Articles | Volume 11, issue 1
https://doi.org/10.5194/wes-11-71-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-11-71-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Jan 2026
Research article |
| 12 Jan 2026
| 12 Jan 2026
Investigation of onshore wind farm wake recovery with in situ aircraft measurements during AWAKEN
Technische Universität Braunschweig, Institute of Flight Guidance, Braunschweig, Germany
Konrad B. Bärfuss
Technische Universität Braunschweig, Institute of Flight Guidance, Braunschweig, Germany
Beatriz Cañadillas
Technische Universität Braunschweig, Institute of Flight Guidance, Braunschweig, Germany
Maik Angermann
Technische Universität Braunschweig, Institute of Flight Guidance, Braunschweig, Germany
Mark Bitter
Technische Universität Braunschweig, Institute of Flight Guidance, Braunschweig, Germany
Matthias Cremer
Technische Universität Braunschweig, Institute of Flight Guidance, Braunschweig, Germany
Thomas Feuerle
Technische Universität Braunschweig, Institute of Flight Guidance, Braunschweig, Germany
Jonas Spoor
Technische Universität Braunschweig, Institute of Flight Guidance, Braunschweig, Germany
Julie K. Lundquist
Johns Hopkins University, Baltimore, MD, USA
National Renewable Energy Laboratory, Golden, CO, USA
Patrick Moriarty
National Renewable Energy Laboratory, Golden, CO, USA
Astrid Lampert
Technische Universität Braunschweig, Institute of Flight Guidance, Braunschweig, Germany
Related authors
No articles found.
Nicola Bodini, Patrick Moriarty, Regis Thedin, Paula Doubrawa, Cristina Archer, Myra Blaylock, Carlo Bottasso, Bruno Carmo, Lawrence Cheung, Camille Dubreuil, Rogier Floors, Thomas Herges, Daniel Houck, Ali Kanjari, Colleen M. Kaul, Christopher Kelley, Ru LI, Julie K. Lundquist, Desirae Major, Anh Kiet Nguyen, Mike Optis, Luan R. C. Parada, Alfredo Peña, Julian Quick, David Ricarte, William C. Radünz, Raj K. Rai, Oscar Garcia Santiago, Jonas Schulte, Knut S. Seim, M. Paul van der Laan, Kisorthman Vimalakanthan, and Adam Wise
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-34, https://doi.org/10.5194/wes-2026-34, 2026
Preprint under review for WES
Short summary
Short summary
Predicting wind farm energy production is challenging because wind patterns are complex. We tested 16 different models against real data from a major field experiment to see which worked best. Surprisingly, the most expensive and detailed models were not always more accurate than simpler ones. We found that feeding models better weather data was the most effective way to improve accuracy. These results help the industry choose the right tools for designing more efficient wind farms.
Nathan J. Agarwal and Julie K. Lundquist
Wind Energ. Sci., 11, 883–910, https://doi.org/10.5194/wes-11-883-2026, https://doi.org/10.5194/wes-11-883-2026, 2026
Short summary
Short summary
Areas with hills and valleys can be either beneficial or challenging for wind energy applications, depending on the wind patterns. Unfortunately, predicting wind patterns in these areas is also challenging, and investing in measurement towers to improve wind forecasts can be expensive. We evaluate ways in which wind farm developers and other stakeholders interested in improving atmospheric forecasts in these areas can do so in a more cost-effective way.
Kira Gramitzky, Florian Jäger, Doron Callies, Tabea Hildebrand, Julie K. Lundquist, and Lukas Pauscher
Wind Energ. Sci., 11, 861–882, https://doi.org/10.5194/wes-11-861-2026, https://doi.org/10.5194/wes-11-861-2026, 2026
Short summary
Short summary
This study introduces an extended sea surface levelling method for the accurate offshore calibration of scanning lidars. This method can determine the alignment of the laser beam, including any vertical shift, and is independent of the scan pattern. Tests using real measurement data and a detailed uncertainty study confirm its reliability. The study offers a versatile calibration approach and improves confidence in offshore wind measurements with scanning lidars.
Nicola Bodini, Aliza Abraham, Paula Doubrawa, Stefano Letizia, Julie K. Lundquist, Patrick Moriarty, and Ryan Scott
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-33, https://doi.org/10.5194/wes-2026-33, 2026
Preprint under review for WES
Short summary
Short summary
Wind turbines create "wakes" of slowed air that reduce power for nearby turbines. To help improve wind energy models, we analyzed data from a large field experiment. We focused on a single day with changing weather patterns. We found that even simple terrain features interacted with the wind to create large variations in power output – up to 80 percent – across a wind farm. This detailed dataset provides a real-world case needed to validate and improve wind energy design tools.
Nicola Bodini, Joseph Olson, Brian Gaudet, Giacomo Valerio Iungo, Mojtaba Shams Solari, Sayahnya Roy, Julie K. Lundquist, Nathan Agarwal, Timothy A. Myers, Bianca Adler, Jeffrey D. Mirocha, Eric James, Laura Bianco, James M. Wilczak, and David D. Turner
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-17, https://doi.org/10.5194/wes-2026-17, 2026
Revised manuscript under review for WES
Short summary
Short summary
To improve offshore wind forecasts, the Third Wind Forecast Improvement Project monitored the United States East Coast for eighteen months. We compiled a daily log of weather events using advanced scanners and expert notes. This public dataset identifies important wind patterns, helping scientists test computer models and choose specific cases to study.
Georgios Deskos, Jiali Wang, Sanjay Arwade, Murray Fisher, Brian Hirth, Xiaoli Guo Larsén, Julie K. Lundquist, Andrew Myers, Weichiang Pang, William J. Pringle, Robert Rogers, Miguel Sanchez-Gomez, Chao Sun, Atsushi Yamaguchi, and Paul Veers
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-32, https://doi.org/10.5194/wes-2026-32, 2026
Preprint under review for WES
Short summary
Short summary
Wind energy is increasingly built in coastal and offshore areas exposed to powerful tropical storms. This paper explains why current wind turbine design approaches are often insufficient for these conditions and identifies what must change to improve resilience. By combining insights from weather modeling, engineering, and risk analysis, we highlight key gaps in data and standards, and show how addressing them can enable safer more reliable wind energy in storm-prone regions.
