Articles | Volume 11, issue 7
https://doi.org/10.5194/wes-11-2543-2026
© Author(s) 2026. 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-11-2543-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Jul 2026
Research article |
| 21 Jul 2026
| 21 Jul 2026
Idealized simulations of wind farm interactions with intermittent turbulence in stable boundary layer conditions
Department of Civil and Environmental Engineering, University of California, Berkeley, California 94720, USA
Lawrence Livermore National Laboratory, Livermore, California 94550, USA
Robert S. Arthur
Lawrence Livermore National Laboratory, Livermore, California 94550, USA
Jeffrey D. Mirocha
Lawrence Livermore National Laboratory, Livermore, California 94550, USA
Julie K. Lundquist
Johns Hopkins University, Baltimore, Maryland 21218, USA
Fotini K. Chow
Lawrence Livermore National Laboratory, Livermore, California 94550, USA
Related authors
Nicola Bodini, Patrick Moriarty, Regis Thedin, Paula Doubrawa, Cristina Archer, Myra Blaylock, Carlo Bottasso, Bruno Carmo, Lawrence Cheung, Camille Dubreuil, Rogier Floors, Thomas Herges, Daniel Houck, Ali Kanjari, Colleen M. Kaul, Christopher Kelley, Ru Li, Julie K. Lundquist, Desirae Major, Anh Kiet Nguyen, Mike Optis, Luan R. C. Parada, Alfredo Peña, Julian Quick, David Ricarte, William C. Radünz, Raj K. Rai, Oscar García-Santiago, Jonas Schulte, Knut S. Seim, M. Paul van der Laan, Kisorthman Vimalakanthan, and Adam Wise
Wind Energ. Sci., 11, 3107–3135, https://doi.org/10.5194/wes-11-3107-2026, https://doi.org/10.5194/wes-11-3107-2026, 2026
Short summary
Short summary
Predicting wind farm energy production is challenging because wind patterns are complex. We tested 16 different models against real data from a major field experiment to see which worked best. Surprisingly, the most expensive and detailed models were not always more accurate than simpler ones. We found that feeding models better weather data was the most effective way to improve accuracy. These results help the industry choose the right tools for designing more-efficient wind farms.
Adam S. Wise, Harish Gopalan, Aaron M. Lattanzi, Ann S. Almgren, David Wiersema, Akshay Gowardhan, Soonpil Kang, Jeffrey D. Mirocha, David J. Gardner, and Katherine A. Lundquist
EGUsphere, https://doi.org/10.5194/egusphere-2026-4030, https://doi.org/10.5194/egusphere-2026-4030, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Fast and accurate models of how wind and hazardous materials move throughout cities and suburban areas are needed for emergency response. This article describes a new method for representing buildings in the exascale Energy Research and Forecasting model for accelerated, high-fidelity urban dispersion modeling. We validate the capability using realistic data from a field experiment conducted in Oklahoma City and examine assess accuracy and speed tradeoffs for different model configurations.
William C. 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., 10, 2365–2393, https://doi.org/10.5194/wes-10-2365-2025, https://doi.org/10.5194/wes-10-2365-2025, 2025
Short summary
Short summary
We explore how simple terrain influences spatial variations in wind speed and wind farm performance during a low-level jet. Using simulations, field observations, and turbine production data, we find that downstream turbines produce more power than upstream ones, despite being subjected to wake effects. This counterintuitive result arises because the low-level jet and winds near turbine rotors are highly sensitive to topographic features, leading to stronger winds at the downstream turbines.
Adam S. Wise, Robert S. Arthur, Aliza Abraham, Sonia Wharton, Raghavendra Krishnamurthy, Rob Newsom, Brian Hirth, John Schroeder, Patrick Moriarty, and Fotini K. Chow
Wind Energ. Sci., 10, 1007–1032, https://doi.org/10.5194/wes-10-1007-2025, https://doi.org/10.5194/wes-10-1007-2025, 2025
Short summary
Short summary
Wind farms can be subject to rapidly changing weather events. In the United States Great Plains, some of these weather events can result in waves in the atmosphere that ultimately affect how much power a wind farm can produce. We modeled a specific event of waves observed in Oklahoma. We determined how to accurately model the event and analyzed how it affected a wind farm’s power production, finding that the waves both decreased power and made it more variable.
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
Short summary
Short summary
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.
