This research investigates a novel method for directly estimating wind velocity variances from averaged Doppler spectra in the frequency domain. Compared to the conventional time-domain approach, the proposed method offers a substantial improvement. Despite some limitations, this study marks a significant advancement in turbulence estimation using pulsed Doppler lidars, which presents promising potential for wind turbine load assessments.

Dual-lidar measurements are explored here as a cost-effective alternative for measuring the wind at great heights. From measurements at a mountainous site, we showed that this methodology can accurately capture mean wind speeds and turbulence under different flow conditions, and we recommended optimal lidar placement and sampling rates. This methodology allows the construction of vertical wind profiles up to 430 m, surpassing traditional meteorological mast heights and single-lidar capabilities.

Three-dimensional wind fields can be accurately measured by sonic anemometers. However, the traditional mast-mounted sonic anemometers are not flexible in various applications, which can be potentially overcome by drones. Therefore, we conducted a proof-of-concept study by applying three continuous-wave Doppler lidars to characterize the complex flow around a drone to validate the results obtained by CFD simulations. Both methods show good agreement, with a velocity difference of 0.1 m s^-1.

By sampling the spectra from continuous-wave Doppler lidars very fast, the rain-induced Doppler signal can be suppressed and the bias in the wind velocity estimation can be reduced. The method normalizes 3 kHz spectra by their peak values before averaging them down to 50 Hz. Over 3 h, we observe a significant reduction in the bias of the lidar data relative to the reference sonic data when the largest lidar focus distance is used. The more it rains, the more the bias is reduced.

This study presents the first experimental investigation using two nacelle-mounted wind lidars that reveal the upwind and downwind conditions relative to a full-scale floating wind turbine. We find that in the case of floating wind turbines with small pitch and roll oscillating motions (< 1°), the ambient turbulence is the main driving factor that determines the propagation of the wake characteristics.