The wind farm wakes were characterized by flying meander patterns, i.e. individual flight tracks, called legs, oriented perpendicular to the prevailing wind direction. Typically, in the framework of the campaigns conducted in the projects WIPAFF and X-Wakes, the transects through the wake region were spaced with a horizontal distance of 10 km. The closest flight leg was at a distance of 500 m downstream of the wind farm due to flight permissions. For neutral and unstable conditions, the distance between the flight legs was reduced. In this way, the horizontal extent and the wind speed recovery of wind farm wakes were quantified. Under neutral conditions, wakes extend several kilometres downstream , under stable atmospheric conditions at least several tens of kilometres . The recovery of the wind speed can be approximated by an exponential function, with a typical recovery up to 95 % of the undisturbed wind speed after a downstream distance of around 55 km for stable and 20 km for unstable conditions , with high variability . This recovery, in particular for stable conditions, is significantly slower than usually assumed in industry models but on the order of magnitude predicted by simple analytical stability-dependent wind farm models such as EFFWAKE . The wake length and also the initial wind speed deficit directly downwind of the wind farms strongly depend on atmospheric stability, but also other parameters, like wind farm geometry and density, seem to have a major influence. Downstream of wind farms, vertical mixing is enhanced at the altitude range of the rotor blades. This vertical mixing can result in micrometeorological changes: either warming or cooling of the air at hub height and either drying or moistening of the downstream atmosphere, which depends on the altitude of the temperature inversion and the moisture distribution with height . The airborne measurements show that directly above the wind farms, turbulence is strongly increased .

Although the methods of airborne in situ measurements of wakes and satellite-borne remote sensing observations based on synthetic aperture radar (SAR) are highly different and sample on other spatial and temporal scales, the quantification of wakes based on the regular SAR overpasses is important for monitoring and is compared to almost-simultaneous airborne measurements in the following. Figure  shows the wind speed at 10 m height derived from SAR imagery. The radar data were obtained on 11 September 2021 at 17:17 UTC, and the 10 m wind speed with a horizontal resolution of 300 m was derived from the normalized radar cross-section (NRCS) using a C-band geophysical model function CMOD5N . For a potential explanation of the higher wind speed values close to the coast, the reader is referred to Sect. . Figure  displays an almost temporally overlapping measurement flight carried out in the same area between 12:51–17:13 UTC. The SAR snapshot provides a quasi-synoptic view of the near-surface wind field at the end of the flight period, while the aircraft sampled the same flow along several transects at hub height and in vertical profiles. Downstream of the wind farm cluster N4, both observations capture a decrease in the wind speed for the individual wind farms Amrumbank West, Nordsee Ost, and Meerwind Süd-Ost (from north to south) and a speed-up in the so-called “Kaskasi gap” between Amrumbank West and Nordsee Ost, where the wind farm Kaskasi was built after the flights were conducted. For a more detailed comparison, Fig.  shows the wind speed measured along three horizontal transects (T6, T7, and T8).

Satellite-based estimations typically derive the wind speed at hub height in several steps indirectly from the height of the capillary waves at the surface, which are an indicator for the wind speed and roughness at the surface. From those, the 10 m wind speed was derived first. Then the SAR-derived 10 m wind speed was extrapolated to hub height using stability information obtained from the airborne profiles. One should note that extrapolating from SAR-derived 10 m wind speed towards hub height introduces uncertainty related to atmospheric stability, surface roughness, and the vertical wind profile. This uncertainty is reduced here by using stability information from the flight profiles, but the stability functions used in the extrapolation are still idealized . Airborne in situ measurements along these transects show substantial small-scale variability and enhanced turbulence, whereas the collocated SAR-derived hub-height winds provide smoother fields because of the finite radar footprint and the averaging applied in the retrieval . Despite the high-frequency fluctuations observed in the airborne data, the SAR-derived wind speed closely follows the mean airborne signal for all transects. Small discrepancies are visible, particularly in regions of enhanced turbulence (e.g. at distances of around 0.5–1.5 km for the transects at 16:49 UTC and 17:04 UTC), but the overall agreement is high in both magnitude and spatial pattern. For transects T6–T8, the SAR-derived and airborne wind speeds show an overall agreement with a root mean square (rms) of 0.8 ms-1 and a correlation coefficient of 0.8. The bias varies between the individual transects (including a change in sign between T6 and T7/T8), indicating that the differences are not systematic across all transects and may depend on local atmospheric boundary layer conditions. The standard deviation of the SAR and airborne wind speed differences is about 0.3 ms-1. The joint analysis highlights the complementarity between airborne and satellite observations in offshore wind farm wake studies. Aircraft-based measurements provide an independent reference at hub height and resolve turbulence and vertical shear on scales that cannot be captured by SAR, thereby supplying essential context for interpreting SAR-derived wake signatures. At the same time, SAR offers an instantaneous, spatially extensive view of the near-surface wind field that cannot be achieved by aircraft alone. Together, these platforms show that SAR-derived wind speed can reliably reproduce the horizontal wind field at hub height when combined with appropriate stability information and can therefore serve as a consistent complement to airborne measurements.