Nicola Bodini, Patrick Moriarty, Regis Thedin, Paula Doubrawa, Cristina Archer, Myra Blaylock, Carlo Bottasso, Bruno Carmo, Lawrence Cheung, Camille Dubreuil, Rogier Floors, Thomas Herges, Daniel Houck, Ali Kanjari, Colleen M. Kaul, Christopher Kelley, Ru Li, Julie K. Lundquist, Desirae Major, Anh Kiet Nguyen, Mike Optis, Luan R. C. Parada, Alfredo Peña, Julian Quick, David Ricarte, William C. Radünz, Raj K. Rai, Oscar García-Santiago, Jonas Schulte, Knut S. Seim, M. Paul van der Laan, Kisorthman Vimalakanthan, and Adam Wise
Wind Energ. Sci., 11, 3107–3135, https://doi.org/10.5194/wes-11-3107-2026, https://doi.org/10.5194/wes-11-3107-2026, 2026
Short summary
Short summary
Predicting wind farm energy production is challenging because wind patterns are complex. We tested 16 different models against real data from a major field experiment to see which worked best. Surprisingly, the most expensive and detailed models were not always more accurate than simpler ones. We found that feeding models better weather data was the most effective way to improve accuracy. These results help the industry choose the right tools for designing more-efficient wind farms.
Georgios Deskos, Jiali Wang, Sanjay Arwade, Murray Fisher, Brian Hirth, Xiaoli Guo Larsén, Julie K. Lundquist, Andrew Myers, Weichiang Pang, William J. Pringle, Robert Rogers, Miguel Sanchez-Gomez, Chao Sun, Atsushi Yamaguchi, and Paul Veers
Wind Energ. Sci., 11, 2749–2782, https://doi.org/10.5194/wes-11-2749-2026, https://doi.org/10.5194/wes-11-2749-2026, 2026
Short summary
Short summary
Wind energy is expanding into regions exposed to tropical cyclones, where extreme winds and waves can damage turbines and disrupt energy production. We reviewed observations, simulations, engineering standards, and risk quantification frameworks to identify the most important gaps. Today's design rules, originally built for milder storms, do not capture cyclone hazards. Closing these gaps requires better offshore measurements, improved models, and risk-informed design standards.
William C. Radünz, Jens H. Kasper, Richard J. A. M. Stevens, and Julie K. Lundquist
Wind Energ. Sci., 11, 2723–2747, https://doi.org/10.5194/wes-11-2723-2026, https://doi.org/10.5194/wes-11-2723-2026, 2026
Short summary
Short summary
Wind farms extract energy from the wind, creating slower, more turbulent flows that can affect other farms downstream. Using high-fidelity simulations for comparison, we find that mesoscale simulations that parameterize wind farms at coarser resolution may underestimate how quickly the wind recovers downstream. This slow recovery results from missing sharp wind gradients and low turbulence. Improving these aspects can help better predict wind energy production over long distances.
Adam S. Wise, Harish Gopalan, Aaron M. Lattanzi, Ann S. Almgren, David Wiersema, Akshay Gowardhan, Soonpil Kang, Jeffrey D. Mirocha, David J. Gardner, and Katherine A. Lundquist
EGUsphere, https://doi.org/10.5194/egusphere-2026-4030, https://doi.org/10.5194/egusphere-2026-4030, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Fast and accurate models of how wind and hazardous materials move throughout cities and suburban areas are needed for emergency response. This article describes a new method for representing buildings in the exascale Energy Research and Forecasting model for accelerated, high-fidelity urban dispersion modeling. We validate the capability using realistic data from a field experiment conducted in Oklahoma City and examine assess accuracy and speed tradeoffs for different model configurations.
Valeria Vasquez-Barros, Nicola Bodini, Seth Zippel, Anthony Kirincich, and Julie K. Lundquist
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-115, https://doi.org/10.5194/wes-2026-115, 2026
Preprint under review for WES
Short summary
Short summary
Offshore wind turbines operate more than 100 meters above the ocean, but atmospheric conditions are often measured only near the sea surface. Using observations from an offshore research platform, we found that surface measurements frequently do not represent the conditions experienced by turbines because the lower atmosphere can become decoupled. These results highlight the need for measurements throughout the turbine height to improve wind energy forecasting and operations.
Nathan J. Agarwal, Julie K. Lundquist, Timothy W. Juliano, and Alex Rybchuk
Wind Energ. Sci., 11, 2369–2403, https://doi.org/10.5194/wes-11-2369-2026, https://doi.org/10.5194/wes-11-2369-2026, 2026
Short summary
Short summary
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.
Miguel Sanchez-Gomez, Georgios Deskos, Mike Optis, Julie K. Lundquist, Michael Sinner, Geng Xia, and Walter Musial
Wind Energ. Sci., 11, 2009–2036, https://doi.org/10.5194/wes-11-2009-2026, https://doi.org/10.5194/wes-11-2009-2026, 2026
Short summary
Short summary
Mesoscale simulations with the Fitch wind farm parameterization were compared to large-domain large-eddy simulations for three planned offshore wind farms under varied atmospheric conditions. Mesoscale runs captured key wake deficit patterns and stability effects in the wind farm wake evolution but underestimated power losses from internal wakes, especially in aligned winds or stable conditions. Results highlight mesoscale strengths for large-scale wakes and limits for turbine-level losses.
