This work presents a new methodology to estimate the motion-induced standard deviation and related turbulence intensity on the retrieved horizontal wind speed by means of the velocity-azimuth-display algorithm applied to the conical scanning pattern of a floating Doppler lidar. The method considers a ZephIR™300 continuous-wave focusable Doppler lidar and does not require access to individual line-of-sight radial-wind information along the scanning pattern. The method combines a software-based velocity-azimuth-display and motion simulator and a statistical recursive procedure to estimate the horizontal wind speed standard deviation—as a well as the turbulence intensity—due to floating lidar buoy motion. The motion-induced error is estimated from the simulator’s side by using basic motional parameters, namely, roll/pitch angular amplitude and period of the floating lidar buoy, as well as reference wind speed and direction measurements at the study height. The impact of buoy motion on the retrieved wind speed and related standard deviation is compared against a reference sonic anemometer and a reference fixed lidar over a 60-day period at the IJmuiden test site (the Netherlands). Individual case examples and an analysis of the overall campaign are presented. After the correction, the mean deviation in the horizontal wind speed standard deviation between the reference and the floating lidar was improved by about 70%, from 0.14 m/s (uncorrected) to −0.04 m/s (corrected), which makes evident the goodness of the method. Equivalently, the error on the estimated turbulence intensity (3–20 m/s range) reduced from 38% (uncorrected) to 4% (corrected).

This work provides a signal-processing and statistical-error analysis methodology to assess key performance indicators for a floating Doppler wind lidar. The study introduces the raw-to-clean data processing chain, error assessment indicators and key performance indicators, as well as two filtering methods at post-processing level to alleviate the impact of angular motion and spatial variability of the wind flow on the performance indicators. Towards this aim, the study mainly revisits horizontal wind speed (HWS) and turbulence intensity measurements with a floating ZephIR 300 lidar buoy during a 38 day nearshore test campaign in Pont del Petroli (Barcelona). Typical day cases along with overall statistics for the whole campaign are discussed to illustrate the methodology and processing tools developed.Peer ReviewedPostprint (author's final draft

UPCommonsPortal del coneixement obert de la UPC http://upcommons.upc.edu/e-prints

No abstract

The standard deviation of the Horizontal Wind Speed as a proxy of wind turbulence is used to compare the apparent wind turbulence measured by an offshore floating Doppler lidar to the one measured by a fixed lidar on a metmast. We use statistical analysis based on clustering the horizontal wind speed measured by the floating lidar as well as buoy angular amplitude and period under the approximation of harmonic motion. Three scenarios with different wave and wind conditions are discussed from the IJmuiden's test campaign (North Sea.).

This work proposes a new wave-period estimation (L-dB) method based on the power-spectral-density (PSD) estimation of pitch and roll motional time series of a Doppler wind lidar buoy under the assumption of small angles (±22 deg) and slow yaw drifts (1 min), and the neglection of translational motion. We revisit the buoy’s simplified two-degrees-of-freedom (2-DoF) motional model and formulate the PSD associated with the eigenaxis tilt of the lidar buoy, which was modelled as a complex-number random process. From this, we present the L-dB method, which estimates the wave period as the average wavelength associated to the cutoff frequency span at which the spectral components drop off L decibels from the peak level. In the framework of the IJmuiden campaign (North Sea, 29 March–17 June 2015), the L-dB method is compared in reference to most common oceanographic wave-period estimation methods by using a TriaxysTM buoy. Parametric analysis showed good agreement (correlation coefficient, ρ = 0.86, root-mean-square error (RMSE) = 0.46 s, and mean difference, MD = 0.02 s) between the proposed L-dB method and the oceanographic zero-crossing method when the threshold L was set at 8 dB.

This paper proposes two methods to estimate the so-called "characteristic motional period" of a floating wind-lidar buoy. These techniques aim to characterise the buoy's pitch and roll tilting as simple harmonic motions by estimating their period. The "peak method" (PM) and the "3-dB method" are introduced as two different aproaches to study the multi-modality of the wave motion. Additionally, the offshore wind measurement campaign at Ijmuiden's is briefly introduced to contextualize where data comes from and the added value of this study.

This paper tackles atmospheric stability typing using a Zephyr TM 300 offshore Doppler Wind Lidar in the context of its progressive acceptance in the offshore wind-energy industry. The lidar-retrieved wind-shear exponent, which is used as a proxy atmospheric stability, is compared against the wind-shear exponent and the potential temperature gradient both retrieved from reference metmast. A total sample of 4319 measurements is analysed from IJmuiden's test campaign, in the North Sea, from This work was supported via Spanish Government-European Regional Development Funds project PGC2018-094132-B-I00 and EU H2020 ACTRIS-2 (GA 654109). The European Institute of Innovation and Technology (EIT), KIC InnoEnergy project NEPTUNE (Offshore Metocean Data Measuring Equipment and Wind, Wave and Current Analysis and Forecasting Software, call FP7) supported measurements campaigns. CommSensLab is a Maríade-Maeztu Unit of Excellence funded by the Agencia Estatal de Investigación (Spanish National Science Foundation) that also funded the project MDM-2016-0600-18-1. Spanish NSF