Articles | Volume 11, issue 8
https://doi.org/10.5194/wes-11-2749-2026
© 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-11-2749-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
03 Aug 2026
Review article |
| 03 Aug 2026
| 03 Aug 2026
Grand challenges in designing resilient wind energy systems in areas prone to tropical cyclones
Parametrica Research & Analytics, Nea Peramos, Attica, 19006, Greece
Jiali Wang
Environmental Science Division, Argonne National Laboratory, Lemont, IL 60439, USA
Sanjay Arwade
Dept. of Civil & Environmental Engineering, University of Massachusetts, Amherst, MA 01003, USA
Murray Fisher
Gulf Wind Technology, Avondale, LA, USA
Brian Hirth
Texas Tech University, Lubbock, TX, USA
Xiaoli Guo Larsén
Department of Wind and Energy Systems, DTU, Roskilde 4000, Denmark
Julie K. Lundquist
Johns Hopkins University, Baltimore, MD, USA
Andrew Myers
Northeastern University, Boston, MA, USA
Weichiang Pang
Clemson University, Clemson 29634, SC, USA
William J. Pringle
Environmental Science Division, Argonne National Laboratory, Lemont, IL 60439, USA
Robert Rogers
Asia-Pacific Typhoon Collaborative Research Center, Shanghai, China
Miguel Sanchez-Gomez
University of Colorado Boulder, Boulder, CO, USA
Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
Atsushi Yamaguchi
School of Engineering, Ashikaga University, Ashikaga, Japan
Paul Veers
North American Wind Energy Academy, Boulder, CO, USA
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Miguel Sanchez-Gomez, Georgios Deskos, Mike Optis, Julie K. Lundquist, Michael Sinner, Geng Xia, and Walter Musial
Wind Energ. Sci., 11, 2009–2036, https://doi.org/10.5194/wes-11-2009-2026, https://doi.org/10.5194/wes-11-2009-2026, 2026
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Mesoscale simulations with the Fitch wind farm parameterization were compared to large-domain large-eddy simulations for three planned offshore wind farms under varied atmospheric conditions. Mesoscale runs captured key wake deficit patterns and stability effects in the wind farm wake evolution but underestimated power losses from internal wakes, especially in aligned winds or stable conditions. Results highlight mesoscale strengths for large-scale wakes and limits for turbine-level losses.
Geng Xia, Mike Optis, Georgios Deskos, Michael Sinner, Daniel Mulas Hernando, Julie Kay Lundquist, Andrew Kumler, Miguel Sanchez Gomez, Paul Fleming, and Walter Musial
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-154, https://doi.org/10.5194/wes-2025-154, 2025
Revised manuscript under review for WES
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This study examines energy losses from cluster wakes in offshore wind farms along the U.S. East Coast. Simulations based on real lease projects show that large wind speed deficits do not always cause equally large energy losses. The energy loss method revealed wake areas up to 30 % larger than traditional estimates, underscoring the need to consider both wind speed deficit and energy loss in planning offshore wind development.
William J. Shaw, Larry K. Berg, Mithu Debnath, Georgios Deskos, Caroline Draxl, Virendra P. Ghate, Charlotte B. Hasager, Rao Kotamarthi, Jeffrey D. Mirocha, Paytsar Muradyan, William J. Pringle, David D. Turner, and James M. Wilczak
Wind Energ. Sci., 7, 2307–2334, https://doi.org/10.5194/wes-7-2307-2022, https://doi.org/10.5194/wes-7-2307-2022, 2022
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This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.
William C. Radünz, Jens H. Kasper, Richard J. A. M. Stevens, and Julie K. Lundquist
Wind Energ. Sci., 11, 2723–2747, https://doi.org/10.5194/wes-11-2723-2026, https://doi.org/10.5194/wes-11-2723-2026, 2026
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Wind farms extract energy from the wind, creating slower, more turbulent flows that can affect other farms downstream. Using high-fidelity simulations for comparison, we find that mesoscale simulations that parameterize wind farms at coarser resolution may underestimate how quickly the wind recovers downstream. This slow recovery results from missing sharp wind gradients and low turbulence. Improving these aspects can help better predict wind energy production over long distances.