Nicola Bodini, Joseph Olson, Brian Gaudet, Giacomo Valerio Iungo, Mojtaba Shams Solari, Sayahnya Roy, Julie K. Lundquist, Nathan Agarwal, Timothy A. Myers, Bianca Adler, Jeffrey D. Mirocha, Eric James, Laura Bianco, James M. Wilczak, and David D. Turner
Wind Energ. Sci., 11, 1949–1961, https://doi.org/10.5194/wes-11-1949-2026, https://doi.org/10.5194/wes-11-1949-2026, 2026
Short summary
Short summary
To improve offshore wind forecasts, the Third Wind Forecast Improvement Project monitored the United States east coast for 18 months. We compiled a daily log of weather events using advanced scanners and expert notes. This public dataset identifies important wind patterns, helping scientists test computer models and choose specific cases to study.
Jishi Zhang, Jean-Christophe Golaz, Matthew Vincent Signorotti, Hsiang-He Lee, Peter Bogenschutz, Minda Monteagudo, Paul Aaron Ullrich, Robert S. Arthur, Stephen Po-Chedley, Philip Cameron-Smith, and Jean-Paul Watson
Geosci. Model Dev., 19, 4513–4545, https://doi.org/10.5194/gmd-19-4513-2026, https://doi.org/10.5194/gmd-19-4513-2026, 2026
Short summary
Short summary
We ran a convection-permitting model with regional mesh refinement (3.25 km and 800 m) to simulate present-day wind and solar capacity factors over California, coupling it to an energy generation model. The high-resolution models captured realistic seasonal and diurnal cycles, with wind markedly better than a 25 km model and solar outperforming a 3 km operational forecast. We highlight the critical role of resolution, modeling assumptions, and data reliability in renewable energy assessment.
Nathan J. Agarwal and Julie K. Lundquist
Wind Energ. Sci., 11, 883–910, https://doi.org/10.5194/wes-11-883-2026, https://doi.org/10.5194/wes-11-883-2026, 2026
Short summary
Short summary
Areas with hills and valleys can be either beneficial or challenging for wind energy applications, depending on the wind patterns. Unfortunately, predicting wind patterns in these areas is also challenging, and investing in measurement towers to improve wind forecasts can be expensive. We evaluate ways in which wind farm developers and other stakeholders interested in improving atmospheric forecasts in these areas can do so in a more cost-effective way.
Kira Gramitzky, Florian Jäger, Doron Callies, Tabea Hildebrand, Julie K. Lundquist, and Lukas Pauscher
Wind Energ. Sci., 11, 861–882, https://doi.org/10.5194/wes-11-861-2026, https://doi.org/10.5194/wes-11-861-2026, 2026
Short summary
Short summary
This study introduces an extended sea surface levelling method for the accurate offshore calibration of scanning lidars. This method can determine the alignment of the laser beam, including any vertical shift, and is independent of the scan pattern. Tests using real measurement data and a detailed uncertainty study confirm its reliability. The study offers a versatile calibration approach and improves confidence in offshore wind measurements with scanning lidars.
Nicola Bodini, Aliza Abraham, Paula Doubrawa, Stefano Letizia, Julie K. Lundquist, Patrick Moriarty, and Ryan Scott
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-33, https://doi.org/10.5194/wes-2026-33, 2026
Revised manuscript under review for WES
Short summary
Short summary
Wind turbines create "wakes" of slowed air that reduce power for nearby turbines. To help improve wind energy models, we analyzed data from a large field experiment. We focused on a single day with changing weather patterns. We found that even simple terrain features interacted with the wind to create large variations in power output – up to 80 percent – across a wind farm. This detailed dataset provides a real-world case needed to validate and improve wind energy design tools.
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., 11, 71–88, https://doi.org/10.5194/wes-11-71-2026, https://doi.org/10.5194/wes-11-71-2026, 2026
Short summary
Short summary
This study analyzes onshore wind farm wakes in a semi-complex terrain with data conducted with the research aircraft of TU Braunschweig during the American WAKE experimeNt (AWAKEN). 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 at a distance of 10 km.
