Articles | Volume 7, issue 5
https://doi.org/10.5194/wes-7-2085-2022
© Author(s) 2022. 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-7-2085-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The sensitivity of the Fitch wind farm parameterization to a three-dimensional planetary boundary layer scheme
National Renewable Energy Laboratory, Golden, Colorado, USA
Department of Mechanical Engineering, University of Colorado Boulder, Boulder, Colorado, USA
Timothy W. Juliano
Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA
Julie K. Lundquist
National Renewable Energy Laboratory, Golden, Colorado, USA
Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado, USA
David Rosencrans
National Renewable Energy Laboratory, Golden, Colorado, USA
Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado, USA
Nicola Bodini
National Renewable Energy Laboratory, Golden, Colorado, USA
Mike Optis
National Renewable Energy Laboratory, Golden, Colorado, USA
Related authors
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.
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
Short summary
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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.
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.
Nathan J. Agarwal and Julie K. Lundquist
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-144, https://doi.org/10.5194/wes-2025-144, 2025
Preprint under review for WES
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Areas with mountains and valleys can either be 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 that wind farm developers and other stakeholders interested in improving atmospheric forecasts in these areas can do so in a more cost-effective way.
Miguel Sanchez-Gomez, Georgios Deskos, Mike Optis, Julie K. Lundquist, Michael Sinner, Geng Xia, and Walter Musial
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-152, https://doi.org/10.5194/wes-2025-152, 2025
Preprint under review for WES
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Mesoscale WRF simulations with the Fitch wind farm parameterization were compared to large-domain LES 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.
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.
Aliza Abraham, Matteo Puccioni, Arianna Jordan, Emina Maric, Nicola Bodini, Nicholas Hamilton, Stefano Letizia, Petra M. Klein, Elizabeth N. Smith, Sonia Wharton, Jonathan Gero, Jamey D. Jacob, Raghavendra Krishnamurthy, Rob K. Newsom, Mikhail Pekour, William Radünz, and Patrick Moriarty
Wind Energ. Sci., 10, 1681–1705, https://doi.org/10.5194/wes-10-1681-2025, https://doi.org/10.5194/wes-10-1681-2025, 2025
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This study is the first to use real-world atmospheric measurements to show that large wind plants can increase the height of the planetary boundary layer, the part of the atmosphere near the surface where life takes place. The planetary boundary layer height governs processes like pollutant transport and cloud formation and is a key parameter for modeling the atmosphere. The results of this study provide important insights into interactions between wind plants and their local environment.
Yelena L. Pichugina, Alan W. Brewer, Sunil Baidar, Robert Banta, Edward Strobach, Brandi McCarty, Brian Carroll, Nicola Bodini, Stefano Letizia, Richard Marchbanks, Michael Zucker, Maxwell Holloway, and Patrick Moriarty
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-79, https://doi.org/10.5194/wes-2025-79, 2025
Preprint under review for WES
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The truck-based Doppler lidar system was used during the American Wake Experiment (AWAKEN) to obtain the high-frequency, simultaneous measurements of the horizontal wind speed, direction, and vertical-velocity from a moving platform. The paper presents the unique capability of the novel lidar system to characterize the temporal, vertical, and spatial variability of winds at various distances from operating turbines and obtain quantitative estimates of wind speed reduction in the waked flow.
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.
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
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.
Ye Liu, Timothy W. Juliano, Raghavendra Krishnamurthy, Brian J. Gaudet, and Jungmin Lee
Wind Energ. Sci., 10, 483–495, https://doi.org/10.5194/wes-10-483-2025, https://doi.org/10.5194/wes-10-483-2025, 2025
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Our study reveals how different weather patterns influence wind conditions off the US West Coast. We identified key weather patterns affecting wind speeds at potential wind farm sites using advanced machine learning. This research helps improve weather prediction models, making wind energy production more reliable and efficient.
