The American WAKE ExperimeNt (AWAKEN) is a multi-institutional field campaign focused on gathering critical observations of wind farm–atmosphere interactions. These interactions are responsible for a large portion of the uncertainty in wind plant modeling tools that are used to represent wind plant performance both prior to construction and during operation and can negatively impact wind energy profitability. The AWAKEN field campaign will provide data for validation, ultimately improving modeling and lowering these uncertainties. The field campaign is designed to address seven testable hypotheses through the analysis of the observations collected by numerous instruments at 13 ground-based locations and on five wind turbines. The location of the field campaign in Northern Oklahoma was chosen to leverage existing observational facilities operated by the U.S. Department of Energy Atmospheric Radiation Measurement program in close proximity to five operating wind plants. The vast majority of the observations from the experiment are publicly available to researchers and industry members worldwide, which the authors hope will advance the state of the science for wind plants and lead to lower cost and increased reliability of wind energy systems.

The interplay of momentum surrounding wind farms significantly influences wake recovery, affecting the speed at which wakes return to their freestream velocities. Under stable atmospheric conditions, wind farm wakes can extend over considerable distances, leading to sustained vertical momentum flux downstream, with variations observed throughout the diurnal cycle. Particularly in regions such as the US Great Plains, stable conditions can induce low-level jets (LLJs), impacting wind farm performance and power output. This study examines the implications of wake recovery using long-term observations of vertical momentum flux profiles across diverse atmospheric conditions. In these observations, several key findings were observed, such as (a) LLJ heights being altered downstream of a wind farm, especially when the LLJs are below 250 m above ground level; (b) a notable impact of LLJ height on wake recovery being observed using momentum flux profiles at upwind and downwind locations, wherein LLJs between 250 and 500 m above ground level resulted in larger momentum transfer within the wake (i.e., smaller velocity deficit) compared to LLJs below 250 m above ground level; (c) the largest momentum flux variability being observed during stable atmospheric conditions, with non-negligible variability observed during neutral and unstable atmospheric conditions; (d) detection of wake effects almost always being observed throughout the atmospheric boundary layer height; and finally (e) enhancement of wake recovery being observed in the presence of propagating gravity waves. These insights deepen our understanding of the intricate dynamics governing wake recovery in wind farms, advancing efforts to model and predict their behavior across varying atmospheric contexts. In addition, the performance of large-eddy-simulation-based semi-empirical internal boundary layer height model estimates incorporating real-world atmospheric and turbine inputs was evaluated using observations during LLJ conditions.

Engineering wake models provide the invaluable advantage to predict wind turbine wakes, power capture, and, in turn, annual energy production for an entire wind farm with very low computational costs compared to higher-fidelity numerical tools. However, wake and power predictions obtained with engineering wake models can be insufficiently accurate for wind farm optimization problems due to the ad hoc tuning of the model parameters, which are typically strongly dependent on the characteristics of the site and power plant under investigation. In this paper, lidar measurements collected for individual turbine wakes evolving over a flat terrain are leveraged to perform optimal tuning of the parameters of four widely used engineering wake models. The average wake velocity fields, used as a reference for the optimization problem, are obtained through a cluster analysis of lidar measurements performed under a broad range of turbine operative conditions, namely rotor thrust coefficients, and incoming wind characteristics, namely turbulence intensity at hub height. The sensitivity analysis of the optimally tuned model parameters and the respective physical interpretation are presented. The performance of the optimally tuned engineering wake models is discussed, while the results suggest that the optimally tuned Bastankhah and Ainslie wake models provide very good predictions of wind turbine wakes. Specifically, the Bastankhah wake model should be tuned only for the far-wake region, namely where the wake velocity field can be well approximated with a Gaussian profile in the radial direction. In contrast, the Ainslie model provides the advantage of using as input an arbitrary near-wake velocity profile, which can be obtained through other wake models, higher-fidelity tools, or experimental data. The good prediction capabilities of the Ainslie model indicate that the mixing-length model is a simple yet efficient turbulence closure to capture effects of incoming wind and wake-generated turbulence on the wake downstream evolution and predictions of turbine power yield.

