Articles | Volume 10, issue 2
https://doi.org/10.5194/wes-10-361-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-361-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Feb 2025
Research article |
| 04 Feb 2025
| 04 Feb 2025
Observations of wind farm wake recovery at an operating wind farm
Pacific Northwest National Laboratory, Richland, WA 99352, United States of America
Rob K. Newsom
Pacific Northwest National Laboratory, Richland, WA 99352, United States of America
Colleen M. Kaul
Pacific Northwest National Laboratory, Richland, WA 99352, United States of America
Stefano Letizia
National Renewable Energy Laboratory, Golden, CO 80401, United States of America
Mikhail Pekour
Pacific Northwest National Laboratory, Richland, WA 99352, United States of America
Nicholas Hamilton
National Renewable Energy Laboratory, Golden, CO 80401, United States of America
Duli Chand
Pacific Northwest National Laboratory, Richland, WA 99352, United States of America
Donna Flynn
Pacific Northwest National Laboratory, Richland, WA 99352, United States of America
Nicola Bodini
National Renewable Energy Laboratory, Golden, CO 80401, United States of America
Patrick Moriarty
National Renewable Energy Laboratory, Golden, CO 80401, United States of America
Related authors
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
Short summary
Short summary
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.
Damao Zhang, Jennifer Comstock, Chitra Sivaraman, Kefei Mo, Raghavendra Krishnamurthy, Jingjing Tian, Tianning Su, Zhanqing Li, and Natalia Roldán-Henao
EGUsphere, https://doi.org/10.5194/egusphere-2024-3959, https://doi.org/10.5194/egusphere-2024-3959, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Planetary boundary layer height (PBLHT) is an important parameter in atmospheric process studies and numerical model simulations. We use machine learning methods to produce a best-estimate planetary boundary layer height (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements. We demonstrated that PBLHT-BE-ML greatly improved the comparisons against sounding-derived PBLHT.
Aliza Abraham, Matteo Puccioni, Arianna Jordan, Emina Maric, Nicola Bodini, Nicholas Hamilton, Stefano Letizia, Petra M. Klein, Elizabeth Smith, Sonia Wharton, Jonathan Gero, Jamey D. Jacob, Raghavendra Krishnamurthy, Rob K. Newsom, Mikhail Pekour, and Patrick Moriarty
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-148, https://doi.org/10.5194/wes-2024-148, 2024
Preprint under review for WES
Short summary
Short summary
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.
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. Discuss., https://doi.org/10.5194/wes-2024-84, https://doi.org/10.5194/wes-2024-84, 2024
Revised manuscript accepted for WES
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.
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.
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
Short summary
Short summary
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.
Raghavendra Krishnamurthy, Gabriel García Medina, Brian Gaudet, William I. Gustafson Jr., Evgueni I. Kassianov, Jinliang Liu, Rob K. Newsom, Lindsay M. Sheridan, and Alicia M. Mahon
Earth Syst. Sci. Data, 15, 5667–5699, https://doi.org/10.5194/essd-15-5667-2023, https://doi.org/10.5194/essd-15-5667-2023, 2023
Short summary
Short summary
Our understanding and ability to observe and model air–sea processes has been identified as a principal limitation to our ability to predict future weather. Few observations exist offshore along the coast of California. To improve our understanding of the air–sea transition zone and support the wind energy industry, two buoys with state-of-the-art equipment were deployed for 1 year. In this article, we present details of the post-processing, algorithms, and analyses.
Sheng-Lun Tai, Larry K. Berg, Raghavendra Krishnamurthy, Rob Newsom, and Anthony Kirincich
Wind Energ. Sci., 8, 433–448, https://doi.org/10.5194/wes-8-433-2023, https://doi.org/10.5194/wes-8-433-2023, 2023
Short summary
Short summary
Turbulence intensity is critical for wind turbine design and operation as it affects wind power generation efficiency. Turbulence measurements in the marine environment are limited. We use a model to derive turbulence intensity and test how sea surface temperature data may impact the simulated turbulence intensity and atmospheric stability. The model slightly underestimates turbulence, and improved sea surface temperature data reduce the bias. Error with unrealistic mesoscale flow is identified.
Lindsay M. Sheridan, Raghu Krishnamurthy, Gabriel García Medina, Brian J. Gaudet, William I. Gustafson Jr., Alicia M. Mahon, William J. Shaw, Rob K. Newsom, Mikhail Pekour, and Zhaoqing Yang
Wind Energ. Sci., 7, 2059–2084, https://doi.org/10.5194/wes-7-2059-2022, https://doi.org/10.5194/wes-7-2059-2022, 2022
Short summary
Short summary
Using observations from lidar buoys, five reanalysis and analysis models that support the wind energy community are validated offshore and at rotor-level heights along the California Pacific coast. The models are found to underestimate the observed wind resource. Occasions of large model error occur in conjunction with stable atmospheric conditions, wind speeds associated with peak turbine power production, and mischaracterization of the diurnal wind speed cycle in summer months.
Raghavendra Krishnamurthy, Rob K. Newsom, Larry K. Berg, Heng Xiao, Po-Lun Ma, and David D. Turner
Atmos. Meas. Tech., 14, 4403–4424, https://doi.org/10.5194/amt-14-4403-2021, https://doi.org/10.5194/amt-14-4403-2021, 2021
Short summary
Short summary
Planetary boundary layer (PBL) height is a critical parameter in atmospheric models. Continuous PBL height measurements from remote sensing measurements are important to understand various boundary layer mechanisms, especially during daytime and evening transition periods. Due to several limitations in existing methodologies to detect PBL height from a Doppler lidar, in this study, a machine learning (ML) approach is tested. The ML model is observed to improve the accuracy by over 50 %.
Daniel Vassallo, Raghavendra Krishnamurthy, and Harindra J. S. Fernando
Wind Energ. Sci., 6, 295–309, https://doi.org/10.5194/wes-6-295-2021, https://doi.org/10.5194/wes-6-295-2021, 2021
Short summary
Short summary
Machine learning is quickly becoming a commonly used technique for wind speed and power forecasting and is especially useful when combined with other forecasting techniques. This study utilizes a popular machine learning algorithm, random forest, in an attempt to predict the forecasting error of a statistical forecasting model. Various atmospheric characteristics are used as random forest inputs in an effort to discern the most useful atmospheric information for this purpose.
Daniel Vassallo, Raghavendra Krishnamurthy, and Harindra J. S. Fernando
Wind Energ. Sci., 5, 959–975, https://doi.org/10.5194/wes-5-959-2020, https://doi.org/10.5194/wes-5-959-2020, 2020
Short summary
Short summary
Model error and uncertainty is a challenge in the wind energy industry, potentially leading to mischaracterization of millions of dollars' worth of wind resource. This paper combines meteorological knowledge with machine learning techniques, specifically artificial neural networks (ANNs), to better extrapolate wind speeds. It is found that ANNs can reduce power-law extrapolation error by up to 52 % while simultaneously reducing uncertainty. A test case is shown to help decipher the ANN results.
Laura Bianco, Irina V. Djalalova, James M. Wilczak, Joseph B. Olson, Jaymes S. Kenyon, Aditya Choukulkar, Larry K. Berg, Harindra J. S. Fernando, Eric P. Grimit, Raghavendra Krishnamurthy, Julie K. Lundquist, Paytsar Muradyan, Mikhail Pekour, Yelena Pichugina, Mark T. Stoelinga, and David D. Turner
Geosci. Model Dev., 12, 4803–4821, https://doi.org/10.5194/gmd-12-4803-2019, https://doi.org/10.5194/gmd-12-4803-2019, 2019
Short summary
Short summary
During the second Wind Forecast Improvement Project, improvements to the parameterizations were applied to the High Resolution Rapid Refresh model and its nested version. The impacts of the new parameterizations on the forecast of 80 m wind speeds and power are assessed, using sodars and profiling lidars observations for comparison. Improvements are evaluated as a function of the model’s initialization time, forecast horizon, time of the day, season, site elevation, and meteorological phenomena.
Nicola Bodini, Julie K. Lundquist, Raghavendra Krishnamurthy, Mikhail Pekour, Larry K. Berg, and Aditya Choukulkar
Atmos. Chem. Phys., 19, 4367–4382, https://doi.org/10.5194/acp-19-4367-2019, https://doi.org/10.5194/acp-19-4367-2019, 2019
Short summary
Short summary
To improve the parameterization of the turbulence dissipation rate (ε) in numerical weather prediction models, we have assessed its temporal and spatial variability at various scales in the Columbia River Gorge during the WFIP2 field experiment. The turbulence dissipation rate shows large spatial variability, even at the microscale, with larger values in sites located downwind of complex orographic structures or in wind farm wakes. Distinct diurnal and seasonal cycles in ε have also been found.
Pietro Bortolotti, Lee Jay Fingersh, Nicholas Hamilton, Arlinda Huskey, Chris Ivanov, Mark Iverson, Jonathan Keller, Scott Lambert, Jason Roadman, Derek Slaughter, Syhoune Thao, and Consuelo Wells
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-8, https://doi.org/10.5194/wes-2025-8, 2025
Preprint under review for WES
Short summary
Short summary
This study compares a wind turbine with blades behind the tower (downwind) to the traditional upwind design. Testing a 1.5 MW turbine at NREL’s Flatirons Campus, we measured performance, loads, and noise. Numerical models matched well with observations. The downwind setup showed higher fatigue loads and sound variations but also an unexpected power improvement. Downwind rotors might be a valid alternative for future floating offshore wind applications.
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
Short summary
Short summary
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.
Damao Zhang, Jennifer Comstock, Chitra Sivaraman, Kefei Mo, Raghavendra Krishnamurthy, Jingjing Tian, Tianning Su, Zhanqing Li, and Natalia Roldán-Henao
EGUsphere, https://doi.org/10.5194/egusphere-2024-3959, https://doi.org/10.5194/egusphere-2024-3959, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Planetary boundary layer height (PBLHT) is an important parameter in atmospheric process studies and numerical model simulations. We use machine learning methods to produce a best-estimate planetary boundary layer height (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements. We demonstrated that PBLHT-BE-ML greatly improved the comparisons against sounding-derived PBLHT.
Gopal R. Yalla, Kenneth Brown, Lawrence Cheung, Dan Houck, Nathaniel deVelder, and Nicholas Hamilton
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-14, https://doi.org/10.5194/wes-2025-14, 2025
Preprint under review for WES
Short summary
Short summary
When wind reaches the first set of turbines in a wind farm, energy is extracted, reducing the energy available for downstream turbines. This study examines emerging technologies aimed at re-energizing the wind between turbines in a wind farm to improve overall power production. Optimizing these technologies depends on understanding complex features of the atmosphere and the wakes behind turbines, which is accomplished using high fidelity computer simulations and data analysis techniques.
Sheng-Lun Tai, Zhao Yang, Brian Gaudet, Koichi Sakaguchi, Larry Berg, Colleen Kaul, Yun Qian, Ye Liu, and Jerome Fast
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-599, https://doi.org/10.5194/essd-2024-599, 2025
Preprint under review for ESSD
Short summary
Short summary
Our study created a high-resolution soil moisture dataset for the eastern U.S. by integrating satellite data with a land surface model and advanced algorithms, achieving 1-km scale analyses. Validated against multiple networks and datasets, it demonstrated superior accuracy. This dataset is vital for understanding soil moisture dynamics, especially during droughts, and highlights the need for improved modeling of clay soils to refine future predictions.
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.
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
Preprint under review for WES
Short summary
Short summary
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.
Jerome D. Fast, Adam C. Varble, Fan Mei, Mikhail Pekour, Jason Tomlinson, Alla Zelenyuk, Art J. Sedlacek III, Maria Zawadowicz, and Louisa Emmons
Atmos. Chem. Phys., 24, 13477–13502, https://doi.org/10.5194/acp-24-13477-2024, https://doi.org/10.5194/acp-24-13477-2024, 2024
Short summary
Short summary
Aerosol property measurements recently collected on the ground and by a research aircraft in central Argentina during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign exhibit large spatial and temporal variability. These measurements coupled with coincident meteorological information provide a valuable data set needed to evaluate and improve model predictions of aerosols in a traditionally data-sparse region of South America.
Fan Mei, Jennifer M. Comstock, Mikhail S. Pekour, Jerome D. Fast, Krista L. Gaustad, Beat Schmid, Shuaiqi Tang, Damao Zhang, John E. Shilling, Jason M. Tomlinson, Adam C. Varble, Jian Wang, L. Ruby Leung, Lawrence Kleinman, Scot Martin, Sebastien C. Biraud, Brian D. Ermold, and Kenneth W. Burk
Earth Syst. Sci. Data, 16, 5429–5448, https://doi.org/10.5194/essd-16-5429-2024, https://doi.org/10.5194/essd-16-5429-2024, 2024
Short summary
Short summary
Our study explores a comprehensive dataset from airborne field studies (2013–2018) conducted using the US Department of Energy's Gulfstream 1 (G-1). The 236 flights span diverse regions, including the Arctic, US Southern Great Plains, US West Coast, eastern North Atlantic, Amazon Basin in Brazil, and Sierras de Córdoba range in Argentina. This dataset provides unique insights into atmospheric dynamics, aerosols, and clouds and makes data available in a more accessible format.
Aliza Abraham, Matteo Puccioni, Arianna Jordan, Emina Maric, Nicola Bodini, Nicholas Hamilton, Stefano Letizia, Petra M. Klein, Elizabeth Smith, Sonia Wharton, Jonathan Gero, Jamey D. Jacob, Raghavendra Krishnamurthy, Rob K. Newsom, Mikhail Pekour, and Patrick Moriarty
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-148, https://doi.org/10.5194/wes-2024-148, 2024
Preprint under review for WES
Short summary
Short summary
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.
