Articles | Volume 11, issue 10
https://doi.org/10.5194/wes-11-3823-2026
https://doi.org/10.5194/wes-11-3823-2026
Research article
 | 
09 Oct 2026
Research article |  | 09 Oct 2026

Identification of optimal ERA5 model level for wind resource assessments in mountainous terrain

Juan Contreras, Nicole van Lipzig, Esteban Samaniego, and Daniela Ballari

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Cited articles

Abdelsattar, M., Ismeil, M. A., Menoufi, K., Moety, A. A., and Emad-Eldeen, A.: Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors, PLOS One, 20, https://doi.org/10.1371/journal.pone.0317619, 2025. 
Basse, A., Callies, D., Grötzner, A., and Pauscher, L.: Seasonal effects in the long-term correction of short-term wind measurements using reanalysis data, Wind Energ. Sci., 6, 1473–1490, https://doi.org/10.5194/wes-6-1473-2021, 2021. 
Bodini, N., Castagneri, S., and Optis, M.: Long-term uncertainty quantification in WRF-modeled offshore wind resource off the US Atlantic coast, Wind Energ. Sci., 8, 607–620, https://doi.org/10.5194/wes-8-607-2023, 2023. 
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Borowski, J., Schwegmann, S., Avila, K., and Dörenkämper, M.: Evaluating the impact of inter-annual variability on long-term wind speed predictions, Wind Energ. Sci., 11, 661–677, https://doi.org/10.5194/wes-11-661-2026, 2026. 
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Short summary
We researched how to improve wind speed estimates for wind resource assessments in the Andes mountains of Ecuador. Instead of relying only on near-ground data from a global reanalysis dataset, we tested wind speeds from higher levels in the atmosphere and combined them with masts measurements through a machine learning model. This strongly improved accuracy and reduced energy calculation errors, offering a more reliable and affordable way to obtain data for planning wind power in complex terrain.
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