Yelena Pichugina, Alan W. Brewer, Sunil Baidar, Robert Banta, Edward Strobach, Brandi McCarty, Brian Carroll, Nicola Bodini, Stefano Letizia, Richard Marchbanks, Michael Zucker, Maxwell Holloway, and Patrick Moriarty
Wind Energ. Sci., 11, 417–442, https://doi.org/10.5194/wes-11-417-2026, https://doi.org/10.5194/wes-11-417-2026, 2026
Short summary
Short summary
The truck-based Doppler lidar system was used during the American Wake Experiment (AWAKEN) to obtain the high-frequency, simultaneous measurements of the horizontal wind speed, direction, and vertical velocity from a moving platform. The paper presents the unique capability of the novel lidar system to characterize the temporal, vertical, and spatial variability in winds at various distances from operating turbines and obtain quantitative estimates of wind speed reduction in the waked flow.
Astrid Lampert, Beatriz Cañadillas, Thomas Rausch, Lea Schmitt, Bughsin' Djath, Johannes Schulz-Stellenfleth, Andreas Platis, Kjell zum Berge, Ines Schäfer, Jens Bange, Thomas Neumann, Martin Dörenkämper, Bernhard Stoevesandt, Julia Gottschall, Lukas Vollmer, Stefan Emeis, Mares Barekzai, Simon Siedersleben, Martin Kühn, Gerald Steinfeld, Detlev Heinemann, Joachim Peinke, Hendrik Heißelmann, Jörge Schneemann, Gabriele Centurelli, Philipp Waldmann, and Konrad Bärfuss
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-277, https://doi.org/10.5194/wes-2025-277, 2026
Preprint under review for WES
Short summary
Short summary
Two major aircraft measurement campaigns above the North Sea provide insights into modifications of the wind field and sea surface induced by wind farms: The aircraft performed transects at hub height upstream and downstream of wind farm clusters, and identified different effects, e.g., how long it takes for the wind speed to recover after the wind farm, how changes across the coastline interact with wind energy, and if wind farms are well represented in numerical simulations.
Adam S. Wise, Robert S. Arthur, Jeffrey D. Mirocha, Julie K. Lundquist, and Fotini K. Chow
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-246, https://doi.org/10.5194/wes-2025-246, 2025
Revised manuscript under review for WES
Short summary
Short summary
At night, wind farms can experience a wide range of intermittent wind events that can affect how much power is produced. We modeled a specific type of intermittent wind event that happens as the atmosphere gets colder and colder throughout a night. We identified a threshold to determine when these events impact wind farms and ultimately found that mainly the front of a wind farm tends to be most impacted by these sorts of events.
Amanda Sellmaier, Ellen Damm, Torsten Sachs, Benjamin Kirbus, Inge Wiekenkamp, Annette Rinke, Falk Pätzold, Daiki Nomura, Astrid Lampert, and Markus Rex
Atmos. Chem. Phys., 25, 17685–17700, https://doi.org/10.5194/acp-25-17685-2025, https://doi.org/10.5194/acp-25-17685-2025, 2025
Short summary
Short summary
This study presents continuous ship-borne measurements of methane (CH4) concentration and isotopic composition monitored during an ice drift expedition in 2020. Using trajectory analysis, we linked atmospheric CH4 variabilities to air mass pathways transported over open water or sea-ice. The study highlights the potential of ship-borne observations to fill significant data gaps in the high Arctic.
Eric Förster, Heidi Huntrieser, Michael Lichtenstern, Falk Pätzold, Lutz Bretschneider, Andreas Schlerf, Sven Bollmann, Astrid Lampert, Jarosław Nęcki, Paweł Jagoda, Justyna Swolkień, Dominika Pasternak, Robert A. Field, and Anke Roiger
Atmos. Meas. Tech., 18, 7153–7176, https://doi.org/10.5194/amt-18-7153-2025, https://doi.org/10.5194/amt-18-7153-2025, 2025
Short summary
Short summary
We introduce a helicopter-borne mass balance approach, utilizing the HELiPOD platform, to accurately quantify methane (CH4) emissions from coal mining activities. The comparison of our top-down mass flux estimates (up to 3000 kg h−1) against those from bottom-up in-mine CH4 safety sensors revealed very good agreement. This approach also has a great potential in quantifying emission source strengths (down to 20 kg h−1) from a wide range of other CH4 emitters (e.g. landfills, oil & gas industry).
William C. Radünz, Bruno Carmo, Julie K. Lundquist, Stefano Letizia, Aliza Abraham, Adam S. Wise, Miguel Sanchez Gomez, Nicholas Hamilton, Raj K. Rai, and Pedro S. Peixoto
Wind Energ. Sci., 10, 2365–2393, https://doi.org/10.5194/wes-10-2365-2025, https://doi.org/10.5194/wes-10-2365-2025, 2025
Short summary
Short summary
We explore how simple terrain influences spatial variations in wind speed and wind farm performance during a low-level jet. Using simulations, field observations, and turbine production data, we find that downstream turbines produce more power than upstream ones, despite being subjected to wake effects. This counterintuitive result arises because the low-level jet and winds near turbine rotors are highly sensitive to topographic features, leading to stronger winds at the downstream turbines.
Stefano Letizia, David D. Turner, Aliza Abraham, Luc Rochette, and Patrick J. Moriarty
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-198, https://doi.org/10.5194/wes-2025-198, 2025
Revised manuscript accepted for WES
Short summary
Short summary
Characterizing the wind resource is much more than just measuring wind speeds. In fact, the physics of the atmosphere is governed by a complex interplay of different quantities, temperature being one of the most important. We used a new technology to remotely sense temperature profiles around wind farms at AWAKEN. Here, we discuss the methodology and guide readers through a comprehensive, step-by-step validation effort to quantify the accuracy of temperature profiling for wind energy.
