Wind turbines have grown so tall that many of them now spin through layers of the atmosphere where turbulence has never been properly measured. The design standards that govern their structural safety still rely on turbulence models developed from observations made within roughly the first 100 metres above the ground, a region known as the atmospheric surface layer. Modern multi-megawatt machines, with hub heights well beyond that level, operate in a regime where the classical assumptions behind those models begin to fray. A new study published in the journal Wind Energy Science tackles this gap head-on, using an unusual combination of instruments at a near-coastal site in northern Germany to characterise turbulence across a wide range of atmospheric conditions.
The research, led by Lennart Vogt of the University of Stavanger together with Julia Gottschall of Fraunhofer IWES and Jasna Bogunović Jakobsen of the University of Stavanger, draws on measurements collected at the Testfeld BHV site in Bremerhaven, on the east bank of the Weser estuary along the North Sea coast. The site hosts an Adwen AD8-180 prototype wind turbine and an IEC-compliant meteorological mast equipped with sonic and cup anemometers at several heights. Crucially, the team also deployed three synchronized short-range continuous-wave lidars based on the Short-Range WindScanner technology developed at the Technical University of Denmark. Between October 2021 and April 2022, the three lidars scanned a bow-tie-shaped pattern in a vertical plane 125 metres from the turbine, centred at hub height, allowing the full three-dimensional wind vector to be reconstructed at points spread across the plane.
The scale of the dataset is one of the study’s most striking features. The researchers analysed nearly 500 hours of lidar measurements acquired over about 90 days of scanning, alongside 533 days of mast data collected between January 2021 and August 2022. After rigorous quality control, which removed wake-affected samples, non-stationary records, and cases with unrealistic fluctuations, roughly 7,500 hours of sonic anemometer data, 8,000 hours of cup anemometer data, and 700 hours of lidar data remained available for spectral analysis. From these records, the team derived the fundamental building blocks of turbulence characterisation: auto-spectra describing how turbulent energy is distributed across frequencies, integral length scales describing the size of the largest coherent eddies, and spatial coherence describing how correlated the wind is at two points separated in space.
The technical approach involved reconstructing the three wind velocity components from the line-of-sight velocities measured by each lidar, using the known scanning azimuth and elevation angles of the instruments. Each bow-tie scan took two seconds, with the lidars sampling radial velocities at 322 hertz. Time series were extracted at grid cells along the scanning trajectory, with an effective temporal resolution of 1 hertz at the centre of the pattern. The spectra were then classified according to atmospheric stability, quantified by the non-dimensional stability parameter zeta, the ratio of measurement height to the Obukhov length, which expresses the balance between buoyancy-driven and shear-driven turbulence production. Fifteen stability classes were used, spanning highly unstable convective conditions through neutral stratification to highly stable nighttime regimes.
The results reveal how profoundly atmospheric stability reshapes the turbulence that wind turbines experience. Under unstable, convective conditions, the combined influence of mechanical shear and thermal buoyancy produces a pronounced spectral plateau in the horizontal velocity components, a broad band of enhanced low-frequency energy that the standard Kaimal model used in turbine design does not capture. As conditions approach neutral, the plateau narrows and eventually gives way to the familiar single spectral peak. Under stable stratification, negative buoyancy suppresses microscale turbulence, shifting the spectral peak toward higher frequencies while a distinct spectral gap opens up, separating the turbulence peak from a reservoir of low-frequency energy attributed to mesoscale motions. These large, quasi-two-dimensional structures are typically neglected in aeroelastic simulations, yet they become increasingly relevant for floating wind turbines whose platform natural frequencies lie on the order of several minutes.
The integral length scales tell a parallel story. Under neutral conditions, the along-wind length scale ranged from roughly 100 to 250 metres, the cross-wind scale from 30 to 100 metres, and the vertical scale from 10 to 50 metres. Moving from neutral to slightly unstable conditions, all three length scales increased, consistent with buoyancy enhancing turbulence generation. From neutral to slightly stable conditions, they decreased substantially as buoyancy flipped from amplifying to suppressing turbulence, and the ratio of along-wind to vertical length scales roughly doubled, reflecting the flattening of turbulent eddies as vertical mixing weakened. Intriguingly, under highly stable conditions the horizontal length scales increased slightly again, which the authors attribute to mesoscale motions being captured within the 30-minute averaging window.