Majid Bastankhah, Marcus Becker, Matthew Churchfield, Caroline Draxl, Jay Prakash Goit, Mehtab Khan, Luis A. Martinez Tossas, Johan Meyers, Patrick Moriarty, Wim Munters, Asim Önder, Sara Porchetta, Eliot Quon, Ishaan Sood, Nicole van Lipzig, Jan-Willem van Wingerden, Paul Veers, and Simon Watson
Wind Energ. Sci., 9, 2171–2174, https://doi.org/10.5194/wes-9-2171-2024, https://doi.org/10.5194/wes-9-2171-2024, 2024
Short summary
Short summary
Dries Allaerts was born on 19 May 1989 and passed away at his home in Wezemaal, Belgium, on 10 October 2024 after battling cancer. Dries started his wind energy career in 2012 and had a profound impact afterward on the community, in terms of both his scientific realizations and his many friendships and collaborations in the field. His scientific acumen, open spirit of collaboration, positive attitude towards life, and playful and often cheeky sense of humor will be deeply missed by many.
Fan Mei, Qi Zhang, Damao Zhang, Jerome Fast, Gourihar Kulkarni, Mikhail Pekour, Christopher Niedek, Susanne Glienke, Isarel Silber, Beat Schmid, Jason Tomlinson, Hardeep Mehta, Xena Mansoura, Zezhen Cheng, Gregory Vandergrift, Nurun Nahar Lata, Swarup China, and Zihua Zhu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3089, https://doi.org/10.5194/egusphere-2024-3089, 2024
Short summary
Short summary
This study highlights the unique capability of the ArcticShark UAS in measuring vertically resolved atmospheric properties over the Southern Great Plains. Data from 32 research flights in 2023 reveal seasonal patterns and correlations with conventional measurements. The consistency and complementarity of in situ and remote sensing methods are highlighted. The study demonstrates the ArcticShark’s versatility in bridging data gaps and improving the understanding of vertical atmospheric structures.
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, and Ethan Young
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-115, https://doi.org/10.5194/wes-2024-115, 2024
Revised manuscript under review for WES
Short summary
Short summary
Three recent wind resource datasets are assessed for their skills in representing annual average wind speeds and seasonal, diurnal, and inter-annual trends in the wind resource to support customers interested in small and midsize wind energy.
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. Discuss., https://doi.org/10.5194/wes-2024-84, https://doi.org/10.5194/wes-2024-84, 2024
Revised manuscript accepted for WES
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.
Kenneth Brown, Pietro Bortolotti, Emmanuel Branlard, Mayank Chetan, Scott Dana, Nathaniel deVelder, Paula Doubrawa, Nicholas Hamilton, Hristo Ivanov, Jason Jonkman, Christopher Kelley, and Daniel Zalkind
Wind Energ. Sci., 9, 1791–1810, https://doi.org/10.5194/wes-9-1791-2024, https://doi.org/10.5194/wes-9-1791-2024, 2024
Short summary
Short summary
This paper presents a study of the popular wind turbine design tool OpenFAST. We compare simulation results to measurements obtained from a 2.8 MW land-based wind turbine. Measured wind conditions were used to generate turbulent flow fields through several techniques. We show that successful validation of the tool is not strongly dependent on the inflow generation technique used for mean quantities of interest. The type of inflow assimilation method has a larger effect on fatigue quantities.
Daphne Quint, Julie K. Lundquist, Nicola Bodini, and David Rosencrans
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-53, https://doi.org/10.5194/wes-2024-53, 2024
Preprint under review for WES
Short summary
Short summary
Offshore wind farms along the US east coast can have limited effects on local weather. Studying this, we used a weather model to compare conditions with and without wind farms near Massachusetts and Rhode Island. We analyzed changes in wind, temperature, and turbulence. Results show reduced wind speeds near and downwind of wind farms, especially during stability and high winds. Turbulence increases near wind farms, affecting boundary-layer height and wake size.
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.
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
Short summary
Short summary
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
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.
Raghavendra Krishnamurthy, Gabriel García Medina, Brian Gaudet, William I. Gustafson Jr., Evgueni I. Kassianov, Jinliang Liu, Rob K. Newsom, Lindsay M. Sheridan, and Alicia M. Mahon
Earth Syst. Sci. Data, 15, 5667–5699, https://doi.org/10.5194/essd-15-5667-2023, https://doi.org/10.5194/essd-15-5667-2023, 2023
Short summary
Short summary
Our understanding and ability to observe and model air–sea processes has been identified as a principal limitation to our ability to predict future weather. Few observations exist offshore along the coast of California. To improve our understanding of the air–sea transition zone and support the wind energy industry, two buoys with state-of-the-art equipment were deployed for 1 year. In this article, we present details of the post-processing, algorithms, and analyses.
Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma
Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, https://doi.org/10.5194/gmd-16-6355-2023, 2023
Short summary
Short summary
To assess the ability of Earth system model (ESM) predictions, we developed a tool called ESMAC Diags to understand how aerosols, clouds, and aerosol–cloud interactions are represented in ESMs. This paper describes its version 2 functionality. We compared the model predictions with measurements taken by planes, ships, satellites, and ground instruments over four regions across the world. Results show that this new tool can help identify model problems and guide future development of ESMs.
Paul Hulsman, Luis A. Martínez-Tossas, Nicholas Hamilton, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-112, https://doi.org/10.5194/wes-2023-112, 2023
Manuscript not accepted for further review
Short summary
Short summary
This paper presents an approach to analytically estimate the wake deficit within the near-wake region by modifying the curled wake model. This is done by incorporating a new initial condition at the rotor using an azimuth-dependent Gaussian profile, an adjusted turbulence model in the near-wake region and the far-wake region and an iterative process to determine the velocity field, while considering the relation of the pressure gradient and accounting the conservation of mass.
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.
Paul Veers, Carlo L. Bottasso, Lance Manuel, Jonathan Naughton, Lucy Pao, Joshua Paquette, Amy Robertson, Michael Robinson, Shreyas Ananthan, Thanasis Barlas, Alessandro Bianchini, Henrik Bredmose, Sergio González Horcas, Jonathan Keller, Helge Aagaard Madsen, James Manwell, Patrick Moriarty, Stephen Nolet, and Jennifer Rinker
Wind Energ. Sci., 8, 1071–1131, https://doi.org/10.5194/wes-8-1071-2023, https://doi.org/10.5194/wes-8-1071-2023, 2023
Short summary
Short summary
Critical unknowns in the design, manufacturing, and operation of future wind turbine and wind plant systems are articulated, and key research activities are recommended.
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
Short summary
Short summary
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.
Ryan Scott, Luis Martínez-Tossas, Juliaan Bossuyt, Nicholas Hamilton, and Raúl B. Cal
Wind Energ. Sci., 8, 449–463, https://doi.org/10.5194/wes-8-449-2023, https://doi.org/10.5194/wes-8-449-2023, 2023
Short summary
Short summary
In this work we examine the relationship between wind speed and turbulent stresses within a wind turbine wake. This relationship changes further from the turbine as the driving physical phenomena vary throughout the wake. We propose a model for this process and test the effectiveness of our model against existing formulations. Our approach increases the accuracy of wind speed predictions, which will lead to better estimates of wind plant performance and promote more efficient wind plant design.
Sheng-Lun Tai, Larry K. Berg, Raghavendra Krishnamurthy, Rob Newsom, and Anthony Kirincich
Wind Energ. Sci., 8, 433–448, https://doi.org/10.5194/wes-8-433-2023, https://doi.org/10.5194/wes-8-433-2023, 2023
Short summary
Short summary
Turbulence intensity is critical for wind turbine design and operation as it affects wind power generation efficiency. Turbulence measurements in the marine environment are limited. We use a model to derive turbulence intensity and test how sea surface temperature data may impact the simulated turbulence intensity and atmospheric stability. The model slightly underestimates turbulence, and improved sea surface temperature data reduce the bias. Error with unrealistic mesoscale flow is identified.
Siegfried Schobesberger, Emma L. D'Ambro, Lejish Vettikkat, Ben H. Lee, Qiaoyun Peng, David M. Bell, John E. Shilling, Manish Shrivastava, Mikhail Pekour, Jerome Fast, and Joel A. Thornton
Atmos. Meas. Tech., 16, 247–271, https://doi.org/10.5194/amt-16-247-2023, https://doi.org/10.5194/amt-16-247-2023, 2023
Short summary
Short summary
We present a new, highly sensitive technique for measuring atmospheric ammonia, an important trace gas that is emitted mainly by agriculture. We deployed the instrument on an aircraft during research flights over rural Oklahoma. Due to its fast response, we could analyze correlations with turbulent winds and calculate ammonia emissions from nearby areas at 1 to 2 km resolution. We observed high spatial variability and point sources that are not resolved in the US National Emissions Inventory.
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.
Lindsay M. Sheridan, Raghu Krishnamurthy, Gabriel García Medina, Brian J. Gaudet, William I. Gustafson Jr., Alicia M. Mahon, William J. Shaw, Rob K. Newsom, Mikhail Pekour, and Zhaoqing Yang
Wind Energ. Sci., 7, 2059–2084, https://doi.org/10.5194/wes-7-2059-2022, https://doi.org/10.5194/wes-7-2059-2022, 2022
Short summary
Short summary
Using observations from lidar buoys, five reanalysis and analysis models that support the wind energy community are validated offshore and at rotor-level heights along the California Pacific coast. The models are found to underestimate the observed wind resource. Occasions of large model error occur in conjunction with stable atmospheric conditions, wind speeds associated with peak turbine power production, and mischaracterization of the diurnal wind speed cycle in summer months.
Fan Mei, Mikhail S. Pekour, Darielle Dexheimer, Gijs de Boer, RaeAnn Cook, Jason Tomlinson, Beat Schmid, Lexie A. Goldberger, Rob Newsom, and Jerome D. Fast
Earth Syst. Sci. Data, 14, 3423–3438, https://doi.org/10.5194/essd-14-3423-2022, https://doi.org/10.5194/essd-14-3423-2022, 2022
Short summary
Short summary
This work focuses on an expanding number of data sets observed using ARM TBS (133 flights) and UAS (seven flights) platforms by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility. These data streams provide new perspectives on spatial variability of atmospheric and surface parameters, helping to address critical science questions in Earth system science research, such as the aerosol–cloud interaction in the boundary layer.
Emmanouil M. Nanos, Carlo L. Bottasso, Filippo Campagnolo, Franz Mühle, Stefano Letizia, G. Valerio Iungo, and Mario A. Rotea
Wind Energ. Sci., 7, 1263–1287, https://doi.org/10.5194/wes-7-1263-2022, https://doi.org/10.5194/wes-7-1263-2022, 2022
Short summary
Short summary
The paper describes the design of a scaled wind turbine in detail, for studying wakes and wake control applications in the known, controllable and repeatable conditions of a wind tunnel. The scaled model is characterized by conducting experiments in two wind tunnels, in different conditions, using different measurement equipment. Results are also compared to predictions obtained with models of various fidelity. The analysis indicates that the model fully satisfies the initial requirements.
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.
Fan Mei, Steven Spielman, Susanne Hering, Jian Wang, Mikhail S. Pekour, Gregory Lewis, Beat Schmid, Jason Tomlinson, and Maynard Havlicek
Atmos. Meas. Tech., 14, 7329–7340, https://doi.org/10.5194/amt-14-7329-2021, https://doi.org/10.5194/amt-14-7329-2021, 2021
Short summary
Short summary
This study focuses on understanding a versatile water-based condensation particle counter (vWCPC 3789) performance under various ambient pressure conditions (500–1000 hPa). A vWCPC has the advantage of avoiding health and safety concerns. However, its performance characterization under low pressure is rare but crucial for ensuring successful airborne deployment. This paper provides advanced knowledge of operating a vWCPC 3789 to capture the spatial variations of atmospheric aerosols.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
Offshore wind turbines are huge, with rotor blades soon to extend up to nearly 300 m. Accurate modeling of winds across these heights is crucial for accurate estimates of energy production. However, we lack sufficient observations at these heights but have plenty of near-surface observations. Here we show that a basic machine-learning model can provide very accurate estimates of winds in this area, and much better than conventional techniques.
Raghavendra Krishnamurthy, Rob K. Newsom, Larry K. Berg, Heng Xiao, Po-Lun Ma, and David D. Turner
Atmos. Meas. Tech., 14, 4403–4424, https://doi.org/10.5194/amt-14-4403-2021, https://doi.org/10.5194/amt-14-4403-2021, 2021
Short summary
Short summary
Planetary boundary layer (PBL) height is a critical parameter in atmospheric models. Continuous PBL height measurements from remote sensing measurements are important to understand various boundary layer mechanisms, especially during daytime and evening transition periods. Due to several limitations in existing methodologies to detect PBL height from a Doppler lidar, in this study, a machine learning (ML) approach is tested. The ML model is observed to improve the accuracy by over 50 %.
Alayna Farrell, Jennifer King, Caroline Draxl, Rafael Mudafort, Nicholas Hamilton, Christopher J. Bay, Paul Fleming, and Eric Simley
Wind Energ. Sci., 6, 737–758, https://doi.org/10.5194/wes-6-737-2021, https://doi.org/10.5194/wes-6-737-2021, 2021
Short summary
Short summary
Most current wind turbine wake models struggle to accurately simulate spatially variant wind conditions at a low computational cost. In this paper, we present an adaptation of NREL's FLOw Redirection and Induction in Steady State (FLORIS) wake model, which calculates wake losses in a heterogeneous flow field using local weather measurement inputs. Two validation studies are presented where the adapted model consistently outperforms previous versions of FLORIS that simulated uniform flow only.