Raghavendra Krishnamurthy, Rob K. Newsom, Colleen M. Kaul, Stefano Letizia, Mikhail Pekour, Nicholas Hamilton, Duli Chand, Donna Flynn, Nicola Bodini, and Patrick Moriarty
Wind Energ. Sci., 10, 361–380, https://doi.org/10.5194/wes-10-361-2025, https://doi.org/10.5194/wes-10-361-2025, 2025
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This study examines how atmospheric phenomena affect the recovery of wind farm wake – the disturbed air behind turbines. In regions like Oklahoma, where wind farms are often clustered, understanding wake recovery is crucial. We found that wind farms can alter phenomena like low-level jets, which are common in Oklahoma, by deflecting them above the wind farm. As a result, the impact of wakes can be observed up to 1–2 km above ground level.
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
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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
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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.
Pedro Angel Jimenez y Munoz, Maria Frediani, Masih Eghdami, Daniel Rosen, Michael Kavulich, and Timothy W. Juliano
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-124, https://doi.org/10.5194/gmd-2024-124, 2024
Revised manuscript under review for GMD
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We present the Community Fire Behavior model (CFBM) a fire behavior model designed to facilitate coupling to atmospheric models. We describe its implementation in the Unified Forecast System (UFS). Simulations of the Cameron Peak ire allowed us to verify our implementation. Our vision is to foster collaborative development in fire behavior modeling with the ultimate goal of increasing our fundamental understanding of fire science and minimizing the adverse impacts of wildland fires.
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
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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.
Lindsay M. Sheridan, Raghavendra Krishnamurthy, William I. Gustafson Jr., Ye Liu, Brian J. Gaudet, Nicola Bodini, Rob K. Newsom, and Mikhail Pekour
Wind Energ. Sci., 9, 741–758, https://doi.org/10.5194/wes-9-741-2024, https://doi.org/10.5194/wes-9-741-2024, 2024
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In 2020, lidar-mounted buoys owned by the US Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas and provided valuable year-long analyses of offshore low-level jet (LLJ) characteristics at heights relevant to wind turbines. In addition to the LLJ climatology, this work provides validation of LLJ representation in atmospheric models that are essential for assessing the potential energy yield of offshore wind farms.
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
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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.
Timothy W. Juliano, Fernando Szasdi-Bardales, Neil P. Lareau, Kasra Shamsaei, Branko Kosović, Negar Elhami-Khorasani, Eric P. James, and Hamed Ebrahimian
Nat. Hazards Earth Syst. Sci., 24, 47–52, https://doi.org/10.5194/nhess-24-47-2024, https://doi.org/10.5194/nhess-24-47-2024, 2024
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Following the destructive Lahaina Fire in Hawaii, our team has modeled the wind and fire spread processes to understand the drivers of this devastating event. The simulation results show that extreme winds with high variability, a fire ignition close to the community, and construction characteristics led to continued fire spread in multiple directions. Our results suggest that available modeling capabilities can provide vital information to guide decision-making during wildfire events.
Tasnim Zaman, Timothy Juliano, Pat Hawbecker, and Marina Astitha
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-148, https://doi.org/10.5194/wes-2023-148, 2024
Preprint withdrawn
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We suggest a model configuration to predict offshore wind speed and wind power density in the Northeast US. We focused on wind droughts, long periods of low wind speed that affect the reliability of wind power generation. We show that wind prediction depends primarily on the initial and boundary conditions, and that it is important to evaluate the connection of wind speed to wind power generation, to select the best model configuration.
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.
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.
Nicola Bodini, Simon Castagneri, and Mike Optis
Wind Energ. Sci., 8, 607–620, https://doi.org/10.5194/wes-8-607-2023, https://doi.org/10.5194/wes-8-607-2023, 2023
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The National Renewable Energy Laboratory (NREL) has published updated maps of the wind resource along all US coasts. Given the upcoming offshore wind development, it is essential to quantify the uncertainty that comes with the modeled wind resource data set. The paper proposes a novel approach to quantify this numerical uncertainty by leveraging available observations along the US East Coast.
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.
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.
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.
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.
