81 citations as recorded by crossref.

1. Using high-frequency SCADA data for wind turbine performance monitoring: A sensitivity study E. Gonzalez et al. https://doi.org/10.1016/j.renene.2018.07.068
2. Validation of turbulence intensity as simulated by the Weather Research and Forecasting model off the US northeast coast S. Tai et al. https://doi.org/10.5194/wes-8-433-2023
3. Wind turbine power curve modeling using radial basis function neural networks and tabu search D. Karamichailidou et al. https://doi.org/10.1016/j.renene.2020.10.020
4. On the measurement of stability parameter over complex mountainous terrain E. Cantero et al. https://doi.org/10.5194/wes-7-221-2022
5. Atmospheric stability from numerical weather prediction models and microwave radiometer observations for onshore and offshore wind energy applications D. Cimini et al. https://doi.org/10.5194/amt-18-2041-2025
6. Impact of atmospheric turbulence on performance and loads of wind turbines: knowledge gaps and research challenges B. Kosović et al. https://doi.org/10.5194/wes-11-509-2026
7. Turbulence characteristics of a thermally stratified wind turbine array boundary layer via proper orthogonal decomposition N. Ali et al. https://doi.org/10.1017/jfm.2017.492
8. Capturing Day-to-day Diurnal Variations in Stability in the Convective Atmospheric Boundary Layer Using Large Eddy Simulation J. Nielson & K. Bhaganagar https://doi.org/10.2174/1874282301812010107
9. Using wind speed from a blade-mounted flow sensor for power and load assessment on modern wind turbines M. Pedersen et al. https://doi.org/10.5194/wes-2-547-2017
10. Sensitivity study of a wind farm maintenance decision - A performance and revenue analysis J. Tautz-Weinert et al. https://doi.org/10.1016/j.renene.2018.07.110
11. Turbulence modeling to aid tidal energy resource characterization in the Western Passage, Maine, USA M. Deb et al. https://doi.org/10.1016/j.renene.2023.04.100
12. Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing R. Worsnop et al. https://doi.org/10.5194/wes-3-371-2018
13. Wind turbine wake influence on the mixing of relative humidity quantified through wind tunnel experiments M. Obligado et al. https://doi.org/10.1063/5.0039090
14. Temperature profiling at the American WAKE ExperimeNt (AWAKEN): methodology and uncertainty quantification S. Letizia et al. https://doi.org/10.5194/wes-11-1653-2026
15. Free-flow wind speed from a blade-mounted flow sensor M. Pedersen et al. https://doi.org/10.5194/wes-3-121-2018
16. Characteristics of Wind Environment in Dongbok･Bukchon Wind Farm on Jeju H. Jeong et al. https://doi.org/10.7849/ksnre.2022.2027
17. Review of atmospheric stability estimations for wind power applications C. Pérez Albornoz et al. https://doi.org/10.1016/j.rser.2022.112505
18. Evaluating the Performance of Machine Learning-Based Stacking Ensemble Models for Wind Power Prediction H. Kim & B. Kim https://doi.org/10.7849/ksnre.2025.0025
19. Effect of Turbulence Intensity on the Induction Factor and Power Efficiency of Wind Turbines T. Revaz & F. Porté‐Agel https://doi.org/10.1002/we.70040
20. Wind power variation by wind veer characteristics with two wind farms U. Tumenbayar & K. Ko https://doi.org/10.1038/s41598-023-37957-6
21. Modeling of the atmospheric boundary layer under stability stratification for wind turbine wake production M. Ichenial et al. https://doi.org/10.1177/0309524X19880929
22. Decision model for cross-border electricity trade considering renewable energy sources M. Eghlimi et al. https://doi.org/10.1016/j.egyr.2022.08.220
23. The effect of wind direction shear on turbine performance in a wind farm in central Iowa M. Sanchez Gomez & J. Lundquist https://doi.org/10.5194/wes-5-125-2020
24. A decision-tree-based measure–correlate–predict approach for peak wind gust estimation from a global reanalysis dataset S. Kartal et al. https://doi.org/10.5194/wes-8-1533-2023
25. Modeling the effect of wind speed and direction shear on utility‐scale wind turbine power production S. Mata et al. https://doi.org/10.1002/we.2917
26. Nacelle-Mounted Doppler Lidar for Real-Time Wind Field Measurement and Wake Deficit Optimization in Wind Farms Y. Zhong et al. https://doi.org/10.1109/TIM.2026.3664560
27. Recent advances in data-driven prediction for wind power Y. Liu et al. https://doi.org/10.3389/fenrg.2023.1204343
28. Analysis of Turbulence and Wind Shear Characteristics in the Hilly Terrain of the Geba Catchment Tigray, North Ethiopia M. Kahsay et al. https://doi.org/10.3390/atmos16121374