Adam S. Wise, Robert S. Arthur, Jeffrey D. Mirocha, Julie K. Lundquist, and Fotini K. Chow
Wind Energ. Sci., 11, 2543–2565, https://doi.org/10.5194/wes-11-2543-2026, https://doi.org/10.5194/wes-11-2543-2026, 2026
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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.
Valeria Vasquez-Barros, Nicola Bodini, Seth Zippel, Anthony Kirincich, and Julie K. Lundquist
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-115, https://doi.org/10.5194/wes-2026-115, 2026
Preprint under review for WES
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Offshore wind turbines operate more than 100 meters above the ocean, but atmospheric conditions are often measured only near the sea surface. Using observations from an offshore research platform, we found that surface measurements frequently do not represent the conditions experienced by turbines because the lower atmosphere can become decoupled. These results highlight the need for measurements throughout the turbine height to improve wind energy forecasting and operations.
Nathan J. Agarwal, Julie K. Lundquist, Timothy W. Juliano, and Alex Rybchuk
Wind Energ. Sci., 11, 2369–2403, https://doi.org/10.5194/wes-11-2369-2026, https://doi.org/10.5194/wes-11-2369-2026, 2026
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Models of wind behavior inform offshore wind farm site investment decisions. Here we compare a newly developed model to another, historically used model based on how these models represent winds and turbulence at two North Sea sites. The best model depends on the site. While the older model performs best at the site above a wind farm, the newer model performs best at the site that is at the same altitude as the wind farm. We support using the new model to represent winds at the turbine level.
Miguel Sanchez-Gomez, Georgios Deskos, Mike Optis, Julie K. Lundquist, Michael Sinner, Geng Xia, and Walter Musial
Wind Energ. Sci., 11, 2009–2036, https://doi.org/10.5194/wes-11-2009-2026, https://doi.org/10.5194/wes-11-2009-2026, 2026
Short summary
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Mesoscale simulations with the Fitch wind farm parameterization were compared to large-domain large-eddy simulations for three planned offshore wind farms under varied atmospheric conditions. Mesoscale runs captured key wake deficit patterns and stability effects in the wind farm wake evolution but underestimated power losses from internal wakes, especially in aligned winds or stable conditions. Results highlight mesoscale strengths for large-scale wakes and limits for turbine-level losses.
Nicola Bodini, Joseph Olson, Brian Gaudet, Giacomo Valerio Iungo, Mojtaba Shams Solari, Sayahnya Roy, Julie K. Lundquist, Nathan Agarwal, Timothy A. Myers, Bianca Adler, Jeffrey D. Mirocha, Eric James, Laura Bianco, James M. Wilczak, and David D. Turner
Wind Energ. Sci., 11, 1949–1961, https://doi.org/10.5194/wes-11-1949-2026, https://doi.org/10.5194/wes-11-1949-2026, 2026
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To improve offshore wind forecasts, the Third Wind Forecast Improvement Project monitored the United States east coast for 18 months. We compiled a daily log of weather events using advanced scanners and expert notes. This public dataset identifies important wind patterns, helping scientists test computer models and choose specific cases to study.
Keeta Chapman-Smith, Xiaoli Guo Larsén, and Mark Laier Brodersen
Wind Energ. Sci., 11, 1889–1912, https://doi.org/10.5194/wes-11-1889-2026, https://doi.org/10.5194/wes-11-1889-2026, 2026
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This study presents a method to estimate wind speeds that could occur in a 50-year period. The 50-year wind speed is calculated for three regions: Taiwan, Japan, and the east coast of the US. The method performs well in Taiwan and Japan, which can be attributed to the large dataset size located in a limited spatial area. The east coast of the US performs less well due to the smaller dataset size and wider spatial region that they cover.
Xiaoli Guo Larsén, Marc Imberger, and Rogier Floors
Wind Energ. Sci., 11, 1853–1869, https://doi.org/10.5194/wes-11-1853-2026, https://doi.org/10.5194/wes-11-1853-2026, 2026
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This study delivers a method and datasets for a global offshore atlas for turbulence intensity from a height of 10 m to 200 m. The method innovatively includes both two-dimensional and three-dimensional turbulence, along with stability, wave age and height. Results show satisfactory agreement with measurements and data from the literature.
