Abstract. The increasing scales of modern wind energy systems, with rotor diameters exceeding 250 m and hub heights above 150 m, introduces new challenges in understanding interactions between atmospheric dynamics and wind farm performance. This study investigates the impact of atmospheric boundary layer height (BLH) as a key parameter influencing wind farm efficiency and turbine loads. Using mesoscale simulations from the Weather Research and Forecasting (WRF) model combined with lidar measurements, we quantify BLH variability and its associated uncertainty across three representative sites in the North and Baltic Seas. A series of Computational Fluid Dynamics (CFD) simulations for a wind farm, containing 100 15 MW turbines, under varying BLH and wind speed conditions reveal significant efficiency differences linked to atmospheric stratification, with lower BLH generally reducing farm efficiency. Seasonal and site-specific climatologies highlight that Baltic Sea conditions, characterized by larger extent of low BLH conditions, lead to reduced performance compared to North Sea sites. Furthermore, we assess the influence of large-scale coherent turbulence structures on turbine loads through aeroelastic simulations of both bottom-fixed and floating configurations. The results show that low-frequency fluctuations, often absent in standard design models, increase fatigue loads within wind farms, particularly for turbines in wake-affected regions. These findings underscore the need to incorporate BLH variability and large-scale turbulence effects into engineering models for reliable performance and load predictions of next-generation offshore wind farms.

Spectral turbulence models commonly used in the design and certification of wind turbines have only been validated at heights up to 70 m in the atmosphere, but many offshore wind turbines now operate at heights above 150 m. Moreover, there is a lack of measurement data on the spatial structure of turbulence at such heights in the marine atmospheric boundary layer (MBL). Consequently, it is uncertain whether these turbulence models are valid for the design of tall offshore wind turbines. To fill this gap, we present measurements of one-point auto-spectra and two-point spectral coherence at heights of 150-250 m and lateral separations up to 241 m providing lateral coherence of turbulence in the MBL that has never been measured before for these heights and separations. Five light detection and ranging (lidar) instruments were deployed on the west coast of Denmark, and we reconstructed the along-wind and cross-wind components at the lidar beam intersection points. The measurements were compared with the theoretical predictions of auto-spectra and lateral coherence from the Mann model and its extension, the Syed-Mann model. The latter models turbulence down to frequencies of 1 h $<^>{-1}$ through the $-5/3$ scaling observed in the mesoscale range. The results show that the Mann model did not compare well with the measurements under stable and near-neutral conditions. On the other hand, the Syed-Mann model predicted the lateral coherence for a range of different conditions. However, the lateral coherence was under predicted in about $8\,\%$ of the data, possibly due to gravity waves. We believe that the high coherence from mesoscale turbulence at these heights can influence the loads on floating wind turbines and large offshore wind farms.

Abstract Conventional measurement of deflection and twist in full-scale wind turbine blades is predominantly conducted using permanently integrated sensor systems, notably strain gauges and fiber optic sensors. However, these integrated systems are constrained by several critical drawbacks: high cost, laborious installation procedures, and an intrinsic lack of reparability after the blade structure is sealed. To overcome these limitations, we introduce a novel, non-contact monitoring framework that employs a synchronized array of three automotive lidar sensors to measure full-field blade deflections and torsional twist across diverse meteorological environments. Flapwise deflection and pitch obtained from lidar data were validated against measurements from strain gauges at a location 1.4 m from the rotor plane center and against 50 Hz pitch signals from the turbine’s SCADA system. Our results demonstrate the effectiveness of lidar sensors in measuring both parameters along the blade span. The present analysis focuses on flapwise deflection, providing a comparative assessment and introducing a method to correct for the accuracy-degrading effects of boom bending.

Testing of full-scale wind turbine blades and tower by the traditional sensors, such as strain gages and accelerometers, is expensive, arduous, and time-consuming. In this study, we investigate a novel technique to accelerate prototype wind turbine testing under different atmospheric conditions by applying automotive lidars. A proof-of-concept field measurement was conducted, scanning the DTU V52 wind turbine with a frequency-modulated-continuous-wave (FMCW) automotive lidar at the Risø campus of the Technical University of Denmark (DTU). It was observed that the upper part of the tower was displaced by about 7 cm after the wind turbine had been stopped. The obtained power spectral density diagram shown that the dominant frequency related to the tower's oscillation is about 1.25 Hz. These results show that automotive lidars may have a great potential to be integrated into the wind energy industry to provide accurate measurements of blade deflection and tower oscillation, which will speed up the testing process and reduce the time-to-market of new turbine models.

