Articles | Volume 10, issue 1
https://doi.org/10.5194/wes-10-117-2025
© 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-10-117-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jan 2025
Research article |
| 14 Jan 2025
| 14 Jan 2025
Simulations suggest offshore wind farms modify low-level jets
Daphne Quint
Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado 80309-0311, United States
National Renewable Energy Laboratory, Golden, Colorado 80401, United States
Julie K. Lundquist
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado 80309-0311, United States
National Renewable Energy Laboratory, Golden, Colorado 80401, United States
Ralph O'Conor Sustainable Energy Institute, Johns Hopkins University, Baltimore, Maryland 21218, United States
David Rosencrans
Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado 80309-0311, United States
Related authors
Daphne Quint, Julie K. Lundquist, Nicola Bodini, and David Rosencrans
Wind Energ. Sci., 10, 1269–1301, https://doi.org/10.5194/wes-10-1269-2025, https://doi.org/10.5194/wes-10-1269-2025, 2025
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Offshore wind farms along the US East Coast can have limited effects on local weather. To study these effects, we include wind farms near Massachusetts and Rhode Island, and we test different amounts of turbulence in our model. We analyze changes in wind, temperature, and turbulence. Simulated effects on surface temperature and turbulence change depending on how much turbulence is added to the model. The extent of the wind farm wake depends on how deep the atmospheric boundary layer is.
Annelies Sticker, François Massonnet, Thierry Fichefet, Patricia DeRepentigny, Alexandra Jahn, David Docquier, Christopher Wyburn-Powell, Daphne Quint, Erica Shivers, and Makayla Ortiz
EGUsphere, https://doi.org/10.5194/egusphere-2024-1873, https://doi.org/10.5194/egusphere-2024-1873, 2024
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Our study analyses rapid Arctic sea ice loss events (RILEs), which are significant reductions in sea ice extent. RILEs are expected throughout the year, varying in frequency and duration with the seasons. Our research gives a year-round analysis of their characteristics in climate models and suggests that summer RILEs could begin before the mid-century. Understanding these events is crucial as they can have profound impacts on the Arctic environment.
Geng Xia, Mike Optis, Georgios Deskos, Michael Sinner, Daniel Mulas Hernando, Julie Kay Lundquist, Andrew Kumler, Miguel Sanchez Gomez, Paul Fleming, and Walter Musial
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-154, https://doi.org/10.5194/wes-2025-154, 2025
Preprint under review for WES
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This study examines energy losses from cluster wakes in offshore wind farms along the U.S. East Coast. Simulations based on real lease projects show that large wind speed deficits do not always cause equally large energy losses. The energy loss method revealed wake areas up to 30 % larger than traditional estimates, underscoring the need to consider both wind speed deficit and energy loss in planning offshore wind development.
Anna Voss, Konrad B. Bärfuss, Beatriz Cañadillas, Maik Angermann, Mark Bitter, Matthias Cremer, Thomas Feuerle, Jonas Spoor, Julie K. Lundquist, Patrick Moriarty, and Astrid Lampert
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-113, https://doi.org/10.5194/wes-2025-113, 2025
Preprint under review for WES
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This study analyses onshore wind farm wakes in a semi-complex terrain with data conducted with the research aircraft of TU Braunschweig during the AWAKEN project. Vertical profiles of temperature, humidity and wind give insights into the stratification of the atmospheric boundary layer, while horizontal profiles downwind of wind farms reveal an amplification of the reduction in wind speed in a semi-complex terrain in particular in a distance of 10 km.
Daphne Quint, Julie K. Lundquist, Nicola Bodini, and David Rosencrans
Wind Energ. Sci., 10, 1269–1301, https://doi.org/10.5194/wes-10-1269-2025, https://doi.org/10.5194/wes-10-1269-2025, 2025
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Offshore wind farms along the US East Coast can have limited effects on local weather. To study these effects, we include wind farms near Massachusetts and Rhode Island, and we test different amounts of turbulence in our model. We analyze changes in wind, temperature, and turbulence. Simulated effects on surface temperature and turbulence change depending on how much turbulence is added to the model. The extent of the wind farm wake depends on how deep the atmospheric boundary layer is.
Robert S. Arthur, Alex Rybchuk, Timothy W. Juliano, Gabriel Rios, Sonia Wharton, Julie K. Lundquist, and Jerome D. Fast
Wind Energ. Sci., 10, 1187–1209, https://doi.org/10.5194/wes-10-1187-2025, https://doi.org/10.5194/wes-10-1187-2025, 2025
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This paper evaluates a new model configuration for wind energy forecasting in complex terrain. We compare model results to observations in the Altamont Pass (California, USA), where wind channeling through a mountain gap leads to increased energy production. We demonstrate that the new model configuration performs similarly to a more established approach, with some evidence of improved wind speed predictions, and provide guidance for future model testing.