Aliza Abraham, Nicola Bodini, Nicholas Hamilton, Brian Hirth, John Schroeder, and Patrick Moriarty
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-82, https://doi.org/10.5194/wes-2026-82, 2026
Preprint under review for WES
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Thunderstorms can cause sudden changes in wind speed and direction called "wind ramps". While wind turbines are designed to withstand simplified versions of such wind ramps, this paper shows that real wind ramps are much more variable and complex than those prescribed in the design standard. This variability makes it difficult for wind farm operators to predict the conditions that each wind turbine will experience, adding uncertainty to the prediction of wind farm power.
Chunyong Jung, Pengfei Xue, Chenfu Huang, William Pringle, Mrinal Biswas, Geeta Nain, and Jiali Wang
Wind Energ. Sci., 11, 1321–1341, https://doi.org/10.5194/wes-11-1321-2026, https://doi.org/10.5194/wes-11-1321-2026, 2026
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We developed a new modeling system that combines air, ocean, and wave processes to better understand hurricanes. Using Hurricane Henri as a test case, we found that including ocean and wave interactions improves the accuracy of storm intensity and wind patterns. These results show that accounting for these interactions is important for assessing risks to offshore energy systems and coastal regions.
Sara Müller, Xiaoli Guo Larsén, and Fei Hu
Wind Energ. Sci., 11, 961–981, https://doi.org/10.5194/wes-11-961-2026, https://doi.org/10.5194/wes-11-961-2026, 2026
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Wind farms are being developed in areas prone to tropical cyclones. However, it remains unclear whether turbulence models in current design standards, such as the Mann uniform shear model, are suitable for these conditions. For the first time, the Mann model is assessed in depth using high-frequency measurements from four typhoons. Larger-than-predicted spectral energy is found at small wavenumbers in the outer cyclone and, in some cases, in the crosswind component in the inner cyclone.
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
Revised manuscript under review for WES
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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
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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
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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
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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.
Shadan Mozafari, Jennifer Marie Rinker, Paul Veers, and Katherine Dykes
Wind Energ. Sci., 11, 621–641, https://doi.org/10.5194/wes-11-621-2026, https://doi.org/10.5194/wes-11-621-2026, 2026
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The study showcases the added value of using structural response measurements in lifetime extension assessments within wind farms. In addition, it answers two of the common questions in different methods of assessment. First, it assesses the applicability of the Frandsen model for estimating conservative waked turbulence in the compact layout of wind farms. Second, it showcases probabilistic extrapolation of short- to mid-term data for long-term site-specific fatigue assessments.
Branko Kosović, Sukanta Basu, Jacob Berg, Larry K. Berg, Sue E. Haupt, Xiaoli G. Larsén, Joachim Peinke, Richard J. A. M. Stevens, Paul Veers, and Simon Watson
Wind Energ. Sci., 11, 509–555, https://doi.org/10.5194/wes-11-509-2026, https://doi.org/10.5194/wes-11-509-2026, 2026
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Most human activity happens in the layer of the atmosphere which extends a few hundred meters to a couple of kilometers above the surface of the Earth. The flow in this layer is turbulent. Turbulence impacts wind power production and turbine lifespan. Optimizing wind turbine performance requires understanding how turbulence affects both wind turbine efficiency and reliability. This paper points to gaps in our knowledge that need to be addressed to effectively utilize wind resources.
Carlo L. Bottasso, Sandrine Aubrun, Nicolaos A. Cutululis, Julia Gottschall, Athanasios Kolios, Jakob Mann, and Paul Veers
Wind Energ. Sci., 11, 347–348, https://doi.org/10.5194/wes-11-347-2026, https://doi.org/10.5194/wes-11-347-2026, 2026
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This editorial celebrates the 10th anniversary of Wind Energy Science, reflecting on a decade of rapid scientific progress and the journal’s role in advancing fundamental, interdisciplinary research. It highlights key developments in wind energy, the importance of open science and academia–industry collaboration, and emerging challenges such as data sharing and artificial intelligence. Above all, it honors the research community that has shaped the journal and looks ahead to the next decade.