Maria A. Zawadowicz, Kaitlyn Suski, Jiumeng Liu, Mikhail Pekour, Jerome Fast, Fan Mei, Arthur J. Sedlacek, Stephen Springston, Yang Wang, Rahul A. Zaveri, Robert Wood, Jian Wang, and John E. Shilling
Atmos. Chem. Phys., 21, 7983–8002, https://doi.org/10.5194/acp-21-7983-2021, https://doi.org/10.5194/acp-21-7983-2021, 2021
Short summary
Short summary
This paper describes the results of a recent field campaign in the eastern North Atlantic, where two mass spectrometers were deployed aboard a research aircraft to measure the chemistry of aerosols and trace gases. Very clean conditions were found, dominated by local sulfate-rich acidic aerosol and very aged organics. Evidence of
long-range transport of aerosols from the continents was also identified.
Luis A. Martínez-Tossas, Jennifer King, Eliot Quon, Christopher J. Bay, Rafael Mudafort, Nicholas Hamilton, Michael F. Howland, and Paul A. Fleming
Wind Energ. Sci., 6, 555–570, https://doi.org/10.5194/wes-6-555-2021, https://doi.org/10.5194/wes-6-555-2021, 2021
Short summary
Short summary
In this paper a three-dimensional steady-state solver for flow through a wind farm is developed and validated. The computational cost of the solver is on the order of seconds for large wind farms. The model is validated using high-fidelity simulations and SCADA.
Stefano Letizia, Lu Zhan, and Giacomo Valerio Iungo
Atmos. Meas. Tech., 14, 2065–2093, https://doi.org/10.5194/amt-14-2065-2021, https://doi.org/10.5194/amt-14-2065-2021, 2021
Short summary
Short summary
A LiDAR Statistical Barnes Objective Analysis (LiSBOA) for the optimal design of lidar scans and retrieval of velocity statistics is proposed. The LiSBOA is validated and characterized via a Monte Carlo approach applied to a synthetic velocity field. The optimal design of lidar scans is formulated as a two-cost-function optimization problem, including the minimization of the volume not sampled with adequate spatial resolution and the minimization of the error on the mean of the velocity field.
Stefano Letizia, Lu Zhan, and Giacomo Valerio Iungo
Atmos. Meas. Tech., 14, 2095–2113, https://doi.org/10.5194/amt-14-2095-2021, https://doi.org/10.5194/amt-14-2095-2021, 2021
Short summary
Short summary
The LiDAR Statistical Barnes Objective Analysis (LiSBOA) is applied to lidar data collected in the wake of wind turbines to reconstruct mean wind speed and turbulence intensity. Various lidar scans performed during a field campaign for a wind farm in complex terrain are analyzed. The results endorse the application of the LiSBOA for lidar-based wind resource assessment and farm diagnosis.
Daniel Vassallo, Raghavendra Krishnamurthy, and Harindra J. S. Fernando
Wind Energ. Sci., 6, 295–309, https://doi.org/10.5194/wes-6-295-2021, https://doi.org/10.5194/wes-6-295-2021, 2021
Short summary
Short summary
Machine learning is quickly becoming a commonly used technique for wind speed and power forecasting and is especially useful when combined with other forecasting techniques. This study utilizes a popular machine learning algorithm, random forest, in an attempt to predict the forecasting error of a statistical forecasting model. Various atmospheric characteristics are used as random forest inputs in an effort to discern the most useful atmospheric information for this purpose.
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
Short summary
Short summary
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.
Lu Zhan, Stefano Letizia, and Giacomo Valerio Iungo
Wind Energ. Sci., 5, 1601–1622, https://doi.org/10.5194/wes-5-1601-2020, https://doi.org/10.5194/wes-5-1601-2020, 2020
Short summary
Short summary
Lidar measurements of wakes generated by isolated wind turbines are leveraged for optimal tuning of parameters of four engineering wake models. The lidar measurements are retrieved as ensemble averages of clustered data with incoming wind speed and turbulence intensity. It is shown that the optimally tuned wake models enable a significantly increased accuracy for predictions of wakes. The optimally tuned models are expected to enable generally enhanced performance for wind farms on flat terrain.
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
Short summary
Short summary
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.
Peter Brugger, Mithu Debnath, Andrew Scholbrock, Paul Fleming, Patrick Moriarty, Eric Simley, David Jager, Jason Roadman, Mark Murphy, Haohua Zong, and Fernando Porté-Agel
Wind Energ. Sci., 5, 1253–1272, https://doi.org/10.5194/wes-5-1253-2020, https://doi.org/10.5194/wes-5-1253-2020, 2020
Short summary
Short summary
A wind turbine can actively influence its wake by turning the rotor out of the wind direction to deflect the wake away from a downstream wind turbine. This technique was tested in a field experiment at a wind farm, where the inflow and wake were monitored with remote-sensing instruments for the wind speed. The behaviour of the wake deflection agrees with the predictions of two analytical models, and a bias of the wind direction perceived by the yawed wind turbine led to suboptimal power gains.
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
Short summary
Short summary
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.
Daniel Vassallo, Raghavendra Krishnamurthy, and Harindra J. S. Fernando
Wind Energ. Sci., 5, 959–975, https://doi.org/10.5194/wes-5-959-2020, https://doi.org/10.5194/wes-5-959-2020, 2020
Short summary
Short summary
Model error and uncertainty is a challenge in the wind energy industry, potentially leading to mischaracterization of millions of dollars' worth of wind resource. This paper combines meteorological knowledge with machine learning techniques, specifically artificial neural networks (ANNs), to better extrapolate wind speeds. It is found that ANNs can reduce power-law extrapolation error by up to 52 % while simultaneously reducing uncertainty. A test case is shown to help decipher the ANN results.
Paul Fleming, Jennifer King, Eric Simley, Jason Roadman, Andrew Scholbrock, Patrick Murphy, Julie K. Lundquist, Patrick Moriarty, Katherine Fleming, Jeroen van Dam, Christopher Bay, Rafael Mudafort, David Jager, Jason Skopek, Michael Scott, Brady Ryan, Charles Guernsey, and Dan Brake
Wind Energ. Sci., 5, 945–958, https://doi.org/10.5194/wes-5-945-2020, https://doi.org/10.5194/wes-5-945-2020, 2020
Short summary
Short summary
This paper presents the results of a field campaign investigating the performance of wake steering applied at a section of a commercial wind farm. It is the second phase of the study for which the first phase was reported in a companion paper (https://wes.copernicus.org/articles/4/273/2019/). The authors implemented wake steering on two turbine pairs and compared results with the latest FLORIS model of wake steering, showing good agreement in overall energy increase.
Nicola Bodini and Mike Optis
Wind Energ. Sci., 5, 489–501, https://doi.org/10.5194/wes-5-489-2020, https://doi.org/10.5194/wes-5-489-2020, 2020
Short summary
Short summary
An accurate assessment of the wind resource at hub height is necessary for an efficient and bankable wind farm project. Conventional techniques for wind speed vertical extrapolation include a power law and a logarithmic law. Here, we propose a round-robin validation to assess the benefits that a machine-learning-based approach can provide in vertically extrapolating wind speed at a location different from the training site – the most practically useful application for the wind energy industry.
Nicholas Hamilton
Atmos. Meas. Tech., 13, 1019–1032, https://doi.org/10.5194/amt-13-1019-2020, https://doi.org/10.5194/amt-13-1019-2020, 2020
Short summary
Short summary
The identification of atmospheric conditions within a multivariable atmospheric data set is an important step in validating emerging and existing models used to simulate wind plant flows and operational strategies. The total variation approach developed here offers a method founded in tested mathematical metrics and can be used to identify and characterize periods corresponding to quiescent conditions or specific events of interest for study or wind energy development.
Fan Mei, Jian Wang, Jennifer M. Comstock, Ralf Weigel, Martina Krämer, Christoph Mahnke, John E. Shilling, Johannes Schneider, Christiane Schulz, Charles N. Long, Manfred Wendisch, Luiz A. T. Machado, Beat Schmid, Trismono Krisna, Mikhail Pekour, John Hubbe, Andreas Giez, Bernadett Weinzierl, Martin Zoeger, Mira L. Pöhlker, Hans Schlager, Micael A. Cecchini, Meinrat O. Andreae, Scot T. Martin, Suzane S. de Sá, Jiwen Fan, Jason Tomlinson, Stephen Springston, Ulrich Pöschl, Paulo Artaxo, Christopher Pöhlker, Thomas Klimach, Andreas Minikin, Armin Afchine, and Stephan Borrmann
Atmos. Meas. Tech., 13, 661–684, https://doi.org/10.5194/amt-13-661-2020, https://doi.org/10.5194/amt-13-661-2020, 2020
Short summary
Short summary
In 2014, the US DOE G1 aircraft and the German HALO aircraft overflew the Amazon basin to study how aerosols influence cloud cycles under a clean condition and around a tropical megacity. This paper describes how to meaningfully compare similar measurements from two research aircraft and identify the potential measurement issue. We also discuss the uncertainty range for each measurement for further usage in model evaluation and satellite data validation.
Laura Bianco, Irina V. Djalalova, James M. Wilczak, Joseph B. Olson, Jaymes S. Kenyon, Aditya Choukulkar, Larry K. Berg, Harindra J. S. Fernando, Eric P. Grimit, Raghavendra Krishnamurthy, Julie K. Lundquist, Paytsar Muradyan, Mikhail Pekour, Yelena Pichugina, Mark T. Stoelinga, and David D. Turner
Geosci. Model Dev., 12, 4803–4821, https://doi.org/10.5194/gmd-12-4803-2019, https://doi.org/10.5194/gmd-12-4803-2019, 2019
Short summary
Short summary
During the second Wind Forecast Improvement Project, improvements to the parameterizations were applied to the High Resolution Rapid Refresh model and its nested version. The impacts of the new parameterizations on the forecast of 80 m wind speeds and power are assessed, using sodars and profiling lidars observations for comparison. Improvements are evaluated as a function of the model’s initialization time, forecast horizon, time of the day, season, site elevation, and meteorological phenomena.
Paul Fleming, Jennifer King, Katherine Dykes, Eric Simley, Jason Roadman, Andrew Scholbrock, Patrick Murphy, Julie K. Lundquist, Patrick Moriarty, Katherine Fleming, Jeroen van Dam, Christopher Bay, Rafael Mudafort, Hector Lopez, Jason Skopek, Michael Scott, Brady Ryan, Charles Guernsey, and Dan Brake
Wind Energ. Sci., 4, 273–285, https://doi.org/10.5194/wes-4-273-2019, https://doi.org/10.5194/wes-4-273-2019, 2019
Short summary
Short summary
Wake steering is a form of wind farm control in which turbines use yaw offsets to affect wakes in order to yield an increase in total energy production. In this first phase of a study of wake steering at a commercial wind farm, two turbines implement a schedule of offsets. For two closely spaced turbines, an approximate 14 % increase in energy was measured on the downstream turbine over a 10° sector, with a 4 % increase in energy production of the combined turbine pair.
Nicola Bodini, Julie K. Lundquist, Raghavendra Krishnamurthy, Mikhail Pekour, Larry K. Berg, and Aditya Choukulkar
Atmos. Chem. Phys., 19, 4367–4382, https://doi.org/10.5194/acp-19-4367-2019, https://doi.org/10.5194/acp-19-4367-2019, 2019
Short summary
Short summary
To improve the parameterization of the turbulence dissipation rate (ε) in numerical weather prediction models, we have assessed its temporal and spatial variability at various scales in the Columbia River Gorge during the WFIP2 field experiment. The turbulence dissipation rate shows large spatial variability, even at the microscale, with larger values in sites located downwind of complex orographic structures or in wind farm wakes. Distinct diurnal and seasonal cycles in ε have also been found.
Jessica M. Tomaszewski, Julie K. Lundquist, Matthew J. Churchfield, and Patrick J. Moriarty
Wind Energ. Sci., 3, 833–843, https://doi.org/10.5194/wes-3-833-2018, https://doi.org/10.5194/wes-3-833-2018, 2018
Short summary
Short summary
Wind energy development has increased rapidly in rural locations of the United States, areas that also serve general aviation airports. The spinning rotor of a wind turbine creates an area of increased turbulence, and we question if this turbulent air could pose rolling hazards for light aircraft flying behind turbines. We analyze high-resolution simulations of wind flowing past a turbine to quantify the rolling risk and find that wind turbines pose no significant roll hazards to light aircraft.
Jeffrey D. Mirocha, Matthew J. Churchfield, Domingo Muñoz-Esparza, Raj K. Rai, Yan Feng, Branko Kosović, Sue Ellen Haupt, Barbara Brown, Brandon L. Ennis, Caroline Draxl, Javier Sanz Rodrigo, William J. Shaw, Larry K. Berg, Patrick J. Moriarty, Rodman R. Linn, Veerabhadra R. Kotamarthi, Ramesh Balakrishnan, Joel W. Cline, Michael C. Robinson, and Shreyas Ananthan
Wind Energ. Sci., 3, 589–613, https://doi.org/10.5194/wes-3-589-2018, https://doi.org/10.5194/wes-3-589-2018, 2018
Short summary
Short summary
This paper validates the use of idealized large-eddy simulations with periodic lateral boundary conditions to provide boundary-layer flow quantities of interest for wind energy applications. Sensitivities to model formulation, forcing parameter values, and grid configurations were also examined, both to ascertain the robustness of the technique and to characterize inherent uncertainties, as required for the evaluation of more general wind plant flow simulation approaches under development.