Nathan J. Agarwal, Julie K. Lundquist, Timothy W. Juliano, and Alex Rybchuk
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-16, https://doi.org/10.5194/wes-2025-16, 2025
Preprint under review for WES
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Models of wind behavior inform offshore wind farm site investment decisions. Here we compare a newly-developed model to another, historically-used, model based on how these models represent winds and turbulence at two North Sea sites. The best model depends on the site. While the older model performs best at the site above a wind farm, the newer model performs best at the site that is at the same altitude as the wind farm. We support using the new model to represent winds at the turbine level.
David Rosencrans, Julie K. Lundquist, Mike Optis, and Nicola Bodini
Wind Energ. Sci., 10, 59–81, https://doi.org/10.5194/wes-10-59-2025, https://doi.org/10.5194/wes-10-59-2025, 2025
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The US offshore wind industry is growing rapidly. Expansion into cold climates will subject turbines and personnel to hazardous icing. We analyze the 21-year icing risk for US east coast wind areas based on numerical weather prediction simulations and further assess impacts from wind farm wakes over one winter season. Sea spray icing at 10 m can occur up to 67 h per month. However, turbine–atmosphere interactions reduce icing hours within wind plant areas.
William Radünz, Bruno Carmo, Julie K. Lundquist, Stefano Letizia, Aliza Abraham, Adam S. Wise, Miguel Sanchez Gomez, Nicholas Hamilton, Raj K. Rai, and Pedro S. Peixoto
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-166, https://doi.org/10.5194/wes-2024-166, 2025
Revised manuscript accepted for WES
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This study investigates how simple terrain can cause significant variations in wind speed, especially during specific atmospheric conditions like low-level jets. By combining simulations and observations from a real wind farm, we found that downstream turbines generate more power than upstream ones, despite wake effects only impacting the upstream turbines. We highlight the crucial role of the strong vertical wind speed gradient in low-level jets in driving this effect.
Rachel Robey and Julie K. Lundquist
Wind Energ. Sci., 9, 1905–1922, https://doi.org/10.5194/wes-9-1905-2024, https://doi.org/10.5194/wes-9-1905-2024, 2024
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Measurements of wind turbine wakes with scanning lidar instruments contain complex errors. We model lidars in a simulated environment to understand how and why the measured wake may differ from the true wake and validate the results with observational data. The lidar smooths out the wake, making it seem more spread out and the slowdown of the winds less pronounced. Our findings provide insights into best practices for accurately measuring wakes with lidar and interpreting observational data.
Annelies Sticker, François Massonnet, Thierry Fichefet, Patricia DeRepentigny, Alexandra Jahn, David Docquier, Christopher Wyburn-Powell, Daphne Quint, Erica Shivers, and Makayla Ortiz
EGUsphere, https://doi.org/10.5194/egusphere-2024-1873, https://doi.org/10.5194/egusphere-2024-1873, 2024
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Our study analyses rapid Arctic sea ice loss events (RILEs), which are significant reductions in sea ice extent. RILEs are expected throughout the year, varying in frequency and duration with the seasons. Our research gives a year-round analysis of their characteristics in climate models and suggests that summer RILEs could begin before the mid-century. Understanding these events is crucial as they can have profound impacts on the Arctic environment.
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.
David Rosencrans, Julie K. Lundquist, Mike Optis, Alex Rybchuk, Nicola Bodini, and Michael Rossol
Wind Energ. Sci., 9, 555–583, https://doi.org/10.5194/wes-9-555-2024, https://doi.org/10.5194/wes-9-555-2024, 2024
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The US offshore wind industry is developing rapidly. Using yearlong simulations of wind plants in the US mid-Atlantic, we assess the impacts of wind turbine wakes. While wakes are the strongest and longest during summertime stably stratified conditions, when New England grid demand peaks, they are predictable and thus manageable. Over a year, wakes reduce power output by over 35 %. Wakes in a wind plant contribute the most to that reduction, while wakes between wind plants play a secondary role.
