9 shows the mean wind speed profiles measured using LIDAR (from 10-min data) and SODAR (from 30-min data) for the campaign period, where the data recovery rate (DRR; dashed lines) decreased with increasing altitude.…”