John E. Shilling, Mikhail S. Pekour, Edward C. Fortner, Paulo Artaxo, Suzane de Sá, John M. Hubbe, Karla M. Longo, Luiz A. T. Machado, Scot T. Martin, Stephen R. Springston, Jason Tomlinson, and Jian Wang
Atmos. Chem. Phys., 18, 10773–10797, https://doi.org/10.5194/acp-18-10773-2018, https://doi.org/10.5194/acp-18-10773-2018, 2018
Short summary
Short summary
We report aircraft observations of the evolution of organic aerosol in the Manaus urban plume as it ages. We observe dynamic changes in the organic aerosol. The mean carbon oxidation state of the OA increases from −0.6 to −0.45. Hydrocarbon-like organic aerosol (HOA) mass is lost and is balanced out by formation of oxygenated organic aerosol (OOA). Because HOA loss is balanced by OOA formation, we observe little change in the net Δorg / ΔCO values with aging.
Nicola Bodini, Julie K. Lundquist, and Rob K. Newsom
Atmos. Meas. Tech., 11, 4291–4308, https://doi.org/10.5194/amt-11-4291-2018, https://doi.org/10.5194/amt-11-4291-2018, 2018
Short summary
Short summary
Turbulence within the atmospheric boundary layer is critically important to transfer heat, momentum, and moisture. Currently, improved turbulence parametrizations are crucially needed to refine the accuracy of model results at fine horizontal scales. In this study, we calculate turbulence dissipation rate from sonic anemometers and discuss a novel approach to derive turbulence dissipation from profiling lidar measurements.
Paul Fleming, Jennifer Annoni, Matthew Churchfield, Luis A. Martinez-Tossas, Kenny Gruchalla, Michael Lawson, and Patrick Moriarty
Wind Energ. Sci., 3, 243–255, https://doi.org/10.5194/wes-3-243-2018, https://doi.org/10.5194/wes-3-243-2018, 2018
Short summary
Short summary
This paper investigates the role of flow structures in wind farm control through yaw misalignment. A pair of counter-rotating vortices is shown to be important in deforming the shape of the wake. Further, we demonstrate that the vortex structures created in wake steering can enable a greater change power generation than currently modeled in control-oriented models. We propose that wind farm controllers can be made more effective if designed to take advantage of these effects.
Dean B. Atkinson, Mikhail Pekour, Duli Chand, James G. Radney, Katheryn R. Kolesar, Qi Zhang, Ari Setyan, Norman T. O'Neill, and Christopher D. Cappa
Atmos. Chem. Phys., 18, 5499–5514, https://doi.org/10.5194/acp-18-5499-2018, https://doi.org/10.5194/acp-18-5499-2018, 2018
Short summary
Short summary
We use in situ measurements of particle light extinction to assess the performance of a typical aerosol remote retrieval method. The retrieved fine-mode fraction of extinction, a property commonly used to characterize the anthropogenic influence on the aerosol optical depth, compares well with the in situ measurements as does the retrieved effective fine-mode radius, which characterizes the average size of the particles that contribute most to scattering.
Naseem Ali, Nicholas Hamilton, Dominic DeLucia, and Raúl Bayoán Cal
Wind Energ. Sci., 3, 43–56, https://doi.org/10.5194/wes-3-43-2018, https://doi.org/10.5194/wes-3-43-2018, 2018
Rob K. Newsom, W. Alan Brewer, James M. Wilczak, Daniel E. Wolfe, Steven P. Oncley, and Julie K. Lundquist
Atmos. Meas. Tech., 10, 1229–1240, https://doi.org/10.5194/amt-10-1229-2017, https://doi.org/10.5194/amt-10-1229-2017, 2017
Short summary
Short summary
Doppler lidars are remote sensing instruments that use infrared light to measure wind velocity in the lowest 2 to 3 km of the atmosphere. Quantifying the uncertainty in these measurements is crucial for applications ranging from wind resource assessment to model data assimilation. In this study, we evaluate three methods for estimating the random uncertainty by comparing the lidar wind measurements with nearly collocated in situ wind measurements at multiple levels on a tall tower.
K. K. Shukla, K. Niranjan Kumar, D. V. Phanikumar, R. K. Newsom, V. R. Kotamarthi, T. B. M. J. Ouarda, and M. V. Ratnam
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2016-162, https://doi.org/10.5194/amt-2016-162, 2016
Revised manuscript not accepted
Short summary
Short summary
Estimation of Cloud base height was carried out by using various ground based instruments (Doppler Lidar and Ceilometer) and satellite datasets (MODIS) over central Himalayan region for the first time. The present study demonstrates the potential of Doppler Lidar in precise estimation of cloud base height and updraft velocities. More such deployments will be invaluable inputs for regional weather prediction models over complex Himalayan terrains.
Naseem Ali, Nicholas Hamilton, and Raúl Bayáon Cal
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2016-23, https://doi.org/10.5194/wes-2016-23, 2016
Revised manuscript not accepted
Short summary
Short summary
The effect of the density of turbines on the wake recovery is important. However, the impact of the tight spacing is still not fully understood. Here, we used proper orthogonal decomposition tool to analyze this impact. Different streamwise and spanwise spacings are chosen to make this work robust. Thus, the power measurements are also applied to investigate the spacing impact.
Christopher D. Cappa, Katheryn R. Kolesar, Xiaolu Zhang, Dean B. Atkinson, Mikhail S. Pekour, Rahul A. Zaveri, Alla Zelenyuk, and Qi Zhang
Atmos. Chem. Phys., 16, 6511–6535, https://doi.org/10.5194/acp-16-6511-2016, https://doi.org/10.5194/acp-16-6511-2016, 2016
Short summary
Short summary
Measurements of size-dependent aerosol optical properties at visible wavelengths made during the 2010 CARES study are reported on, with a special focus on the characterization of supermicron particles. The relationships with and dependence upon particle composition, particle size, photochemical aging, water uptake and heating are discussed, along with broader implications of these in situ measurements for the interpretation of remote sensing products.
A. Lupascu, R. Easter, R. Zaveri, M. Shrivastava, M. Pekour, J. Tomlinson, Q. Yang, H. Matsui, A. Hodzic, Q. Zhang, and J. D. Fast
Atmos. Chem. Phys., 15, 12283–12313, https://doi.org/10.5194/acp-15-12283-2015, https://doi.org/10.5194/acp-15-12283-2015, 2015
D. B. Atkinson, J. G. Radney, J. Lum, K. R. Kolesar, D. J. Cziczo, M. S. Pekour, Q. Zhang, A. Setyan, A. Zelenyuk, and C. D. Cappa
Atmos. Chem. Phys., 15, 4045–4061, https://doi.org/10.5194/acp-15-4045-2015, https://doi.org/10.5194/acp-15-4045-2015, 2015
Short summary
Short summary
This work describes an analysis of measurements of the influence of water uptake on the light-scattering properties of sub- and supermicron-sized particles as observed in the Sacramento, CA, USA region during the 2010 CARES field campaign. The observations are used to derive campaign-average effective hygroscopicity parameters for submicron oxygenated organic aerosol and for supermicron particles, and the influence of chloride displacement reactions on particle hygroscopicity is examined.
E. Kassianov, J. Barnard, M. Pekour, L. K. Berg, J. Shilling, C. Flynn, F. Mei, and A. Jefferson
Atmos. Meas. Tech., 7, 3247–3261, https://doi.org/10.5194/amt-7-3247-2014, https://doi.org/10.5194/amt-7-3247-2014, 2014
J. D. Fast, J. Allan, R. Bahreini, J. Craven, L. Emmons, R. Ferrare, P. L. Hayes, A. Hodzic, J. Holloway, C. Hostetler, J. L. Jimenez, H. Jonsson, S. Liu, Y. Liu, A. Metcalf, A. Middlebrook, J. Nowak, M. Pekour, A. Perring, L. Russell, A. Sedlacek, J. Seinfeld, A. Setyan, J. Shilling, M. Shrivastava, S. Springston, C. Song, R. Subramanian, J. W. Taylor, V. Vinoj, Q. Yang, R. A. Zaveri, and Q. Zhang
Atmos. Chem. Phys., 14, 10013–10060, https://doi.org/10.5194/acp-14-10013-2014, https://doi.org/10.5194/acp-14-10013-2014, 2014
B. Friedman, A. Zelenyuk, J. Beranek, G. Kulkarni, M. Pekour, A. Gannet Hallar, I. B. McCubbin, J. A. Thornton, and D. J Cziczo
Atmos. Chem. Phys., 13, 11839–11851, https://doi.org/10.5194/acp-13-11839-2013, https://doi.org/10.5194/acp-13-11839-2013, 2013
A. Cazorla, R. Bahadur, K. J. Suski, J. F. Cahill, D. Chand, B. Schmid, V. Ramanathan, and K. A. Prather
Atmos. Chem. Phys., 13, 9337–9350, https://doi.org/10.5194/acp-13-9337-2013, https://doi.org/10.5194/acp-13-9337-2013, 2013
M. Gyawali, W. P. Arnott, R. A. Zaveri, C. Song, M. Pekour, B. Flowers, M. K. Dubey, A. Setyan, Q. Zhang, J. W. Harworth, J. G. Radney, D. B. Atkinson, S. China, C. Mazzoleni, K. Gorkowski, R. Subramanian, B. T. Jobson, and H. Moosmüller
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-13-7113-2013, https://doi.org/10.5194/acpd-13-7113-2013, 2013
Revised manuscript not accepted
Related subject area
Thematic area: Wind and the atmosphere | Topic: Wind and turbulence
Periods of constant wind speed: how long do they last in the turbulent atmospheric boundary layer?
Characterization of local wind profiles: a random forest approach for enhanced wind profile extrapolation
Simulations suggest offshore wind farms modify low-level jets
On the lidar-turbulence paradox and possible countermeasures
The actuator farm model for large eddy simulation (LES) of wind-farm-induced atmospheric gravity waves and farm–farm interaction
Understanding the impact of data gaps on long-term offshore wind resource estimates
Converging profile relationships for offshore wind speed and turbulence intensity
An analytical formulation for turbulent kinetic energy added by wind turbines based on large-eddy simulation
A simple steady-state inflow model of the neutral and stable atmospheric boundary layer applied to wind turbine wake simulations
Influences of lidar scanning parameters on wind turbine wake retrievals in complex terrain
Tall Wind Profile Validation Using Lidar Observations and Hindcast Data
Experimental evaluation of wind turbine wake turbulence impacts on a general aviation aircraft
Underestimation of strong wind speeds offshore in ERA5: evidence, discussion and correction
Brief communication: A simple axial induction modification to the Weather Research and Forecasting Fitch wind farm parameterization
Impact of swell waves on atmospheric surface turbulence: wave–turbulence decomposition methods
Flow acceleration statistics: a new paradigm for wind-driven loads, towards probabilistic turbine design
Machine-learning-based estimate of the wind speed over complex terrain using the long short-term memory (LSTM) recurrent neural network
Method to predict the minimum measurement and experiment durations needed to achieve converged and significant results in a wind energy field experiment
Swell Impacts on an Offshore Wind Farm in Stable Boundary Layer: Wake Flow and Energy Budget Analysis
Evaluation of wind farm parameterizations in the WRF model under different atmospheric stability conditions with high-resolution wake simulations
Renewable Energy Complementarity (RECom) maps – a comprehensive visualisation tool to support spatial diversification
Control-oriented modelling of wind direction variability
Machine learning methods to improve spatial predictions of coastal wind speed profiles and low-level jets using single-level ERA5 data
Offshore low-level jet observations and model representation using lidar buoy data off the California coast
Measurement-driven large-eddy simulations of a diurnal cycle during a wake-steering field campaign
The fractal turbulent–non-turbulent interface in the atmosphere
TOSCA – an open-source, finite-volume, large-eddy simulation (LES) environment for wind farm flows
Quantitative comparison of power production and power quality onshore and offshore: a case study from the eastern United States
The wind farm pressure field
Realistic turbulent inflow conditions for estimating the performance of a floating wind turbine
Brief communication: On the definition of the low-level jet
A decision-tree-based measure–correlate–predict approach for peak wind gust estimation from a global reanalysis dataset
Revealing inflow and wake conditions of a 6 MW floating turbine
Stochastic gradient descent for wind farm optimization
Modelling the impact of trapped lee waves on offshore wind farm power output
Applying a random time mapping to Mann-modeled turbulence for the generation of intermittent wind fields
From shear to veer: theory, statistics, and practical application
Quantification and correction of motion influence for nacelle-based lidar systems on floating wind turbines
Gaussian mixture models for the optimal sparse sampling of offshore wind resource
Dependence of turbulence estimations on nacelle lidar scanning strategies
Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques
An investigation of spatial wind direction variability and its consideration in engineering models
From gigawatt to multi-gigawatt wind farms: wake effects, energy budgets and inertial gravity waves investigated by large-eddy simulations
Investigations of correlation and coherence in turbulence from a large-eddy simulation
Validation of turbulence intensity as simulated by the Weather Research and Forecasting model off the US northeast coast
On the laminar–turbulent transition mechanism on megawatt wind turbine blades operating in atmospheric flow
Brief communication: A momentum-conserving superposition method applied to the super-Gaussian wind turbine wake model
Turbulence structures and entrainment length scales in large offshore wind farms
Effect of different source terms and inflow direction in atmospheric boundary modeling over the complex terrain site of Perdigão
Comparison of large eddy simulations against measurements from the Lillgrund offshore wind farm
Daniela Moreno, Jan Friedrich, Matthias Wächter, Jörg Schwarte, and Joachim Peinke
Wind Energ. Sci., 10, 347–360, https://doi.org/10.5194/wes-10-347-2025, https://doi.org/10.5194/wes-10-347-2025, 2025
Short summary
Short summary
Unexpected load events measured on operating wind turbines are not accurately predicted by numerical simulations. We introduce the periods of constant wind speed as a possible cause of such events. We measure and characterize their statistics from atmospheric data. Further comparisons to standard modelled data and experimental turbulence data suggest that such events are not intrinsic to small-scale turbulence and are not accurately described by current standard wind models.