Matteo Puccioni, Sonia Wharton, Stephan F. J. De Wekker, Robert S. Arthur, Tianyi Li, Ye Liu, Sha Feng, Kyle Pressel, Raj K. Rai, Larry K. Berg, and Jerome D. Fast
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-215, https://doi.org/10.5194/wes-2025-215, 2025
Revised manuscript not accepted
Short summary
Short summary
A multi-wind Lidar campaign is conducted in a forest in the United States Southeast, a region featuring low wind resources due to the forest drag. Although the latter reduces the wind for hundreds of meters above ground, we found this effect to be negligible for heights above 8 times the tree height where the wind is dominated by local atmospheric events. This scenario opens to the development of taller turbines harvesting the wind farther away from the ground where the forest drag is minimal.
William C. 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., 10, 2365–2393, https://doi.org/10.5194/wes-10-2365-2025, https://doi.org/10.5194/wes-10-2365-2025, 2025
Short summary
Short summary
We explore how simple terrain influences spatial variations in wind speed and wind farm performance during a low-level jet. Using simulations, field observations, and turbine production data, we find that downstream turbines produce more power than upstream ones, despite being subjected to wake effects. This counterintuitive result arises because the low-level jet and winds near turbine rotors are highly sensitive to topographic features, leading to stronger winds at the downstream turbines.
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
Revised manuscript not accepted
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Mohit L. Dubey, Andre Santos, Andrew B. Moyes, Ken Reichl, James E. Lee, Manvendra K. Dubey, Corentin LeYhuelic, Evan Variano, Emily Follansbee, Fotini K. Chow, and Sébastien C. Biraud
Atmos. Meas. Tech., 18, 2987–3007, https://doi.org/10.5194/amt-18-2987-2025, https://doi.org/10.5194/amt-18-2987-2025, 2025
Short summary
Short summary
Orphaned wells, meaning wells lacking responsible owners, pose a significant and poorly understood environmental challenge. We propose, develop and test a novel method for estimating emissions from orphaned wells using a forced advection sampling technique (FAST) that can overcome many of the limitations in current methods (cost, accuracy, safety). Our results suggest that the FAST method can provide a low-cost alternative to existing methods over a range of leak rates.
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
Short summary
Short summary
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.
Macy Frost Chang, Raghavendra Krishnamurthy, and Fotini Katopodes Chow
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-85, https://doi.org/10.5194/wes-2025-85, 2025
Revised manuscript under review for WES
Short summary
Short summary
The development of offshore wind energy sites hinges on accurate prediction of hub-height wind speeds. This paper compares three machine learning (ML) algorithms to a standard log-law wind extrapolation at two offshore buoy sites. The ML methods demonstrate new capabilities for providing accurate and adaptable predictions of offshore wind characteristics in comparison to conventional approaches. These ML techniques can help inform the development of offshore wind energy projects.
Adam S. Wise, Robert S. Arthur, Aliza Abraham, Sonia Wharton, Raghavendra Krishnamurthy, Rob Newsom, Brian Hirth, John Schroeder, Patrick Moriarty, and Fotini K. Chow
Wind Energ. Sci., 10, 1007–1032, https://doi.org/10.5194/wes-10-1007-2025, https://doi.org/10.5194/wes-10-1007-2025, 2025
Short summary
Short summary
Wind farms can be subject to rapidly changing weather events. In the United States Great Plains, some of these weather events can result in waves in the atmosphere that ultimately affect how much power a wind farm can produce. We modeled a specific event of waves observed in Oklahoma. We determined how to accurately model the event and analyzed how it affected a wind farm’s power production, finding that the waves both decreased power and made it more variable.
Daphne Quint, Julie K. Lundquist, and David Rosencrans