Miguel Sanchez Gomez, Julie K. Lundquist, Jeffrey D. Mirocha, and Robert S. Arthur
Wind Energ. Sci., 8, 1049–1069, https://doi.org/10.5194/wes-8-1049-2023, https://doi.org/10.5194/wes-8-1049-2023, 2023
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The wind slows down as it approaches a wind plant; this phenomenon is called blockage. As a result, the turbines in the wind plant produce less power than initially anticipated. We investigate wind plant blockage for two atmospheric conditions. Blockage is larger for a wind plant compared to a stand-alone turbine. Also, blockage increases with atmospheric stability. Blockage is amplified by the vertical transport of horizontal momentum as the wind approaches the front-row turbines in the array.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
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Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
Alex Rybchuk, Timothy W. Juliano, Julie K. Lundquist, David Rosencrans, Nicola Bodini, and Mike Optis
Wind Energ. Sci., 7, 2085–2098, https://doi.org/10.5194/wes-7-2085-2022, https://doi.org/10.5194/wes-7-2085-2022, 2022
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Numerical weather prediction models are used to predict how wind turbines will interact with the atmosphere. Here, we characterize the uncertainty associated with the choice of turbulence parameterization on modeled wakes. We find that simulated wind speed deficits in turbine wakes can be significantly sensitive to the choice of turbulence parameterization. As such, predictions of future generated power are also sensitive to turbulence parameterization choice.
Rachel Robey and Julie K. Lundquist
Atmos. Meas. Tech., 15, 4585–4622, https://doi.org/10.5194/amt-15-4585-2022, https://doi.org/10.5194/amt-15-4585-2022, 2022
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Our work investigates the behavior of errors in remote-sensing wind lidar measurements due to turbulence. Using a virtual instrument, we measured winds in simulated atmospheric flows and decomposed the resulting error. Dominant error mechanisms, particularly vertical velocity variations and interactions with shear, were identified in ensemble data over three test cases. By analyzing the underlying mechanisms, the response of the error behavior to further varying flow conditions may be projected.
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.
Adam S. Wise, James M. T. Neher, Robert S. Arthur, Jeffrey D. Mirocha, Julie K. Lundquist, and Fotini K. Chow
Wind Energ. Sci., 7, 367–386, https://doi.org/10.5194/wes-7-367-2022, https://doi.org/10.5194/wes-7-367-2022, 2022
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Wind turbine wake behavior in hilly terrain depends on various atmospheric conditions. We modeled a wind turbine located on top of a ridge in Portugal during typical nighttime and daytime atmospheric conditions and validated these model results with observational data. During nighttime conditions, the wake deflected downwards following the terrain. During daytime conditions, the wake deflected upwards. These results can provide insight into wind turbine siting and operation in hilly regions.
Hannah Livingston, Nicola Bodini, and Julie K. Lundquist
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2021-68, https://doi.org/10.5194/wes-2021-68, 2021
Preprint withdrawn
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In this paper, we assess whether hub-height turbulence can easily be quantified from either other hub-height variables or ground-level measurements in complex terrain. We find a large variability across the three considered locations when trying to model hub-height turbulence intensity and turbulence kinetic energy. Our results highlight the nonlinear and complex nature of atmospheric turbulence, so that more powerful techniques should instead be recommended to model hub-height turbulence.
Miguel Sanchez Gomez, Julie K. Lundquist, Petra M. Klein, and Tyler M. Bell
Earth Syst. Sci. Data, 13, 3539–3549, https://doi.org/10.5194/essd-13-3539-2021, https://doi.org/10.5194/essd-13-3539-2021, 2021
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In July 2018, the International Society for Atmospheric Research using Remotely-piloted Aircraft (ISARRA) hosted a flight week to demonstrate unmanned aircraft systems' capabilities in sampling the atmospheric boundary layer. Three Doppler lidars were deployed during this week-long experiment. We use data from these lidars to estimate turbulence dissipation rate. We observe large temporal variability and significant differences in dissipation for lidars with different sampling techniques.
Miguel Sanchez Gomez, Julie K. Lundquist, Jeffrey D. Mirocha, Robert S. Arthur, and Domingo Muñoz-Esparza
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2021-57, https://doi.org/10.5194/wes-2021-57, 2021
Revised manuscript not accepted
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Winds decelerate upstream of a wind plant as turbines obstruct and extract energy from the flow. This effect is known as wind plant blockage. We assess how atmospheric stability modifies the upstream wind plant blockage. We find stronger stability amplifies this effect. We also explore different approaches to quantifying blockage from field-like observations. We find different methodologies may induce errors of the same order of magnitude as the blockage-induced velocity deficits.
Alex Rybchuk, Mike Optis, Julie K. Lundquist, Michael Rossol, and Walt Musial
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-50, https://doi.org/10.5194/gmd-2021-50, 2021
Preprint withdrawn
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We characterize the wind resource off the coast of California by conducting simulations with the Weather Research and Forecasting (WRF) model between 2000 and 2019. We compare newly simulated winds to those from the WIND Toolkit. The newly simulated winds are substantially stronger, particularly in the late summer. We also conduct a refined analysis at three areas that are being considered for commercial development, finding that stronger winds translates to substantially more power here.