Overall, compared to other wake measurement methods, airborne measurements offer several advantages. These include the ability to directly validate satellite-derived wind speed at hub height. Satellite-based techniques typically derive the wind speed at hub height indirectly, using surface roughness to estimate the wind speed at 10 m and subsequently extrapolating to hub height. Airborne measurements also extend the spatial coverage provided by long-range scanning wind lidar systems, which are typically fixed at a single location and can retrieve wind field data over distances of up to approximately 15 km, depending on atmospheric conditions. The effective range of these systems is influenced by several factors, including aerosol concentration (which influences backscatter strength), atmospheric visibility and humidity (which affect signal attenuation), laser pulse energy and optical design (which influence system sensitivity), and the scanning geometry (as longer path lengths or steeper elevation angles can reduce the signal-to-noise ratio). In contrast, point measurements from fixed installations such as the FINO1 meteorological mast provide data with high precision and high resolution in time but are limited to single locations where additional measurements are needed to estimate wake strengths.

Airborne observations provide the opportunity to focus the measurement strategy on covering overlapping wake effects of multiple wind farms and to confirm numerical simulations of the wind field. The interaction of different wind farm wakes has been investigated by numerical simulations of different complexity and by satellite images providing an overview of large scales . Figure  shows airborne measurements which confirm the further reduction in the wind speed downstream of a second wind farm, with the wind speed reduction depending on turbine density and the mode of operation .

Building on these findings, evaluated both engineering and mesoscale models using the in situ airborne measurements behind large-scale wind farm clusters in the German Bight. The study compared four representative measurement flights with simulations from the Weather Research and Forecasting Model (WRF), developed by the National Center of Atmospheric Research widely used in the wind energy community , including wind farm parameterizations and several engineering wake models implemented in the FOXES software tool . Results showed that engineering models, when properly configured or coupled with mesoscale input, are capable of reproducing wake effects over tens of kilometres, including merged wakes from multiple farms. Particularly the engineering model coupled with WRF (EM-WRF; Fig. ) – where a heterogeneous, undisturbed WRF background simulation is used as input to initialize each turbine locally, and turbine wakes are propagated as passive scalars along the atmospheric flow streamlines derived from the WRF data – proved to be most consistent with the airborne observations, especially under stable stratification.

All models (see Fig. ) showed a reduced accuracy in predicting wake position and strength at distances beyond 30 km from the wind farm cluster. Therefore, accurate inflow conditions and proper atmospheric stability characterization are required to improve the performance of all model results over such large scales. Unlike traditional meteorological masts, which only provide single-point time series, or Doppler lidars, which are limited to a scanning range of about 10 km, in situ airborne measurements allow the full, large-scale cluster wake developments that can extend beyond 50 to 70 km to be captured under stable atmospheric conditions . By flying meandering patterns perpendicular to the wind direction at hub height across multiple locations, aircraft data uniquely provide high-spatial-resolution cross-sections of the wind field. This makes it possible to simultaneously evaluate wake deficits, lateral wake extent, and merged wake structures from multiple adjacent wind farm clusters across vast horizontal distances.

On the other hand, the results of the model comparison also underline the difficulties of using only one aircraft, lacking simultaneous inflow measurements. Further, the lack of repetition of similar conditions makes it hard to remove or quantify uncertainty based on aircraft measurements.

