Articles | Volume 6, issue 3
https://doi.org/10.5194/wes-6-935-2021
© Author(s) 2021. 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-6-935-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Jun 2021
Research article |
| 16 Jun 2021
| 16 Jun 2021
New methods to improve the vertical extrapolation of near-surface offshore wind speeds
Mike Optis
CORRESPONDING AUTHOR
National Renewable Energy Laboratory, Golden, Colorado, USA
Nicola Bodini
National Renewable Energy Laboratory, Golden, Colorado, USA
Mithu Debnath
National Renewable Energy Laboratory, Golden, Colorado, USA
Paula Doubrawa
National Renewable Energy Laboratory, Golden, Colorado, USA
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Cited
21 citations as recorded by crossref.
- High-resolution satellite observations to account for coastal gradient in wind resource assessment: application to French coastal areas M. Cathelain et al. 10.1088/1742-6596/2505/1/012027
- Comparing Offshore Ferry Lidar Measurements in the Southern Baltic Sea with ASCAT, FINO2 and WRF D. Hatfield et al. 10.3390/rs14061427
- The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques L. Canché-Cab et al. 10.1007/s10462-024-10962-5
- Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis S. Ren et al. 10.1016/j.apenergy.2022.119876
- Influence of the WRF model and atmospheric reanalysis on the offshore wind resource potential and cost estimation: A case study for Rio de Janeiro State L. de Assis Tavares et al. 10.1016/j.energy.2021.122767
- Near‐surface wind profiles from numerical model predictions. Part I: Algorithms and comparisons with wind profile based on Monin–Obukhov similarity theory Y. Ma 10.1002/qj.4779
- Gap-Filling Sentinel-1 Offshore Wind Speed Image Time Series Using Multiple-Point Geostatistical Simulation and Reanalysis Data S. Hadjipetrou et al. 10.3390/rs15020409
- Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment M. Badger et al. 10.3390/en16093819
- Wind farm response to mesoscale-driven coastal low level jets: a multiscale large eddy simulation study T. Chatterjee et al. 10.1088/1742-6596/2265/2/022004
- Structure of Offshore Low-Level Jet Turbulence and Implications to Mesoscale-to-Microscale Coupling B. Jayaraman et al. 10.1088/1742-6596/2265/2/022064
- High-resolution offshore wind resource assessment at turbine hub height with Sentinel-1 synthetic aperture radar (SAR) data and machine learning L. de Montera et al. 10.5194/wes-7-1441-2022
- Quantitative comparison of power production and power quality onshore and offshore: a case study from the eastern United States R. Foody et al. 10.5194/wes-9-263-2024
- Characterization of wind turbine flow through nacelle-mounted lidars: a review S. Letizia et al. 10.3389/fmech.2023.1261017
- Visual anemometry for physics-informed inference of wind J. Dabiri et al. 10.1038/s42254-023-00626-8
- Analysis of Near-Surface Wind Shear Characteristics over Land in China L. Yuan et al. 10.3390/atmos15050582
- Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble N. Bodini et al. 10.5194/wes-6-1363-2021
- Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques D. Hatfield et al. 10.5194/wes-8-621-2023
- Long-term uncertainty quantification in WRF-modeled offshore wind resource off the US Atlantic coast N. Bodini et al. 10.5194/wes-8-607-2023
- Extreme value analysis of wind droughts in Great Britain P. Potisomporn et al. 10.1016/j.renene.2023.119847
- Machine learning for predicting offshore vertical wind profiles F. Rouholahnejad et al. 10.1088/1742-6596/2626/1/012023
- Enabling Virtual Met Masts for wind energy applications through machine learning-methods S. Schwegmann et al. 10.1016/j.egyai.2022.100209
21 citations as recorded by crossref.
- High-resolution satellite observations to account for coastal gradient in wind resource assessment: application to French coastal areas M. Cathelain et al. 10.1088/1742-6596/2505/1/012027
- Comparing Offshore Ferry Lidar Measurements in the Southern Baltic Sea with ASCAT, FINO2 and WRF D. Hatfield et al. 10.3390/rs14061427
- The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques L. Canché-Cab et al. 10.1007/s10462-024-10962-5
- Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis S. Ren et al. 10.1016/j.apenergy.2022.119876
- Influence of the WRF model and atmospheric reanalysis on the offshore wind resource potential and cost estimation: A case study for Rio de Janeiro State L. de Assis Tavares et al. 10.1016/j.energy.2021.122767
- Near‐surface wind profiles from numerical model predictions. Part I: Algorithms and comparisons with wind profile based on Monin–Obukhov similarity theory Y. Ma 10.1002/qj.4779
- Gap-Filling Sentinel-1 Offshore Wind Speed Image Time Series Using Multiple-Point Geostatistical Simulation and Reanalysis Data S. Hadjipetrou et al. 10.3390/rs15020409
- Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment M. Badger et al. 10.3390/en16093819
- Wind farm response to mesoscale-driven coastal low level jets: a multiscale large eddy simulation study T. Chatterjee et al. 10.1088/1742-6596/2265/2/022004
- Structure of Offshore Low-Level Jet Turbulence and Implications to Mesoscale-to-Microscale Coupling B. Jayaraman et al. 10.1088/1742-6596/2265/2/022064
- High-resolution offshore wind resource assessment at turbine hub height with Sentinel-1 synthetic aperture radar (SAR) data and machine learning L. de Montera et al. 10.5194/wes-7-1441-2022
- Quantitative comparison of power production and power quality onshore and offshore: a case study from the eastern United States R. Foody et al. 10.5194/wes-9-263-2024
- Characterization of wind turbine flow through nacelle-mounted lidars: a review S. Letizia et al. 10.3389/fmech.2023.1261017
- Visual anemometry for physics-informed inference of wind J. Dabiri et al. 10.1038/s42254-023-00626-8
- Analysis of Near-Surface Wind Shear Characteristics over Land in China L. Yuan et al. 10.3390/atmos15050582
- Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble N. Bodini et al. 10.5194/wes-6-1363-2021
- Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques D. Hatfield et al. 10.5194/wes-8-621-2023
- Long-term uncertainty quantification in WRF-modeled offshore wind resource off the US Atlantic coast N. Bodini et al. 10.5194/wes-8-607-2023
- Extreme value analysis of wind droughts in Great Britain P. Potisomporn et al. 10.1016/j.renene.2023.119847
- Machine learning for predicting offshore vertical wind profiles F. Rouholahnejad et al. 10.1088/1742-6596/2626/1/012023
- Enabling Virtual Met Masts for wind energy applications through machine learning-methods S. Schwegmann et al. 10.1016/j.egyai.2022.100209
Latest update: 22 Nov 2024
Short summary
Offshore wind turbines are huge, with rotor blades soon to extend up to nearly 300 m. Accurate modeling of winds across these heights is crucial for accurate estimates of energy production. However, we lack sufficient observations at these heights but have plenty of near-surface observations. Here we show that a basic machine-learning model can provide very accurate estimates of winds in this area, and much better than conventional techniques.
Offshore wind turbines are huge, with rotor blades soon to extend up to nearly 300 m. Accurate...
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