Articles | Volume 5, issue 3
https://doi.org/10.5194/wes-5-1191-2020
© Author(s) 2020. 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-5-1191-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
US East Coast synthetic aperture radar wind atlas for offshore wind energy
Tobias Ahsbahs
DTU Wind Energy, Risø, Roskilde, Denmark
Galen Maclaurin
National Renewable Energy Laboratory, Golden, Colorado, USA
Caroline Draxl
National Renewable Energy Laboratory, Golden, Colorado, USA
Christopher R. Jackson
Global Ocean Associates, Alexandria, Virginia, USA
Frank Monaldo
Applied Physics Laboratory, Johns Hopkins University, Baltimore,
Maryland, USA
DTU Wind Energy, Risø, Roskilde, Denmark
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Lindsay M. Sheridan, Jiali Wang, Caroline Draxl, Nicola Bodini, Caleb Phillips, Dmitry Duplyakin, Heidi Tinnesand, Raj K. Rai, Julia E. Flaherty, Larry K. Berg, Chunyong Jung, Ethan Young, and Rao Kotamarthi
Wind Energ. Sci., 10, 1551–1574, https://doi.org/10.5194/wes-10-1551-2025, https://doi.org/10.5194/wes-10-1551-2025, 2025
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Three recent wind resource datasets are assessed for their skills in representing annual average wind speeds and seasonal, diurnal, and interannual trends in the wind resource in coastal locations to support customers interested in small and midsize wind energy.
Kyle Peco, Jiali Wang, Chunyong Jung, Gökhan Sever, Lindsay Sheridan, Jeremy Feinstein, Rao Kotamarthi, Caroline Draxl, Ethan Young, Avi Purkayastha, and Andrew Kumler
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-13, https://doi.org/10.5194/wes-2025-13, 2025
Revised manuscript accepted for WES
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This study presents a new wind dataset, generated by a climate model, that can help facilitate efforts in wind energy. By providing data across much of North America, this dataset can offer insights into the wind patterns in more understudied regions. By validating the dataset against actual wind observations, we have demonstrated that this dataset is able to accurately capture the wind patterns of different geographic areas, which can help identify locations for wind energy farms.
Nicola Bodini, Mike Optis, Stephanie Redfern, David Rosencrans, Alex Rybchuk, Julie K. Lundquist, Vincent Pronk, Simon Castagneri, Avi Purkayastha, Caroline Draxl, Raghavendra Krishnamurthy, Ethan Young, Billy Roberts, Evan Rosenlieb, and Walter Musial
Earth Syst. Sci. Data, 16, 1965–2006, https://doi.org/10.5194/essd-16-1965-2024, https://doi.org/10.5194/essd-16-1965-2024, 2024
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This article presents the 2023 National Offshore Wind data set (NOW-23), an updated resource for offshore wind information in the US. It replaces the Wind Integration National Dataset (WIND) Toolkit, offering improved accuracy through advanced weather prediction models. The data underwent regional tuning and validation and can be accessed at no cost.
Sue Ellen Haupt, Branko Kosović, Larry K. Berg, Colleen M. Kaul, Matthew Churchfield, Jeffrey Mirocha, Dries Allaerts, Thomas Brummet, Shannon Davis, Amy DeCastro, Susan Dettling, Caroline Draxl, David John Gagne, Patrick Hawbecker, Pankaj Jha, Timothy Juliano, William Lassman, Eliot Quon, Raj K. Rai, Michael Robinson, William Shaw, and Regis Thedin
Wind Energ. Sci., 8, 1251–1275, https://doi.org/10.5194/wes-8-1251-2023, https://doi.org/10.5194/wes-8-1251-2023, 2023
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The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models. Lessons learned and discerned best practices are described in the context of the cases studied for the purpose of enabling further deployment of wind energy. It also points to code, assessment tools, and data for testing the methods.
Stephanie Redfern, Mike Optis, Geng Xia, and Caroline Draxl
Wind Energ. Sci., 8, 1–23, https://doi.org/10.5194/wes-8-1-2023, https://doi.org/10.5194/wes-8-1-2023, 2023
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As wind farm developments expand offshore, accurate forecasting of winds above coastal waters is rising in importance. Weather models rely on various inputs to generate their forecasts, one of which is sea surface temperature (SST). In this study, we evaluate how the SST data set used in the Weather Research and Forecasting model may influence wind characterization and find meaningful differences between model output when different SST products are used.
Merete Badger, Haichen Zuo, Ásta Hannesdóttir, Abdalmenem Owda, and Charlotte Hasager
Wind Energ. Sci., 7, 2497–2512, https://doi.org/10.5194/wes-7-2497-2022, https://doi.org/10.5194/wes-7-2497-2022, 2022
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When wind turbine blades are exposed to strong winds and heavy rainfall, they may be damaged and their efficiency reduced. The problem is most pronounced offshore where turbines are tall and the climate is harsh. Satellites provide global half-hourly rain observations. We use these rain data as input to a model for blade lifetime prediction and find that the satellite-based predictions agree well with predictions based on observations from weather stations on the ground.