Farkhondeh (Hanie) Rouholahnejad and Julia Gottschall
Wind Energ. Sci., 10, 143–159, https://doi.org/10.5194/wes-10-143-2025, https://doi.org/10.5194/wes-10-143-2025, 2025
Short summary
Short summary
In wind energy, precise wind speed prediction at hub height is vital. Our study in the Dutch North Sea reveals that the on-site-trained random forest model outperforms the global reanalysis data, ERA5, in accuracy and precision. Trained within a 200 km range, the model effectively extends the wind speed vertically but experiences bias. It also outperforms ERA5 corrected with measurements in capturing wind speed variations and fine wind patterns, highlighting its potential for site assessment.
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.
Alfredo Peña, Ginka G. Yankova, and Vasiliki Mallini
Wind Energ. Sci., 10, 83–102, https://doi.org/10.5194/wes-10-83-2025, https://doi.org/10.5194/wes-10-83-2025, 2025
Short summary
Short summary
Lidars are vastly used in wind energy, but most users struggle when interpreting lidar turbulence measures. Here, we explain the difficulty in converting them into standard measurements. We show two ways of converting lidar to in situ turbulence measurements, both using neural networks: one of them is based on physics, while the other is purely data-driven. They show promising results when compared to high-quality turbulence measurements from a tall mast.
Sebastiano Stipa, Arjun Ajay, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 2301–2332, https://doi.org/10.5194/wes-9-2301-2024, https://doi.org/10.5194/wes-9-2301-2024, 2024
Short summary
Short summary
This study presents the actuator farm model, a new method for modeling wind turbines within large wind farms. The model greatly reduces computational cost when compared to traditional actuator wind turbine models and is beneficial for studying flow around large wind farms as well as the interaction between multiple wind farms. Results obtained from numerical simulations show excellent agreement with past wind turbine models, demonstrating its utility for future large-scale wind farm simulations.
Martin Georg Jonietz Alvarez, Warren Watson, and Julia Gottschall
Wind Energ. Sci., 9, 2217–2233, https://doi.org/10.5194/wes-9-2217-2024, https://doi.org/10.5194/wes-9-2217-2024, 2024
Short summary
Short summary
Offshore wind measurements are often affected by gaps. We investigated how these gaps affect wind resource assessments and whether filling them reduces their effect. We find that the effect of gaps on the estimated long-term wind resource is lower than expected and that data gap filling does not significantly change the outcome. These results indicate a need to reduce current wind data availability requirements for offshore measurement campaigns.
Gus Jeans
Wind Energ. Sci., 9, 2001–2015, https://doi.org/10.5194/wes-9-2001-2024, https://doi.org/10.5194/wes-9-2001-2024, 2024
Short summary
Short summary
An extensive set of met mast data offshore northwestern Europe are used to reduce uncertainty in offshore wind speed and turbulence intensity. The performance of widely used industry standard relationships is quantified, while some new empirical relationships are derived for practical application. Motivations include encouraging appropriate convergence of traditionally separate technical disciplines within the rapidly growing offshore wind energy industry.
Ali Khanjari, Asim Feroz, and Cristina Archer
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-128, https://doi.org/10.5194/wes-2024-128, 2024
Revised manuscript accepted for WES
Short summary
Short summary
Wind turbines add turbulence to the atmosphere behind them, especially 4–6 diameters downstream and near the rotor top. We propose an equation that predicts the distribution of added turbulence as a function of a turbine parameter (thrust coefficient) and an atmospheric parameter (undisturbed turbulence intensity before the turbine). We find that our equation performs well, although not perfectly. Eventually this equation can be used to better understand how wind turbines affect the atmosphere.
Maarten Paul van der Laan, Mark Kelly, Mads Baungaard, Antariksh Dicholkar, and Emily Louise Hodgson
Wind Energ. Sci., 9, 1985–2000, https://doi.org/10.5194/wes-9-1985-2024, https://doi.org/10.5194/wes-9-1985-2024, 2024
Short summary
Short summary
Wind turbines are increasing in size and operate more frequently above the atmospheric surface layer, which requires improved inflow models for numerical simulations of turbine interaction. In this work, a novel steady-state model of the atmospheric boundary layer (ABL) is introduced. Numerical wind turbine flow simulations subjected to shallow and tall ABLs are conducted, and the proposed model shows improved performance compared to other state-of-the-art steady-state models.
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.
Etienne Cheynet, Jan Markus Diezel, Hilde Haakenstad, Øyvind Breivik, Alfredo Peña, and Joachim Reuder
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-119, https://doi.org/10.5194/wes-2024-119, 2024
Revised manuscript accepted for WES
Short summary
Short summary
This study aims to help future large offshore wind turbines and airborne wind energy systems by providing insights into wind speeds at much higher altitudes than previously examined. We assessed three wind models (ERA5, NORA3, and NEWA) to predict wind speeds up to 500 m. Using lidar data from Norway and the North Sea, we found that ERA5 excels offshore, while NORA3 performs best onshore. However, the performance of the models depends on the locations and the evaluation criteria.
Jonathan D. Rogers
Wind Energ. Sci., 9, 1849–1868, https://doi.org/10.5194/wes-9-1849-2024, https://doi.org/10.5194/wes-9-1849-2024, 2024
Short summary
Short summary
This paper describes the results of a flight experiment to assess the existence of potential safety risks to a general aviation aircraft from added turbulence in the wake of a wind turbine. A general aviation aircraft was flown through the wake of an operating wind turbine at different downwind distances. Results indicated that there were small increases in disturbances to the aircraft due to added turbulence in the wake, but they never approached levels that would pose a safety risk.
Rémi Gandoin and Jorge Garza
Wind Energ. Sci., 9, 1727–1745, https://doi.org/10.5194/wes-9-1727-2024, https://doi.org/10.5194/wes-9-1727-2024, 2024
Short summary
Short summary
ERA5 has become the workhorse of most wind resource assessment applications, as it compares better with in situ measurements than other reanalyses. However, for design purposes, ERA5 suffers from a drawback: it underestimates strong wind speeds offshore (approx. from 10 m s−1). This is not widely discussed in the scientific literature. We address this bias and proposes a simple, robust correction. This article supports the growing need for use-case-specific validations of reanalysis datasets.
Lukas Vollmer, Balthazar Arnoldus Maria Sengers, and Martin Dörenkämper
Wind Energ. Sci., 9, 1689–1693, https://doi.org/10.5194/wes-9-1689-2024, https://doi.org/10.5194/wes-9-1689-2024, 2024
Short summary
Short summary
This study proposes a modification to a well-established wind farm parameterization used in mesoscale models. The wind speed at the location of the turbine, which is used to calculate power and thrust, is corrected to approximate the free wind speed. Results show that the modified parameterization produces more accurate estimates of the turbine’s power curve.
Mostafa Bakhoday Paskyabi
Wind Energ. Sci., 9, 1631–1645, https://doi.org/10.5194/wes-9-1631-2024, https://doi.org/10.5194/wes-9-1631-2024, 2024
Short summary
Short summary
The exchange of momentum and energy between the atmosphere and ocean depends on air–sea processes, especially wave-related ones. Precision in representing these interactions is vital for offshore wind turbine and farm design and operation. The development of a reliable wave–turbulence decomposition method to remove wave-induced interference from single-height wind measurements is essential for these applications and enhances our grasp of wind coherence within the wave boundary layer.
Mark Kelly
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-69, https://doi.org/10.5194/wes-2024-69, 2024
Revised manuscript accepted for WES
Short summary
Short summary
Industrial standards for wind turbine design are based on 10-minute statistics of wind speed at turbine hub-height, treating fluctuations as turbulence. But recent work shows the effect of strong transients is described by flow accelerations. We devise a method to measure the accelerations turbines encounter; the extremes offshore defy 10-minute statistics, due to various phenomena beyond turbulence. These findings are translated into a recipe supporting statistical turbine design.
Cássia Maria Leme Beu and Eduardo Landulfo
Wind Energ. Sci., 9, 1431–1450, https://doi.org/10.5194/wes-9-1431-2024, https://doi.org/10.5194/wes-9-1431-2024, 2024
Short summary
Short summary
Extrapolating the wind profile for complex terrain through the long short-term memory model outperformed the traditional power law methodology, which due to its universal nature cannot capture local features as the machine-learning methodology does. Moreover, considering the importance of investigating the wind potential and the need for alternative energy sources, it is motivating to find that a short observational campaign can produce better results than the traditional techniques.
Daniel R. Houck, Nathaniel B. de Velder, David C. Maniaci, and Brent C. Houchens
Wind Energ. Sci., 9, 1189–1209, https://doi.org/10.5194/wes-9-1189-2024, https://doi.org/10.5194/wes-9-1189-2024, 2024
Short summary
Short summary
Experiments offer incredible value to science, but results must come with an uncertainty quantification to be meaningful. We present a method to simulate a proposed experiment, calculate uncertainties, and determine the measurement duration (total time of measurements) and the experiment duration (total time to collect the required measurement data when including condition variability and time when measurement is not occurring) required to produce statistically significant and converged results.
Xu Ning and Mostafa Bakhoday-Paskyabi
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-38, https://doi.org/10.5194/wes-2024-38, 2024
Revised manuscript accepted for WES
Short summary
Short summary
Waves interact with the overlying wind field by modifying the stresses at the atmosphere-ocean interface. We develop and employ a parameterization method of wave-induced stresses in the numerical simulation of an offshore wind farm in a stable atmospheric boundary layer. This work demonstrates how swells change the kinetic energy transport, and induce wind veer and wake deflection, leading to significant variations in the power output of wind turbines at different positions of the wind farm.
Oscar García-Santiago, Andrea N. Hahmann, Jake Badger, and Alfredo Peña
Wind Energ. Sci., 9, 963–979, https://doi.org/10.5194/wes-9-963-2024, https://doi.org/10.5194/wes-9-963-2024, 2024
Short summary
Short summary
This study compares the results of two wind farm parameterizations (WFPs) in the Weather Research and Forecasting model, simulating a two-turbine array under three atmospheric stabilities with large-eddy simulations. We show that the WFPs accurately depict wind speeds either near turbines or in the far-wake areas, but not both. The parameterizations’ performance varies by variable (wind speed or turbulent kinetic energy) and atmospheric stability, with reduced accuracy in stable conditions.
Til Kristian Vrana and Harald G. Svendsen
Wind Energ. Sci., 9, 919–932, https://doi.org/10.5194/wes-9-919-2024, https://doi.org/10.5194/wes-9-919-2024, 2024
Short summary
Short summary
We developed new ways to plot comprehensive wind resource maps that show the revenue potential of different locations for future wind power developments. The relative capacity factor is introduced as an indicator showing the expected mean power output. The market value factor is introduced, which captures the expected mean market value relative to other wind parks. The Renewable Energy Complementarity (RECom) index combines the two into a single index, resulting in the RECom map.
Scott Dallas, Adam Stock, and Edward Hart
Wind Energ. Sci., 9, 841–867, https://doi.org/10.5194/wes-9-841-2024, https://doi.org/10.5194/wes-9-841-2024, 2024
Short summary
Short summary
This review presents the current understanding of wind direction variability in the context of control-oriented modelling of wind turbines and wind farms in a manner suitable to a wide audience. Motivation comes from the significant and commonly seen yaw error of horizontal axis wind turbines, which carries substantial negative impacts on annual energy production and the levellised cost of wind energy. Gaps in the literature are identified, and the critical challenges in this area are discussed.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Ville Vakkari, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 9, 821–840, https://doi.org/10.5194/wes-9-821-2024, https://doi.org/10.5194/wes-9-821-2024, 2024
Short summary
Short summary
Knowing the wind speed across the rotor of a wind turbine is key in making good predictions of the power production. However, models struggle to capture both the speed and the shape of the wind profile. Using machine learning methods based on the model data, we show that the predictions can be improved drastically. The work focuses on three coastal sites, spread over the Northern Hemisphere (the Baltic Sea, the North Sea, and the US Atlantic coast) with similar results for all sites.
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
Short summary
Short summary
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.
Eliot Quon
Wind Energ. Sci., 9, 495–518, https://doi.org/10.5194/wes-9-495-2024, https://doi.org/10.5194/wes-9-495-2024, 2024
Short summary
Short summary
Engineering models used to design wind farms generally do not account for realistic atmospheric conditions that can rapidly evolve from minute to minute. This paper uses a first-principles simulation technique to predict the performance of five wind turbines during a wind farm control experiment. Challenges included limited observations and atypical conditions. The simulation accurately predicts the aerodynamics of a turbine when it is situated partially within the wake of an upstream turbine.