Airborne measurements were able to show that latent heat fluxes increased above and downstream of wind farm clusters across a range of atmospheric stability regimes . Latent heat fluxes were calculated based on the eddy covariance method, correlating the fluctuations in the vertical wind speed component and the fluctuations in the mixing ratio based on measurements of the Lyman-alpha fast humidity sensor. More details of the averaging method are explained in . Under thermally stable conditions, nearly all flights showed a notable increase in upward latent heat flux above the wind farms, with maximum enhancements reaching +160 Wm-2 relative to background conditions. During near-neutral stratification, the latent heat flux enhancement was generally stronger, with peak values up to +600 Wm-2, although more spatially confined. The data suggest that while the signal is more consistently detectable during stable conditions, it becomes more pronounced under neutral conditions. This is likely due to enhanced vertical mixing induced by the wind farms, which facilitates downward entrainment of drier air into the near-surface layer, enhancing the evaporation potential. The increase in latent heat flux was generally limited to the area above and immediately downstream (up to around 1 km) of the wind farms (Fig. ), while the associated wakes extended further downwind, indicating different spatial scales of momentum and scalar flux modifications. The distance of the flight trajectory to the wind farms varies due to wind park geometry and wind direction, resulting in visible effects of the latent heat fluxes only for part of the areas indicated as downstream of the wind farms in Fig. .

However, attributing the observed effects solely to wind farm influence also remains challenging for airborne observations due to varying flight patterns, measurement altitudes, atmospheric background variability, and limitations in a suitable definition for accurately classifying stability . Additionally, wind farm characteristics such as turbine layout and density likely play a role in modulating the magnitude of these impacts. The observed modification of moisture fluxes at hub height and above suggests potential downstream effects on surface latent heat fluxes, as supported by previous numerical modelling studies. While our measurements represent only short-term snapshots, they support the hypothesis that offshore wind farms may locally alter the surface energy budget and atmospheric structure . For instance, on two flights with near-saturated humidity and low-stability conditions, small cumulus clouds were visually observed forming directly above wind farms, suggesting that increased latent heat flux may contribute to localized cloud development.

The wind speed over the North Sea is typically higher than over land due to the approximately 2-order-of-magnitude-lower surface roughness over water and the higher heat capacity of water. However, the transition of wind from land to sea, and the distance required to reach an equilibrium wind speed, is strongly influenced by atmospheric stability , the formation of internal boundary layers , and mesoscale phenomena such as low-level jets (LLJs) and sea breezes. Previous studies e.g. show that offshore wind speed generally increases with distance from the coast, especially at lower altitudes. This increase is more pronounced under neutral or unstable conditions and is attenuated at higher altitudes. Atmospheric stratification plays a critical role: stable conditions can delay the offshore wind adjustment, while unstable conditions tend to accelerate it. For studies in the German Bight, the equilibrium was typically reached within 50–80 km. A mesoscale modelling study over the Baltic Sea showed potential temperature developments under stable stratification over distances of more than 200 km .

present two contrasting airborne measurement cases illustrating the offshore adjustment of LLJs over the German Bight under offshore-flow conditions. In the first case (Fig. a; 23 July 2020), warmer sea surface temperatures offshore (approximately 17 °C at FINO1 compared to about 13 °C over land during the early morning) reduced atmospheric stability over the sea and enhanced vertical turbulent mixing. This promoted the downward transport of momentum and led to a rapid weakening and gradual erosion of the land-based LLJs as the flow adjusted offshore, accompanied by increasing near-surface wind speed with increasing fetch. In the second case (Fig. b; 23 September 2020), the flow over land was initially much more strongly stratified. Although the warmer sea surface gradually reduced the stability offshore, the boundary layer adjustment occurred more slowly, allowing the LLJs to persist farther offshore before weakening. As a consequence, the wind speed decreased with increasing distance from the coast. The airborne data were analysed in the context of stationary lidar measurements at the island of Norderney and at the meteorological mast FINO1, as well as satellite observations, and complemented with ERA5 reanalysis data and WRF simulations .

Airborne measurements offer significant potential for capturing atmospheric parameters during periods with meteorologically interesting flow conditions (e.g. stable stratification around and above offshore wind farms), which can be compared to simulations. Unlike many ground-based methods, they provide high-resolution data in the range of a few centimetres in both horizontal and vertical directions, often exceeding 1000 m in altitude. This makes them a valuable tool for comparison with simulations and for evaluating parameterizations in numerical models, including those related to wind turbines and turbulence, particularly in complex flow regimes such as those found in offshore environments (e.g. ; ; ).