29. A review on proliferation of artificial intelligence in wind energy forecasting and instrumentation management L. Zhao et al. https://doi.org/10.1007/s11356-022-19902-8
30. Analysis of Vertical Wind Shear Effects on Offshore Wind Energy Prediction Accuracy Applying Rotor Equivalent Wind Speed and the Relationship with Atmospheric Stability G. Ryu et al. https://doi.org/10.3390/app12146949
31. Investigation of wind veer characteristics on complex terrain using ground-based lidar U. Tumenbayar & K. Ko https://doi.org/10.14710/ijred.2023.56352
32. Influence of atmospheric stability on wind farm performance in complex terrain W. Radünz et al. https://doi.org/10.1016/j.apenergy.2020.116149
33. Improvement of wind power prediction from meteorological characterization with machine learning models C. Sasser et al. https://doi.org/10.1016/j.renene.2021.10.034
34. Understanding the Characteristics of Vertical Structures for Wind Speed Observations via Wind-LIDAR on Jeju Island D. Yi et al. https://doi.org/10.3390/atmos14081260
35. Benchmarks for Model Validation based on LiDAR Wake Measurements P. Doubrawa et al. https://doi.org/10.1088/1742-6596/1256/1/012024
36. Influence of turbulence intensity on wind turbine power curves L. Bardal & L. Sætran https://doi.org/10.1016/j.egypro.2017.10.384
37. Quantification of performance degradation due to wind turbine aging: Estimating the reduction in annual energy production using the annual degradation rate D. Kim & B. Kim https://doi.org/10.1016/j.energy.2025.136142
38. Comparing the impact of uncertainties on technical and meteorological parameters in wind power time series modelling in the European Union F. Monforti & I. Gonzalez-Aparicio https://doi.org/10.1016/j.apenergy.2017.08.217
39. Influence of Atmospheric Stability on Wind Turbine Energy Production: A Case Study of the Coastal Region of Yucatan C. Pérez et al. https://doi.org/10.3390/en16104134
40. Using atmospheric inputs for Artificial Neural Networks to improve wind turbine power prediction J. Nielson et al. https://doi.org/10.1016/j.energy.2019.116273
41. Wind farm site selection considering turbulence intensity M. Asadi & K. Pourhossein https://doi.org/10.1016/j.energy.2021.121480
42. Extracting Hidden Features of an Atmospheric Turbulent Flow Through Implementing Discrete Wavelet Analysis on Sodar’s Signal M. Saberivahidaval et al. https://doi.org/10.1007/s13538-021-01039-7
43. Quantification of power losses due to wind turbine wake interactions through SCADA, meteorological and wind LiDAR data S. El‐Asha et al. https://doi.org/10.1002/we.2123
44. Quantification of the axial induction exerted by utility-scale wind turbines by coupling LiDAR measurements and RANS simulations G. Valerio Iungo et al. https://doi.org/10.1088/1742-6596/1037/7/072023
45. The Detection of Wind‐Turbine Noise in Seismic Records O. Marcillo & J. Carmichael https://doi.org/10.1785/0220170271
46. Challenges in detecting wind turbine power loss: the effects of blade erosion, turbulence, and time averaging T. Malik & C. Bak https://doi.org/10.5194/wes-10-227-2025
47. Can synoptic drivers explain rotor‐area wind conditions and predict energy output? J. Coburn & K. Klink https://doi.org/10.1002/we.2807
48. Extracting Atmospheric Stability Information from Dual-Doppler Radar Scans in the AWAKEN Campaign J. Nadolsky et al. https://doi.org/10.1088/1742-6596/2767/4/042012
49. Quantifying the impact of wind shear coefficient on annual energy production of coastal wind farm in balochistan, Pakistan J. Yasir et al. https://doi.org/10.1177/27533735251319764
50. AIVT: Inference of turbulent thermal convection from measured 3D velocity data by physics-informed Kolmogorov-Arnold networks J. Toscano et al. https://doi.org/10.1126/sciadv.ads5236
51. Estimation of the Performance Aging of the Vestas V52 Wind Turbine through Comparative Test Case Analysis D. Astolfi et al. https://doi.org/10.3390/en14040915
52. Contribution of meteorological factors based on explainable artificial intelligence in predicting wind farm power production using machine learning algorithms D. Kim & B. Kim https://doi.org/10.1063/5.0127519
53. Statistical characterization of marine wind structure over wind-turbine-relevant heights in tropical cyclones J. He et al. https://doi.org/10.1016/j.jweia.2024.105874