Wind Energ. Sci., 10, 117–142, https://doi.org/10.5194/wes-10-117-2025, https://doi.org/10.5194/wes-10-117-2025, 2025
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Ansorge, C. and Mellado, J. P.: Global intermittency and collapsing turbulence in the stratified planetary boundary layer, Bound.-Lay. Meteorol., 153, 89–116, 2014. a
Ansorge, C. and Mellado, J. P.: Analyses of external and global intermittency in the logarithmic layer of Ekman flow, J. Fluid Mech., 805, 611–635, 2016. a
Arthur, R. S., Mirocha, J. D., Marjanovic, N., Hirth, B. D., Schroeder, J. L., Wharton, S., and Chow, F. K.: Multi-scale simulation of wind farm performance during a frontal passage, Atmosphere-Basel, 11, 245, https://doi.org/10.3390/atmos11030245, 2020. a, b, c
Banta, R. M.: Stable-boundary-layer regimes from the perspective of the low-level jet, Acta Geophys., 56, 58–87, https://doi.org/10.2478/s11600-007-0049-8, 2008. a, b
Bardina, J., Ferziger, J., and Reynolds, W.: Improved Turbulence Models Based on Large Eddy Simulation of Homgeneous, Imcompressible, Turbulent Flows., Tech. rep., SU-TF-19, Stanford University, 1983. a
Beare, R. J., Macvean, M. K., Holtslag, A. A. M., Cuxart, J., Esau, I., Golaz, J.-C., Jimenez, M. A., Khairoutdinov, M., Kosovic, B., Lewellen, D., Lund, T. S., Lundquist, J. K., Mccabe, A., Moene, A. F., Noh, Y., Raasch, S., and Sullivan, P.: An intercomparison of large-eddy simulations of the stable boundary layer, Bound.-Lay. Meteorol., 118, 247–272, https://doi.org/10.1007/s10546-004-2820-6, 2006. a, b, c, d, e
Bilgili, M. and Alphan, H.: Global growth in offshore wind turbine technology, Clean Technol. Envir., 24, https://doi.org/10.1007/s10098-022-02314-0, 2022. a
Brown, A. R., Hobson, J. M., and Wood, N.: Large-eddy simulation of neutral turbulent flow over rough sinusoidal ridges, Bound.-Lay. Meteorol., 98, 411–441, https://doi.org/10.1023/A:1018703209408, 2001. a
Businger, J. A., Wyngaard, J. C., Izumi, Y., and Bradley, E. F.: Flux-profile relationships in the atmospheric surface layer, J. Atmos. Sci., 28, 181–189, https://doi.org/10.1175/1520-0469(1971)028<0181:FPRITA>2.0.CO;2, 1971. a, b
Carper, M. A. and Porté-Agel, F.: The role of coherent structures in subfilter-scale dissipation of turbulence measured in the atmospheric surface layer, J. Turbul., 5, N40, https://doi.org/10.1088/1468-5248/5/1/040, 2004. a, b
Chenge, Y. and Brutsaert, W.: Flux-profile relationships for wind speed and temperature in the stable atmospheric boundary layer, Bound.-Lay. Meteorol., 114, 519–538, https://doi.org/10.1007/s10546-004-1425-4, 2005. a
Dimitrov, N., Kelly, M. C., Vignaroli, A., and Berg, J.: From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases, Wind Energ. Sci., 3, 767–790, https://doi.org/10.5194/wes-3-767-2018, 2018. a
Draxl, C., Worsnop, R. P., Xia, G., Pichugina, Y., Chand, D., Lundquist, J. K., Sharp, J., Wedam, G., Wilczak, J. M., and Berg, L. K.: Mountain waves can impact wind power generation, Wind Energ. Sci., 6, 45–60, https://doi.org/10.5194/wes-6-45-2021, 2021. a
Ellison, A. M.: Effect of seed dimorphism on the density-dependent dynamics of experimental populations of atriplex triangularis (chenopodiaceae), Am. J. Bot., 74, 1280–1288, https://doi.org/10.1002/j.1537-2197.1987.tb08741.x, 1987. a
Gadde, S. N. and Stevens, R. J. A. M.: Effect of low-level jet height on wind farm performance, J. Renew. Sustain. Ener., 13, 013305, https://doi.org/10.1063/5.0026232, 2021a. a, b, c
Gadde, S. N. and Stevens, R. J. A. M.: Interaction between low-level jets and wind farms in a stable atmospheric boundary layer, Physical Review Fluids, 6, 014603, https://doi.org/10.1103/PhysRevFluids.6.014603, 2021b. a, b, c, d
Gullbrand, J. and Chow, F. K.: The effect of numerical errors and turbulence models in large-eddy simulations of channel flow, with and without explicit filtering, J. Fluid Mech., 495, 323–341, https://doi.org/10.1017/S0022112003006268, 2003. a
Hirth, B. and Schroeder, J.: Documenting wind speed and power deficits behind a utility-scale wind turbine, J. Appl. Meteorol., 52, 39–46, https://doi.org/10.1175/JAMC-D-12-0145.1, 2013. a, b