Nicola Bodini, Weiming Hu, Mike Optis, Guido Cervone, and Stefano Alessandrini
Wind Energ. Sci., 6, 1363–1377, https://doi.org/10.5194/wes-6-1363-2021, https://doi.org/10.5194/wes-6-1363-2021, 2021
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We develop two machine-learning-based approaches to temporally extrapolate uncertainty in hub-height wind speed modeled by a numerical weather prediction model. We test our approaches in the California Outer Continental Shelf, where a significant offshore wind energy development is currently being planned, and we find that both provide accurate results.
Mithu Debnath, Paula Doubrawa, Mike Optis, Patrick Hawbecker, and Nicola Bodini
Wind Energ. Sci., 6, 1043–1059, https://doi.org/10.5194/wes-6-1043-2021, https://doi.org/10.5194/wes-6-1043-2021, 2021
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As the offshore wind industry emerges on the US East Coast, a comprehensive understanding of the wind resource – particularly extreme events – is vital to the industry's success. We leverage a year of data of two floating lidars to quantify and characterize the frequent occurrence of high-wind-shear and low-level-jet events, both of which will have a considerable impact on turbine operation. We find that almost 100 independent long events occur throughout the year.
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.
Mike Optis, Nicola Bodini, Mithu Debnath, and Paula Doubrawa
Wind Energ. Sci., 6, 935–948, https://doi.org/10.5194/wes-6-935-2021, https://doi.org/10.5194/wes-6-935-2021, 2021
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Offshore wind turbines are huge, with rotor blades soon to extend up to nearly 300 m. Accurate modeling of winds across these heights is crucial for accurate estimates of energy production. However, we lack sufficient observations at these heights but have plenty of near-surface observations. Here we show that a basic machine-learning model can provide very accurate estimates of winds in this area, and much better than conventional techniques.
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.
Nicola Bodini and Mike Optis
Wind Energ. Sci., 5, 1435–1448, https://doi.org/10.5194/wes-5-1435-2020, https://doi.org/10.5194/wes-5-1435-2020, 2020
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Calculations of annual energy production (AEP) and its uncertainty are critical for wind farm financial transactions. Standard industry practice assumes that different uncertainty categories within an AEP calculation are uncorrelated and can therefore be combined through a sum of squares approach. In this project, we show the limits of this assumption by performing operational AEP estimates for over 470 wind farms in the United States and propose a more accurate way to combine uncertainties.
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
Archer, C. L., Colle, B. A., Monache, L. D., Dvorak, M. J., Lundquist, J.,
Bailey, B. H., Beaucage, P., Churchfield, M. J., Fitch, A. C., Kosovic, B.,
Lee, S., Moriarty, P. J., Simao, H., Stevens, R. J. A. M., Veron, D., and
Zack, J.: Meteorology for Coastal/Offshore Wind Energy in the United States: Recommendations and Research Needs for the Next 10 Years, B. Am. Meteorol. Soc., 95, 515–519, https://doi.org/10.1175/BAMS-D-13-00108.1, 2014. a, b
Archer, C. L., Wu, S., Vasel-Be-Hagh, A., Brodie, J. F., Delgado, R.,