William C. Radünz, Jens H. Kasper, Richard J. A. M. Stevens, and Julie K. Lundquist
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-147, https://doi.org/10.5194/wes-2025-147, 2025
Preprint under review for WES
Short summary
Short summary
Wind farms extract energy from the wind, creating slower, more turbulent flows that can affect other farms downstream. Using high-fidelity simulations for comparison, we find that models using coarser resolution to represent wind farms may underestimate how quickly the wind recovers. This appears to result from missing sharp wind changes and losing turbulence too quickly. Improving these aspects can help better predict wind energy production over long distances.
Miguel Sanchez-Gomez, Georgios Deskos, Mike Optis, Julie K. Lundquist, Michael Sinner, Geng Xia, and Walter Musial
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-152, https://doi.org/10.5194/wes-2025-152, 2025
Preprint under review for WES
Short summary
Short summary
Mesoscale WRF simulations with the Fitch wind farm parameterization were compared to large-domain LES for three planned offshore wind farms under varied atmospheric conditions. Mesoscale runs captured key wake deficit patterns and stability effects in the wind farm wake evolution, but underestimated power losses from internal wakes, especially in aligned winds or stable conditions. Results highlight mesoscale strengths for large-scale wakes and limits for turbine-level losses.
Geng Xia, Mike Optis, Georgios Deskos, Michael Sinner, Daniel Mulas Hernando, Julie Kay Lundquist, Andrew Kumler, Miguel Sanchez Gomez, Paul Fleming, and Walter Musial
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-154, https://doi.org/10.5194/wes-2025-154, 2025
Preprint under review for WES
Short summary
Short summary
This study examines energy losses from cluster wakes in offshore wind farms along the U.S. East Coast. Simulations based on real lease projects show that large wind speed deficits do not always cause equally large energy losses. The energy loss method revealed wake areas up to 30 % larger than traditional estimates, underscoring the need to consider both wind speed deficit and energy loss in planning offshore wind development.
Aliza Abraham, Matteo Puccioni, Arianna Jordan, Emina Maric, Nicola Bodini, Nicholas Hamilton, Stefano Letizia, Petra M. Klein, Elizabeth N. Smith, Sonia Wharton, Jonathan Gero, Jamey D. Jacob, Raghavendra Krishnamurthy, Rob K. Newsom, Mikhail Pekour, William Radünz, and Patrick Moriarty
Wind Energ. Sci., 10, 1681–1705, https://doi.org/10.5194/wes-10-1681-2025, https://doi.org/10.5194/wes-10-1681-2025, 2025
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.
Daphne Quint, Julie K. Lundquist, Nicola Bodini, and David Rosencrans
Wind Energ. Sci., 10, 1269–1301, https://doi.org/10.5194/wes-10-1269-2025, https://doi.org/10.5194/wes-10-1269-2025, 2025
Short summary
Short summary
Offshore wind farms along the US East Coast can have limited effects on local weather. To study these effects, we include wind farms near Massachusetts and Rhode Island, and we test different amounts of turbulence in our model. We analyze changes in wind, temperature, and turbulence. Simulated effects on surface temperature and turbulence change depending on how much turbulence is added to the model. The extent of the wind farm wake depends on how deep the atmospheric boundary layer is.
Robert S. Arthur, Alex Rybchuk, Timothy W. Juliano, Gabriel Rios, Sonia Wharton, Julie K. Lundquist, and Jerome D. Fast
Wind Energ. Sci., 10, 1187–1209, https://doi.org/10.5194/wes-10-1187-2025, https://doi.org/10.5194/wes-10-1187-2025, 2025
Short summary
Short summary
This paper evaluates a new model configuration for wind energy forecasting in complex terrain. We compare model results to observations in the Altamont Pass (California, USA), where wind channeling through a mountain gap leads to increased energy production. We demonstrate that the new model configuration performs similarly to a more established approach, with some evidence of improved wind speed predictions, and provide guidance for future model testing.
Alexander Mönnig, Ansgar Hahn, Astrid Lampert, and Ulrich Römer
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-112, https://doi.org/10.5194/wes-2025-112, 2025
Revised manuscript not accepted
Short summary
Short summary
To enable efficient load estimation in wind farms, e.g. to assess Remaining Useful Life, we investigated the feasibility of using surrogate models based on open-source turbine models. In a case study, we adapted such a model to match turbines from a real German onshore wind farm, ran aeroelastic simulations, and trained surrogate models to reconstruct loads observed in 5 years of SCADA data. Comparing predicted and measured values showed promising accuracy, especially for blade bending moments.
Adam S. Wise, Robert S. Arthur, Aliza Abraham, Sonia Wharton, Raghavendra Krishnamurthy, Rob Newsom, Brian Hirth, John Schroeder, Patrick Moriarty, and Fotini K. Chow
Wind Energ. Sci., 10, 1007–1032, https://doi.org/10.5194/wes-10-1007-2025, https://doi.org/10.5194/wes-10-1007-2025, 2025
Short summary
Short summary
Wind farms can be subject to rapidly changing weather events. In the United States Great Plains, some of these weather events can result in waves in the atmosphere that ultimately affect how much power a wind farm can produce. We modeled a specific event of waves observed in Oklahoma. We determined how to accurately model the event and analyzed how it affected a wind farm’s power production, finding that the waves both decreased power and made it more variable.
Nathan J. Agarwal, Julie K. Lundquist, Timothy W. Juliano, and Alex Rybchuk
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-16, https://doi.org/10.5194/wes-2025-16, 2025
Revised manuscript under review for WES
Short summary
Short summary
Models of wind behavior inform offshore wind farm site investment decisions. Here we compare a newly-developed model to another, historically-used, model based on how these models represent winds and turbulence at two North Sea sites. The best model depends on the site. While the older model performs best at the site above a wind farm, the newer model performs best at the site that is at the same altitude as the wind farm. We support using the new model to represent winds at the turbine level.
Raghavendra Krishnamurthy, Rob K. Newsom, Colleen M. Kaul, Stefano Letizia, Mikhail Pekour, Nicholas Hamilton, Duli Chand, Donna Flynn, Nicola Bodini, and Patrick Moriarty
Wind Energ. Sci., 10, 361–380, https://doi.org/10.5194/wes-10-361-2025, https://doi.org/10.5194/wes-10-361-2025, 2025
Short summary
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.