Jiménez, M. A. and Cuxart, J.: Large-eddy simulations of the stable boundary layer using the Standard Kolmogorov Theory: range of applicability, Bound.-Lay. Meteorol., 115, 241–261, https://doi.org/10.1007/s10546-004-3470-4, 2005. a, b
Kelley, N., Jonkman, B., and Scott, G.: Great Plains Turbulence Environment: Its Origins, Impact, and Simulation, NREL/CP-500-40176, 2006. a
Kelly, M. and Gryning, S.-E.: Long-term mean wind profiles based on similarity theory, Bound.-Lay. Meteorol., 136, 377–390, https://doi.org/10.1007/s10546-010-9509-9, 2010. a
Kirkil, G., Mirocha, J., Bou-Zeid, E., Chow, F. K., and Kosović, B.: Implementation and evaluation of dynamic subfilter-scale stress models for large-eddy simulation using WRF, Mon. Weather Rev., 140, 266–284, https://doi.org/10.1175/MWR-D-11-00037.1, 2012. a, b, c
Kosović, B.: Subgrid-scale modelling for the large-eddy simulation of high-Reynolds-number boundary layers, J. Fluid Mech., 336, 151–182, https://doi.org/10.1017/S0022112096004697, 1997. a
Kumar, P. and Sharan, M.: An analysis for the applicability of Monin–Obukhov Similarity Theory in stable conditions, J. Atmos. Sci., 69, 1910–1915, https://doi.org/10.1175/JAS-D-11-0250.1, 2012. a
Lanzilao, L. and Meyers, J.: A parametric large-eddy simulation study of wind-farm blockage and gravity waves in conventionally neutral boundary layers, J. Fluid Mech., 979, A54, https://doi.org/10.1017/jfm.2023.1088, 2024. a
Lilly, D. K.: A proposed modification of the Germano subgrid scale closure method, Phys. Fluids A-Fluid, 4, 633–635, https://doi.org/10.1063/1.858280, 1992. a
Lu, H. and Porté-Agel, F.: Large-eddy simulation of a very large wind farm in a stable atmospheric boundary layer, Phys. Fluids, 23, 065101, https://doi.org/10.1063/1.3589857, 2011. 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
Mahrt, L.: Vertical structure and turbulence in the very stable boundary layer, J. Atmos. Sci., 42, 2333–2349, https://doi.org/10.1175/1520-0469(1985)042<2333:VSATIT>2.0.CO;2, 1985. a
Mahrt, L.: Intermittency of atmospheric turbulence, J. Atmos. Sci., 46, 79–95, https://doi.org/10.1175/1520-0469(1989)046<0079:IOAT>2.0.CO;2, 1989. a, b
Mahrt, L.: Stratified atmospheric boundary layers, Bound.-Lay. Meteorol., 90, 375–396, https://doi.org/10.1023/A:1001765727956, 1999. a, b
Marjanovic, N., Mirocha, J. D., Kosović, B., Lundquist, J. K., and Chow, F. K.: Implementation of a generalized actuator line model for wind turbine parameterization in the Weather Research and Forecasting model, J. Renew. Sustain. Ener., 9, 063308, https://doi.org/10.1063/1.4989443, 2017. a
Mason, P. J. and Thomson, D. J.: Stochastic backscatter in large-eddy simulations of boundary layers, J. Fluid Mech., 242, 51–78, https://doi.org/10.1017/S0022112092002271, 1992. a
Mayor, S. D., Jachens, E. R., and Randall, T. N.: Lidar observations of fine-scale gravity waves in the nocturnal boundary layer above an orchard canopy, in: 16th International Symposium for the Advancement of Boundary-Layer Remote Sensing (ISARS), CIRES, University of Colorado and NOAA Earth System Research Laboratory, Boulder, CO, 157–160, https://psl.noaa.gov/events/2012/isars/pdf/isars2012-abstractVolume.pdf (last access: 15 March 2026), 2012. a
Mirocha, J., Kosović, B., and Kirkil, G.: Resolved turbulence characteristics in large-eddy simulations nested within mesoscale simulations using the Weather Research and Forecasting Model, Mon. Weather Rev., 142, 806–831, https://doi.org/10.1175/MWR-D-13-00064.1, 2014a. a
Mirocha, J. D., Kosović, B., Aitken, M. L., and Lundquist, J. K.: Implementation of a generalized actuator disk wind turbine model into the weather research and forecasting model for large-eddy simulation applications, J. Renew. Sustain. Ener., 6, 013104, https://doi.org/10.1063/1.4861061, 2014b. a, b
Mirocha, J. D., Rajewski, D. A., Marjanovic, N., Lundquist, J. K., Kosović, B., Draxl, C., and Churchfield, M. J.: Investigating wind turbine impacts on near-wake flow using profiling lidar data and large-eddy simulations with an actuator disk model, J. Renew. Sustain. Ener., 7, 043143, https://doi.org/10.1063/1.4928873, 2015. a, b
