16 citations as recorded by crossref.

1. Understanding the impact of data gaps on long-term offshore wind resource estimates M. Jonietz Alvarez et al. https://doi.org/10.5194/wes-9-2217-2024
2. Evaluating the impact of inter-annual variability on long-term wind speed predictions J. Borowski et al. https://doi.org/10.5194/wes-11-661-2026
3. Harnessing Offshore Wind Energy along the Mexican Coastline in the Gulf of Mexico—An Exploratory Study including Sustainability Criteria G. Hernández Galvez et al. https://doi.org/10.3390/su14105877
4. Offshore wind farm cluster wakes as observed by long-range-scanning wind lidar measurements and mesoscale modeling B. Cañadillas et al. https://doi.org/10.5194/wes-7-1241-2022
5. Machine learning and statistical approaches for wind speed estimation at partially sampled and unsampled locations; review and open questions F. Houndekindo & T. Ouarda https://doi.org/10.1016/j.enconman.2025.119555
6. Copernicus Data for Offshore Wind Energy: Capabilities, Applications and Emerging Trends P. Poozesh et al. https://doi.org/10.3390/su18041949
7. A compendious approach for renewable energy assessment based on satellite and ground truth data: Bilate catchment, Rift Valley Basin, Ethiopia M. Ayana et al. https://doi.org/10.1007/s40095-022-00484-7
8. Reliability of ERA5 Reanalysis Data for Wind Resource Assessment: A Comparison against Tall Towers G. Gualtieri https://doi.org/10.3390/en14144169
9. Enabling Virtual Met Masts for wind energy applications through machine learning-methods S. Schwegmann et al. https://doi.org/10.1016/j.egyai.2022.100209
10. Machine learning for predicting offshore vertical wind profiles F. Rouholahnejad et al. https://doi.org/10.1088/1742-6596/2626/1/012023
11. Benchmarking engineering wake models for farm-to-farm interactions L. Vollmer et al. https://doi.org/10.1088/1742-6596/2767/9/092095
12. Applying triple collocation for verifying wind resource measurements and reanalysis data J. Gottschall & A. Moskal https://doi.org/10.1088/1742-6596/2875/1/012011
13. Influence of missing wind measurements on wind turbine power production using various measure-correlate-predict methods and reanalysis datasets B. Amarzaya & K. Ko https://doi.org/10.61435/ijred.2025.61557
14. Fit-for-Purpose Information for Offshore Wind Farming Applications—Part-II: Gap Analysis and Recommendations J. Schulz-Stellenfleth et al. https://doi.org/10.3390/jmse11091817
15. Evaluation of Engineering Models for Large‐Scale Cluster Wakes With the Help of In Situ Airborne Measurements K. zum Berge et al. https://doi.org/10.1002/we.2942
16. Wind Resource Assessment for Potential Wind Turbine Operations in the City of Yanbu, Saudi Arabia M. Ramli & H. Bouchekara https://doi.org/10.3390/en18082139