St. Pé, A., Oncley, S., and Semmer, S.: The VERTEX Field Campaign:
Observations of near-Ground Effects of Wind Turbine Wakes, J. Turbulence, 20, 64–92, https://doi.org/10.1080/14685248.2019.1572161, 2019. a
Beiter, P., Musial, W., Duffy, P., Cooperman, A., Shields, M., Heimiller, D.,
and Optis, M.: The Cost of Floating Offshore Wind Energy in California Between 2019 and 2032, Tech. Rep. NREL/TP-5000-77384, NREL – National Renewable Energy Lab., Golden, CO, USA, https://doi.org/10.2172/1710181, 2020. a
Bodini, N., Hu, W., Optis, M., Cervone, G., and Alessandrini, S.: Assessing
Boundary Condition and Parametric Uncertainty in Numerical-Weather-Prediction-Modeled, Long-Term Offshore Wind Speed through
Machine Learning and Analog Ensemble, Wind Energ. Sci., 6, 1363–1377,
https://doi.org/10.5194/wes-6-1363-2021, 2021a. a
Bodini, N., Lundquist, J. K., and Moriarty, P.: Wind Plants Can Impact
Long-Term Local Atmospheric Conditions, Scient. Rep., 11, 22939,
https://doi.org/10.1038/s41598-021-02089-2, 2021b. a
BOEM: Renewable Energy GIS Data|Bureau of Ocean Energy
Management,
https://www.boem.gov/renewable-energy/mapping-and-data/renewable-energy-gis-data
(last access: 31 October 2021), 2020. a
Brower, M., Bernadett, D. W., Elsholz, K. V., Filippelli, M. V., Markus, M. J., Taylor, M. A., and Tensen, J.: Wind Resource Assessment: A Practical
Guide to Developing a Wind Project, John Wiley & Sons, Incorporated, Somerset, USA, ISBN 978-1-118-02232-0, 2012. a
Carvalho, D., Rocha, A., Gómez-Gesteira, M., and Santos, C.: A
Sensitivity Study of the WRF Model in Wind Simulation for an Area of High
Wind Energy, Environ. Model. Softw., 33, 23–34,
https://doi.org/10.1016/j.envsoft.2012.01.019, 2012. a
Carvalho, D., Rocha, A., Gómez-Gesteira, M., and Silva Santos, C.:
Offshore Wind Energy Resource Simulation Forced by Different Reanalyses:
Comparison with Observed Data in the Iberian Peninsula, Appl. Energy, 134, 57–64, https://doi.org/10.1016/j.apenergy.2014.08.018, 2014. a
Draxl, C., Hahmann, A. N., Peña, A., and Giebel, G.: Evaluating Winds and
Vertical Wind Shear from Weather Research and Forecasting Model Forecasts Using Seven Planetary Boundary Layer Schemes, Wind Energy, 17, 39–55, https://doi.org/10.1002/we.1555, 2014. a, b
Fernández-González, S., Martín, M. L., García-Ortega, E.,
Merino, A., Lorenzana, J., Sánchez, J. L., Valero, F., and Rodrigo, J. S.: Sensitivity Analysis of the WRF Model: Wind-Resource Assessment for Complex Terrain, J. Appl. Meteorol. Clim., 57, 733–753, https://doi.org/10.1175/JAMC-D-17-0121.1, 2018. a
Fischereit, J., Brown, R., Larsén, X. G., Badger, J., and Hawkes, G.:
Review of Mesoscale Wind-Farm Parametrizations and Their Applications, Bound.-Lay. Meteorol., 182, 175–224, https://doi.org/10.1007/s10546-021-00652-y, 2022. a, b, c
Gupta, T. and Baidya Roy, S.: Recovery Processes in a Large Offshore Wind Farm, Wind Energ. Sci., 6, 1089–1106, https://doi.org/10.5194/wes-6-1089-2021, 2021. a
Hansen, K. S., Barthelmie, R. J., Jensen, L. E., and Sommer, A.: The Impact of Turbulence Intensity and Atmospheric Stability on Power Deficits Due to Wind Turbine Wakes at Horns Rev Wind Farm, Wind Energy, 15, 183–196,
https://doi.org/10.1002/we.512, 2012. a
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R.,
Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P.,
Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant,
T. E.: Array Programming with NumPy, Nature, 585, 357–362,