Abstract The extreme coherent gust with extreme change (ECD) is a load case in the wind turbine design standards that drives the design of many components. However, there are concerns that the current form of this load case could be too conservative for modern wind turbines. Thus, we aim to characterize gusts detected from a fleet of wind turbines. The turbines are used as anemometers and the rotor-averaged wind speed is reconstructed through a model called the wind speed estimator (WSE). Gusts are detected based on 10 minute statistics of rotor-averaged wind speed along with information on the turbine’s operating state. The wind speed change and rise time are characterized via a wavelet transform and fitting the observations to an error function, while the direction change is measured by a sonic anemometer placed on the nacelle. A comparison of the WSE with measurements from a met-mast showed that the rise time detected by the latter increases when a “shear” is present in the gust, as the event does not arrive at the same time at all altitudes. As it is sensitive to the two-dimensional structure of the gust, we argue that the WSE is more suitable for characterizing extreme wind ramps. The dataset which is equivalent to 21 years of measurements does not include any event that simultaneously matches the features of the ECD. This reinforces our belief that the standard is indeed too conservative. Thus, the insights from this study along with additional work can be used to recommend an update to the ECD load case.

Abstract Accurate measurements of absolute wind direction are essential for maximising wind farm production through strategies such as sector management or wake steering. This study aims to evaluate how accurately a nacelle-mounted sonic anemometer and a forward-looking nacelle-mounted wind lidar can measure wind direction and wind speed, and how this accuracy is affected by intentional yaw misalignment. Ten minute measurements from nacelle sensors are analysed and compared to reference measurements on an upstream met mast, in a flat-terrain site. Calibration and mounting errors are identified, the effect of intentional yaw misalignment and atmospheric stability is assessed, and the effect of land heterogeneity is evaluated using a high-resolution Reynolds Averaged Navier-Stokes simulation. Even in a simple, highly calibrated site, the yaw misalignment bias of nacelle anemometers ranges from 1.5° fixed-offset to up to 6° due to intentional yaw misalignment and varying atmospheric conditions, which may impact the effectiveness of wind farm control strategies.

The Mann turbulence model is widely used in the design and certification of multi-megawatt wind turbines. However, these turbines operate in a region of the atmosphere where the model's assumptions are violated. One of the most significant assumptions is that of neutral stability conditions, which raises concerns about the model's accuracy for load simulations. To investigate this, we compare fatigue loads measured on a 15 MW wind turbine to simulations performed using an aeroelastic solver. The inflow was characterized using data from a meteorological mast equipped with sonic and cup anemometers. The turbulence model was fitted to measurements of auto-spectra under varying wind speeds and stability conditions, while the vertical profile of wind speed was represented by a power law. The resulting wind fields were then used as input to the aeroelastic simulations.We first present a comparison of measured fatigue loads on the tower and blades across different atmospheric stability regimes. On average, the fore-aft loads at the bottom of the tower were 98 % higher under unstable atmospheric conditions as compared to stable conditions. On the other hand, the difference in flapwise blade loads between unstable and stable conditions was 20 % below rated wind speed and -2% at higher wind speeds. These results underscore the importance of accounting for atmospheric stability in wind turbine siting and load verification campaigns. A subsequent comparison of measurements and simulations revealed that measured and simulated loads tend to fall within 3 standard deviations of each other, even under non-neutral conditions. However, the simulated fatigue loads on the tower were overestimated by a margin of 5 standard deviations under some stable conditions, likely due to incorrect predictions of the spectral coherence made by the turbulence model. Shear extrapolation based on the power law might also lead to overestimation of blade loads in the simulations.These results indicate that, despite its simplifying assumptions, the Mann model, when fitted to measurements of turbulence auto-spectra, does not introduce significant errors in fatigue load simulations for solitary multi-megawatt wind turbines.

