The lower wind speeds and increased turbulence that are characteristic of turbine wakes have considerable consequences on large wind farms: turbines located downwind generate less power and experience increased turbulent loads. The structures of wakes and their downwind impacts are sensitive to wind speed and atmospheric variability. Wake characterization can provide important insights for turbine layout optimization in view of decreasing the cost of wind energy. The CWEX-13 field campaign, which took place between June and September 2013 in a wind farm in Iowa, was designed to explore the interaction of multiple wakes in a range of atmospheric stability conditions. Based on lidar wind measurements, we extend, present, and apply a quantitative algorithm to assess wake parameters such as the velocity deficits, the size of the wake boundaries, and the location of the wake centerlines. We focus on wakes from a row of four turbines at the leading edge of the wind farm to explore variations between wakes from the edge of the row (outer wakes) and those from turbines in the center of the row (inner wakes). Using multiple horizontal scans at different elevations, a three-dimensional structure of wakes from the row of turbines can be created. Wakes erode very quickly during unstable conditions and can in fact be detected primarily in stable conditions in the conditions measured here. During stable conditions, important differences emerge between the wakes of inner turbines and the wakes of outer turbines. Further, the strong wind veer associated with stable conditions results in a stretching of the wake structures, and this stretching manifests differently for inner and outer wakes. These insights can be incorporated into low-order wake models for wind farm layout optimization or for wind power forecasting.

The rapid growth of offshore wind energy requires accurate modeling of the wind resource, which can be depleted by wind farm wakes. Turbulence dissipation rate (ϵ) governs the accuracy of model predictions of hub‐height wind speed and the development and erosion of wakes. Here we assess the variability of turbulence kinetic energy and ϵ using 13 months of observations from a profiling lidar deployed on a platform off the Massachusetts coast. Offshore, ϵ is 2 orders of magnitude smaller than onshore, with a subtle diurnal cycle. Wind direction influences the annual cycle of turbulence, with larger values in winter when the wind flows from the land, and smaller values in summer, when the wind flows from open ocean. Because of the weak turbulence, wind plant wakes will be stronger and persist farther downwind in summer.

As the offshore wind industry emerges on the US East Coast, a comprehensive understanding of the wind resource – particularly extreme events – is vital to the industry's success. Such understanding has been hindered by a lack of publicly available wind profile observations in offshore wind energy areas. However, the New York State Energy Research and Development Authority recently funded the deployment of two floating lidars within two current lease areas off the coast of New Jersey. These floating lidars provide publicly available wind speed data from 20 to 200 m height with a 20 m vertical resolution. In this study, we leverage a year of these lidar data to quantify and characterize the frequent occurrence of high-wind-shear and low-level-jet events, both of which will have a considerable impact on turbine operation. In designing a detection algorithm for these events, we find that the typical, non-dimensional power-law-based wind shear exponent is insufficient to identify many of these extreme, high-wind-speed events. Rather, we find that the simple vertical gradient of wind speed better captures the events. Based on this detection method, we find that almost 100 independent events occur throughout the year with mean wind speed at 100 m height and wind speed gradient of 16 m s⁻¹ and 0.05 s⁻¹, respectively. The events have strong seasonal variability, with the highest number of events in summer and the lowest in winter. A detailed analysis reveals that these events are enabled by an induced stable stratification when warmer air from the south flows over the colder mid-Atlantic waters, leading to a positive air–sea temperature difference.

Despite turbulence being a fundamental transport process in the boundary layer, the capability of current numerical models to represent it is undermined by the limits of the adopted assumptions, notably that of local equilibrium. Here we leverage the potential of extensive observations in determining the variability in turbulence dissipation rate ( ϵ ). These observations can provide insights towards the understanding of the scales at which the major assumption of local equilibrium between generation and dissipation of turbulence is invalid. Typically, observations of ϵ require time- and labor-intensive measurements from sonic and/or hot-wire anemometers. We explore the capability of wind Doppler lidars to provide measurements of ϵ . We refine and extend an existing method to accommodate different atmospheric stability conditions. To validate our approach, we estimate ϵ from four wind Doppler lidars during the 3-month XPIA campaign at the Boulder Atmospheric Observatory (Colorado), and we assess the uncertainty of the proposed method by data intercomparison with sonic anemometer measurements of ϵ . Our analysis of this extensive dataset provides understanding of the climatology of turbulence dissipation over the course of the campaign. Further, the variability in ϵ with atmospheric stability, height, and wind speed is also assessed. Finally, we present how ϵ increases as nocturnal turbulence is generated during low-level jet events.

Accurate characterization of the offshore wind resource has been hindered by a sparsity of wind speed observations that span offshore wind turbine rotor-swept heights. Although public availability of floating lidar data is increasing, most offshore wind speed observations continue to come from buoy-based and satellite-based near-surface measurements. The aim of this study is to develop and validate novel vertical extrapolation methods that can accurately estimate wind speed time series across rotor-swept heights using these near-surface measurements. We contrast the conventional logarithmic profile against three novel approaches: a logarithmic profile with a long-term stability correction, a single-column model, and a machine-learning model. These models are developed and validated using 1 year of observations from two floating lidars deployed in US Atlantic offshore wind energy areas. We find that the machine-learning model significantly outperforms all other models across all stability regimes, seasons, and times of day. Machine-learning model performance is considerably improved by including the air–sea temperature difference, which provides some accounting for offshore atmospheric stability. Finally, we find no degradation in machine-learning model performance when tested 83 km from its training location, suggesting promising future applications in extrapolating 10 m wind speeds from spatially resolved satellite-based wind atlases.

The understanding of the sources, spatial distribution and temporal variability of turbulence in the atmospheric boundary layer, and improved simulation of its forcing processes require observations in a broad range of terrain types and atmospheric conditions. In this study, we estimate turbulence kinetic energy dissipation rate ε using multiple techniques, including in situ measurements of sonic anemometers on meteorological towers, a hot-wire anemometer on a tethered lifting system and remote-sensing retrievals from a vertically staring lidar and two lidars performing range–height indicator (RHI) scans. For the retrieval of ε from the lidar RHI scans, we introduce a modification of the Doppler spectral width method. This method uses spatiotemporal averages of the variance in the line-of-sight velocity and the turbulent broadening of the Doppler backscatter spectrum. We validate this method against the observations from the other instruments, also including uncertainty estimations for each method. The synthesis of the results from all instruments enables a detailed analysis of the spatial and temporal variability in ε across a valley between two parallel ridges at the Perdigão 2017 campaign. We analyze in detail how ε varies in the night from 13 to 14 June 2017. We find that the shear zones above and below a nighttime low-level jet experience turbulence enhancements. We also show that turbulence in the valley, approximately 11 rotor diameters downstream of an operating wind turbine, is still significantly enhanced by the wind turbine wake.