Moeng, C.-H.: A large-eddy-simulation model for the study of planetary boundary-layer turbulence, J. Atmos. Sci., 41, 2052–2062, https://doi.org/10.1175/1520-0469(1984)041<2052:ALESMF>2.0.CO;2, 1984. a, b
Moeng, C.-H. and Sullivan, P. P.: A comparison of shear- and buoyancy-driven planetary boundary layer flows, J. Atmos. Sci., 51, 999–1022, https://doi.org/10.1175/1520-0469(1994)051<0999:ACOSAB>2.0.CO;2, 1994. a
Monin, A. S. and Obukhov, A. M.: Basic laws of turbulent mixing in the surface layer of the atmosphere, Contrib. Geophys. Inst. Acad. Sci. USSR, 151, e187, 163–187, 1954. a
Moriarty, P., Hamilton, N., Debnath, M., Herges, T., Isom, B., Lundquist, J., Maniaci, D., Naughton, B., Pauly, R., Roadman, J., Shaw, W., van Dam, J., and Wharton, S.: American WAKE experimeNt (AWAKEN), Tech. Rep. NREL/TP-5000-75789, 1659798, MainId:5894, https://doi.org/10.2172/1659798, 2020. a
Peña, A., Kosović, B., and Mirocha, J. D.: Evaluation of idealized large-eddy simulations performed with the Weather Research and Forecasting model using turbulence measurements from a 250 m meteorological mast, Wind Energ. Sci., 6, 645–661, https://doi.org/10.5194/wes-6-645-2021, 2021. a, b
Radünz, W. C., Carmo, B., Lundquist, J. K., Letizia, S., Abraham, A., Wise, A. S., Sanchez Gomez, M., Hamilton, N., Rai, R. K., and Peixoto, P. S.: Influence of simple terrain on the spatial variability of a low-level jet and wind farm performance in the AWAKEN field campaign, Wind Energ. Sci., 10, 2365–2393, https://doi.org/10.5194/wes-10-2365-2025, 2025. a
Rottman, J. W. and Simpson, J. E.: The formation of internal bores in the atmosphere: a laboratory model, Q. J. Roy. Meteor. Soc., 115, 941–963, https://doi.org/10.1002/qj.49711548809, 1989. a
Sanchez Gomez, M. and Lundquist, J. K.: The effect of wind direction shear on turbine performance in a wind farm in central Iowa, Wind Energ. Sci., 5, 125–139, https://doi.org/10.5194/wes-5-125-2020, 2020. a
Sanchez Gomez, M., Lundquist, J. K., Mirocha, J. D., Arthur, R. S., Muñoz-Esparza, D., and Robey, R.: Can lidars assess wind plant blockage in simple terrain? A WRF-LES study, J. Renew. Sustain. Ener., 14, 063303, https://doi.org/10.1063/5.0103668, 2022. a
Sanchez Gomez, M., Lundquist, J. K., Mirocha, J. D., and Arthur, R. S.: Investigating the physical mechanisms that modify wind plant blockage in stable boundary layers, Wind Energ. Sci., 8, 1049–1069, https://doi.org/10.5194/wes-8-1049-2023, 2023. a, b, c
Simon, J. S., Zhou, B., Mirocha, J. D., and Chow, F. K.: Explicit filtering and reconstruction to reduce grid dependence in convective boundary layer simulations using WRF-LES, Mon. Weather Rev., 147, 1805–1821, https://doi.org/10.1175/MWR-D-18-0205.1, 2019. a
Sisterson, D. L. and Frenzen, P.: Nocturnal boundary-layer wind maxima and the problem of wind power assessment, Environ. Sci. Technol., 12, 218–221, https://doi.org/10.1021/es60138a014, 1978. a
Stolz, S., Adams, N. A., and Kleiser, L.: The approximate deconvolution model for large-eddy simulations of compressible flows and its application to shock-turbulent-boundary-layer interaction, Phys. Fluids, 13, 2985–3001, https://doi.org/10.1063/1.1397277, 2001. a
Stull, R. B.: An introduction to boundary layer meteorology, Springer Science and Business Media, ISBN: 9789027727695, 1988. a
Sullivan, P., Horst, T., Lenschow, D., Moeng, C.-H., and Weil, J.: Structure of subfilter-scale fluxes in the atmospheric surface layer with application to large-eddy simulation modelling, J. Fluid Mech., 482, https://doi.org/10.1017/S0022112003004099, 2003. a, b
Sun, J., Burns, S. P., Lenschow, D. H., Banta, R., Newsom, R., Coulter, R., Frasier, S., Ince, T., Nappo, C., Cuxart, J., Blumen, W., Lee, X., and Hu, X.-Z.: Intermittent turbulence associated with a density current passage in the stable boundary layer, Bound.-Lay. Meteorol., 105, 199–219, https://doi.org/10.1023/A:1019969131774, 2002. a, b
Sun, J., Nappo, C. J., Mahrt, L., Belušić, D., Grisogono, B., Stauffer, D. R., Pulido, M., Staquet, C., Jiang, Q., Pouquet, A., Yagüe, C., Galperin, B., Smith, R. B., Finnigan, J. J., Mayor, S. D., Svensson, G., Grachev, A. A., and Neff, W. D.: Review of wave-turbulence interactions in the stable atmospheric boundary layer, Rev. Geophys., 53, 956–993, https://doi.org/10.1002/2015RG000487, 2015. a