Tyler M. Bell, Petra M. Klein, Julie K. Lundquist, and Sean Waugh
Earth Syst. Sci. Data, 13, 1041–1051, https://doi.org/10.5194/essd-13-1041-2021, https://doi.org/10.5194/essd-13-1041-2021, 2021
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In July 2018, numerous weather sensing remotely piloted aircraft systems (RPASs) were flown in a flight week called Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). As part of LAPSE-RATE, ground-based remote and in situ systems were also deployed to supplement and enhance observations from the RPASs. These instruments include multiple Doppler lidars, thermodynamic profilers, and radiosondes. This paper describes data from these systems.
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.
Jessica M. Tomaszewski and Julie K. Lundquist
Wind Energ. Sci., 6, 1–13, https://doi.org/10.5194/wes-6-1-2021, https://doi.org/10.5194/wes-6-1-2021, 2021
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We use a mesoscale numerical weather prediction model to conduct a case study of a thunderstorm outflow passing over and interacting with a wind farm. These simulations and observations from a nearby radar and surface station confirm that interactions with the wind farm cause the outflow to reduce its speed by over 20 km h−1, with brief but significant impacts on the local meteorology, including temperature, moisture, and winds. Precipitation accumulation across the region was unaffected.
Gijs de Boer, Adam Houston, Jamey Jacob, Phillip B. Chilson, Suzanne W. Smith, Brian Argrow, Dale Lawrence, Jack Elston, David Brus, Osku Kemppinen, Petra Klein, Julie K. Lundquist, Sean Waugh, Sean C. C. Bailey, Amy Frazier, Michael P. Sama, Christopher Crick, David Schmale III, James Pinto, Elizabeth A. Pillar-Little, Victoria Natalie, and Anders Jensen
Earth Syst. Sci. Data, 12, 3357–3366, https://doi.org/10.5194/essd-12-3357-2020, https://doi.org/10.5194/essd-12-3357-2020, 2020
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This paper provides an overview of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) field campaign, held from 14 to 20 July 2018. This field campaign spanned a 1-week deployment to Colorado's San Luis Valley, involving over 100 students, scientists, engineers, pilots, and outreach coordinators. This overview paper provides insight into the campaign for a special issue focused on the datasets collected during LAPSE-RATE.
Antonia Englberger, Julie K. Lundquist, and Andreas Dörnbrack
Wind Energ. Sci., 5, 1623–1644, https://doi.org/10.5194/wes-5-1623-2020, https://doi.org/10.5194/wes-5-1623-2020, 2020
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Wind turbines rotate clockwise. The rotational direction of the rotor interacts with the nighttime veering wind, resulting in a rotational-direction impact on the wake. In the case of counterclockwise-rotating blades the streamwise velocity in the wake is larger in the Northern Hemisphere whereas it is smaller in the Southern Hemisphere.
Antonia Englberger, Andreas Dörnbrack, and Julie K. Lundquist
Wind Energ. Sci., 5, 1359–1374, https://doi.org/10.5194/wes-5-1359-2020, https://doi.org/10.5194/wes-5-1359-2020, 2020
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At night, the wind direction often changes with height, and this veer affects structures near the surface like wind turbines. Wind turbines usually rotate clockwise, but this rotational direction interacts with veer to impact the flow field behind a wind turbine. If another turbine is located downwind, the direction of the upwind turbine's rotation will affect the downwind turbine.
Nicola Bodini, Julie K. Lundquist, and Mike Optis
Geosci. Model Dev., 13, 4271–4285, https://doi.org/10.5194/gmd-13-4271-2020, https://doi.org/10.5194/gmd-13-4271-2020, 2020
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While turbulence dissipation rate (ε) is an essential parameter for the prediction of wind speed, its current representation in weather prediction models is inaccurate, especially in complex terrain. In this study, we leverage the potential of machine-learning techniques to provide a more accurate representation of turbulence dissipation rate. Our results show a 30 % reduction in the average error compared to the current model representation of ε and a total elimination of its average bias.
Patrick Murphy, Julie K. Lundquist, and Paul Fleming
Wind Energ. Sci., 5, 1169–1190, https://doi.org/10.5194/wes-5-1169-2020, https://doi.org/10.5194/wes-5-1169-2020, 2020
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We present and evaluate an improved method for predicting wind turbine power production based on measurements of the wind speed and direction profile across the rotor disk for a wind turbine in complex terrain. By comparing predictions to actual power production from a utility-scale wind turbine, we show this method is more accurate than methods based on hub-height wind speed or surface-based atmospheric characterization.