https://doi.org/10.1038/s41586-020-2649-2, 2020. a
Haupt, S. E., Berg, L. K., Decastro, A., Gagne, D. J., Jimenez, P., Juliano,
T., Kosovic, B., Quon, E., Shaw, W. J., Churchfield, M. J., Draxl, C.,
Hawbecker, P., Jonko, A., Kaul, C. M., Mirocha, J. D., and Rai, R. K.:
Outcomes of the DOE Workshop on Atmospheric Challenges for the Wind
Energy Industry, Tech. Rep. PNNL-30828, PNNL – Pacific Northwest National Lab., Richland, WA, USA, https://doi.org/10.2172/1762812, 2020. a
Hong, S.-Y., Noh, Y., and Dudhia, J.: A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes, Mon. Weather Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1, 2006. a
Hoyer, S. and Hamman, J.: Xarray: N-D Labeled Arrays and Datasets in Python, J. Open Res. Softw., 5, 10, https://doi.org/10.5334/jors.148, 2017. a
Hunter, J. D.: Matplotlib: A 2D Graphics Environment, Comput. Sci. Eng., 9, 90–95, https://doi.org/10.1109/MCSE.2007.55, 2007. a
Janjić, Z. I.: The Step-Mountain Eta Coordinate Model: Further
Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes, Mon. Weather Rev., 122, 927–945,
https://doi.org/10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2, 1994. a
Juliano, T. W., Kosović, B., Jiménez, P. A., Eghdami, M., Haupt, S. E., and Martilli, A.: “Gray Zone” Simulations Using a Three-Dimensional Planetary Boundary Layer Parameterization in the Weather Research and Forecasting Model, Mon. Weather Rev., 150, 1585–1619, https://doi.org/10.1175/MWR-D-21-0164.1, 2022. a, b
Kosović, B., Munoz, P. J., Juliano, T. W., Martilli, A., Eghdami, M.,
Barros, A. P., and Haupt, S. E.: Three-Dimensional Planetary Boundary Layer
Parameterization for High-Resolution Mesoscale Simulations, J. Phys.: Conf. Ser., 1452, 012080, https://doi.org/10.1088/1742-6596/1452/1/012080, 2020. a, b
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
Lee, J. C. Y. and Fields, M. J.: An Overview of Wind-Energy-Production
Prediction Bias, Losses, and Uncertainties, Wind Energ. Sci., 6, 311–365,
https://doi.org/10.5194/wes-6-311-2021, 2021. a, b
Mangara, R. J., Guo, Z., and Li, S.: Performance of the Wind Farm
Parameterization Scheme Coupled with the Weather Research and Forecasting Model under Multiple Resolution Regimes for Simulating an Onshore Wind Farm, Adv. Atmos. Sci., 36, 119–132, https://doi.org/10.1007/s00376-018-8028-3, 2019. a
Mellor, G. L. and Yamada, T.: A Hierarchy of Turbulence Closure Models for Planetary Boundary Layers, J. Atmos. Sci., 31, 1791–1806, https://doi.org/10.1175/1520-0469(1974)031<1791:AHOTCM>2.0.CO;2, 1974. a
Mellor, G. L. and Yamada, T.: Development of a Turbulence Closure Model for
Geophysical Fluid Problems, Rev. Geophysics, 20, 851–875,
https://doi.org/10.1029/RG020i004p00851, 1982. a, b, c, d
Met Office: Cartopy: A Cartographic Python Library with a Matplotlib
Interface, Met Office, Exeter, Devon, http://scitools.org.uk/cartopy (last access: 15 October 2022), 2010. a
Miles, A., Jakirkham, Bussonnier, M., Moore, J., Fulton, A., Bourbeau, J., Onalan, T., Hamman, J., Patel, Z., Rocklin, M., de Andrade, E. S., Lee, G. R., Abernathey, R., Bennett, D., Durant, M., Schut, V., Dussin, R., Barnes, C., Williams, B., Noyes, C., Shikharsg, Jelenak, A., Banihirwe, A., Baddeley, D., Younkin, E., Sakkis, G., Hunt-Isaak, I., Funke, J., and Kelleher, J.: Zarr-Developers/Zarr-Python, Zenodo [code], https://doi.org/10.5281/zenodo.5579625, 2021. a
Nakanishi, M. and Niino, H.: Development of an Improved Turbulence Closure
