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With the increased availability of experimental measurements aiming at probing wind resources and wind turbine operations, machine learning (ML) models are poised to advance our understanding of the physics underpinning the interaction between the atmospheric boundary layer and wind turbine arrays, the generated wakes and their interactions, and wind energy harvesting. However, the majority of the existing ML models for predicting wind turbine wakes merely recreate CFD-simulated data with analogous accuracy but reduced computational costs, thus providing surrogate models rather than enhanced data-enabled physics insights. Although ML-based surrogate models are useful to overcome current limitations associated with the high computational costs of CFD models, using ML to unveil processes from experimental data or enhance modeling capabilities is deemed a potential research direction to pursue. In this letter, we discuss recent achievements in the realm of ML modeling of wind turbine wakes and operations, along with new promising research strategies. 

Abstract The power performance and the wind velocity field of an onshore wind farm are predicted with machine learning models and the pseudo‐2D RANS model, then assessed against SCADA data. The wind farm under investigation is one of the sites involved with the American WAKE experimeNt (AWAKEN). The performed simulations enable predictions of the power capture at the farm and turbine levels while providing insights into the effects on power capture associated with wake interactions that operating upstream turbines induce, as well as the variability caused by atmospheric stability. The machine learning models show improved accuracy compared to the pseudo‐2D RANS model in the predictions of turbine power capture and farm power capture with roughly half the normalized error. The machine learning models also entail lower computational costs upon training. Further, the machine learning models provide predictions of the wind turbulence intensity at the turbine level for different wind and atmospheric conditions with very good accuracy, which is difficult to achieve through RANS modeling. Additionally, farm‐to‐farm interactions are noted, with adverse impacts on power predictions from both models. 

Abstract Understanding the organization and dynamics of turbulence structures in the atmospheric surface layer (ASL) is important for fundamental and applied research in different fields, including weather prediction, snow settling, particle and pollutant transport, and wind energy. The main challenges associated with probing and modeling turbulence in the ASL are: i) the broad range of turbulent scales associated with the different eddies present in high Reynolds-number boundary layers ranging from the viscous scale (𝒪(mm)) up to large energy-containing structures (𝒪(km)); ii) the non-stationarity of the wind conditions and the variability associated with the daily cycle of the atmospheric stability; iii) the interactions among eddies of different sizes populating different layers of the ASL, which contribute to momentum, energy, and scalar turbulent fluxes. Creative and innovative measurement techniques are required to probe near-surface turbulence by generating spatio-temporally-resolved data in the proximity of the ground and, at the same time, covering the entire ASL height with large enough streamwise extent to characterize the dynamics of larger eddies evolving aloft. To this aim, the U.S. National Science Foundation sponsored the development of the Grand-scale Atmospheric Imaging Apparatus (GAIA) enabling super-large snow particle image velocimetry (SLPIV) in the near-surface region of the ASL. This inaugural version of GAIA provides a comprehensive measuring system by coupling SLPIV and two scanning Doppler LiDARs to probe the ASL at an unprecedented resolution. A field campaign performed in 2021–2022 and its preliminary results are presented herein elucidating new research opportunities enabled by the GAIA measuring system. 

Abstract A field experiment was conducted to investigate the effects of the thrust force induced by utility-scale wind turbines on the incoming wind field. Five wind profiling LiDARs and a scanning Doppler pulsed wind LiDAR were deployed in the proximity of a row of four wind turbines located over relatively flat terrain, both before and after the construction of the wind farm. The analysis of the LiDAR data collected during the pre-construction phase enables quantifying the wind map of the site, which is then leveraged to correct the post-construction LiDAR data and isolate rotor-induced effects on the incoming wind field. The analysis of the profiling LiDAR data allows for the identification of the induction zone upstream of the turbine rotors, with an increasing velocity deficit moving from the top tip towards the bottom tip of the rotor. The largest wind speed reduction (about 5%) is observed for convective conditions and incoming hub-height wind speed between cut-in and rated wind speeds. The scanning LiDAR data indicate the presence of speedup regions within the gaps between adjacent turbine rotors. Speedup increases with reducing the transverse distance between the rotors, atmospheric instability (maximum 15%), while a longer streamwise extent of the speedup region is observed under stable atmospheric conditions. 

Summary Over the last decades, pulsed light detection and ranging (LiDAR) anemometry has gained growing attention in probing the marine atmospheric boundary layer (MABL) due to its ease of use combined with compelling spatio‐temporal resolution. Among several scanning strategies, fixed scans represent the most prominent choice when high‐frequency resolution is required; however, no information is provided about the spatial heterogeneity of the wind field. On the other hand, volumetric scans allow for the characterization of the spatial variability of the wind field with much lower temporal resolution than fixed scans. In this work, the recently developed “LiDAR Statistical Barnes Objective Analysis” (LiSBOA) algorithm for the optimal design of LiDAR scans and retrieval of wind velocity statistics is tailored for applications in the MABL. The LiDAR data, collected during a recent experimental campaign over Lake Lavon in Texas, show a good consistency of mean velocity profiles between fixed and LiSBOA‐interpolated volumetric data, thus further encouraging the use of coupled fixed and volumetric scans for simultaneous characterizations of wind turbulence statistics along the vertical direction and volumetric heterogeneity of the wind field. 

Abstract Flow modifications induced by wind turbine rotors on the incoming atmospheric boundary layer (ABL), such as blockage and speedups, can be important factors affecting the power performance and annual energy production (AEP) of a wind farm. Further, these rotor‐induced effects on the incoming ABL can vary significantly with the characteristics of the incoming wind, such as wind shear, veer, and turbulence intensity, and turbine operative conditions. To better characterize the complex flow physics underpinning the interaction between turbine rotors and the ABL, a field campaign was performed by deploying profiling wind LiDARs both before and after the construction of an onshore wind turbine array. Considering that the magnitude of these rotor‐induced flow modifications represents a small percentage of the incoming wind speed ( ), high accuracy needs to be achieved for the analysis of the experimental data and generation of flow predictions. Further, flow distortions induced by the site topography and effects of the local climatology need to be quantified and differentiated from those induced by wind turbine rotors. To this aim, a suite of statistical and machine learning models, such as k‐means cluster analysis coupled with random forest predictions, are used to quantify and predict flow modifications for different wind and atmospheric conditions. The experimental results show that wind velocity reductions of up to 3% can be observed at an upstream distance of 1.5 rotor diameter from the leading wind turbine rotor, with more significant effects occurring for larger positive wind shear. For more complex wind conditions, such as negative shear and low‐level jet, the rotor induction becomes highly complex entailing either velocity reductions (down to 9%) below hub height and velocity increases (up to 3%) above hub height. The effects of the rotor induction on the incoming wind velocity field seem to be already roughly negligible at an upstream distance of three rotor diameters. The results from this field experiment will inform models to simulate wind‐turbine and wind‐farm operations with improved accuracy for flow predictions in the proximity of the rotor area, which will be instrumental for more accurate quantification of wind farm blockage and relative effects on AEP.