Cited articles
Abkar, M., Sharifi, A., and Porté-Agel, F.: Wake flow in a wind farm during a diurnal cycle, J. Turbul., 17, 420–441, https://doi.org/10.1080/14685248.2015.1127379, 2016. a
Angevine, W. M., Tjernström, M., and Žagar, M.: Modeling of the Coastal Boundary Layer and Pollutant Transport in New England, J. Appl. Meteorol. Clim., 45, 137–154, https://doi.org/10.1175/JAM2333.1, 2006. a
Baas, P., Bosveld, F. C., Baltink, H. K., and Holtslag, A. a. M.: A Climatology of Nocturnal Low-Level Jets at Cabauw, J. Appl. Meteorol. Clim., 48, 1627–1642, https://doi.org/10.1175/2009JAMC1965.1, 2009. a
Banta, R., Newsom, R. K., Lundquist, J. K., Pichugina, Y. L., Coulter, R. L., and Mahrt, L.: Nocturnal Low-Level Jet Characteristics Over Kansas During Cases-99, Bound.-Lay. Meteorol., 105, 221–252, https://doi.org/10.1023/A:1019992330866, 2002. a, b, c
Blackadar, A. K.: Boundary Layer Wind Maxima and Their Significance for the Growth of Nocturnal Inversions, B. Am. Meteorol. Soc., 38, 283–290, https://doi.org/10.1175/1520-0477-38.5.283, 1957. a
Bodini, N., Lundquist, J. K., Zardi, D., and Handschy, M.: Year-to-year correlation, record length, and overconfidence in wind resource assessment, Wind Energ. Sci., 1, 115–128, https://doi.org/10.5194/wes-1-115-2016, 2016. a
Bodini, N., Lundquist, J. K., and Kirincich, A.: U.S. East Coast Lidar Measurements Show Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence, Geophys. Res. Lett., 46, 5582–5591, https://doi.org/10.1029/2019GL082636, 2019. a
Bodini, N., Lundquist, J. K., and Kirincich, A.: Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence, J. Phys. Conf. Ser., 1452, 012023, https://doi.org/10.1088/1742-6596/1452/1/012023, 2020. a
Bodini, N., Optis, M., Redfern, S., Rosencrans, D., Rybchuk, A., Lundquist, J. K., Pronk, V., Castagneri, S., Purkayastha, A., Draxl, C., Krishnamurthy, R., Young, E., Roberts, B., Rosenlieb, E., and Musial, W.: The 2023 National Offshore Wind data set (NOW-23), Earth Syst. Sci. Data, 16, 1965–2006, https://doi.org/10.5194/essd-16-1965-2024, 2024. a
Bonner, W. D.: CLIMATOLOGY OF THE LOW LEVEL JET, Mon. Weather Rev., 96, 833–850, https://doi.org/10.1175/1520-0493(1968)096<0833:COTLLJ>2.0.CO;2, 1968. a, b, c
Cañadillas, B., Beckenbauer, M., Trujillo, J. J., Dörenkämper, M., Foreman, R., Neumann, T., and Lampert, A.: Offshore wind farm cluster wakes as observed by long-range-scanning wind lidar measurements and mesoscale modeling, Wind Energ. Sci., 7, 1241–1262, https://doi.org/10.5194/wes-7-1241-2022, 2022. a
Chatterjee, T., Li, J., Yellapantula, S., Jayaraman, B., and Quon, E.: Wind farm response to mesoscale-driven coastal low level jets: a multiscale large eddy simulation study, J. Phys. Conf. Ser., 2265, 022004, https://doi.org/10.1088/1742-6596/2265/2/022004, 2022. a
de Jong, E., Quon, E., and Yellapantula, S.: Mechanisms of Low-Level Jet Formation in the U.S. Mid-Atlantic Offshore, J. Atmos. Sci., 81, 31–52, https://doi.org/10.1175/JAS-D-23-0079.1, 2023. a, b
Doosttalab, A., Siguenza-Alvarado, D., Pulletikurthi, V., Jin, Y., Bocanegra Evans, H., Chamorro, L. P., and Castillo, L.: Interaction of low-level jets with wind turbines: On the basic mechanisms for enhanced performance, J. Renew. Sustain. Ener., 12, 053301, https://doi.org/10.1063/5.0017230, 2020. a
Dörenkämper, M., Optis, M., Monahan, A., and Steinfeld, G.: On the Offshore Advection of Boundary-Layer Structures and the Influence on Offshore Wind Conditions, Bound.-Lay. Meteorol., 155, 459–482, https://doi.org/10.1007/s10546-015-0008-x, 2015. a
Fischereit, J., Larsén, X. G., and Hahmann, A. N.: Climatic Impacts of Wind-Wave-Wake Interactions in Offshore Wind Farms, Frontiers in Energy Research, 10, 21 pp., https://doi.org/10.3389/fenrg.2022.881459, 2022. a