Sima Hamzeloo, Xiaoli Guo Larsén, Alfredo Peña, Jana Fischereit, and Oscar García-Santiago
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-267, https://doi.org/10.5194/wes-2025-267, 2026
Revised manuscript under review for WES
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We studied how winds and ocean waves affect each other during a North Sea storm. Using a multiscale approach that captures processes from kilometers down to meters, we linked wind and wave models and compared the results with real measurements. Our aim was to improve current simulation methods, and the findings show that this detailed approach provides more accurate storm predictions up to 100 m height.
Anna Voss, Konrad B. Bärfuss, Beatriz Cañadillas, Maik Angermann, Mark Bitter, Matthias Cremer, Thomas Feuerle, Jonas Spoor, Julie K. Lundquist, Patrick Moriarty, and Astrid Lampert
Wind Energ. Sci., 11, 71–88, https://doi.org/10.5194/wes-11-71-2026, https://doi.org/10.5194/wes-11-71-2026, 2026
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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.
Kyle Peco, Jiali Wang, Chunyong Jung, Gökhan Sever, Lindsay Sheridan, Jeremy Feinstein, Rao Kotamarthi, Caroline Draxl, Ethan Young, Avi Purkayastha, and Andrew Kumler
Wind Energ. Sci., 11, 13–35, https://doi.org/10.5194/wes-11-13-2026, https://doi.org/10.5194/wes-11-13-2026, 2026
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This study presents a new wind dataset, generated by a convection-permitting regional climate model across North America. By validating the dataset against wind observations, we have demonstrated that this dataset captures the wind patterns over complex terrains more realistically than the European Centre for
Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5). Additionally, this study quantifies model uncertainty in wind speed, comparing it against interannual variability, to better inform wind farm siting.
Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5). Additionally, this study quantifies model uncertainty in wind speed, comparing it against interannual variability, to better inform wind farm siting.
Jana Fischereit, Bjarke T. E. Olsen, Marc Imberger, Henrik Vedel, Kristian H. Møller, Andrea N. Hahmann, and Xiaoli Guo Larsén
EGUsphere, https://doi.org/10.5194/egusphere-2025-5407, https://doi.org/10.5194/egusphere-2025-5407, 2025
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We evaluated how operating wind farms influence the atmosphere in numerical weather prediction using two wind farm parameterizations in the HARMONIE-AROME model, applied by over 10 European weather services. Accurate yield forecasts require including both onshore and offshore turbines. Wind turbines slightly alter near-surface temperature (<1 K on average). We also present an open-access European wind turbine dataset combining multiple data sources.
Nathalia Correa-Sánchez, Xiaoli Guo Larsén, Giorgia Fosser, Eleonora Dallan, Marco Borga, and Francesco Marra
Wind Energ. Sci., 10, 2551–2561, https://doi.org/10.5194/wes-10-2551-2025, https://doi.org/10.5194/wes-10-2551-2025, 2025
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We examined the power spectra of wind speed in three convection-permitting models in central Europe and found that these models have a better representation of wind variability characteristics than standard wind datasets like the New European Wind Atlas, due to different simulation approaches, providing more reliable extreme wind predictions.
Nathalia Correa-Sánchez, Xiaoli Guo Larsén, Eleonora Dallan, Marco Borga, and Fracesco Marra
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-172, https://doi.org/10.5194/wes-2025-172, 2025
Revised manuscript accepted for WES
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This research presents the first use of SMEV for wind extremes, extending it to wind energy applications. We use a categories framework combining climate, roughness, and topography for CPM evaluation. We find that model formulation drives inter-model uncertainties, rather than surface conditions. Also, there is a higher model agreement in winter (synoptic) and lower in summer (convective). CPM uncertainty analysis improves the reliability of extreme winds for design parameters.
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
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We explore how simple terrain influences spatial variations in wind speed and wind farm performance during a low-level jet. Using simulations, field observations, and turbine production data, we find that downstream turbines produce more power than upstream ones, despite being subjected to wake effects. This counterintuitive result arises because the low-level jet and winds near turbine rotors are highly sensitive to topographic features, leading to stronger winds at the downstream turbines.