54. Characterization of Turbulence in Wind Turbine Wakes under Different Stability Conditions from Static Doppler LiDAR Measurements V. Kumer et al. https://doi.org/10.3390/rs9030242
55. Fault-tolerant sensorless control of wind turbines achieving efficiency maximization in the presence of electrical faults M. Corradini et al. https://doi.org/10.1016/j.jfranklin.2018.01.003
56. On the Wind Energy Resource above High-Rise Buildings G. Vita et al. https://doi.org/10.3390/en13143641
57. Atmospheric turbulence affects wind turbine nacelle transfer functions C. St. Martin et al. https://doi.org/10.5194/wes-2-295-2017
58. Wind farm power curve characterization under different atmospheric stability regimes J. Gonzalez et al. https://doi.org/10.1177/0309524X241254473
59. Assessing the impact of waves and platform dynamics on floating wind-turbine energy production A. Fontanella et al. https://doi.org/10.5194/wes-9-1393-2024
60. The lattice Boltzmann method for wind farm simulations: a review H. Korb et al. https://doi.org/10.5194/wes-11-983-2026
61. Simulation of Wind Speeds with Spatio-Temporal Correlation M. Cordeiro-Costas et al. https://doi.org/10.3390/app11083355
62. The Effects of Wind Veer During the Morning and Evening Transitions M. Sanchez Gomez & J. Lundquist https://doi.org/10.1088/1742-6596/1452/1/012075
63. Low-level jets' influence on the power conversion efficiency of offshore wind turbines J. Paulsen et al. https://doi.org/10.5194/wes-11-321-2026
64. Full-scale wind turbine performance assessment using the turbine performance integral (TPI) method: a study of aerodynamic degradation and operational influences T. Malik & C. Bak https://doi.org/10.5194/wes-9-2017-2024
65. More accurate aeroelastic wind-turbine load simulations using detailed inflow information M. Pedersen et al. https://doi.org/10.5194/wes-4-303-2019
66. A workflow for including atmospheric stability effects in wind resource and yield assessment and its evaluation against wind measurements and SCADA M. Diallo et al. https://doi.org/10.1088/1742-6596/2507/1/012018
67. Augmenting insights from wind turbine data through data-driven approaches C. Moss et al. https://doi.org/10.1016/j.apenergy.2024.124116
68. Using field data–based large eddy simulation to understand role of atmospheric stability on energy production of wind turbines J. Nielson & K. Bhaganagar https://doi.org/10.1177/0309524X18824540
69. Power curve performance of coastal turbines subject to low turbulence intensity offshore winds Y. Sakagami et al. https://doi.org/10.1007/s40430-022-03942-9
70. Reconstructing the upwind field of wind turbines using LiDAR data M. Khezri et al. https://doi.org/10.1016/j.seta.2025.104382
71. A Methodology for Turbine-Level Possible Power Prediction and Uncertainty Estimations Using Farm-Wide Autoregressive Information on High-Frequency Data F. Jara Ávila et al. https://doi.org/10.3390/en18143764
72. The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production M. Optis & J. Perr-Sauer https://doi.org/10.1016/j.rser.2019.05.031
73. A novel probabilistic power curve model to predict the power production and its uncertainty for a wind farm over complex terrain G. Qian & T. Ishihara https://doi.org/10.1016/j.energy.2022.125171
74. Pitch angle control of a wind turbine operating above the rated wind speed: A sliding mode control approach L. Colombo et al. https://doi.org/10.1016/j.isatra.2019.07.002
75. Modeling weights for co-location feasibility in hybrid wind-wave device O. Aristondo et al. https://doi.org/10.1016/j.energy.2025.138548
76. Changes in wind turbine power characteristics and annual energy production due to atmospheric stability, turbulence intensity, and wind shear D. Kim et al. https://doi.org/10.1016/j.energy.2020.119051
77. Impacts of inflow turbulence on the flow past a permeable disk Y. Li et al. https://doi.org/10.1017/jfm.2024.876
78. Modified Power Curves for Prediction of Power Output of Wind Farms M. Vahidzadeh & C. Markfort https://doi.org/10.3390/en12091805
79. Ensemble offshore Wind Turbine Power Curve modelling – An integration of Isolation Forest, fast Radial Basis Function Neural Network, and metaheuristic algorithm T. Li et al. https://doi.org/10.1016/j.energy.2021.122340
80. Atmospheric Stability Effects on Offshore and Coastal Wind Resource Characteristics in South Korea for Developing Offshore Wind Farms G. Ryu et al. https://doi.org/10.3390/en15041305
81. How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine P. Murphy et al. https://doi.org/10.5194/wes-5-1169-2020