12 citations as recorded by crossref.

1. Diurnal surface heat-flux forcing controls wind-farm performance, blockage, and gravity-wave formation M. Pasupula et al. https://doi.org/10.1063/5.0334039
2. Grand challenges in designing resilient wind energy systems in areas prone to tropical cyclones G. Deskos et al. https://doi.org/10.5194/wes-11-2749-2026
3. Modeling Multiscale Atmospheric Interactions in Wind-Farm Power Spectra Y. Liu & R. Stevens https://doi.org/10.1103/dsz3-zftt
4. Doppler lidar gravity wave observations within North Sea wind farms S. Knoop et al. https://doi.org/10.1088/1742-6596/3224/2/022006
5. Impact of Stable Boundary Layer Dynamics on Wind Turbine Wake Characteristics: Insights from LES and Field Data at the WiValdi Wind Park L. Bührend et al. https://doi.org/10.1088/1742-6596/3224/3/032042
6. Modelling global offshore turbulence intensity including large-scale turbulence, stability and sea state X. Larsén et al. https://doi.org/10.5194/wes-11-1853-2026
7. How well can the Mann model describe typhoon turbulence? S. Müller et al. https://doi.org/10.5194/wes-11-961-2026
8. Time-varying wind-turbine wakes at high Reynolds numbers N. Wei et al. https://doi.org/10.1103/18jm-6bzj
9. AI-Assisted Aerodynamic Enhancement in Low-Speed Wind Energy Harvesting: A Case Study on Archimedes Spiral Turbines E. Mohammadian Bishe et al. https://doi.org/10.1016/j.energy.2026.142029
10. Low-frequency wind speed variations and their impact on wind farm performance Y. Liu et al. https://doi.org/10.1063/5.0332176
11. Reducing the number of wind turbine prototype measurement campaigns for power curve model validation using a model-validity predictor J. Murcia Leon et al. https://doi.org/10.1016/j.renene.2026.126122
12. Extreme turbulence effects on wind turbine loads: A case study for the North China Plain using nacelle lidar F. Guo et al. https://doi.org/10.1016/j.renene.2026.125774