Model for the Atmospheric Boundary Layer, J. Meteorol. Soc. Jpn. Ser. II, 87, 895–912, https://doi.org/10.2151/jmsj.87.895, 2009. a, b, c, d
Nygaard, N. G. and Hansen, S. D.: Wake Effects between Two Neighbouring Wind
Farms, J. Phys.: Conf. Ser., 753, 032020, https://doi.org/10.1088/1742-6596/753/3/032020, 2016. a
Olsen, B. T., Hahmann, A. N., Sempreviva, A. M., Badger, J., and Jørgensen, H. E.: An Intercomparison of Mesoscale Models at Simple Sites for Wind Energy Applications, Wind Energ. Sci., 2, 211–228, https://doi.org/10.5194/wes-2-211-2017, 2017. a, b
Olson, J. B., Kenyon, J. S., Angevine, W. A., Brown, J. M., Pagowski, M., and
Sušelj, K.: A Description of the MYNN-EDMF Scheme and the Coupling to Other Components in WRF–ARW, in: NOAA Technical Memorandum OAR GSD, 61,
NOAA, https://doi.org/10.25923/N9WM-BE49, 2019. a
Optis, M., Rybchuk, O., Bodini, N., Rossol, M., and Musial, W.: Offshore Wind
Resource Assessment for the California Pacific Outer Continental Shelf (2020), Tech. Rep. NREL/TP-5000-77642, NREL – National Renewable Energy Lab., Golden, CO, USA, https://doi.org/10.2172/1677466, 2020. a
Pan, Y. and Archer, C. L.: A Hybrid Wind-Farm Parametrization for Mesoscale and Climate Models, Bound.-Lay. Meteorol., 168, 469–495, https://doi.org/10.1007/s10546-018-0351-9, 2018. a
Pleim, J. E.: A Combined Local and Nonlocal Closure Model for the Atmospheric Boundary Layer. Part I: Model Description and Testing, J. Appl. Meteorol. Clim., 46, 1383–1395, https://doi.org/10.1175/JAM2539.1, 2007. a
Pryor, S. C., Shepherd, T. J., Volker, P. J. H., Hahmann, A. N., and
Barthelmie, R. J.: “Wind Theft” from Onshore Wind Turbine Arrays:
Sensitivity to Wind Farm Parameterization and Resolution, J. Appl. Meteorol. Clim., 59, 153–174, https://doi.org/10.1175/JAMC-D-19-0235.1, 2020. a
Redfern, S., Olson, J. B., Lundquist, J. K., and Clack, C. T. M.: Incorporation of the Rotor-Equivalent Wind Speed into the Weather Research and Forecasting Model's Wind Farm Parameterization, Mon. Weather Rev., 147, 1029–1046, https://doi.org/10.1175/MWR-D-18-0194.1, 2019. a
Rocklin, M.: Dask: Parallel Computation with Blocked Algorithms and Task
Scheduling, in: Proceedings of the 14th Python in Science Conference, vol. 130, Citeseer, p. 136, 2015. a
Roubeyrie, L. and Celles, S.: Windrose: A Python Matplotlib, Numpy Library to Manage Wind and Pollution Data, Draw Windrose, J. Open Source Softw., 3, 268, https://doi.org/10.21105/joss.00268, 2018. a
Rybchuk, A., Juliano, T. W. Lundquist, J. K., Rosencrans, D., Bodini, N., and Optis, M.: Supporting Material for The Sensitivity of the Fitch Wind Farm Parameterization to a Three-Dimensional Planetary Boundary Layer Scheme, Zenodo [code and data set], https://doi.org/10.5281/zenodo.5565399, 2021. a
Sanchez Gomez, M., Lundquist, J. K., Mirocha, J. D., Arthur, R. S., and Muñoz-Esparza, D.: Quantifying wind plant blockage under stable atmospheric conditions, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2021-57, 2021. a
Schneemann, J., Theuer, F., Rott, A., Dörenkämper, M., and Kühn,
M.: Offshore Wind Farm Global Blockage Measured with Scanning Lidar, Wind
Energ. Sci., 6, 521–538, https://doi.org/10.5194/wes-6-521-2021, 2021. a
Shaw, W. J., Draxl, C., Mirocha, J. D., Muradyan, P., Ghate, V. P., Optis, M., and Lemke, A.: Workshop on Research Needs for Offshore Wind Resource