Fitch, A. C., Olson, J. B., Lundquist, J. K., Dudhia, J., Gupta, A. K., Michalakes, J., and Barstad, I.: Local and Mesoscale Impacts of Wind Farms as Parameterized in a Mesoscale NWP Model, Mon. Weather Rev., 140, 3017–3038, https://doi.org/10.1175/MWR-D-11-00352.1, 2012. a
Gadde, S. N. and Stevens, R. J. A. M.: Interaction between low-level jets and wind farms in a stable atmospheric boundary layer, Phys. Rev. Fluids, 6, 014603, https://doi.org/10.1103/PhysRevFluids.6.014603, 2021. a, b
Gryning, S.-E., Batchvarova, E., Brümmer, B., Jørgensen, H., and Larsen, S.: On the extension of the wind profile over homogeneous terrain beyond the surface boundary layer, Bound.-Lay. Meteorol., 124, 251–268, https://doi.org/10.1007/s10546-007-9166-9, 2007. a
Gutierrez, W., Araya, G., Kiliyanpilakkil, P., Ruiz-Columbie, A., Tutkun, M., and Castillo, L.: Structural impact assessment of low level jets over wind turbines, J. Renew. Sustain. Ener., 8, 023308, https://doi.org/10.1063/1.4945359, 2016. a, b
Hallgren, C., Arnqvist, J., Ivanell, S., Körnich, H., Vakkari, V., and Sahlée, E.: Looking for an Offshore Low-Level Jet Champion among Recent Reanalyses: A Tight Race over the Baltic Sea, Energies, 13, 3670, https://doi.org/10.3390/en13143670, 2020. a, b
Hasager, C. B., Barthelmie, R. J., Christiansen, M. B., Nielsen, M., and Pryor, S. C.: Quantifying offshore wind resources from satellite wind maps: study area the North Sea, Wind Energy, 9, 63–74, https://doi.org/10.1002/we.190, 2006. a
Holton, J. R.: The diurnal boundary layer wind oscillation above sloping terrain1, Tellus, 19, 199–205, https://doi.org/10.1111/j.2153-3490.1967.tb01473.x, 1967. a, b
Högström, U. and Smedman-Högström, A.-S.: The wind regime in coastal areas with special reference to results obtained from the Swedish wind energy program, Bound.-Lay. Meteorol., 30, 351–373, https://doi.org/10.1007/BF00121961, 1984. a
Kalverla, P., Steeneveld, G.-J., Ronda, R., and Holtslag, A. A.: Evaluation of three mainstream numerical weather prediction models with observations from meteorological mast IJmuiden at the North Sea, Wind Energy, 22, 34–48, https://doi.org/10.1002/we.2267, 2019. a, b, c
Larsén, X. G. and Fischereit, J.: A case study of wind farm effects using two wake parameterizations in the Weather Research and Forecasting (WRF) model (V3.7.1) in the presence of low-level jets, Geosci. Model Dev., 14, 3141–3158, https://doi.org/10.5194/gmd-14-3141-2021, 2021. a, b, c
Lee, J. C. Y., Fields, M. J., and Lundquist, J. K.: Assessing variability of wind speed: comparison and validation of 27 methodologies, Wind Energ. Sci., 3, 845–868, https://doi.org/10.5194/wes-3-845-2018, 2018. a
Lundquist, J. K.: Wind Shear and Wind Veer Effects on Wind Turbines, in: Handbook of Wind Energy Aerodynamics, edited by: Stoevesandt, B., Schepers, G., Fuglsang, P., and Yuping, S., 1–22, Springer International Publishing, Cham, ISBN 978-3-030-05455-7, https://doi.org/10.1007/978-3-030-05455-7_44-1, 2020. a
Lundquist, J. K. and Mirocha, J. D.: Interaction of Nocturnal Low-Level Jets with Urban Geometries as Seen in Joint Urban 2003 Data, J. Appl. Meteorol. Clim., 47, 44–58, https://doi.org/10.1175/2007JAMC1581.1, 2008. a
Lundquist, J. K., DuVivier, K. K., Kaffine, D., and Tomaszewski, J. M.: Costs and consequences of wind turbine wake effects arising from uncoordinated wind energy development, Nature Energy, 4, 26–34, https://doi.org/10.1038/s41560-018-0281-2, 2019. a, b
Mahrt, L., Vickers, D., and Andreas, E. L.: Low-Level Wind Maxima and Structure of the Stably Stratified Boundary Layer in the Coastal Zone, J. Appl. Meteorol. Clim., 53, 363–376, https://doi.org/10.1175/JAMC-D-13-0170.1, 2014. a