As wind is a driving force for sea state development, the reduced wind speed downstream of wind farms leads to changes in the wave spectra. The enhanced laser scanner surface reflectance in wake areas indicates that the water surface is smoother, and a larger percentage of the laser pulse is reflected back to the receiver. These changes in the surface roughness can take place on a horizontal scale of less than 1 km. Modifications of the wave spectra, i.e. the energy per wavelength, were observed in wake areas for developing sea state conditions .

A significant reduction in wind speed in the wake of a wind farm also results in a decrease in significant wave height. Under stable atmospheric conditions, influences on sea state characteristics can be detected that can be attributed to a sea surface wake induced by the atmospheric wake. For the atmospheric wake, different parameters such as wind speed reduction are characteristic for the wake length. Characteristics for the sea surface wake can be selected as well. A simple, yet meaningful description can be provided by the reduction in the significant wave height . For all meander flights performed during WIPAFF and X-Wakes, the atmospheric wake length was found to be visible up to larger distances than wakes at the sea surface, defined as reduced significant wave height, but long-reaching surface wakes of several tens of kilometres have also been identified . More detailed analyses can be performed using the spectral sea surface energy distribution. Figure  shows such an energy distribution of the measurement flight on 24 July 2020. The area influenced by the wind farm is depicted within the manually inserted black lines, and from a graphical perspective, the wind originates from the left, i.e. from 270°. Within the black lines, a reduction in wind speed is observed, which can be noted as the wake of the wind farm. As the wind speed decreases, the energy density spectrum is also modified over a wide frequency range. This can be observed to a downstream distance of at least 29 km behind the wind farm. At downstream distances of 70 and 76 km, very low spectral energy densities are observed (black colour), particularly at angular frequencies below approximately 1.5 rads-1. Overall, the spectra at these large distances from the wind farm exhibit a more homogeneous distribution, with energy levels predominantly around 0.2 m2rad3s-3 (green range of the colour scale), leaving low-energy regions to appear black due to the applied range. Wind farms can have a significant impact on developing sea state conditions, particularly on young wind waves. Reduced wind speed disrupts wave growth, resulting in a less energetic spectrum. A larger impact on sea conditions is observed for stable atmospheric conditions with more pronounced reductions in wind speed.

The global blockage effect (GBE) is the combined induction zone of all turbines in a wind farm . The upstream pressure field is responsible for deflecting the upstream flow to the side and above the wind farm . The upward deflection reaches the upper atmospheric layers where stable stratification is always present and thus induces gravity waves that further intensify the GBE . Therefore, GBE depends on atmospheric boundary layer height and stratification of the inversion layer and the free atmosphere . also demonstrate that the GBE is sensitive to the stratification of the atmospheric boundary layer, as justified by . It is still the subject of research if and to which extent it leads to a net power loss in a wind farm or rather a power redistribution . Compared to wind turbine or wind farm wake effects the GBE is much harder to investigate experimentally . While wind speed gradients in wind farm wakes appear on the order of metres per second over short distances of some hundreds of metres , the wind speed gradient in GBE spreads over distances of several kilometres, with much smaller wind speed differences of only tenths of a metre per second . Similar to the previously described downstream wind farm wake investigations, airborne measurements with horizontal flight legs were performed upstream of wind farms. The flight path was oriented perpendicular to the main wind direction with the goal of quantifying GBE-induced wind speed differences in front of and beside the farm. The investigations were limited by the flight permission, which only allowed operation with a safety distance of at least 500 m to the wind turbines.

Figure  shows an exemplary data set and illustrates how the GBE was investigated: the flight patterns were aligned close to the wind farm cluster N4 with distances of 500 m to 2 km upstream. The wind speed was normalized to 1 in the undisturbed areas next to the areas affected by wakes, and latitudinal and longitudinal transects are displayed. Based on all flights with such patterns, small average numbers of wind speed reductions in the wake areas compared to the undisturbed areas were observed in the range of 2 % for the closest flight distance of 500 m with a high scatter. On averaged data, a non-significant trend of reduced wind speed in the direction towards the farm is visible, which cannot be reliably attributed to GBE. In this overview, different days with different atmospheric conditions are compared. The wind speed difference is calculated over large distances not considering mesoscale changes in the wind field. In summary, it turned out that the GBE is potentially too small to be determined by airborne measurements for single flights and also on average.