Daphne Quint, Julie K. Lundquist, and David Rosencrans
Wind Energ. Sci., 10, 117–142, https://doi.org/10.5194/wes-10-117-2025, https://doi.org/10.5194/wes-10-117-2025, 2025
Short summary
Short summary
Offshore wind farms will be built along the East Coast of the United States. Low-level jets (LLJs) – layers of fast winds at low altitudes – also occur here. LLJs provide wind resources and also influence moisture and pollution transport, so it is important to understand how they might change. We develop and validate an automated tool to detect LLJs and compare 1 year of simulations with and without wind farms. Here, we describe LLJ characteristics and how they change with wind farms.
David Rosencrans, Julie K. Lundquist, Mike Optis, and Nicola Bodini
Wind Energ. Sci., 10, 59–81, https://doi.org/10.5194/wes-10-59-2025, https://doi.org/10.5194/wes-10-59-2025, 2025
Short summary
Short summary
The US offshore wind industry is growing rapidly. Expansion into cold climates will subject turbines and personnel to hazardous icing. We analyze the 21-year icing risk for US east coast wind areas based on numerical weather prediction simulations and further assess impacts from wind farm wakes over one winter season. Sea spray icing at 10 m can occur up to 67 h per month. However, turbine–atmosphere interactions reduce icing hours within wind plant areas.
Hassnae Erraji, Philipp Franke, Astrid Lampert, Tobias Schuldt, Ralf Tillmann, Andreas Wahner, and Anne Caroline Lange
Atmos. Chem. Phys., 24, 13913–13934, https://doi.org/10.5194/acp-24-13913-2024, https://doi.org/10.5194/acp-24-13913-2024, 2024
Short summary
Short summary
Four-dimensional variational data assimilation allows for the simultaneous optimisation of initial values and emission rates by using trace-gas profiles from drone observations in a regional air quality model. Assimilated profiles positively impact the representation of air pollutants in the model by improving their vertical distribution and ground-level concentrations. This case study highlights the potential of drone data to enhance air quality analyses including local emission evaluation.
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.
Astrid Lampert, Rudolf Hankers, Thomas Feuerle, Thomas Rausch, Matthias Cremer, Maik Angermann, Mark Bitter, Jonas Füllgraf, Helmut Schulz, Ulf Bestmann, and Konrad B. Bärfuss
Earth Syst. Sci. Data, 16, 4777–4792, https://doi.org/10.5194/essd-16-4777-2024, https://doi.org/10.5194/essd-16-4777-2024, 2024
Short summary
Short summary
We conducted flights above the North Sea and investigated changes in the wind field. The research aircraft measured wind speed, wind direction, temperature, humidity and sea surface at high resolution. Wind parks reduce the wind speed, and the data help to determine how long it takes for the wind speed to recover. The coast also plays an important role, and the wind speed varies with distance from the coast. The results help in wind park planning and better estimating the energy yield.
Rachel Robey and Julie K. Lundquist
Wind Energ. Sci., 9, 1905–1922, https://doi.org/10.5194/wes-9-1905-2024, https://doi.org/10.5194/wes-9-1905-2024, 2024
Short summary
Short summary
Measurements of wind turbine wakes with scanning lidar instruments contain complex errors. We model lidars in a simulated environment to understand how and why the measured wake may differ from the true wake and validate the results with observational data. The lidar smooths out the wake, making it seem more spread out and the slowdown of the winds less pronounced. Our findings provide insights into best practices for accurately measuring wakes with lidar and interpreting observational data.
Nicola Bodini, Mike Optis, Stephanie Redfern, David Rosencrans, Alex Rybchuk, Julie K. Lundquist, Vincent Pronk, Simon Castagneri, Avi Purkayastha, Caroline Draxl, Raghavendra Krishnamurthy, Ethan Young, Billy Roberts, Evan Rosenlieb, and Walter Musial
Earth Syst. Sci. Data, 16, 1965–2006, https://doi.org/10.5194/essd-16-1965-2024, https://doi.org/10.5194/essd-16-1965-2024, 2024
Short summary
Short summary
This article presents the 2023 National Offshore Wind data set (NOW-23), an updated resource for offshore wind information in the US. It replaces the Wind Integration National Dataset (WIND) Toolkit, offering improved accuracy through advanced weather prediction models. The data underwent regional tuning and validation and can be accessed at no cost.
David Rosencrans, Julie K. Lundquist, Mike Optis, Alex Rybchuk, Nicola Bodini, and Michael Rossol
Wind Energ. Sci., 9, 555–583, https://doi.org/10.5194/wes-9-555-2024, https://doi.org/10.5194/wes-9-555-2024, 2024
Short summary
Short summary
The US offshore wind industry is developing rapidly. Using yearlong simulations of wind plants in the US mid-Atlantic, we assess the impacts of wind turbine wakes. While wakes are the strongest and longest during summertime stably stratified conditions, when New England grid demand peaks, they are predictable and thus manageable. Over a year, wakes reduce power output by over 35 %. Wakes in a wind plant contribute the most to that reduction, while wakes between wind plants play a secondary role.
Barbara Harm-Altstädter, Konrad Bärfuss, Lutz Bretschneider, Martin Schön, Jens Bange, Ralf Käthner, Radovan Krejci, Mauro Mazzola, Kihong Park, Falk Pätzold, Alexander Peuker, Rita Traversi, Birgit Wehner, and Astrid Lampert
Aerosol Research, 1, 39–64, https://doi.org/10.5194/ar-1-39-2023, https://doi.org/10.5194/ar-1-39-2023, 2023
Short summary
Short summary
We present observations of aerosol particles and meteorological parameters in the horizontal and vertical distribution measured with uncrewed aerial systems in the Arctic. The field campaign was carried out during the snow melting season, when ultrafine aerosol particles (UFPs) with a size between 3 and 12 nm occurred frequently. A high variability of the measured UFPs was identified in the spatial scale, which was strongly associated with different atmospheric boundary layer properties.
