Articles | Volume 10, issue 4
https://doi.org/10.5194/wes-10-733-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-733-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Apr 2025
Research article |
| 22 Apr 2025
| 22 Apr 2025
Tall wind profile validation of ERA5, NORA3, and NEWA datasets using lidar observations
Geophysical Institute, University of Bergen, Bergen, Norway
Bergen Offshore Wind Centre, University of Bergen, Bergen, Norway
Jan Markus Diezel
Geophysical Institute, University of Bergen, Bergen, Norway
Bergen Offshore Wind Centre, University of Bergen, Bergen, Norway
Hilde Haakenstad
Norwegian Meteorological Institute, Bergen, Norway.
Øyvind Breivik
Geophysical Institute, University of Bergen, Bergen, Norway
Norwegian Meteorological Institute, Bergen, Norway.
Alfredo Peña
DTU Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark
Joachim Reuder
Geophysical Institute, University of Bergen, Bergen, Norway
Bergen Offshore Wind Centre, University of Bergen, Bergen, Norway
Bjerknes Centre for Climate Research, Bergen, Norway
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We carry out a probabilistic calibration of the Dynamic Wake Meandering (DWM) model using high-spatial- and high-temporal-resolution nacelle-based lidar measurements of the wake flow field. The experimental data were collected from the Scaled Wind Farm Technology (SWiFT) facility in Texas. The analysis includes the velocity deficit, wake-added turbulence, and wake meandering features under various inflow wind and atmospheric-stability conditions.
Davide Conti, Vasilis Pettas, Nikolay Dimitrov, and Alfredo Peña
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We define two lidar-based procedures for improving the accuracy of wind turbine load assessment under wake conditions. The first approach incorporates lidar observations directly into turbulence fields serving as inputs for aeroelastic simulations; the second approach imposes lidar-fitted wake deficit time series on the turbulence fields. The uncertainty in the lidar-based power and load predictions is quantified for a variety of scanning configurations and atmosphere turbulence conditions.
Alfredo Peña, Branko Kosović, and Jeffrey D. Mirocha
Wind Energ. Sci., 6, 645–661, https://doi.org/10.5194/wes-6-645-2021, https://doi.org/10.5194/wes-6-645-2021, 2021
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Pedro Santos, Alfredo Peña, and Jakob Mann
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-960, https://doi.org/10.5194/acp-2020-960, 2020
Preprint withdrawn
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We show that the vector of vertical flux of horizontal momentum and the vector of the mean vertical gradient of horizontal velocity are not aligned, based on Doppler wind lidar observations up to 500 m, both offshore and onshore. We illustrate that a mesoscale model output matches the observed mean wind speed and momentum fluxes well, but that this model output as well as idealized large-eddy simulations have deviations with the observations when looking at the turning of the wind.
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Short summary
This study analyses wind speed data at heights up to 500 m to support the design of future large offshore wind turbines and airborne wind energy systems. We compared three wind models (ERA5, NORA3, and NEWA) with lidar measurements at five sites using four performance metrics. ERA5 and NORA3 performed equally well offshore, with NORA3 typically outperforming the other two models onshore. More generally, the optimal choice of model depends on site, altitude, and evaluation criteria.
This study analyses wind speed data at heights up to 500 m to support the design of future large...
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