Abstract. Permanently integrated sensor systems, such as strain gauges and fiber optic sensors, are the predominant means of measuring deflection in full-scale wind turbine blades. However, these approaches suffer from several key limitations, including complex calibration procedures, labor-intensive installation, and the inability to repair sensors once the blade structure is sealed. Furthermore, they are severely limited in measuring torsional deformation, a parameter of increasing importance for large wind turbine blades. To address these limitations, this study presents a novel non-contact monitoring framework based on a synchronized array of three automotive-grade lidars, enabling full-scale measurement of blade deflection and torsional deformation under diverse operating conditions. Lidar-derived flapwise deflection measurements (sampled at 33.3 Hz) are validated against co-located strain gauge data acquired at 1.4 m from the rotor plane center (sampled at 50 Hz), while lidar-based pitch angle estimates are validated against SCADA measurements after both signals are resampled to 2 Hz. The measured blade torsional deformation reaches approximately 0.8° under above-rated wind speed conditions, consistent with expected aerodynamic behavior. The dependence of median flapwise deflection on mean hub-height wind speed, rotor azimuth angle, and wind shear is also systematically analyzed. The results demonstrate that the proposed lidar-based system can accurately capture both flapwise deflection and pitch deformation along the blade span. These findings highlight the potential of cost-effective automotive lidar sensors for reliable, high-resolution monitoring of wind turbine structural dynamics under challenging field conditions.

There is a lack of measurement data on the spatial structure of turbulence at heights greater than 100 m in the marine atmospheric boundary layer (MBL). Consequently, turbulence models like the Mann and Kaimal models, which are referred to in industry standards, have not been validated at the operational heights of large offshore wind turbines. To address this gap, we carried out an experimental campaign using a total of five lidars placed at two locations on the west coast of Denmark. This setup allowed us to measure the horizontal wind components at the intersections of the lidar beams which were 150 to 250 m above the sea surface. As a result, lateral coherence can be assessed up to separations of 240 m. Due to differences in data quality from each lidar, the data availability at each intersection point was different, being in the range between 17 % and 50 % over the entire 360° sector. Thus, this measurement dataset can be used to test and validate turbulence models in the MBL at heights relevant for offshore wind turbines.

We investigate the impact of low-frequency wind fluctuations on the loads and response of a large reference offshore wind turbine. Synthetic wind fields containing low-frequency fluctuations down to 1 h−1 are used in aeroelastic simulations with the HAWC2 code. The dynamic response and damage equivalent loads (DEL) for tower and blade moments are evaluated. Both monopile and floating configurations are tested against three wind fields: (i) high-frequency turbulence (3D), (ii) combined low- and high-frequency turbulence (2D+3D), and (iii) high-frequency turbulence scaled to match the measured standard deviation. Low-frequency fluctuations increase DEL for the fore–aft and flapwise moments at the tower base and the blade root, especially at low wind speeds. These are out-of-plane bending moments caused by longitudinal forces. Torsional moments, such as tower top yaw, exhibit reduced DEL across most wind speeds due to increased coherence. The strongest dynamic response to low-frequency turbulence occurs in the tower fore–aft and blade root flapwise moments at frequencies below 2×10-3 Hz. For the floating turbine, the platform's surge and pitch motions, and the windward mooring line tension, show pronounced responses. This study underscores the importance of accounting for low-frequency wind fluctuations when simulating the loads and response of large offshore wind turbines.

A simple adaptive variant of the Doppler beam swinging (DBS) method is presented to enhance the availability of wind velocity measurements in profiling lidars. The adaptive method dynamically selects Doppler velocities from beams that have sufficient signal-to-noise ratios (SNRs) in their backscattered signals; then it uses those Doppler velocities for wind velocity reconstruction, rather than relying on the standard approach which discards the entire scan whenever even one beam's backscattered signal does not meet the SNR requirement. The adaptive method was validated in two measurement campaigns at the & Oslash;sterild wind turbine test field in Denmark using three BEAM 6x profiling lidars from Lumibird. In the first campaign, a lidar measured up to 500 m in proximity to a meteorological mast; in the second campaign, the first lidar was replaced by two other lidar units to increase the maximum measurement range up to 1 km. Validation against cup anemometers and wind vanes at four different heights of the met mast showed excellent agreement for mean wind speed and wind direction, with results similar to those from the standard approach. Availability assessments indicated improvements for all three lidars at high altitudes, showing a maximum increment of 16.9 percentage points over the standard approach. Due to its simplicity, the adaptive method can be implemented in lidar software without requiring any hardware modifications.