Characterization: Summary Report, Tech. rep., PNNL – Pacific Northwest National Lab., Richland, WA, USA, https://www.energy.gov/eere/wind/downloads/workshop-research-needs-offshore-wind-resource-characterization (last access: 13 October 2022), 2019. a
Shepherd, T. J., Barthelmie, R. J., and Pryor, S. C.: Sensitivity of Wind
Turbine Array Downstream Effects to the Parameterization Used in WRF, J. Appl. Meteorol. Clim., 59, 333–361, https://doi.org/10.1175/JAMC-D-19-0135.1, 2020. a
Siedersleben, S. K., Platis, A., Lundquist, J. K., Djath, B., Lampert, A.,
Bärfuss, K., Cañadillas, B., Schulz-Stellenfleth, J., Bange, J.,
Neumann, T., and Emeis, S.: Turbulent Kinetic Energy over Large Offshore Wind
Farms Observed and Simulated by the Mesoscale Model WRF (3.8.1), Geosci. Model Dev., 13, 249–268, https://doi.org/10.5194/gmd-13-249-2020, 2020. a, b
Storm, B. and Basu, S.: The WRF Model Forecast-Derived Low-Level Wind Shear
Climatology over the United States Great Plains, Energies, 3, 258–276,
https://doi.org/10.3390/en3020258, 2010. a
Tomaszewski, J. M. and Lundquist, J. K.: Simulated Wind Farm Wake Sensitivity
to Configuration Choices in the Weather Research and Forecasting
Model Version 3.8.1, Geosci. Model Dev., 13, 2645–2662,
https://doi.org/10.5194/gmd-13-2645-2020, 2020. a, b, c
Vanderwende, B. J., Kosović, B., Lundquist, J. K., and Mirocha, J. D.:
Simulating Effects of a Wind-Turbine Array Using LES and RANS, J. Adv. Model. Earth Syst., 8, 1376–1390, https://doi.org/10.1002/2016MS000652, 2016. a
Volker, P. J. H., Badger, J., Hahmann, A. N., and Ott, S.: The Explicit Wake
Parametrisation V1.0: A Wind Farm Parametrisation in the Mesoscale Model WRF, Geosci. Model Dev., 8, 3715–3731, https://doi.org/10.5194/gmd-8-3715-2015, 2015. a
White House: Fact Sheet: Biden Administration Jumpstarts Offshore Wind
Energy Projects to Create Jobs,
https://www.whitehouse.gov/briefing-room/statements-releases/2021/03/29/fact-sheet-biden-administration-jumpstarts-offshore-wind-energy
(last access: 13 October 2022), 2021. a
Yang, B., Qian, Y., Berg, L. K., Ma, P.-L., Wharton, S., Bulaevskaya, V., Yan, H., Hou, Z., and Shaw, W. J.: Sensitivity of Turbine-Height Wind Speeds
to Parameters in Planetary Boundary-Layer and Surface-Layer Schemes in the Weather Research and Forecasting Model, Bound.-Lay. Meteorol., 162, 117–142, https://doi.org/10.1007/s10546-016-0185-2, 2017. a
Yang, B., Berg, L. K., Qian, Y., Wang, C., Hou, Z., Liu, Y., Shin, H. H., Hong, S., and Pekour, M.: Parametric and Structural Sensitivities of
Turbine-Height Wind Speeds in the Boundary Layer Parameterizations in the Weather Research and Forecasting Model, J. Geophys. Res.-Atmos., 124, 5951–5969, https://doi.org/10.1029/2018JD029691, 2019. a
Yang, Q., Berg, L. K., Pekour, M., Fast, J. D., Newsom, R. K., Stoelinga, M.,
and Finley, C.: Evaluation of WRF-Predicted Near-Hub-Height Winds and Ramp Events over a Pacific Northwest Site with Complex Terrain, J. Appl. Meteorol. Clim., 52, 1753–1763, https://doi.org/10.1175/JAMC-D-12-0267.1, 2013. a
Zhang, X., Bao, J.-W., Chen, B., and Grell, E. D.: A Three-Dimensional
Scale-Adaptive Turbulent Kinetic Energy Scheme in the WRF-ARW Model, Mon. Weather Rev., 146, 2023–2045, https://doi.org/10.1175/MWR-D-17-0356.1, 2018. a
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.
Numerical weather prediction models are used to predict how wind turbines will interact with the...
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