A multifaceted future for wind power: Modern wind turbines already represent a tightly optimized confluence of materials science and aerodynamic engineering. Veers et al. review the challenges and opportunities for further expanding this technology, with an emphasis on the need for interdisciplinary collaboration. They highlight the need to better understand atmospheric physics in the regions where taller turbines will operate as well as the materials constraints associated with the scale-up. The mutual interaction of turbine sites with one another and with the evolving features of the overall electricity grid will furthermore necessitate a systems approach to future development. Science , this issue p. eaau2027

Wind tunnel measurements of snowdrift in a turbulent, logarithmic velocity boundary layer have been made in Davos, Switzerland, using natural snow. Regression analysis gives the drift threshold friction velocity (u_*t), assuming an exponential drift profile and a simple drift to friction velocity relationship. Measurements over 15 snow covers show that u_*t is influenced more by snow density and particle size than by ambient temperature and humidity, and varies from 0.27 to 0.69 ms^–1. Schmidt’s threshold algorithm and a modified version used in SNOWPACK (a snow-cover model) agree well with observations if small bond sizes are assumed. Using particle hydraulic diameters, obtained from image processing, Bagnold’s threshold parameter is 0.18. Roughness lengths (z₀) vary between snow covers but are constant until the start of drift. Threshold roughness lengths are proportional to . The influence of macroscopic objects on the roughness length is shown by the lower values measured over the smooth and flat snow surface of the wind tunnel (0.04 ≤ z₀ ≤ 0.13 mm), compared to field measurements. Mean drifting-snow grain sizes for mainly new and partly decomposed snow are 100–175 μm, and independent of surface particle size.

Using detailed upwind and nacelle-based measurements from a General Electric (GE) 1.5sle model with a 77 m rotor diameter, we calculate power curves and annual energy production (AEP) and explore their sensitivity to different atmospheric parameters to provide guidelines for the use of stability and turbulence filters in segregating power curves. The wind measurements upwind of the turbine include anemometers mounted on a 135 m meteorological tower as well as profiles from a lidar. We calculate power curves for different regimes based on turbulence parameters such as turbulence intensity (TI) as well as atmospheric stability parameters such as the bulk Richardson number (R_B). We also calculate AEP with and without these atmospheric filters and highlight differences between the results of these calculations. The power curves for different TI regimes reveal that increased TI undermines power production at wind speeds near rated, but TI increases power production at lower wind speeds at this site, the US Department of Energy (DOE) National Wind Technology Center (NWTC). Similarly, power curves for different R_B regimes reveal that periods of stable conditions produce more power at wind speeds near rated and periods of unstable conditions produce more power at lower wind speeds. AEP results suggest that calculations without filtering for these atmospheric regimes may overestimate the AEP. Because of statistically significant differences between power curves and AEP calculated with these turbulence and stability filters for this turbine at this site, we suggest implementing an additional step in analyzing power performance data to incorporate effects of atmospheric stability and turbulence across the rotor disk.

Wind-profiling lidars are now regularly used in boundary-layer meteorology and in applications such as wind energy and air quality. Lidar wind profilers exploit the Doppler shift of laser light backscattered from particulates carried by the wind to measure a line-of-sight (LOS) velocity. The Doppler beam swinging (DBS) technique, used by many commercial systems, considers measurements of this LOS velocity in multiple radial directions in order to estimate horizontal and vertical winds. The method relies on the assumption of homogeneous flow across the region sampled by the beams. Using such a system in inhomogeneous flow, such as wind turbine wakes or complex terrain, will result in errors. To quantify the errors expected from such violation of the assumption of horizontal homogeneity, we simulate inhomogeneous flow in the atmospheric boundary layer, notably stably stratified flow past a wind turbine, with a mean wind speed of 6.5 m s⁻¹ at the turbine hub-height of 80 m. This slightly stable case results in 15° of wind direction change across the turbine rotor disk. The resulting flow field is sampled in the same fashion that a lidar samples the atmosphere with the DBS approach, including the lidar range weighting function, enabling quantification of the error in the DBS observations. The observations from the instruments located upwind have small errors, which are ameliorated with time averaging. However, the downwind observations, particularly within the first two rotor diameters downwind from the wind turbine, suffer from errors due to the heterogeneity of the wind turbine wake. Errors in the stream-wise component of the flow approach 30% of the hub-height inflow wind speed close to the rotor disk. Errors in the cross-stream and vertical velocity components are also significant: cross-stream component errors are on the order of 15% of the hub-height inflow wind speed (1.0 m s⁻¹) and errors in the vertical velocity measurement exceed the actual vertical velocity. By three rotor diameters downwind, DBS-based assessments of wake wind speed deficits based on the stream-wise velocity can be relied on even within the near wake within 1.0 m s⁻¹ (or 15% of the hub-height inflow wind speed), and the cross-stream velocity error is reduced to 8% while vertical velocity estimates are compromised. Measurements of inhomogeneous flow such as wind turbine wakes are susceptible to these errors, and interpretations of field observations should account for this uncertainty.