Lars Neuhaus, Matthias Wächter, and Joachim Peinke
Wind Energ. Sci., 9, 439–452, https://doi.org/10.5194/wes-9-439-2024, https://doi.org/10.5194/wes-9-439-2024, 2024
Short summary
Short summary
Future wind turbines reach unprecedented heights and are affected by wind conditions that have not yet been studied in detail. With increasing height, a transition to laminar conditions with a turbulent–non-turbulent interface (TNTI) becomes more likely. In this paper, the presence and fractality of this TNTI in the atmosphere are studied. Typical fractalities known from ideal laboratory and numerical studies and a frequent occurrence of the TNTI at heights of multi-megawatt turbines are found.
Sebastiano Stipa, Arjun Ajay, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 297–320, https://doi.org/10.5194/wes-9-297-2024, https://doi.org/10.5194/wes-9-297-2024, 2024
Short summary
Short summary
In the current study, we introduce TOSCA (Toolbox fOr Stratified Convective Atmospheres), an open-source computational fluid dynamics (CFD) tool, and demonstrate its capabilities by simulating the flow around a large wind farm, operating in realistic flow conditions. This is one of the grand challenges of the present decade and can yield better insight into physical phenomena that strongly affect wind farm operation but which are not yet fully understood.
Rebecca Foody, Jacob Coburn, Jeanie A. Aird, Rebecca J. Barthelmie, and Sara C. Pryor
Wind Energ. Sci., 9, 263–280, https://doi.org/10.5194/wes-9-263-2024, https://doi.org/10.5194/wes-9-263-2024, 2024
Short summary
Short summary
Using lidar-derived wind speed measurements at approx. 150 m height at onshore and offshore locations, we quantify the advantages of deploying wind turbines offshore in terms of the amount of electrical power produced and the higher reliability and predictability of the electrical power.
Ronald B. Smith
Wind Energ. Sci., 9, 253–261, https://doi.org/10.5194/wes-9-253-2024, https://doi.org/10.5194/wes-9-253-2024, 2024
Short summary
Short summary
Recent papers have investigated the impact of turbine drag on local wind patterns, but these studies have not given a full explanation of the induced pressure field. The pressure field blocks and deflects the wind and in other ways modifies farm efficiency. Current gravity wave models are complex and provide no estimation tools. We dig deeper into the cause of the pressure field and provide approximate closed-form expressions for pressure field effects.
Cédric Raibaudo, Jean-Christophe Gilloteaux, and Laurent Perret
Wind Energ. Sci., 8, 1711–1725, https://doi.org/10.5194/wes-8-1711-2023, https://doi.org/10.5194/wes-8-1711-2023, 2023
Short summary
Short summary
The work presented here proposes interfacing experimental measurements performed in a wind tunnel with simulations conducted with the aeroelastic code FAST and applied to a floating wind turbine model under wave-induced motion. FAST simulations using experiments match well with those obtained using the inflow generation method provided by TurbSim. The highest surge motion frequencies show a significant decrease in the mean power produced by the turbine and a mitigation of the flow dynamics.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 8, 1651–1658, https://doi.org/10.5194/wes-8-1651-2023, https://doi.org/10.5194/wes-8-1651-2023, 2023
Short summary
Short summary
Low-level jets (LLJs) are special types of non-ideal wind profiles affecting both wind energy production and loads on a wind turbine. However, among LLJ researchers, there is no consensus regarding which definition to use to identify these profiles. In this work, we compare two different ways of identifying the LLJ – the falloff definition and the shear definition – and argue why the shear definition is better suited to wind energy applications.
Serkan Kartal, Sukanta Basu, and Simon J. Watson
Wind Energ. Sci., 8, 1533–1551, https://doi.org/10.5194/wes-8-1533-2023, https://doi.org/10.5194/wes-8-1533-2023, 2023
Short summary
Short summary
Peak wind gust is a crucial meteorological variable for wind farm planning and operations. Unfortunately, many wind farms do not have on-site measurements of it. In this paper, we propose a machine-learning approach (called INTRIGUE, decIsioN-TRee-based wInd GUst Estimation) that utilizes numerous inputs from a public-domain reanalysis dataset, generating long-term, site-specific peak wind gust series.
Nikolas Angelou, Jakob Mann, and Camille Dubreuil-Boisclair
Wind Energ. Sci., 8, 1511–1531, https://doi.org/10.5194/wes-8-1511-2023, https://doi.org/10.5194/wes-8-1511-2023, 2023
Short summary
Short summary
This study presents the first experimental investigation using two nacelle-mounted wind lidars that reveal the upwind and downwind conditions relative to a full-scale floating wind turbine. We find that in the case of floating wind turbines with small pitch and roll oscillating motions (< 1°), the ambient turbulence is the main driving factor that determines the propagation of the wake characteristics.
Julian Quick, Pierre-Elouan Rethore, Mads Mølgaard Pedersen, Rafael Valotta Rodrigues, and Mikkel Friis-Møller
Wind Energ. Sci., 8, 1235–1250, https://doi.org/10.5194/wes-8-1235-2023, https://doi.org/10.5194/wes-8-1235-2023, 2023
Short summary
Short summary
Wind turbine positions are often optimized to avoid wake losses. These losses depend on atmospheric conditions, such as the wind speed and direction. The typical optimization scheme involves discretizing the atmospheric inputs, then considering every possible set of these discretized inputs in every optimization iteration. This work presents stochastic gradient descent (SGD) as an alternative, which randomly samples the atmospheric conditions during every optimization iteration.
Sarah J. Ollier and Simon J. Watson
Wind Energ. Sci., 8, 1179–1200, https://doi.org/10.5194/wes-8-1179-2023, https://doi.org/10.5194/wes-8-1179-2023, 2023
Short summary
Short summary
This modelling study shows that topographic trapped lee waves (TLWs) modify flow behaviour and power output in offshore wind farms. We demonstrate that TLWs can substantially alter the wind speeds at individual wind turbines and effect the power output of the turbine and whole wind farm. The impact on wind speeds and power is dependent on which part of the TLW wave cycle interacts with the wind turbines and wind farm. Positive and negative impacts of TLWs on power output are observed.
Khaled Yassin, Arne Helms, Daniela Moreno, Hassan Kassem, Leo Höning, and Laura J. Lukassen
Wind Energ. Sci., 8, 1133–1152, https://doi.org/10.5194/wes-8-1133-2023, https://doi.org/10.5194/wes-8-1133-2023, 2023
Short summary
Short summary
The current turbulent wind field models stated in the IEC 61400-1 standard underestimate the probability of extreme changes in wind velocity. This underestimation can lead to the false calculation of extreme and fatigue loads on the turbine. In this work, we are trying to apply a random time-mapping technique to one of the standard turbulence models to adapt to such extreme changes. The turbulent fields generated are compared with a standard wind field to show the effects of this new mapping.
Mark Kelly and Maarten Paul van der Laan
Wind Energ. Sci., 8, 975–998, https://doi.org/10.5194/wes-8-975-2023, https://doi.org/10.5194/wes-8-975-2023, 2023
Short summary
Short summary
The turning of the wind with height, which is known as veer, can affect wind turbine performance. Thus far meteorology has only given idealized descriptions of veer, which has not yet been related in a way readily usable for wind energy. Here we derive equations for veer in terms of meteorological quantities and provide practically usable forms in terms of measurable shear (change in wind speed with height). Flow simulations and measurements at turbine heights support these developments.
Moritz Gräfe, Vasilis Pettas, Julia Gottschall, and Po Wen Cheng
Wind Energ. Sci., 8, 925–946, https://doi.org/10.5194/wes-8-925-2023, https://doi.org/10.5194/wes-8-925-2023, 2023
Short summary
Short summary
Inflow wind field measurements from nacelle-based lidar systems offer great potential for different applications including turbine control, load validation and power performance measurements. On floating wind turbines nacelle-based lidar measurements are affected by the dynamic behavior of the floating foundations. Therefore, the effects on lidar wind speed measurements induced by floater dynamics must be well understood. A new model for quantification of these effects is introduced in our work.
Robin Marcille, Maxime Thiébaut, Pierre Tandeo, and Jean-François Filipot
Wind Energ. Sci., 8, 771–786, https://doi.org/10.5194/wes-8-771-2023, https://doi.org/10.5194/wes-8-771-2023, 2023
Short summary
Short summary
A novel data-driven method is proposed to design an optimal sensor network for the reconstruction of offshore wind resources. Based on unsupervised learning of numerical weather prediction wind data, it provides a simple yet efficient method for the siting of sensors, outperforming state-of-the-art methods for this application. It is applied in the main French offshore wind energy development areas to provide guidelines for the deployment of floating lidars for wind resource assessment.
Wei Fu, Alessandro Sebastiani, Alfredo Peña, and Jakob Mann
Wind Energ. Sci., 8, 677–690, https://doi.org/10.5194/wes-8-677-2023, https://doi.org/10.5194/wes-8-677-2023, 2023
Short summary
Short summary
Nacelle lidars with different beam scanning locations and two types of systems are considered for inflow turbulence estimations using both numerical simulations and field measurements. The turbulence estimates from a sonic anemometer at the hub height of a Vestas V52 turbine are used as references. The turbulence parameters are retrieved using the radial variances and a least-squares procedure. The findings from numerical simulations have been verified by the analysis of the field measurements.
Daniel Hatfield, Charlotte Bay Hasager, and Ioanna Karagali
Wind Energ. Sci., 8, 621–637, https://doi.org/10.5194/wes-8-621-2023, https://doi.org/10.5194/wes-8-621-2023, 2023
Short summary
Short summary
Wind observations at heights relevant to the operation of modern offshore wind farms, i.e. 100 m and more, are required to optimize their positioning and layout. Satellite wind retrievals provide observations of the wind field over large spatial areas and extensive time periods, yet their temporal resolution is limited and they are only representative at 10 m height. Machine-learning models are applied to lift these satellite winds to higher heights, directly relevant to wind energy purposes.
Anna von Brandis, Gabriele Centurelli, Jonas Schmidt, Lukas Vollmer, Bughsin' Djath, and Martin Dörenkämper
Wind Energ. Sci., 8, 589–606, https://doi.org/10.5194/wes-8-589-2023, https://doi.org/10.5194/wes-8-589-2023, 2023
Short summary
Short summary
We propose that considering large-scale wind direction changes in the computation of wind farm cluster wakes is of high relevance. Consequently, we present a new solution for engineering modeling tools that accounts for the effect of such changes in the propagation of wakes. The new model is evaluated with satellite data in the German Bight area. It has the potential to reduce uncertainty in applications such as site assessment and short-term power forecasting.
Oliver Maas
Wind Energ. Sci., 8, 535–556, https://doi.org/10.5194/wes-8-535-2023, https://doi.org/10.5194/wes-8-535-2023, 2023
Short summary
Short summary
The study compares small vs. large wind farms regarding the flow and power output with a turbulence-resolving simulation model. It shows that a large wind farm (90 km length) significantly affects the wind direction and that the wind speed is higher in the large wind farm wake. Both wind farms excite atmospheric gravity waves that also affect the power output of the wind farms.
Regis Thedin, Eliot Quon, Matthew Churchfield, and Paul Veers
Wind Energ. Sci., 8, 487–502, https://doi.org/10.5194/wes-8-487-2023, https://doi.org/10.5194/wes-8-487-2023, 2023
Short summary
Short summary
We investigate coherence and correlation and highlight their importance for disciplines like wind energy structural dynamic analysis, in which blade loading and fatigue depend on turbulence structure. We compare coherence estimates to those computed using a model suggested by international standards. We show the differences and highlight additional information that can be gained using large-eddy simulation, further improving analytical coherence models used in synthetic turbulence generators.
Sheng-Lun Tai, Larry K. Berg, Raghavendra Krishnamurthy, Rob Newsom, and Anthony Kirincich
Wind Energ. Sci., 8, 433–448, https://doi.org/10.5194/wes-8-433-2023, https://doi.org/10.5194/wes-8-433-2023, 2023
Short summary
Short summary
Turbulence intensity is critical for wind turbine design and operation as it affects wind power generation efficiency. Turbulence measurements in the marine environment are limited. We use a model to derive turbulence intensity and test how sea surface temperature data may impact the simulated turbulence intensity and atmospheric stability. The model slightly underestimates turbulence, and improved sea surface temperature data reduce the bias. Error with unrealistic mesoscale flow is identified.
Brandon Arthur Lobo, Özge Sinem Özçakmak, Helge Aagaard Madsen, Alois Peter Schaffarczyk, Michael Breuer, and Niels N. Sørensen
Wind Energ. Sci., 8, 303–326, https://doi.org/10.5194/wes-8-303-2023, https://doi.org/10.5194/wes-8-303-2023, 2023
Short summary
Short summary
Results from the DAN-AERO and aerodynamic glove projects provide significant findings. The effects of inflow turbulence on transition and wind turbine blades are compared to computational fluid dynamic simulations. It is found that the transition scenario changes even over a single revolution. The importance of a suitable choice of amplification factor is evident from the simulations. An agreement between the power spectral density plots from the experiment and large-eddy simulations is seen.
Frédéric Blondel
Wind Energ. Sci., 8, 141–147, https://doi.org/10.5194/wes-8-141-2023, https://doi.org/10.5194/wes-8-141-2023, 2023
Short summary
Short summary
Accurate wind farm flow predictions based on analytical wake models are crucial for wind farm design and layout optimization. Wake superposition methods play a key role and remain a substantial source of uncertainty. In the present work, a momentum-conserving superposition method is extended to the superposition of super-Gaussian-type velocity deficit models, allowing the full wake velocity deficit estimation and design of closely packed wind farms.