Abstract. Vertical momentum entrainment above offshore wind farms plays a key role in the recovery of wind turbine and wind farm wakes but remains poorly documented by field measurements. The LOLland offshore Lidar EXperiment (LOLLEX) campaign introduced a novel measurement approach to address this knowledge gap. The primary objective of this campaign was to develop a new atmospheric measurement strategy to characterise and quantify the vertical momentum entrainment inside and outside an offshore wind farm using Doppler wind lidar technology. LOLLEX was conducted from September 2022 to September 2023 in Denmark in and around the Rødsand II wind farm just south of the island of Lolland. During this campaign, two pulsed Doppler wind lidars, a scanning and a profiling instrument, were deployed onboard a crew transfer vessel (CTV) commuting daily between the harbour and the offshore wind farm Rødsand II. Additionally, a scanning pulsed Doppler wind lidar was mounted on a transformer platform north of the wind farm to perform range height indicator scans across the farm. Motion-corrected mean wind speed data were collected up to 300 m above the sea surface by the profiler lidar. The scanning lidar collected data up to 2.5 km alternating between the profiling mode and vertical stare mode. The latter scan operated with a sampling frequency of 1 Hz and along-beam spatial resolution of 10 m, allowing for the study of the turbulent vertical wind velocity component. The dataset includes several thousand hours of vertical scans. As a result of the moving vessel, many of the observations occurred inside or in the close vicinity of the wind farm, providing insight into the near and far wakes of individual and multiple turbines. The potential and limitations of the new measurement strategy is illustrated using four case studies: (1) the observation of a Kelvin–Helmholtz instability above the wind farm, examined further in a companion paper; (2) turbulent mixing propagating downward from the top of the boundary layer, enhancing momentum entrainment; (3) internal atmospheric waves and (4) wake characterisation inside the wind farm using the range-height indicator scans from the lidar deployed on the platform. This work demonstrates a novel methodology integrating remote sensing with a mobile offshore platform to measure turbulence at unprecedented altitudes. The dataset offers valuable data for wind energy research, boundary-layer meteorology, and further development of atmospheric measurement techniques.

This work presents a simulator approach to quantify and parametrize the inherent averaging error in wind turbulence measurements by Doppler Wind Lidars (DWL). The simulator replicates the instrumental setup of the IJmuiden campaign, which included an offshore meteorological mast with a DWL and an anemometer measuring wind at the same height as a reference (90 m a.s.l.). The Mann turbulence model was used to simulate wind turbulence boxes with a wide range of turbulence conditions, enabling the quantification of measurement errors as a function of the turbulence spectral characteristics via the Mann model parameters. The study found that only the Mann turbulence length scale (LMM) affected the DWL measurement of turbulence. Additionally, the inherent averaging error for the IJmuiden setup was parameterized using an analytical function, significantly reducing the computational effort required to retrieve it.

We present a Doppler lidar designed to detect the molecular spectrum characteristics, which are attributed to the Rayleigh-Brillouin scattering, in the atmospheric boundary layer. The suggested system is a continuous-wave, infrared Doppler lidar based on a bi-static transceiver and a coherent in-phase/quadrature detection scheme. For the detection of the features of the Rayleigh-Brillouin spectrum we use fiber-coupled, balanced photodetectors and a digitizer with a 1.6 GHz bandwidth. This broad bandwidth is necessary for the detection of Doppler shifts not only at frequencies of atmospheric winds, but also of the ones corresponding to molecular and acoustic speed that extend over several hundred megahertz. We demonstrate that using this configuration it is possible to detect the molecular Rayleigh-Brillouin spectrum over 30-minute time periods. The observational range of this system is focused on the lower part of the atmosphere (< 200 m) and the objective is to investigate if the resolved features of the Rayleigh-Brillouin spectrum can be related to the temperature, which could lead to the development of a novel vertical profiler of atmospheric temperature.

We demonstrate that we can measure spectral coherence of offshore atmospheric turbulence at heights and with lateral displacements relevant for dynamic loads on modern, large wind turbines. This is done by five coordinated, pulsed Doppler lidars standing on the coast of the North Sea with beams intersecting almost perpendicularly. The six crossing points are 150 to 250 m above the ocean and have lateral separations of up to 200 m, reflecting the scale of modern offshore wind turbines. We compare the measurements with spectral and cross-spectral models. The model of Syed and Mann (Boundary-Layer Meteorology, 2024, vol 190), in general fits the spectra well and predicts the lateral coherences well. However, there are cases where the measured lateral coherence of the v-component is much larger than predicted. This seems not to be due to malfunction of the instruments, but rather due to non-turbulence processes in the atmosphere, e.g . interval gravity waves. We will also touch upon the potential consequences for loads on wind turbines.