van der Laan, M. P. and Sørensen, N. N.: Why the Coriolis force turns a wind farm wake clockwise in the Northern Hemisphere, Wind Energ. Sci., 2, 285–294, https://doi.org/10.5194/wes-2-285-2017, 2017. a, b
van der Laan, M. P., Baungaard, M., and Kelly, M.: Brief communication: A clarification of wake recovery mechanisms, Wind Energ. Sci., 8, 247–254, https://doi.org/10.5194/wes-8-247-2023, 2023. a
van der Linden, S. J. A., van de Wiel, B. J. H., Petenko, I., van Heerwaarden, C. C., Baas, P., and Jonker, H. J. J.: A businger mechanism for intermittent bursting in the stable boundary layer, J. Atmos. Sci., 77, 3343–3360, https://doi.org/10.1175/JAS-D-19-0309.1, 2020. a, b
Vanderwende, B. J. and Lundquist, J. K.: The modification of wind turbine performance by statistically distinct atmospheric regimes, Environ. Res. Lett., 7, 034035, https://doi.org/10.1088/1748-9326/7/3/034035, 2012. a
Vanderwende, B. J., Lundquist, J. K., Rhodes, M. E., Takle, E. S., and Irvin, S. L.: Observing and simulating the summertime low-level jet in Central Iowa, Mon. Weather Rev., 143, 2319–2336, https://doi.org/10.1175/MWR-D-14-00325.1, 2015. a
Veers, P., Bottasso, C. L., Manuel, L., Naughton, J., Pao, L., Paquette, J., Robertson, A., Robinson, M., Ananthan, S., Barlas, T., Bianchini, A., Bredmose, H., Horcas, S. G., Keller, J., Madsen, H. A., Manwell, J., Moriarty, P., Nolet, S., and Rinker, J.: Grand challenges in the design, manufacture, and operation of future wind turbine systems, Wind Energ. Sci., 8, 1071–1131, https://doi.org/10.5194/wes-8-1071-2023, 2023. a
Wharton, S. and Lundquist, J. K.: Atmospheric stability affects wind turbine power collection, Environ. Res. Lett., 7, 014005, https://doi.org/10.1088/1748-9326/7/1/014005, 2012. a
Wise, A. S.: WRFv4.4-DRM_GAD, Zenodo [code], https://doi.org/10.5281/zenodo.15492528, 2025. a
Wise, A. S., Neher, J. M. T., Arthur, R. S., Mirocha, J. D., Lundquist, J. K., and Chow, F. K.: Meso- to microscale modeling of atmospheric stability effects on wind turbine wake behavior in complex terrain, Wind Energ. Sci., 7, 367–386, https://doi.org/10.5194/wes-7-367-2022, 2022. a
Wise, A. S., Arthur, R. S., Abraham, A., Wharton, S., Krishnamurthy, R., Newsom, R., Hirth, B., Schroeder, J., Moriarty, P., and Chow, F. K.: Large-eddy simulation of an atmospheric bore and associated gravity wave effects on wind farm performance in the southern Great Plains, Wind Energ. Sci., 10, 1007–1032, https://doi.org/10.5194/wes-10-1007-2025, 2025. a, b, c, d
Wong, V. C. and Lilly, D. K.: A comparison of two dynamic subgrid closure methods for turbulent thermal convection, Phys. Fluids, 6, 1016–1023, https://doi.org/10.1063/1.868335, 1994. a
Wyngaard, J. C.: Turbulence in the Atmosphere, Cambridge University Press, ISBN: 9780511840524, 2010. a
Zang, Y., Street, R., and Koseff, J.: A dynamic mixed subgrid-scale model and its application to turbulent recirculating flows, Phys. Fluids A-Fluid, 5, 3186, https://doi.org/10.1063/1.858675, 1993. a
Zhou, B. and Chow, F. K.: Turbulence modeling for the stable atmospheric boundary layer and implications for wind energy, Flow Turbul. Combust., 88, 255–277, https://doi.org/10.1007/s10494-011-9359-7, 2012. a, b, c, d
Zhou, B. and Chow, F. K.: Nested large-eddy simulations of the intermittently turbulent stable atmospheric boundary layer over real terrain, J. Atmos. Sci., 71, 1021–1039, https://doi.org/10.1175/JAS-D-13-0168.1, 2014. a, b, c, d
Short summary
At night, wind farms can experience a wide range of intermittent wind events that can affect how much power is produced. We modeled a specific type of intermittent wind event that happens as the atmosphere gets colder and colder throughout a night. We identified a threshold to determine when these events impact wind farms and ultimately found that mainly the front of a wind farm tends to be most impacted by these sorts of events.
At night, wind farms can experience a wide range of intermittent wind events that can affect how...
Altmetrics
Final-revised paper
Preprint