Konrad B. Bärfuss, Holger Schmithüsen, and Astrid Lampert
Atmos. Meas. Tech., 16, 3739–3765, https://doi.org/10.5194/amt-16-3739-2023, https://doi.org/10.5194/amt-16-3739-2023, 2023
Short summary
Short summary
The first atmospheric soundings with an electrically powered small uncrewed aircraft system (UAS) up to an altitude of 10 km are presented and assessed for quality, revealing the potential to augment atmospheric observations and fill observation gaps for numerical weather prediction. This is significant because of the need for high-resolution meteorological data, in particular in remote areas with limited in situ measurements, and for reference data for satellite measurement calibration.
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.
Miguel Sanchez Gomez, Julie K. Lundquist, Jeffrey D. Mirocha, and Robert S. Arthur
Wind Energ. Sci., 8, 1049–1069, https://doi.org/10.5194/wes-8-1049-2023, https://doi.org/10.5194/wes-8-1049-2023, 2023
Short summary
Short summary
The wind slows down as it approaches a wind plant; this phenomenon is called blockage. As a result, the turbines in the wind plant produce less power than initially anticipated. We investigate wind plant blockage for two atmospheric conditions. Blockage is larger for a wind plant compared to a stand-alone turbine. Also, blockage increases with atmospheric stability. Blockage is amplified by the vertical transport of horizontal momentum as the wind approaches the front-row turbines in the array.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
Short summary
Short summary
Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
Alex Rybchuk, Timothy W. Juliano, Julie K. Lundquist, David Rosencrans, Nicola Bodini, and Mike Optis
Wind Energ. Sci., 7, 2085–2098, https://doi.org/10.5194/wes-7-2085-2022, https://doi.org/10.5194/wes-7-2085-2022, 2022
Short summary
Short summary
Numerical weather prediction models are used to predict how wind turbines will interact with the atmosphere. Here, we characterize the uncertainty associated with the choice of turbulence parameterization on modeled wakes. We find that simulated wind speed deficits in turbine wakes can be significantly sensitive to the choice of turbulence parameterization. As such, predictions of future generated power are also sensitive to turbulence parameterization choice.
Rachel Robey and Julie K. Lundquist
Atmos. Meas. Tech., 15, 4585–4622, https://doi.org/10.5194/amt-15-4585-2022, https://doi.org/10.5194/amt-15-4585-2022, 2022
Short summary
Short summary
Our work investigates the behavior of errors in remote-sensing wind lidar measurements due to turbulence. Using a virtual instrument, we measured winds in simulated atmospheric flows and decomposed the resulting error. Dominant error mechanisms, particularly vertical velocity variations and interactions with shear, were identified in ensemble data over three test cases. By analyzing the underlying mechanisms, the response of the error behavior to further varying flow conditions may be projected.
Beatriz Cañadillas, Maximilian Beckenbauer, Juan J. Trujillo, Martin Dörenkämper, Richard Foreman, Thomas Neumann, and Astrid Lampert
Wind Energ. Sci., 7, 1241–1262, https://doi.org/10.5194/wes-7-1241-2022, https://doi.org/10.5194/wes-7-1241-2022, 2022
Short summary
Short summary
Scanning lidar measurements combined with meteorological sensors and mesoscale simulations reveal the strong directional and stability dependence of the wake strength in the direct vicinity of wind farm clusters.
Vincent Pronk, Nicola Bodini, Mike Optis, Julie K. Lundquist, Patrick Moriarty, Caroline Draxl, Avi Purkayastha, and Ethan Young
Wind Energ. Sci., 7, 487–504, https://doi.org/10.5194/wes-7-487-2022, https://doi.org/10.5194/wes-7-487-2022, 2022
Short summary
Short summary
In this paper, we have assessed to which extent mesoscale numerical weather prediction models are more accurate than state-of-the-art reanalysis products in characterizing the wind resource at heights of interest for wind energy. The conclusions of our work will be of primary importance to the wind industry for recommending the best data sources for wind resource modeling.
Adam S. Wise, James M. T. Neher, Robert S. Arthur, Jeffrey D. Mirocha, Julie K. Lundquist, and Fotini K. Chow
Wind Energ. Sci., 7, 367–386, https://doi.org/10.5194/wes-7-367-2022, https://doi.org/10.5194/wes-7-367-2022, 2022
Short summary
Short summary
Wind turbine wake behavior in hilly terrain depends on various atmospheric conditions. We modeled a wind turbine located on top of a ridge in Portugal during typical nighttime and daytime atmospheric conditions and validated these model results with observational data. During nighttime conditions, the wake deflected downwards following the terrain. During daytime conditions, the wake deflected upwards. These results can provide insight into wind turbine siting and operation in hilly regions.
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.
Miguel Sanchez Gomez, Julie K. Lundquist, Petra M. Klein, and Tyler M. Bell
Earth Syst. Sci. Data, 13, 3539–3549, https://doi.org/10.5194/essd-13-3539-2021, https://doi.org/10.5194/essd-13-3539-2021, 2021
Short summary
Short summary
In July 2018, the International Society for Atmospheric Research using Remotely-piloted Aircraft (ISARRA) hosted a flight week to demonstrate unmanned aircraft systems' capabilities in sampling the atmospheric boundary layer. Three Doppler lidars were deployed during this week-long experiment. We use data from these lidars to estimate turbulence dissipation rate. We observe large temporal variability and significant differences in dissipation for lidars with different sampling techniques.
Miguel Sanchez Gomez, Julie K. Lundquist, Jeffrey D. Mirocha, Robert S. Arthur, and Domingo Muñoz-Esparza
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2021-57, https://doi.org/10.5194/wes-2021-57, 2021
Revised manuscript not accepted
Short summary
Short summary
Winds decelerate upstream of a wind plant as turbines obstruct and extract energy from the flow. This effect is known as wind plant blockage. We assess how atmospheric stability modifies the upstream wind plant blockage. We find stronger stability amplifies this effect. We also explore different approaches to quantifying blockage from field-like observations. We find different methodologies may induce errors of the same order of magnitude as the blockage-induced velocity deficits.