The American wake experiment (AWAKEN) is taking place in northern Oklahoma, USA, close to the Atmospheric Radiation Measurement Southern Great Plains (ARM SGP) atmospheric observatory. The planning for the deployment of the instruments in this observational field campaign required an assessment of the wind characteristics of the site. This paper analyzes long-term data collected by instruments at the ARM SGP observatory to characterize the winds near the AWAKEN site. The analysis shows that this site experiences high wind shear and veer events with a large number of nocturnal low-level jets. A total of 7086 low-level jet wind profiles over 6 years are examined and found to be dominant from the south and southeast. Significant nocturnal wind veer is observed, which causes southerly wind near the surface to become westerly wind aloft. By identifying a strong relationship between atmospheric stability and wind shear, the wind shear at the site is predicted using the Monin–Obukhov similarity theory (MOST) and validated with the observational data collected by a scanning Doppler lidar. The results show that wind speed at a height of 91 m, a proxy hub height for wind turbines in this area, can be predicted from data collected at a height of 10 m with a bias of −0.35 and 0.65 m s−1 in unstable and stable atmospheric boundary layers, respectively. The bias of the predicted wind speed is mostly in the region of low wind speed, and wind speed above 5 m s−1 at a height of 91 m can be predicted with a bias of less than 0.2 m s−1, and the limitations of the MOST in predicting winds during the stably stratified boundary layer is well-observed.

Next-generation models of wind farm flows are increasingly needed to assist the design, operation, and performance diagnostic of modern wind power plants. Accuracy in the descriptions of the wind farm aerodynamics, including the effects of atmospheric stability, coalescing wakes, and the pressure field induced by the turbine rotors are necessary attributes for such tools as well as low computational costs. The Pseudo-2D RANS model is formulated to provide an efficient solution of the Navier–Stokes equations governing wind-farm flows installed in flat terrain and offshore. The turbulence closure and actuator disk model are calibrated based on wind light detection and ranging measurements of wind turbine wakes collected under different operative and atmospheric conditions. A shallow-water formulation is implemented to achieve a converged solution for the velocity and pressure fields across a farm with computational costs comparable to those of mid-fidelity engineering wake models. The theoretical foundations and numerical scheme of the Pseudo-2D RANS model are provided, together with a detailed description of the verification and validation processes. The model is assessed against a large dataset of power production for an onshore wind farm located in North Texas showing a normalized mean absolute error of 5.6% on the 10-min-averaged active power and 3% on the clustered wind farm efficiency, which represent 8% and 24%, respectively, improvements with respect to the best-performing engineering wake model tested in this work.

In wind energy research, scientific challenges are often associated with complex terrain sites, where orography, vegetation, and buildings disrupt flow uniformity. However, even sites characterized as simple terrain can exhibit significant spatial variability in wind speed, particularly during stable boundary layers (SBLs) and low-level jets (LLJs). This study investigates these terrain interactions using both simulations and observations from the American WAKe ExperimeNt (AWAKEN). We employ a multiscale Weather Research and Forecasting (WRF) model simulation, integrating mesoscale forcing in the coarse domains and representing three rows of turbines from the King Plains wind farm as generalized actuator disks (GAD) in the large-eddy simulation (LES) domains. During a nocturnal LLJ event on 3 April 2023, the downstream, wake-affected turbine rows outperformed the upstream, unwaked row by 25 %–51 %. This counterintuitive result arises from terrain-induced streamwise variations in hub-height wind speed of approximately 4 m s⁻¹ over 5 km – equivalent to ∼50 % of the upstream reference speed. This enhancement outweighs the wake-induced reduction in mean wind speed (∼12 %) and global blockage effects reported in the literature (∼1 %–3.4 %). The multiscale simulations capture the intra-farm spatial variability in power performance observed in SCADA data. Terrain-induced vertical displacement of the LLJ, coupled with large wind shear below the jet maximum, drives the substantial streamwise acceleration within the wind farm. These findings underscore the importance of accounting for spatial variability related to terrain, even in simple landscapes, particularly during LLJ conditions. Incorporating such effects into reduced-order modeling frameworks for wind farm design and control could significantly enhance their effectiveness.