The paper presents the measurement strategy and data set collected during the COTUR (COherence of TURbulence with lidars) campaign. This field experiment took place from February 2019 to April 2020 on the southwestern coast of Norway. The coherence quantifies the spatial correlation of eddies and is little known in the marine atmospheric boundary layer. The study was motivated by the need to better characterize the lateral coherence, which partly governs the dynamic wind load on multi-megawatt offshore wind turbines. During the COTUR campaign, the coherence was studied using land-based remote sensing technology. The instrument setup consisted of three long-range scanning Doppler wind lidars, one Doppler wind lidar profiler and one passive microwave radiometer. Both the WindScanner software and LidarPlanner software were used jointly to simultaneously orient the three scanner heads into the mean wind direction, which was provided by the lidar wind profiler. The radiometer instrument complemented these measurements by providing temperature and humidity profiles in the atmospheric boundary layer. The scanning beams were pointed slightly upwards to record turbulence characteristics both within and above the surface layer, providing further insight on the applicability of surface-layer scaling to model the turbulent wind load on offshore wind turbines. The preliminary results show limited variations of the lateral coherence with the scanning distance. A slight increase in the identified Davenport decay coefficient with the height is partly due to the limited pointing accuracy of the instruments. These results underline the importance of achieving pointing errors under 0.1^∘ to study properly the lateral coherence of turbulence at scanning distances of several kilometres.

Direct covariance flux (DCF) measurements taken from floating platforms are contaminated by wave-induced platform motions that need to be removed before computation of the turbulent fluxes. Several correction algorithms have been developed and successfully applied in earlier studies from research vessels and, most recently, by the use of moored buoys. The validation of those correction algorithms has so far been limited to short-duration comparisons against other floating platforms. Although these comparisons show in general a good agreement, there is still a lack of a rigorous validation of the method, required to understand the strengths and weaknesses of the existing motion-correction algorithms. This paper attempts to provide such a validation by a comparison of flux estimates from two DCF systems, one mounted on a moored buoy and one on the Air–Sea Interaction Tower (ASIT) at the Martha’s Vineyard Coastal Observatory, Massachusetts. The ASIT was specifically designed to minimize flow distortion over a wide range of wind directions from the open ocean for flux measurements. The flow measurements from the buoy system are corrected for wave-induced platform motions before computation of the turbulent heat and momentum fluxes. Flux estimates and cospectra of the corrected buoy data are found to be in very good agreement with those obtained from the ASIT. The comparison is also used to optimize the filter constants used in the motion-correction algorithm. The quantitative agreement between the buoy data and the ASIT demonstrates that the DCF method is applicable for turbulence measurements from small moving platforms, such as buoys.

We quantify the percentage of sea surface covered by whitecaps from images taken by a non-stationary camera mounted on a moored buoy using an Adaptive Thresholding Segmentation (ATS) method and an Iterative Between Class Variance (IBCV) approach. In the ATS algorithm, the optimal value for the threshold is determined as the last inflection point of the smoothed cumulative histogram of the scene. This makes the method more effective in finding the optimal value of the threshold and reduces the computational efforts compared to the conventional Automated Whitecap Extraction (AWE) technique. In the IBCV method, the optimum criterion for determining the value of the threshold corresponds to the measure of separability between the segmented water and whitecap pixels. In our experiments, the fraction of each image covered by the whitecap is determined using the aforementioned dynamical thresholding techniques for images taken under complex forcing and lighting conditions. Comparisons between different techniques suggest the effectiveness of the proposed methodologies, in particular the ATS algorithm to separate the whitecap features from the darker water pixels

The remotely piloted aircraft system (RPAS) SUMO (Small Unmanned Meteorological Observer) has been equipped with a miniaturized 5-hole probe sensor system for measurement of the 3-dimensional flow vector with a temporal resolution of 100Hz. Due to its’ weight and size this system is particularly well suited for operations in the vicinity of wind turbines. To qualify for full scale measurements in turbine wakes the system has been characterized by several laboratory and field tests described in this study.A wind tunnel test against a hot-wire anemometer shows the capability of the 5-hole probe to react to turbulence in the same manner as the hot-wire system. The resulting spectra from the two platforms show in general good agreement for both laminar and turbulent flows. The 5-hole probe system is able to resolve turbulence up to frequencies around 20 − 30Hz when using a tubing length of 15cm between the probe and the pressure transducers.In addition, an environmental parallel test against to two sonic anemometers mounted on the roof-top of a car was performed at Bergen airport Flesland. Despite several issues with the self-made and low-cost experimental setup, important system characteristics could be tested and verified. In particular the velocity spectral components of the sonic anemometer system and the 5-hole probe are in close resemblance to each other. This is at least a strong indication that the 5-hole probe is suitable for atmospheric turbulence measurements onboard the RPAS SUMO platform