National Renewable Energy Laboratory: 2023 National Offshore Wind data set (NOW-23) [data set], https://doi.org/10.25984/1821404, 2020. a
Nunalee, C. G. and Basu, S.: Mesoscale modeling of coastal low-level jets: implications for offshore wind resource estimation, Wind Energy, 17, 1199–1216, https://doi.org/10.1002/we.1628, 2014. a, b
Nygaard, N. G.: Wakes in very large wind farms and the effect of neighbouring wind farms, J. Phys. Conf. Ser., 524, 012162, https://doi.org/10.1088/1742-6596/524/1/012162, 2014. a
Pichugina, Y. L., Brewer, W. A., Banta, R. M., Choukulkar, A., Clack, C. T. M., Marquis, M. C., McCarty, B. J., Weickmann, A. M., Sandberg, S. P., Marchbanks, R. D., and Hardesty, R. M.: Properties of the offshore low level jet and rotor layer wind shear as measured by scanning Doppler Lidar: Offshore low level jet from scanning doppler lidar measurements, Wind Energy, 20, 987–1002, https://doi.org/10.1002/we.2075, 2017. a, b
Platis, A., Siedersleben, S. K., Bange, J., Lampert, A., Bärfuss, K., Hankers, R., Cañadillas, B., Foreman, R., Schulz-Stellenfleth, J., Djath, B., Neumann, T., and Emeis, S.: First in situ evidence of wakes in the far field behind offshore wind farms, Sci. Rep., 8, 2163, https://doi.org/10.1038/s41598-018-20389-y, 2018. a
Platis, A., Hundhausen, M., Lampert, A., Emeis, S., and Bange, J.: The Role of Atmospheric Stability and Turbulence in Offshore Wind-Farm Wakes in the German Bight, Bound.-Lay. Meteorol., 182, 441–469, https://doi.org/10.1007/s10546-021-00668-4, 2022. a
Quint, D.: doqhne/offshore_wind_research: meteorological impacts of offshore wind turbines (Version v1), Zenodo [code], https://doi.org/10.5281/zenodo.10993298, 2024. a
Quint, D., Lundquist, J. K., Bodini, N., and Rosencrans, D.: Meteorological Impacts of Offshore Wind Turbines as Simulated in the Weather Research and Forecasting Model, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2024-53, in review, 2024. a
Radünz, W. C., de Almeida, E., Gutiérrez, A., Acevedo, O. C., Sakagami, Y., Petry, A. P., and Passos, J. C.: Nocturnal jets over wind farms in complex terrain, Appl. Energ., 314, 118959, https://doi.org/10.1016/j.apenergy.2022.118959, 2022. a
Rife, D. L., Pinto, J. O., Monaghan, A. J., Davis, C. A., and Hannan, J. R.: Global Distribution and Characteristics of Diurnally Varying Low-Level Jets, J. Climate, 23, 5041–5064, https://doi.org/10.1175/2010JCLI3514.1, 2010. a, b
Rybchuk, A., Juliano, T. W., Lundquist, J. K., Rosencrans, D., Bodini, N., and Optis, M.: The sensitivity of the Fitch wind farm parameterization to a three-dimensional planetary boundary layer scheme, Wind Energ. Sci., 7, 2085–2098, https://doi.org/10.5194/wes-7-2085-2022, 2022. a
Sathe, A., Gryning, S.-E., and Peña, A.: Comparison of the atmospheric stability and wind profiles at two wind farm sites over a long marine fetch in the North Sea, Wind Energy, 14, 767–780, https://doi.org/10.1002/we.456, 2011. a
Sharma, V., Parlange, M. B., and Calaf, M.: Perturbations to the Spatial and Temporal Characteristics of the Diurnally-Varying Atmospheric Boundary Layer Due to an Extensive Wind Farm, Bound.-Lay. Meteorol., 162, 255–282, https://doi.org/10.1007/s10546-016-0195-0, 2017. a
Sheridan, L. M., Krishnamurthy, R., Gustafson Jr., W. I., Liu, Y., Gaudet, B. J., Bodini, N., Newsom, R. K., and Pekour, M.: Offshore low-level jet observations and model representation using lidar buoy data off the California coast, Wind Energ. Sci., 9, 741–758, https://doi.org/10.5194/wes-9-741-2024, 2024. a, b
Siedersleben, S. K., Lundquist, J. K., Platis, A., Bange, J., Bärfuss, K., Lampert, A., Cañadillas, B., Neumann, T., and Emeis, S.: Micrometeorological impacts of offshore wind farms as seen in observations and simulations, Environ. Res. Lett., 13, 124012, https://doi.org/10.1088/1748-9326/aaea0b, 2018. a
