Retrieving accurate turbulence intensity from motion-corrupted floating lidar measurements is one of the main challenges of the offshore wind energy industry today. Toward overcoming this challenge, this work describes a method to retrieve the motion-corrected turbulence intensity from floating lidar measurements. The method relies on an atmospheric turbulence model combined with lidar-based flow characteristics estimates. The method simulates floating-lidar and anemometer-like measurements via synthetically-generated wind fields in turbulence boxes. We highlight the importance of accurate modeling of turbulence-box parameters to virtually eliminate motion-induced error. The proposed method was successfully tested over a 3-month campaign at IJmuiden by comparing floating-lidar turbulence measurements against those from measurements from a reference meteorological mast.

This work focuses on the feasibility of radial spatial averaging to improve satellite Synthetic Aperture Radar (SAR) ocean Horizontal Wind Speed (HWS) retrievals even in neutral atmosphere situations. The reference for comparison are in-situ HWS measurements from the FINO-1 offshore metmast. The data used from the Sentinel-1 SAR are the Level 2 Ocean (OCN) products for wind, wave, and current applications, retrieving the information from the Ocean Wind Field (OWI) component through Geophysical Model Function (GMF) CMOD-IFR2. The GMF allows for estimation of the equivalent wind speed at 10 meters and, therefore, stable and convective regimes can bias the estimations. The best agreement between SAR and FINO-1 was found -as expected- for neutral atmospheres, which yielded a determination coefficient of R2= 0.90 and a root mean squared error (RMSE) of 1.2 m/s. In non-neutral scenarios, statistical indicators showed that fine-tuned spatial averaging is also able to improve SAR HWS retrievals and yielded comparable statistical indicators (R2= 0.87, RMSE = 1.1 m/s) for the HWS estimates.

This work presents a novel method to statistically compensate for the apparent motion-induced turbulence in floating Doppler-Wind LiDAR measurements. The method relies on the analytical model [1], recently published by the authors, applied to the ZephIRTM300 continuous-wave LiDAR. Performance of the method is studied over two floating LiDAR measurement sites/configurations, a mild near-offshore Mediterranean site and an open-sea site (IJmuiden, North Sea), and compared to fixed LiDAR measurements as a reference. The method outperforms other motion compensation approaches based on turbulence increment estimation with less computational effort.

This work analyzes the influence of open-sea wave motion on the performance of the floating Doppler wind lidar motion-compensation method [1], recently published by the authors. The method is applied to a floating ZephIR 300 LiDAR and compared to a fixed LiDAR as a reference. The motion-correction results are studied as a function of the wave-motion intensity by clustering the IJmuiden experimental campaign into three categories: low, medium, and high motion.

This work studies the capabilities of the k-nearest-neighbours (KNN) method to estimate the atmospheric stability from floating Doppler wind lidar measurements of the vertical wind profile. A procedure to generate synthetic wind profiles to be used as training data for the KNN is presented. The method is validated with experimental data gathered during IJmuiden campaign by a floating Doppler wind lidar against a metmast as a reference. A global hit rate of 70.2% is obtained when comparing the predicted stability classes to the reference ones.

Floating-lidar-derived wind profiles are used to retrieve the vertical wind speed gradient, dimensionless wind shear, and dimensionless stability using Monin-Obukhov similarity theory (MOST) in the context of IJmuiden campaign. Reference retrievals are obtained by applying the same methodology to metmast-derived wind profiles. Wind gradient estimates from floating lidar measurements are successfully compared to those from the reference metmast at three different heights, yielding coefficients of determination higher than ρ2= 0.83. MOST flux relationships are also re-encountered when representing the dimensionless wind shear estimates as a function of dimensionless stability.

Wind profiles measured at the research platform FINO1 are used to retrieve surface boundary-layer parameters, namely, the friction velocity, heat flux, and Obukhov length. The latter is used to assess atmospheric stability. Two different retrieval algorithms, the 2D parametric solver [1] and the Hybrid-Wind method [2], are compared with reference to sonic-anemometers retrievals. Regarding atmospheric stability classification, the 2D algorithm provided better performance than the Hybrid-Wind method. When comparing the 2D-estimated (HW-estimated) friction velocity to the anemometers’ reference, a determination coefficient of ρ2= 0.70 (ρ2= 0.00) were obtained.

We present preliminary research on a method to estimate Vertical Air Motion (VAM) at a particular height by comparing the measured rain-rate (RR) by a vertically-pointing S-band Frequency-Modulated Continuous-Wave (FMCW) radar with that of a ground-based disdrometer. The method is based on a constrained parametric solver, assuming high correlation between 5-min averaged rain rates measured by the radar and disdrometer. The method is tested over disdrometer and radar observations during the Verification of the ORigins Tornado EXperiment in South East US (VORTEX-SE) project. Finally, the results are partially validated by means of fitting a gamma distribution to the VAM-corrected DSD profiles and studying its parameters.

This work studies the influence of the number of lidar measurement heights on the performance of the floating Doppler wind lidar motion-correction algorithm, recently published by the authors. The work is in the context of offshore wind energy and continuous-wave focusable ZephirTM 300 lidar. A downsampling technique applied over the lidar-measured wind speed time-series is used to simulate different height-sounding configurations. The operation of the filter under one, three, and five measurement heights of the lidar is studied by using data from El Pont del Petroli measurement campaign. The filter is proved to remove apparent turbulence addition in all three cases, showing a deterioration of statistical indicators as the number of sounding heights increase.

In this work, the 2D parametric-solver algorithm [1] used to assess atmospheric stability from floating Doppler wind lidar (FDWL) measurements is revisited. The algorithm performance is studied using data from IJmuiden campaign. Mast-measured temperature and wind-speed provided the reference parameters used to evaluate the performance of the stability estimation algorithm. From 5,922 10-min samples available, the algorithm classified the atmosphere as stable (52% of the cases), neutral (31%) and unstable (17%), which successfully agreed with the mast-derived reference classification (53%, 30% and 17%, respectively).

This study presents a wind retrieval simulator of a floating Doppler Wind Lidar (DWL) with 6 Degrees of Freedom (DoF) motion. The simulator considers a continuous-wave conical-scanning floating DWL which retrieves the wind vector from 50 radial velocities per scan. The 6 DoF motion framework as well as the most important buoy motion equations are summarized. Rotational and translational motion effects over HWS measurements are studied parametrically.

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 off-shore 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.).

The following topics are dealt with: remote sensing; geophysical image processing; synthetic aperture radar; radar imaging; remote sensing by radar; image classification; learning (artificial intelligence); feature extraction; neural nets; vegetation.

This paper tackles atmospheric stability typing using a Zephyr™ 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 April 1stto 30th, 2015. Concerning stability typing, both lidar- and metmast-derived wind-shear indicators overestimated by 4% and 14%, respectively, the most frequent atmospheric stability case, which was convective (48% of the cases) according to the potential temperature reference indicator.