Abdul Haseeb Syed, Jakob Mann, Andreas Platis, and Jens Bange
Wind Energ. Sci., 8, 125–139, https://doi.org/10.5194/wes-8-125-2023, https://doi.org/10.5194/wes-8-125-2023, 2023
Short summary
Short summary
Wind turbines extract energy from the incoming wind flow, which needs to be recovered. In very large offshore wind farms, the energy is recovered mostly from above the wind farm in a process called entrainment. In this study, we analyzed the effect of atmospheric stability on the entrainment process in large offshore wind farms using measurements recorded by a research aircraft. This is the first time that in situ measurements are used to study the energy recovery process above wind farms.
Kartik Venkatraman, Trond-Ola Hågbo, Sophia Buckingham, and Knut Erik Teigen Giljarhus
Wind Energ. Sci., 8, 85–108, https://doi.org/10.5194/wes-8-85-2023, https://doi.org/10.5194/wes-8-85-2023, 2023
Short summary
Short summary
This paper is focused on the impact of modeling different effects, such as forest canopy and Coriolis forces, on the wind resource over a complex terrain site located near Perdigão, Portugal. A numerical model is set up and results are compared with field measurements. The results show that including a forest canopy improves the predictions close to the ground at some locations on the site, while the model with inflow from a precursor performed better at other locations.
Ishaan Sood, Elliot Simon, Athanasios Vitsas, Bart Blockmans, Gunner C. Larsen, and Johan Meyers
Wind Energ. Sci., 7, 2469–2489, https://doi.org/10.5194/wes-7-2469-2022, https://doi.org/10.5194/wes-7-2469-2022, 2022
Short summary
Short summary
In this work, we conduct a validation study to compare a numerical solver against measurements obtained from the offshore Lillgrund wind farm. By reusing a previously developed inflow turbulent dataset, the atmospheric conditions at the wind farm were recreated, and the general performance trends of the turbines were captured well. The work increases the reliability of numerical wind farm solvers while highlighting the challenges of accurately representing large wind farms using such solvers.
Cited articles
Abkar, M. and Porté-Agel, F.: The effect of free-atmosphere stratification on boundary-layer flow and power output from very large wind farms, Energies, 6, 2338–2361, https://doi.org/10.3390/en6052338, 2013.
Abkar, M. and Porté-Agel, F.: Mean and turbulent kinetic energy budgets inside and above very large wind farms under conventionally-neutral condition, Renew. Energ., 70, 142–152, https://doi.org/10.1016/j.renene.2014.03.050, 2014.
Abkar, M. and Porté-Agel, F.: Influence of atmospheric stability on wind-turbine wakes: A large-eddy simulation study, Physics of fluids, 27, 035104, https://doi.org/10.1063/1.4913695, 2015.
Ahsbahs, T., Nygaard, N. G., Newcombe, A., and Badger, M.: Wind farm wakes from SAR and Doppler radar, Remote Sens., 12, 462, https://doi.org/10.3390/rs12030462, 2020.
Allaerts, D. and Meyers, J.: Boundary-layer development and gravity waves in conventionally neutral wind farms, J. Fluid Mech., 814, 95–130, https://doi.org/10.1017/jfm.2017.11, 2017.
Baidya Roy, S., Pacala, S. W., and Walko, R. L.: Can large wind farms affect local meteorology?, J. Geophys. Res.-Atmos., 109, D19101, https://doi.org/10.1029/2004JD004763, 2004.
Banta, R. M., Pichugina, Y. L., Brewer, W. A., Lundquist, J. K., Kelley, N. D., Sandberg, S. P., Alvarez II, R. J., Hardesty, R. M., and Weickmann, A. M.: 3D volumetric analysis of wind turbine wake properties in the atmosphere using high-resolution Doppler lidar, J. Atmos. Ocean. Tech., 32, 904–914, https://doi.org/10.1175/JTECH-D-14-00078.1, 2015.
Barthelmie, R. J., Hansen, K. S., and Pryor, S. C.: Meteorological controls on wind turbine wakes, P. IEEE, 101, 1010–1019, https://doi.org/10.1109/JPROC.2012.2204029, 2012.
Berg, L. K., Riihimaki, L. D., Qian, Y., Yan, H., and Huang, M.: The low-level jet over the southern Great Plains determined from observations and reanalyses and its impact on moisture transport, J. Climate, 28, 6682–6706, https://doi.org/10.1175/JCLI-D-14-00719.1, 2015.
Berg, L. K., Newsom, R. K., and Turner, D. D.: Year-long vertical velocity statistics derived from Doppler lidar data for the continental convective boundary layer, J. Appl. Meteorol. Clim., 56, 2441–2454, https://doi.org/10.1175/JAMC-D-16-0359.1, 2017.
Blackadar, A. K.: Boundary layer wind maxima and their significance for the growth of nocturnal inversions, B. Am. Meteorol. Soc., 38, 283–290, https://doi.org/10.1175/1520-0477-38.5.283, 1957.
Bodini, N., Lundquist, J. K., and Moriarty, P.: Wind plants can impact long-term local atmospheric conditions, Sci. Rep., 11, 22939, https://doi.org/10.1038/s41598-021-02089-2, 2021.
Bonin, T. A., Choukulkar, A., Brewer, W. A., Sandberg, S. P., Weickmann, A. M., Pichugina, Y. L., Banta, R. M., Oncley, S. P., and Wolfe, D. E.: Evaluation of turbulence measurement techniques from a single Doppler lidar, Atmos. Meas. Tech., 10, 3021–3039, https://doi.org/10.5194/amt-10-3021-2017, 2017.
Calaf, M., Parlange, M. B., and Meneveau, C.: Large eddy simulation study of scalar transport in fully developed wind-turbine array boundary layers, Phys. Fluids, 23, 126603, https://doi.org/10.1063/1.3663376, 2011.
Carbone, R. E., Conway, J. W., Crook, N. A., and Moncrieff, M. W.: The generation and propagation of a nocturnal squall line. Part I: Observations and implications for mesoscale predictability, Mon. Weather Rev., 118, 26–49, https://doi.org/10.1175/1520-0493(1990)118<0026:TGAPOA>2.0.CO;2, 1990.
Cook, D. R. and Sullivan, R. C.: Eddy Correlation Flux Measurement System (ECOR) Instrument Handbook, edited by: Robert Stafford, ARM user facility, DOE/SC-ARM/TR-052, https://doi.org/10.2172/1467448, 2020.
Davis, C. A., Manning, K. W., Carbone, R. E., Trier, S. B., and Tuttle, J. D.: Coherence of warm-season continental rainfall in numerical weather prediction models, Mon. Weather Rev., 131, 2667–2679, https://doi.org/10.1175/1520-0493(2003)131<2667:COWCRI>2.0.CO;2, 2003.
Debnath, M., Moriarty, P., Krishnamurthy, R., Bodini, N., Newsom, R., Quon, E., Lundquist, J. K., Letizia, S., Iungo, V., and Klein, P.: Characterization of wind speed and directional shear at the AWAKEN field campaign site, J. Renew. Sustain. Ener., 15, 033308, https://doi.org/10.1063/5.0139737, 2023.
Djath, B. and Schulz-Stellenfleth, J.: Wind speed deficits downstream offshore wind parks – A new automised estimation technique based on satellite synthetic aperture radar data, Meteorol. Z., 28, 499–515, https://doi.org/10.1127/metz/2019/0992, 2019.
Djath, B., Schulz-Stellenfleth, J., and Cañadillas, B.: Impact of atmospheric stability on X-band and C-band Synthetic Aperture Radar imagery of offshore windpark wakes, Journal of Sustainable and Renewable Energy, 10, 2018, https://doi.org/10.1063/1.5020437.
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.
Eberhard, W. L., Cupp, R. E., and Healy, K. R.: Doppler lidar measurement of profiles of turbulence and momentum flux, J. Atmos. Ocean. Tech., 6, 809–819, https://doi.org/10.1175/1520-0426(1989)006<0809:DLMOPO>2.0.CO;2, 1989.
Fernando, H. J. S., Mann, J., Palma, J. M. L. M., Lundquist, J. K., Barthelmie, R. J., Belo-Pereira, M., Brown, W. O. J., Chow, F. K., Gerz, T., Hocut, C. M., Klein, P. M., Leo, L. S., Matos, J. C., Oncley, S. P., Pryor, S. C., Bariteau, L., Bell, T. M., Bodini, N., Carney, M. B., Courtney, M. S., Creegan, E. D., Dimitrova, R., Gomes, S., Hagen, M., Hyde, J. O., Kigle, S., Krishnamurthy, R., Lopes, J. C., Mazzaro, L., Neher, J. M. T., Menke, R., Murphy, P., Oswald, L., Otarola-Bustos, S., Pattantyus, A. K., Rodrigues, C. V., Schady, A., Sirin, N., Spuler, S., Svensson, E., Tomaszewski, J., Turner, D. D., van Veen, L., Vasiljević, N., Vassallo, D., Voss, S., Wildmann, N., and Wang, Y.: The Perdigão: The Perdigao: Peering into microscale details of mountain winds, B. Am. Meteorol. Soc., 100, 799–819, https://doi.org/10.1175/BAMS-D-17-0227.1, 2019.
Frehlich, R.: Coherent Doppler lidar signal covariance including wind shear and wind turbulence, Appl. Opt., 33, 6472–6481, https://doi.org/10.1364/AO.33.006472, 1994.
Frehlich, R.: Estimation of velocity error for Doppler lidar measurements, J. Atmos. Ocean. Tech., 18, 1628–1639, https://doi.org/10.1175/1520-0426(2001)018<1628:EOVEFD>2.0.CO;2, 2001.
Frehlich, R. and Cornman, L.: Estimating spatial velocity statistics with coherent Doppler lidar, J. Atmos. Ocean. Tech., 19, 355–366, https://doi.org/10.1175/1520-0426-19.3.355, 2002.
Frehlich, R., Meillier, Y., Jensen, M. L., Balsley, B., and Sharman, R.: Measurements of boundary layer profiles in an urban environment, J. Appl. Meteorol. Clim., 45, 821–837, https://doi.org/10.1175/JAM2368.1, 2006
Fleming, P., King, J., Dykes, K., Simley, E., Roadman, J., Scholbrock, A., Murphy, P., Lundquist, J. K., Moriarty, P., Fleming, K., van Dam, J., Bay, C., Mudafort, R., Lopez, H., Skopek, J., Scott, M., Ryan, B., Guernsey, C., and Brake, D.: Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1, Wind Energ. Sci., 4, 273–285, https://doi.org/10.5194/wes-4-273-2019, 2019.
Gadde, S. N. and Stevens, R. J.: 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, 2021.
Garratt, J. R.: The internal boundary layer – A review, Bound.-Lay. Meteorol., 50, 171–203, https://doi.org/10.1007/BF00120524, 1990.
Geerts, B., Parsons, D., Ziegler, C. L., Weckwerth, T. M., Biggerstaff, M. I., Clark, R. D., Coniglio, M. C., Demoz, B. B., Ferrare, R. A., Gallus Jr., W. A., and Haghi, K.: The 2015 plains elevated convection at night field project, B. Am. Meteorol. Soc., 98, 767–786, https://doi.org/10.1175/BAMS-D-15-00257.1, 2017.
Goring, D. G. and Nikora, V. I.: Despiking acoustic Doppler velocimeter data, J. Hydraul. Eng., 128, 117–126, https://doi.org/10.1061/(ASCE)0733-9429(2002)128:1(117), 2002.
Hamilton, N.: Ceilometer, Wind Data Hub [data set], https://doi.org/10.21947/2221789, 2024.
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.
Hirth, B. D., Schroeder, J. L., Gunter, W. S., and Guynes, J. G.: Measuring a utility-scale turbine wake using the TTUKa mobile research radars, J. Atmos. Ocean. Tech., 29, 765–771, https://doi.org/10.1175/JTECH-D-12-00039.1, 2012.
Kalverla, P. C., Duncan Jr., J. B., Steeneveld, G.-J., and Holtslag, A. A. M.: Low-level jets over the North Sea based on ERA5 and observations: together they do better, Wind Energ. Sci., 4, 193–209, https://doi.org/10.5194/wes-4-193-2019, 2019.
Kotthaus, S., Bravo-Aranda, J. A., Collaud Coen, M., Guerrero-Rascado, J. L., Costa, M. J., Cimini, D., O'Connor, E. J., Hervo, M., Alados-Arboledas, L., Jiménez-Portaz, M., Mona, L., Ruffieux, D., Illingworth, A., and Haeffelin, M.: Atmospheric boundary layer height from ground-based remote sensing: a review of capabilities and limitations, Atmos. Meas. Tech., 16, 433–479, https://doi.org/10.5194/amt-16-433-2023, 2023.
Krishnamurthy, R., Calhoun, R., Billings, B., and Doyle, J.: Wind turbulence estimates in a valley by coherent Doppler lidar, Meteorol. Appl., 18, 361–371, https://doi.org/10.1002/met.263, 2011.
Krishnamurthy, R., Choukulkar, A., Calhoun, R., Fine, J., Oliver, A., and Barr, K.S.: Coherent Doppler lidar for wind farm characterization, Wind Energy, 16, 189–206, https://doi.org/10.1002/we.539, 2013.
Krishnamurthy, R., Reuder, J., Svardal, B., Fernando, H. J. S., and Jakobsen, J. B.: Offshore wind turbine wake characteristics using scanning Doppler lidar, Enrgy Proced., 137, 428–442, https://doi.org/10.1016/j.egypro.2017.10.367, 2017.