Cited articles
Abkar, M., Sharifi, A., and Porté-Agel, F.: Wake flow in a wind farm during a diurnal cycle, J. Turbul., 17, 420–441, 2016. a
Abraham, A., Puccioni, M., Jordan, A., Maric, E., Bodini, N., Hamilton, N., Letizia, S., Klein, P. M., Smith, E. N., Wharton, S., Gero, J., Jacob, J. D., Krishnamurthy, R., Newsom, R. K., Pekour, M., Radünz, W., and Moriarty, P.: Operational wind plants increase planetary boundary layer height: an observational study, Wind Energ. Sci., 10, 1681–1705, https://doi.org/10.5194/wes-10-1681-2025, 2025. a
Adkins, K. A. and Sescu, A.: Observations of relative humidity in the near-wake of a wind turbine using an instrumented unmanned aerial system, Int. J. Green Energy, 14, 845–860, 2017. a
Alaoui-Sosse, S., Durand, P., and Médina, P.: In situ observations of wind turbines wakes with unmanned aerial vehicle BOREAL within the MOMEMTA project, Atmosphere, 13, 775, https://doi.org/10.3390/atmos13050775, 2022. a
Armstrong, A., Burton, R. R., Lee, S. E., Mobbs, S., Ostle, N., Smith, V., Waldron, S., and Whitaker, J.: Ground-level climate at a peatland wind farm in Scotland is affected by wind turbine operation, Environ. Res. Lett., 11, 044024, https://doi.org/10.1088/1748-9326/11/4/044024, 2016. a
Banta, R., Newsom, R., Lundquist, J., Pichugina, Y., Coulter, R., and Mahrt, L.: Nocturnal low-level jet characteristics over Kansas during CASES-99, Bound.-Lay. Meteorol., 105, 221–252, 2002. a
Bärfuss, K., Spoor, J., Bestmann, U., Cremer, M., Feuerle, T., Angermann, M., Bitter, M., and Lampert, A.: In-situ aircraft measurements of wind farm wake effects during the AWAKEN campaign in Oklahoma (September 2023), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.984783, 2025. a
Bastankhah, M. and Porté-Agel, F.: Wind tunnel study of the wind turbine interaction with a boundary-layer flow: Upwind region, turbine performance, and wake region, Phys. Fluids, 29, 065105, https://doi.org/10.1063/1.4984078, 2017. a
Bodini, N., Letizia, S., and Zalkind, D.: AWAKEN Site A1 - NREL Profiling Lidar (Windcube v2.1)/Derived Data, Wind Data Hub [data set], https://doi.org/10.21947/2205733, 2025. a, b, c, d
Cañadillas, B., Foreman, R., Barth, V., Siedersleben, S., Lampert, A., Platis, A., Djath, B., Schulz-Stellenfleth, J., Bange, J., Emeis, S., and Neumann, T.: Offshore wind farm wake recovery: Airborne measurements and its representation in engineering models, Wind Energy, 23, 1249–1265, 2020. a, b, c, d
Cañadillas, B., Beckenbauer, M., Trujillo, J. J., Dörenkämper, M., Foreman, R., Neumann, T., and Lampert, A.: Offshore wind farm cluster wakes as observed by long-range-scanning wind lidar measurements and mesoscale modeling, Wind Energ. Sci., 7, 1241–1262, https://doi.org/10.5194/wes-7-1241-2022, 2022. a, b
Cañadillas, B., Foreman, R., Steinfeld, G., and Robinson, N.: Cumulative interactions between the global blockage and wake effects as observed by an engineering model and large-eddy simulations, Energies, 16, 2949, https://doi.org/10.3390/en16072949, 2023. a
Corsmeier, U., Hankers, R., and Wieser, A.: Airborne turbulence measurements in the lower troposphere onboard the research aircraft Dornier 128-6, D-IBUF, Meteorol. Z., 10, 315–330, 2001. a
Debnath, M., Scholbrock, A. K., Zalkind, D., Moriarty, P., Simley, E., Hamilton, N., Ivanov, C., Arthur, R. S., Barthelmie, R., Bodini, N., Brewer, A., Goldberger, L., Herges, T., Hirth, B., Valerio Iungo, G., Jager, D., Kaul, C., Klein, P., Krishnamurthy, R., Letizia, S., Lundquist, J. K., Maniaci, D., Newsom, R., Pekour, M., Pryor, Sara C Ritsche, M. T., Roadman, J., Schroeder, J., Shaw, W. J., Van Dam, J., and Wharton, S.: Design of the American Wake Experiment (AWAKEN) field campaign, J. Phys. Conf. Ser., 2265, 022058, https://doi.org/10.1088/1742-6596/2265/2/022058, 2022. a, b, c, d
Desalegn, B., Gebeyehu, D., Tamrat, B., Tadiwose, T., and Lata, A.: Onshore versus offshore wind power trends and recent study practices in modeling of wind turbines’ life-cycle impact assessments, Cleaner Engineering and Technology, 17, 100691, https://doi.org/10.1016/j.clet.2023.100691, 2023. a
Dörenkämper, M., Witha, B., Steinfeld, G., Heinemann, D., and Kühn, M.: The impact of stable atmospheric boundary layers on wind-turbine wakes within offshore wind farms, J. Wind Eng. Ind. Aerod., 144, 146–153, 2015. a
Foreman, R. J., Cañadillas, B., and Robinson, N.: The Atmospheric Stability Dependence of Far Wakes on the Power Output of Downstream Wind Farms, Energies, 17, https://doi.org/10.3390/en17020488, 2024. a
Gadde, S. N. and Stevens, R. J. A. M.: Interaction between low-level jets and wind farms in a stable atmospheric boundary layer, Phys. Rev. Fluids, 6, 014603, https://doi.org/10.1103/PhysRevFluids.6.014603, 2021. a, b
Garratt, J. R.: The atmospheric boundary layer, Earth-Sci. Rev., 37, 89–134, 1994. a
Gero, J, and Hackel, D: Atmospheric Emitted Radiance Interferometer (AERI) Instrument Handbook, US Department of Energy, Atmospheric Radiation Measurement user facility, Richland, Washington, DOE/SC-ARM-TR-054, https://doi.org/10.2172/1020273, 2025. a