One of the study’s central contributions is a careful cross-comparison of the three measurement technologies. The sonic anemometers, sampling at 20 hertz, delivered reliable spectra up to approximately 2 to 6 hertz. The cup anemometers, despite operating at only 1 hertz, agreed well with the sonics up to about 0.07 hertz, above which spatial averaging over the cup geometry and temporal filtering by the rotor inertia attenuated the high-frequency signal. The lidar system showed strong agreement with the sonic measurements at low and intermediate frequencies for all three velocity components, but its finite probe volume, ranging from 10 to 40 metres depending on focus distance, caused attenuation above roughly 0.04 hertz. This meant the along-wind turbulence peak was captured across all stability regimes, but the vertical velocity peak could not be fully resolved. The authors caution that lidar-based spectral estimates should be interpreted carefully in the absence of a sonic reference, particularly for the vertical component.
Perhaps the most valuable outcome concerns spatial coherence, the two-point statistic that governs how aerodynamic loads distribute across a rotor. The bow-tie scanning strategy proved highly suitable for coherence analysis, offering flexibility in separation direction, distance, and height that conventional mast-based measurements cannot match. Vertical and lateral coherence were estimated for separations ranging from 10 to 160 metres at heights up to 230 metres. The coherence of all velocity components decreased from unstable to stable conditions as turbulent structures shrank, and lateral coherence was systematically higher than vertical coherence, especially under stable stratification. Notably, the high-frequency attenuation that plagued the auto-spectra had little effect on coherence estimates, because the attenuation occurred at frequencies beyond those governing coherence decay, and partly cancelled when cross-spectra were normalised by the correspondingly attenuated auto-spectra.
From these observations, the team built an improved coherence model. The classical Davenport formulation assumes coherence decays exponentially with a normalised wavenumber and reaches unity at zero frequency, an assumption that fails for large separations and low heights. By introducing a scaling coefficient that permits coherence below unity at zero wavenumber, and by expressing both the scaling and decay coefficients as exponential functions of the ratio of separation distance to mean measurement height, the researchers arrived at a three-parameter model that accurately reproduces the observations for all velocity components. The fitted coefficients, tabulated as functions of atmospheric stability, are intended as inputs for generating synthetic wind fields in dynamic turbine response simulations. The model is recommended for normalised separations up to 0.5, with additional measurements at larger separations suggested to extend its validity.
The implications for wind energy are considerable. Previous studies have shown that reduced lateral coherence increases tower torsion on large turbines, while highly coherent inflow amplifies surge and pitch motions and mooring line fatigue on floating platforms. By providing stability-dependent spectral and coherence parameters grounded in nearly two years of field data, this work gives turbine designers a more realistic picture of the winds their machines will actually encounter, particularly in the coastal transition zone where flows alternate between overland and overwater fetch. The observed diurnal cycle, with unstable conditions peaking near 50 percent around midday and stable conditions dominating nighttime hours at up to 75 percent, underscores how dramatically the turbulence climate can shift within a single day. As turbines continue to grow and floating platforms push into deeper waters, datasets of this kind will become essential for ensuring that the synthetic winds used in certification simulations reflect the full richness of the real atmosphere.
Subject of Research: Atmospheric turbulence characterization for wind energy using triple-lidar and mast measurements in a near-coastal environment
Article Title: Turbulence characterization in near-coastal environment using triple short-range lidars and mast anemometry
Article References: Vogt, L., Gottschall, J., & Bogunović Jakobsen, J. (2026). Turbulence characterization in near-coastal environment using triple short-range lidars and mast anemometry. Wind Energy Science, 11(9), 3587-3614. https://doi.org/10.5194/wes-11-3587-2026
Image Credits: AI Generated
Keywords: wind energy, atmospheric turbulence, lidar, spatial coherence, atmospheric stability, wind spectra, integral length scales, boundary layer meteorology, wind turbine loads, Kaimal spectrum, coastal site, anemometry
News Source: Faith Mcneil. (October 10, 2026). Triple-Lidar Campaign Reveals How Coastal Turbulence Shifts With the Weather. Scienmag.