Skamarock, C., Klemp, B., Dudhia, J., Gill, O., Liu, Z., Berner, J., Wang, W., Powers, G., Duda, G., Barker, D., and Huang, X.-y.: A Description of the Advanced Research WRF Model Version 4.3, Tech. Rep. NCAR/TN-556+STR, https://opensky.ucar.edu/islandora/object/technotes:588/ (last access: 10 December 2024), 2021. a
Smedman, A.-S., Tjernström, M., and Högström, U.: Analysis of the turbulence structure of a marine low-level jet, Bound.-Lay. Meteorol., 66, 105–126, https://doi.org/10.1007/BF00705462, 1993. a
Smedman, A.-S., Bergström, H., and Högström, U.: Spectra, variances and length scales in a marine stable boundary layer dominated by a low level jet, Bound.-Lay. Meteorol., 76, 211–232, https://doi.org/10.1007/BF00709352, 1995. a
Smith, E. N., Gibbs, J. A., Fedorovich, E., and Klein, P. M.: WRF Model Study of the Great Plains Low-Level Jet: Effects of Grid Spacing and Boundary Layer Parameterization, J. Appl. Meteorol. Climatol., 57, 2375–2397, https://doi.org/10.1175/JAMC-D-17-0361.1, 2018. a
Smith, E. N., Gebauer, J. G., Klein, P. M., Fedorovich, E., and Gibbs, J. A.: The Great Plains Low-Level Jet during PECAN: Observed and Simulated Characteristics, Mon. Weather Rev., 147, 1845–1869, https://doi.org/10.1175/MWR-D-18-0293.1, 2019. a
Song, J., Liao, K., Coulter, R. L., and Lesht, B. M.: Climatology of the Low-Level Jet at the Southern Great Plains Atmospheric Boundary Layer Experiments Site, J. Appl. Meteorol., 44, 1593–1606, https://doi.org/10.1175/JAM2294.1, 2005. a, b
Strobach, E., Sparling, L. C., Rabenhorst, S. D., and Demoz, B.: Impact of Inland Terrain on Mid-Atlantic Offshore Wind and Implications for Wind Resource Assessment: A Case Study, J. Appl. Meteorol. Climatol., 57, 777–796, https://doi.org/10.1175/JAMC-D-17-0143.1, 2018. a
Tay, K., Koh, T.-Y., and Skote, M.: Characterizing mesoscale variability in low-level jet simulations for CBLAST-LOW 2001 campaign, Meteorol. Atmos. Phys., 133, 163–179, https://doi.org/10.1007/s00703-020-00736-3, 2021. a
U.S. Department of Energy: DOE Releases Strategy to Accelerate and Expand Domestic Offshore Wind Deployment, https://www.energy.gov/articles/doe-releases-strategy-accelerate-and-expand-domestic-offshore-wind-deployment (last access: 10 December 2024), 2023. a
Wagner, D., Steinfeld, G., Witha, B., Wurps, H., and Reuder, J.: Low Level Jets over the Southern North Sea, Meteorologische Z., 28, 389–415, https://doi.org/10.1127/metz/2019/0948, 2019. a, b
Whiteman, C. D., Bian, X., and Zhong, S.: Low-Level Jet Climatology from Enhanced Rawinsonde Observations at a Site in the Southern Great Plains, J. Appl. Meteorol. Clim., 36, 1363–1376, https://doi.org/10.1175/1520-0450(1997)036<1363:LLJCFE>2.0.CO;2, 1997. a, b, c
Zhang, D.-L., Zhang, S., and Weaver, S. J.: Low-Level Jets over the Mid-Atlantic States: Warm-Season Climatology and a Case Study, J. Appl. Meteorol. Clim., 45, 194–209, https://doi.org/10.1175/JAM2313.1, 2006. a, b, c, d
Short summary
Offshore wind farms will be built along the East Coast of the United States. Low-level jets (LLJs) – layers of fast winds at low altitudes – also occur here. LLJs provide wind resources and also influence moisture and pollution transport, so it is important to understand how they might change. We develop and validate an automated tool to detect LLJs and compare 1 year of simulations with and without wind farms. Here, we describe LLJ characteristics and how they change with wind farms.
Offshore wind farms will be built along the East Coast of the United States. Low-level jets...
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