Krishnamurthy, R., Newsom, R. K., Chand, D., and Shaw, W. J.: Boundary layer climatology at arm southern great plains (No. PNNL-30832), Pacific Northwest National Lab. (PNNL), Richland, WA, United States, https://doi.org/10.2172/1778833, 2021a.
Krishnamurthy, R., Newsom, R. K., Berg, L. K., Xiao, H., Ma, P.-L., and Turner, D. D.: On the estimation of boundary layer heights: a machine learning approach, Atmos. Meas. Tech., 14, 4403–4424, https://doi.org/10.5194/amt-14-4403-2021, 2021b.
Krishnamurthy, R., Fernando, H. J. S., Alappattu, D., Creegan, E., and Wang, Q.: Observations of offshore internal boundary layers, J. Geophys. Res.-Atmos., 128, e2022JD037425, https://doi.org/10.1029/2022JD037425, 2023.
Lampert, A., Bärfuss, K., Platis, A., Siedersleben, S., Djath, B., Cañadillas, B., Hunger, R., Hankers, R., Bitter, M., Feuerle, T., Schulz, H., Rausch, T., Angermann, M., Schwithal, A., Bange, J., Schulz-Stellenfleth, J., Neumann, T., and Emeis, S.: In situ airborne measurements of atmospheric and sea surface parameters related to offshore wind parks in the German Bight, Earth Syst. Sci. Data, 12, 935–946, https://doi.org/10.5194/essd-12-935-2020, 2020.
Liu, S. and Liang, X. Z.: Observed diurnal cycle climatology of planetary boundary layer height, J. Climate, 23, 5790–5809, https://doi.org/10.1175/2010JCLI3552.1, 2010.
Lu, H. and Porté-Agel, F.: On the impact of wind farms on a convective atmospheric boundary layer, Bound.-Lay. Meteorol., 157, 81–96, https://doi.org/10.1007/s10546-015-0049-1, 2015.
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.
Maddox, R. A.: Mesoscale convective complexes, B. Am. Meteorol. Soc., 1374–1387, 1980.
Mann, J., Peña, A., Bingöl, F., Wagner, R., and Courtney, M. S.: Lidar scanning of momentum flux in and above the atmospheric surface layer, J. Atmos. Ocean. Tech., 27, 959–976, https://doi.org/10.1175/2010JTECHA1389.1, 2010.
Miller, L. M. and Keith, D. W.: Climatic impacts of wind power, Joule, 2, 2618–2632, https://doi.org/10.1016/j.joule.2018.09.009, 2018.
Moriarty, P., Bodini, N., Letizia, S., Abraham, A., Ashley, T., Bärfuss, K. B., Barthelmie, R. J., Brewer, A., Brugger, P., Feuerle, T., Frère, A., Goldberger, L., Gottschall, J., Hamilton, N., Herges, T., Hirth, B., Hung, L. Y., Iungo, G. V., Ivanov, H., Kaul, C., Kern, S., Klein, P., Krishnamurthy, R., Lampert, A., Lundquist, J. K., Morris, V. R., Newsom, R., Pekour, M., Pichugina, Y., Porté-Angel, F., Pryor, S. C., Scholbrock, A., Schroeder, J., Shartzer, S., Simley, E., Vöhringer, L., Wharton, S., and Zalkind, D.: Overview of preparation for the American WAKE ExperimeNt (AWAKEN), J. Renew. Sustain. Ener., 16, 053306, https://doi.org/10.1063/5.0141683, 2024.
Murphy, P., Lundquist, J. K., and Fleming, P.: How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine, Wind Energ. Sci., 5, 1169–1190, https://doi.org/10.5194/wes-5-1169-2020, 2020.
Newsom, R. and Krishnamurthy, R.: Doppler Lidar Site A2, Wind Data Hub [data set], https://doi.org/10.21947/2283040, 2024a.
Newsom, R. and Krishnamurthy, R.: Doppler Lidar at Site H, Wind Data Hub [data set], https://doi.org/10.5439/1890922, 2024b.
Newsom, R. and Krishnamurthy, R.: Doppler Lidar (DLPPI2) Site A1 (S4) for AWAKEN, ARM Data Discovery [data set], https://doi.org/10.5439/1890922, 2024c.
Newsom, R. K. and Krishnamurthy, R.: Doppler Lidar (DL) instrument handbook (No. DOE/SC-ARM/TR-101), DOE Office of Science Atmospheric Radiation Measurement (ARM) User Facility, https://doi.org/10.2172/1034640, 2022.
Newsom, R. K., Brewer, W. A., Wilczak, J. M., Wolfe, D. E., Oncley, S. P., and Lundquist, J. K.: Validating precision estimates in horizontal wind measurements from a Doppler lidar, Atmos. Meas. Tech., 10, 1229–1240, https://doi.org/10.5194/amt-10-1229-2017, 2017.
Pekour, M.: Sonic Anemometer, Wind Data Hub [data set],https://doi.org/10.21947/1899850, 2024.
Parsons, D. B., Lillo, S. P., Rattray, C. P., Bechtold, P., Rodwell, M. J., and Bruce, C. M.: The role of continental mesoscale convective systems in forecast busts within global weather prediction systems, Atmosphere, 10, 681, https://doi.org/10.3390/atmos10110681, 2019.
Peña, A., Hasager, C. B., Gryning, S.-E., Courtney, M., Antoniou, I., and Mikkelsen, T.: Offshore wind profiling using light detection and ranging measurements, Wind Energy, 12, 105–124, https://doi.org/10.1002/we.283, 2009.
Platis, A., Siedersleben, S. K., Bange, J., Lampert, A., Bärfuss, K., Hankers, R., Cañadillas, B., Foreman, R., Schulz-Stellenfleth, J., Djath, B., Neumann, T., and Emeis, S.: First in situ evidence of wakes in the far field behind offshore wind farms, Sci. Rep., 8, 2163, https://doi.org/10.1038/s41598-018-20389-y, 2018.
Pritchard, M. S., Moncrieff, M. W., and Somerville, R. C.: Orogenic propagating precipitation systems over the United States in a global climate model with embedded explicit convection, J. Atmos. Sci., 68, 1821–1840, https://doi.org/10.1175/2011JAS3699.1, 2011.
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.
Sathe, A., Mann, J., Vasiljevic, N., and Lea, G.: A six-beam method to measure turbulence statistics using ground-based wind lidars, Atmos. Meas. Tech., 8, 729–740, https://doi.org/10.5194/amt-8-729-2015, 2015.
Schneemann, J., Rott, A., Dörenkämper, M., Steinfeld, G., and Kühn, M.: Cluster wakes impact on a far-distant offshore wind farm's power, Wind Energ. Sci., 5, 29–49, https://doi.org/10.5194/wes-5-29-2020, 2020.
Shaw, W. J., Berg, L. K., Debnath, M., Deskos, G., Draxl, C., Ghate, V. P., Hasager, C. B., Kotamarthi, R., Mirocha, J. D., Muradyan, P., Pringle, W. J., Turner, D. D., and Wilczak, J. M.: Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer, Wind Energ. Sci., 7, 2307–2334, https://doi.org/10.5194/wes-7-2307-2022, 2022.
Siedersleben, S. K., Lundquist, J. K., Platis, A., Bange, J., Bärfuss, K., Lampert, A., and Emeis, S.: Micrometeorological impacts of offshore wind farms as seen in observations and simulations, Environ. Res. Lett., 13, 124012, https://doi.org/10.1088/1748-9326/aaea0b, 2018.
Smalikho, I., Köpp, F., and Rahm, S.: Measurement of Atmospheric Turbulence by 2-μm Doppler Lidar, J. Atmos. Ocean. Tech., 22, 1733–1747, https://doi.org/10.1175/JTECH1815.1, 2005.
Smalikho, I., Köpp, F., and Rahm, S.: Measurement of atmospheric turbulence by 2-µm Doppler lidar, J. Atmos. Ocean. Tech., 22, 1733–1747, https://doi.org/10.1175/JTECH1815.1, 2015.
Smith, C. M., Barthelmie, R. J., and Pryor, S. C.: In situ observations of the influence of a large onshore wind farm on near-surface temperature, turbulence intensity and wind speed profiles, Environ. Res. Lett., 8, 034006, https://doi.org/10.1088/1748-9326/8/3/034006, 2013.
Song, J., Liao, K., Coulter, R. L., and Lesht, B. M.: Climatology of the low-level jet at the southern Great Plains atmospheric boundary layer experiments site, J. Appl. Meteorol. Clim., 44, 1593–1606, https://doi.org/10.1175/JAM2294.1, 2005.
Stevens, R. J.: Dependence of optimal wind turbine spacing on wind farm length, Wind energy, 19, 651–663, https://doi.org/10.1002/we.1857, 2016.
Stevens, R. J. and Meneveau, C.: Flow structure and turbulence in wind farms, Annu. Rev. Fluid Mech., 49, 311–339, https://doi.org/10.1146/annurev-fluid-010816-060206, 2017.
Stull, R. B.: Boundary layer clouds, in: An Introduction to Boundary Layer Meteorology, 545–585, Springer, Dordrecht, the Netherlands, ISBN 13 978-90-277-2769-5, https://doi.org/10.1007/978-94-009-3027-8, 1988.
Syed, A. H., Mann, J., Platis, A., and Bange, J.: Turbulence structures and entrainment length scales in large offshore wind farms, Wind Energ. Sci., 8, 125–139, https://doi.org/10.5194/wes-8-125-2023, 2023.
Taylor, P.: On wind and shear stress profiles above a change in surface roughness, Q. J. Roy. Meteor. Soc., 95, 77–91, https://doi.org/10.1002/qj.49709540306, 1969.
Tucker, S. C., Senff, C. J., Weickmann, A. M., Brewer, W. A., Banta, R. M., Sandberg, S. P., Law, D. C., and Hardesty, R. M.: Doppler lidar estimation of mixing height using turbulence, shear, and aerosol profiles, J. Atmos. Ocean. Tech., 26, 673–688, https://doi.org/10.1175/2008JTECHA1157.1, 2009.
U.S. Department of Energy: A2E Wind Data Hub, https://www.a2e.energy.gov, last access: 31 October 2024.
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.
VerHulst, C. and Meneveau, C.: Large eddy simulation study of the kinetic energy entrainment by energetic turbulent flow structures in large wind farms, Phys. Fluids, 26, 025113, https://doi.org/10.1063/1.4865755, 2014.
Wang, D., Giangrande, S. E., Feng, Z., Hardin, J. C., and Prein, A. F.: Updraft and downdraft core size and intensity as revealed by radar wind profilers: MCS observations and idealized model comparisons, J. Geophys. Res.-Atmos., 125, e2019JD031774, https://doi.org/10.1029/2019JD031774, 2020.
Whiteman, C. D., Bian, X. and Zhong, S.: Low-level jet climatology from enhanced rawinsonde observations at a site in the southern Great Plains, J. Appl. Meteorol., 36, 1363–1376, https://doi.org/10.1175/1520-0450(1997)036<1363:LLJCFE>2.0.CO;2, 1997.
Wilczak, J. M., Oncley, S. P., and Stage, S. A.: Sonic anemometer tilt correction algorithms, Bound.-Lay. Meteorol., 99, 127–150, https://doi.org/10.1023/A:1018966204465, 2001.
Wilczak, J. M., Stoelinga, M., Berg, L. K., Sharp, J., Draxl, C., McCaffrey, K., Banta, R. M., Bianco, L., Djalalova, I., Lundquist, J. K., Muradyan, P., Choukulkar, A., Leo, L., Bonin, T., Pichugina, Y., Eckman, R., Long, C. N., Lantz, K., Worsnop, R. P., Bickford, J., Bodini, N., Chand, D., Clifton, A., Cline, J., Cook, D. R., Fernando, H. J. S., Friedrich, K., Krishnamurthy, R., Marquis, M., McCaa, J., Olson, J. B., Otarola-Bustos, S., Scott, G., Shaw, W. J., Wharton, S., and White, A. B.: The second wind forecast improvement project (WFIP2): Observational field campaign, B. Am. Meteorol. Soc., 100, 1701–1723, https://doi.org/10.1175/BAMS-D-18-0035.1, 2019.
Wildmann, N., Bodini, N., Lundquist, J. K., Bariteau, L., and Wagner, J.: Estimation of turbulence dissipation rate from Doppler wind lidars and in situ instrumentation for the Perdigão 2017 campaign, Atmos. Meas. Tech., 12, 6401–6423, https://doi.org/10.5194/amt-12-6401-2019, 2019.
Willingham, D., Anderson, W., Christensen, K. T., and Barros, J. M.: Turbulent boundary layer flow over transverse aerodynamic roughness transitions: induced mixing and flow characterization, Phys. Fluids, 26, 025111, https://doi.org/10.1063/1.4864105, 2014.
Yang, D., Meneveau, C., and Shen, L.: Large-eddy simulation of offshore wind farm, Phys. Fluids, 26, 025101, https://doi.org/10.1063/1.4863096, 2014.
Zhan, L., Letizia, S., and Valerio Iungo, G.: LiDAR measurements for an onshore wind farm: Wake variability for different incoming wind speeds and atmospheric stability regimes, Wind Energy, 23, 501–527, https://doi.org/10.1002/we.2430, 2020.
Zhang, D., Comstock, J., and Morris, V.: Comparison of planetary boundary layer height from ceilometer with ARM radiosonde data, Atmos. Meas. Tech., 15, 4735–4749, https://doi.org/10.5194/amt-15-4735-2022, 2022.
Short summary
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.
This study examines how atmospheric phenomena affect the recovery of wind farm wake – the...
Altmetrics
Final-revised paper
Preprint