Turbulence spectral analysis is a critical aspect of wind tunnel experiments. In this study, a modification of the Mann uniform shear model (M94), based on the Rapid Distortion theory, is proposed to adapt M94 for wind tunnel conditions and model the complete second-order turbulence structure. First, the one-point spectra measured at heights ranging from 0.3 to 1.5 m are analyzed. The total absolute error χ2 for the modified M94 (M94-2) prediction is 0.998, compared to 1.6357 and 1.183 for M94 and the von Kármán spectral model, respectively; the results demonstrate the validity of the modification. Second, the spatial coherence is analyzed, with the spatial separations Δy and Δz ranging from 3.5 to 50 cm, M94-2 provides better predictions compared to the Krenk exponential coherence model. Notably, M94-2 is able to predict the turnaround of coherence at low wavenumber. Third, the phase angle of the cross-spectrum for two vertically separated points is predicted by M94-2, M94-2 tends to overestimate the measurement due to noise contamination. In conclusion, the anisotropic spectrum of boundary layer wind tunnel turbulence can be modeled by M94-2 effectively with three parameters: αε2/3, L, and Γ, and the entire work is conducted within a unified theoretical framework.

Enhancing our understanding of the structural response of trees exposed to wind loading is important, since the knowledge of their aerodynamic behaviour is necessary for a realistic risk assessment of tree damage during extreme wind conditions. Here, we first present an analytical model of the aerodynamic admittance function that relates the turbulence fluctuations of the wind at a single point to their spatial average over the crown's frontal area. The latter is responsible for the wind-induced bending moments at the base of a tree's stem. We employ the aerodynamic admittance function to model the dynamic structural response of an open-grown oak tree. The analysis is performed along two axes to express both the longitudinal and transverse response with respect to the mean wind direction. The resulting predictions are compared with strain gauge observations taken at the lower part of the stem. The presented framework shows that the spatial averaging over the crown's frontal area has a stronger effect on the tree's movements in the streamwise wind direction compared to the spanwise direction. Further, the aerodynamic damping is also stronger in the streamwise direction and generally correlates positively with the inflow wind speed.

Accurate estimation of second-order turbulence statistics using pulsed Doppler lidar has been a challenge for a long time, mainly due to the negative influence of probe volume averaging. The present study aims to investigate a novel approach to extracting first- and second-order turbulence statistics directly from the average Doppler spectra in the frequency domain. The main hypothesis is that averaging Doppler spectra over 10 min intervals can mitigate the influence of probe volume averaging and random noise in velocity retrievals, thereby improving estimates of velocity variance. To achieve this, we develop a new analytical model for the time-averaged Doppler spectrum, beginning with a theoretical formulation based on the beat signal within the range gate. The model is applied to 10 min averaged Doppler spectra collected by a pulsed lidar system pointing toward a sonic anemometer mounted on a meteorological mast in front of a Vestas V52 wind turbine at the DTU Risø campus in Denmark. Validation results demonstrate that the Doppler spectra model, when fitted to 400 ns nominal pulse durations, closely matches sonic anemometer measurements in both mean radial velocities and standard deviations. This agreement is quantified by the orthogonal least squares fit slopes of 0.976 for the mean velocities and 0.983 for the standard deviations. In comparison to the conventional time-domain approach, which accounts for only 72.1 % of the standard deviation, the proposed spectral method captures 98.3 % of the standard deviation observed in the sonic anemometer. However, this model does not accurately estimate variances using the short pulse (200 ns) of the instrument. Despite this limitation for the short pulse, the proposed method is an important step towards better turbulence estimation from pulsed Doppler lidars.

This perspective paper provides motivation and guidance to the wind energy community for suggested future investments and a long-term strategy for wind-energy-related field campaigns that will provide much-needed observations for improving wind energy science and model validation. We synthesize key lessons learned from past field campaigns, identify critical science gaps that we think should drive future field efforts, and provide a suggested pipeline on how future endeavors should be developed. When considering future grand field campaigns, we stress the need for international cooperation across funding bodies, research institutes, and industry partners to collect observations to overcome current and future grand challenges for wind energy.

Retrieving accurate turbulence intensity (TI) 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 TI 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.