Hyungwon John Park, Jeffrey S. Reid, Livia S. Freire, Christopher Jackson, and David H. Richter
Atmos. Meas. Tech., 15, 7171–7194, https://doi.org/10.5194/amt-15-7171-2022, https://doi.org/10.5194/amt-15-7171-2022, 2022
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We use numerical models to study field measurements of sea spray aerosol particles and conclude that both the atmospheric state and the methods of instrument sampling are causes for the variation in the production rate of aerosol particles: a critical metric to learn the aerosol's effect on processes like cloud physics and radiation. This work helps field observers improve their experimental design and interpretation of measurements because of turbulence in the atmosphere.
William J. Shaw, Larry K. Berg, Mithu Debnath, Georgios Deskos, Caroline Draxl, Virendra P. Ghate, Charlotte B. Hasager, Rao Kotamarthi, Jeffrey D. Mirocha, Paytsar Muradyan, William J. Pringle, David D. Turner, and James M. Wilczak
Wind Energ. Sci., 7, 2307–2334, https://doi.org/10.5194/wes-7-2307-2022, https://doi.org/10.5194/wes-7-2307-2022, 2022
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This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.
Geng Xia, Caroline Draxl, Michael Optis, and Stephanie Redfern
Wind Energ. Sci., 7, 815–829, https://doi.org/10.5194/wes-7-815-2022, https://doi.org/10.5194/wes-7-815-2022, 2022
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In this study, we propose a new method to detect sea breeze events from the Weather Research and Forecasting simulation. Our results suggest that the method can identify the three different types of sea breezes in the model simulation. In addition, the coastal impact, seasonal distribution and offshore wind potential associated with each type of sea breeze differ significantly, highlighting the importance of identifying the correct type of sea breeze in numerical weather/wind energy forecasting.
Vincent Pronk, Nicola Bodini, Mike Optis, Julie K. Lundquist, Patrick Moriarty, Caroline Draxl, Avi Purkayastha, and Ethan Young
Wind Energ. Sci., 7, 487–504, https://doi.org/10.5194/wes-7-487-2022, https://doi.org/10.5194/wes-7-487-2022, 2022
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In this paper, we have assessed to which extent mesoscale numerical weather prediction models are more accurate than state-of-the-art reanalysis products in characterizing the wind resource at heights of interest for wind energy. The conclusions of our work will be of primary importance to the wind industry for recommending the best data sources for wind resource modeling.
Rogier Floors, Merete Badger, Ib Troen, Kenneth Grogan, and Finn-Hendrik Permien
Wind Energ. Sci., 6, 1379–1400, https://doi.org/10.5194/wes-6-1379-2021, https://doi.org/10.5194/wes-6-1379-2021, 2021
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Wind turbines are frequently placed in forests. We investigate the potential of using satellites to characterize the land surface for wind flow modelling. Maps of forest properties are generated from satellite data and converted to flow modelling maps. Validation is carried out at 10 sites. Using the novel satellite-based maps leads to lower errors of the power density than land cover databases, which demonstrates the value of using satellite-based land cover maps for flow modelling.
Alayna Farrell, Jennifer King, Caroline Draxl, Rafael Mudafort, Nicholas Hamilton, Christopher J. Bay, Paul Fleming, and Eric Simley
Wind Energ. Sci., 6, 737–758, https://doi.org/10.5194/wes-6-737-2021, https://doi.org/10.5194/wes-6-737-2021, 2021
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Most current wind turbine wake models struggle to accurately simulate spatially variant wind conditions at a low computational cost. In this paper, we present an adaptation of NREL's FLOw Redirection and Induction in Steady State (FLORIS) wake model, which calculates wake losses in a heterogeneous flow field using local weather measurement inputs. Two validation studies are presented where the adapted model consistently outperforms previous versions of FLORIS that simulated uniform flow only.
Caroline Draxl, Rochelle P. Worsnop, Geng Xia, Yelena Pichugina, Duli Chand, Julie K. Lundquist, Justin Sharp, Garrett Wedam, James M. Wilczak, and Larry K. Berg
Wind Energ. Sci., 6, 45–60, https://doi.org/10.5194/wes-6-45-2021, https://doi.org/10.5194/wes-6-45-2021, 2021
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Mountain waves can create oscillations in low-level wind speeds and subsequently in the power output of wind plants. We document such oscillations by analyzing sodar and lidar observations, nacelle wind speeds, power observations, and Weather Research and Forecasting model simulations. This research describes how mountain waves form in the Columbia River basin and affect wind energy production and their impact on operational forecasting, wind plant layout, and integration of power into the grid.
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Short summary
Before constructing wind farms we need to know how much energy they will produce. This requires knowledge of long-term wind conditions from either measurements or models. At the US East Coast there are few wind measurements and little experience with offshore wind farms. Therefore, we created a satellite-based high-resolution wind resource map to quantify spatial variations in the wind conditions over potential sites for wind farms and found larger variation than modelling suggested.
Before constructing wind farms we need to know how much energy they will produce. This requires...
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