Göçmen, T., Van der Laan, P., Réthoré, P.-E., Diaz, A. P., Larsen, G. C., and Ott, S.: Wind turbine wake models developed at the technical university of Denmark: A review, Renewable and Sustainable Energy Reviews, 60, 752–769, 2016. a
Hansen, K. S., Barthelmie, R. J., Jensen, L. E., and Sommer, A.: The impact of turbulence intensity and atmospheric stability on power deficits due to wind turbine wakes at Horns Rev wind farm, Wind Energy, 15, 183–196, 2012. a
Harm-Altstädter, B., Voß, A., Aust, S., Bärfuss, K., Bretschneider, L., Merkel, M., Pätzold, F., Schlerf, A., Weinhold, K., Wiedensohler, A., Winkler, U., and Lampert, A.: First study using a fixed-wing drone for systematic measurements of aerosol vertical distribution close to a civil airport, Frontiers in Environmental Science, 12, 1376980, https://doi.org/10.3389/fenvs.2024.1376980, 2024. a
Kaimal, J. C. and Finnigan, J. J.: Atmospheric boundary layer flows: their structure and measurement, Oxford University Press, https://doi.org/10.1093/oso/9780195062397.001.0001, 1994. a, b
Krishnamurthy, R., Newsom, R. K., Chand, D., and Shaw, W. J.: Boundary layer climatology at ARM southern great plains, Tech. rep., Pacific Northwest National Lab. (PNNL), Richland, WA, United States, https://doi.org/10.2172/1778833, 2021. a, b, c
Krishnamurthy, R., Newsom, R. K., Kaul, C. M., Letizia, S., Pekour, M., Hamilton, N., Chand, D., Flynn, D., Bodini, N., and Moriarty, P.: Observations of wind farm wake recovery at an operating wind farm, Wind Energ. Sci., 10, 361–380, https://doi.org/10.5194/wes-10-361-2025, 2025. a, b, c, d, e, f, g
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. a
Lampert, A., Hankers, R., Feuerle, T., Rausch, T., Cremer, M., Angermann, M., Bitter, M., Füllgraf, J., Schulz, H., Bestmann, U., and Bärfuss, K. B.: In situ airborne measurements of atmospheric parameters and airborne sea surface properties related to offshore wind parks in the German Bight during the project X-Wakes, Earth Syst. Sci. Data, 16, 4777–4792, https://doi.org/10.5194/essd-16-4777-2024, 2024. a, b, c, d, e, f
Lee, J. C. and Lundquist, J. K.: Observing and simulating wind-turbine wakes during the evening transition, Bound.-Lay. Meteorol., 164, 449–474, 2017. a
Lenschow, D.: The measurement of air velocity and temperature using the NCAR Buffalo aircraft measuring system, National Center for Atmospheric Research Boulder, https://doi.org/10.5065/D6C8277W, 1972. a
Letizia, S. and Bodini, N.: AWAKEN Site H LiDAR (Cornell University)/Derived Data - Wind Statistics, Wind Data Hub [data set], https://doi.org/10.21947/2375438, 2024. a, b, c
Letizia, S., Bodini, N., Brugger, P., Scholbrock, A., Hamilton, N., Porté-Agel, F., Doubrawa, P., and Moriarty, P.: Holistic scan optimization of nacelle-mounted lidars for inflow and wake characterization at the RAAW and AWAKEN field campaigns, J. Phys. Conf. Ser., 2505, 012048, https://doi.org/10.1088/1742-6596/2505/1/012048, 2023. a, b, c
Lu, H. and Porté-Agel, F.: On the impact of wind farms on a convective atmospheric boundary layer, Bound.-Lay. Meteorol., 157, 81–96, 2015. a
Lundquist, J. K., DuVivier, K. K., Kaffine, D., and Tomaszewski, J. M.: Costs and consequences of wind turbine wake effects arising from uncoordinated wind energy development, Nature Energy, 4, 26–34, 2019. a
Magnusson, M. and Smedman, A.-S.: Influence of atmospheric stability on wind turbine wakes, Wind Engineering, 18, 139–152, https://www.jstor.org/stable/43749538 (last access: 4 February 2025), 1994. a
Mahrt, L. and Vickers, D.: Contrasting vertical structures of nocturnal boundary layers, Bound.-Lay. Meteorol., 105, 351–363, 2002. a
Menke, R., Vasiljević, N., Hansen, K. S., Hahmann, A. N., and Mann, J.: Does the wind turbine wake follow the topography? A multi-lidar study in complex terrain, Wind Energ. Sci., 3, 681–691, https://doi.org/10.5194/wes-3-681-2018, 2018. a
Mittelmeier, N., Blodau, T., and Kühn, M.: Monitoring offshore wind farm power performance with SCADA data and an advanced wake model, Wind Energ. Sci., 2, 175–187, https://doi.org/10.5194/wes-2-175-2017, 2017. a
Moriarty, P., Hamilton, N., Debnath, M., Herges, T., Isom, B., Lundquist, J. K., Maniaci, D., Naughton, B., Pauly, R., Roadman, J., Shaw, W., van Damn, J., and Wharton, S.: American WAKe ExperimeNt (AWAKEN), Tech. rep., Lawrence Livermore National Lab. (LLNL), Livermore, CA, United States, https://doi.org/10.2172/1659798, 2020. a
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. L., 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), Journal of Renewable and Sustainable Energy, 16, https://doi.org/10.1063/5.0141683, 2024. a, b, c
Newsom, R. and Krishnamurthy, R.: Doppler Lidar (DL) instrument handbook, Tech. rep., DOE Office of Science Atmospheric Radiation Measurement (ARM) User Facility, https://doi.org/10.2172/1034640, 2022. a, b
Nygaard, N. G., Steen, S. T., Poulsen, L., and Pedersen, J. G.: Modelling cluster wakes and wind farm blockage, J. Phys. Conf. Ser., 1618, 062072, https://doi.org/10.1088/1742-6596/1618/6/062072, 2020. a
Pekour, M.: Site A1 - PNNL Surface Flux Station/Daily Fluxes, Wind Data Hub [data set], https://doi.org/10.21947/1991103, 2025. a, b, c
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, Scientific Reports, 8, 2163, https://doi.org/10.1038/s41598-018-20389-y, 2018. a, b, c, d
Puccioni, M., Iungo, G. V., Moss, C., Solari, M. S., Letizia, S., Bodini, N., and Moriarty, P.: LiDAR measurements to investigate farm-to-farm interactions at the AWAKEN experiment, J. Phys. Conf. Ser., 2505, 012045, https://doi.org/10.1088/1742-6596/2505/1/012045, 2023. a, b
Quint, D., Lundquist, J. K., and Rosencrans, D.: Simulations suggest offshore wind farms modify low-level jets, Wind Energ. Sci., 10, 117–142, https://doi.org/10.5194/wes-10-117-2025, 2025. a
Radünz, W. C., Carmo, B., Lundquist, J. K., Letizia, S., Abraham, A., Wise, A. S., Sanchez Gomez, M., Hamilton, N., Rai, R. K., and Peixoto, P. S.: Influence of simple terrain on the spatial variability of a low-level jet and wind farm performance in the AWAKEN field campaign, Wind Energ. Sci., 10, 2365–2393, https://doi.org/10.5194/wes-10-2365-2025, 2025. a
Radünz, W. C., Sakagami, Y., Haas, R., Petry, A. P., Passos, J. C., Miqueletti, M., and Dias, E.: Influence of atmospheric stability on wind farm performance in complex terrain, Appl. Energ., 282, 116149, https://doi.org/10.1016/j.apenergy.2020.116149, 2021. a, b
Reuder, J., Båserud, L., Kral, S., Kumer, V., Wagenaar, J. W., and Knauer, A.: Proof of concept for wind turbine wake investigations with the RPAS SUMO, Energy Procedia, 94, 452–461, 2016. a
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. a
Schneemann, J., Theuer, F., Rott, A., Dörenkämper, M., and Kühn, M.: Offshore wind farm global blockage measured with scanning lidar, Wind Energ. Sci., 6, 521–538, https://doi.org/10.5194/wes-6-521-2021, 2021. a
Segalini, A. and Dahlberg, J.-Å.: Blockage effects in wind farms, Wind Energy, 23, 120–128, 2020. a
Sickler, M., Ummels, B., Zaaijer, M., Schmehl, R., and Dykes, K.: Offshore wind farm optimisation: a comparison of performance between regular and irregular wind turbine layouts, Wind Energ. Sci., 8, 1225–1233, https://doi.org/10.5194/wes-8-1225-2023, 2023. a
Siedersleben, S. K., Lundquist, J. K., Platis, A., Bange, J., Bärfuss, K., Lampert, A., Cañadillas, B., Neumann, T., and Emeis, S.: Micrometeorological impacts of offshore wind farms as seen in observations and simulations, Environ. Res. Lett., 13, 124012, https://doi.org/10.1088/1748-9326/aaea0b, 2018a. a, b, c
Siedersleben, S. K., Platis, A., Lundquist, J. K., Lampert, A., Bärfuss, K., Cañadillas, B., Djath, B., Schulz-Stellenfleth, J., Bange, J., Neumann, T., and Emeis, S.: Evaluation of a wind farm parametrization for mesoscale atmospheric flow models with aircraft measurements, Meteorol. Z., 27, 401–415, https://doi.org/10.1127/metz/2018/0900, 2018b. a
Sisterson, D., Peppler, R., Cress, T., Lamb, P., and Turner, D.: The ARM southern great plains (SGP) site, Meteorological Monographs, 57, 6–1, 2016. a
Smedman, A., Högström, U., Bergström, H., and Kahma, K.: The marine atmospheric boundary layer during swell, according to recent studies in the Baltic Sea, in: Air-Sea Exchange: Physics, Chemistry and Dynamics, Springer, 175–196, https://doi.org/10.1007/978-94-015-9291-8_7, 1999. a
Stevens, R. J., Hobbs, B. F., Ramos, A., and Meneveau, C.: Combining economic and fluid dynamic models to determine the optimal spacing in very large wind farms, Wind Energy, 20, 465–477, 2017. a
Vermeer, L., Sørensen, J. N., and Crespo, A.: Wind turbine wake aerodynamics, Prog. Aerosp. Sci., 39, 467–510, 2003. a
Wetz, T. and Wildmann, N.: Multi-point in situ measurements of turbulent flow in a wind turbine wake and inflow with a fleet of uncrewed aerial systems, Wind Energ. Sci., 8, 515–534, https://doi.org/10.5194/wes-8-515-2023, 2023. a
Wu, D., Grodsky, S. M., Xu, W., Liu, N., Almeida, R. M., Zhou, L., Miller, L. M., Roy, S. B., Xia, G., Agrawal, A. A., Houlton, B. Z., Flecker, A. S., and Xu, X.: Observed impacts of large wind farms on grassland carbon cycling, Sci. Bull., 68, 2889–2892, 2023. a
Zhou, L., Roy, S. B., and Xia, G.: Weather, climatic and ecological impacts of onshore wind farms, Reference Module in Earth Systems and Environmenta l Sciences, Elsevier, p. B9780128197271001000, https://doi.org/10.1016/B978-0-12-819727-1.00018-2, 2022. a
Short summary
This study analyzes onshore wind farm wakes in a semi-complex terrain with data conducted with the research aircraft of TU Braunschweig during the American WAKE experimeNt (AWAKEN). Vertical profiles of temperature, humidity, and wind give insights into the stratification of the atmospheric boundary layer, while horizontal profiles downwind of wind farms reveal an amplification of the reduction in wind speed in a semi-complex terrain, in particular at a distance of 10 km.
This study analyzes onshore wind farm wakes in a semi-complex terrain with data conducted with...
Altmetrics
Final-revised paper
Preprint