Geng Xia, Mike Optis, Georgios Deskos, Michael Sinner, Daniel Mulas Hernando, Julie Kay Lundquist, Andrew Kumler, Miguel Sanchez Gomez, Paul Fleming, and Walter Musial
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-154, https://doi.org/10.5194/wes-2025-154, 2025
Revised manuscript under review for WES
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This study examines energy losses from cluster wakes in offshore wind farms along the U.S. East Coast. Simulations based on real lease projects show that large wind speed deficits do not always cause equally large energy losses. The energy loss method revealed wake areas up to 30 % larger than traditional estimates, underscoring the need to consider both wind speed deficit and energy loss in planning offshore wind development.
Lindsay M. Sheridan, Jiali Wang, Caroline Draxl, Nicola Bodini, Caleb Phillips, Dmitry Duplyakin, Heidi Tinnesand, Raj K. Rai, Julia E. Flaherty, Larry K. Berg, Chunyong Jung, Ethan Young, and Rao Kotamarthi
Wind Energ. Sci., 10, 1551–1574, https://doi.org/10.5194/wes-10-1551-2025, https://doi.org/10.5194/wes-10-1551-2025, 2025
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Three recent wind resource datasets are assessed for their skills in representing annual average wind speeds and seasonal, diurnal, and interannual trends in the wind resource in coastal locations to support customers interested in small and midsize wind energy.
Daphne Quint, Julie K. Lundquist, Nicola Bodini, and David Rosencrans
Wind Energ. Sci., 10, 1269–1301, https://doi.org/10.5194/wes-10-1269-2025, https://doi.org/10.5194/wes-10-1269-2025, 2025
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Offshore wind farms along the US East Coast can have limited effects on local weather. To study these effects, we include wind farms near Massachusetts and Rhode Island, and we test different amounts of turbulence in our model. We analyze changes in wind, temperature, and turbulence. Simulated effects on surface temperature and turbulence change depending on how much turbulence is added to the model. The extent of the wind farm wake depends on how deep the atmospheric boundary layer is.
Robert S. Arthur, Alex Rybchuk, Timothy W. Juliano, Gabriel Rios, Sonia Wharton, Julie K. Lundquist, and Jerome D. Fast
Wind Energ. Sci., 10, 1187–1209, https://doi.org/10.5194/wes-10-1187-2025, https://doi.org/10.5194/wes-10-1187-2025, 2025
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This paper evaluates a new model configuration for wind energy forecasting in complex terrain. We compare model results to observations in the Altamont Pass (California, USA), where wind channeling through a mountain gap leads to increased energy production. We demonstrate that the new model configuration performs similarly to a more established approach, with some evidence of improved wind speed predictions, and provide guidance for future model testing.
Lara Tobias-Tarsh, Chunyong Jung, Jiali Wang, Vishal Bobde, Akintomide A. Akinsanola, and V. Rao Kotamarthi
EGUsphere, https://doi.org/10.5194/egusphere-2025-1805, https://doi.org/10.5194/egusphere-2025-1805, 2025
Preprint archived
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We use a high-resolution regional climate model to better understand hurricanes in the North Atlantic over the past 20 years. The model closely matches observed storm frequency and captures stronger storms more accurately than traditional datasets. It also shows better performance in areas with limited data, like the Caribbean. These results can help improve local storm preparedness and planning for critical infrastructure.
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
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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.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
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We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Daphne Quint, Julie K. Lundquist, and David Rosencrans
Wind Energ. Sci., 10, 117–142, https://doi.org/10.5194/wes-10-117-2025, https://doi.org/10.5194/wes-10-117-2025, 2025
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Offshore wind farms will be built along the East Coast of the United States. Low-level jets (LLJs) – layers of fast winds at low altitudes – also occur here. LLJs provide wind resources and also influence moisture and pollution transport, so it is important to understand how they might change. We develop and validate an automated tool to detect LLJs and compare 1 year of simulations with and without wind farms. Here, we describe LLJ characteristics and how they change with wind farms.
David Rosencrans, Julie K. Lundquist, Mike Optis, and Nicola Bodini
Wind Energ. Sci., 10, 59–81, https://doi.org/10.5194/wes-10-59-2025, https://doi.org/10.5194/wes-10-59-2025, 2025
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The US offshore wind industry is growing rapidly. Expansion into cold climates will subject turbines and personnel to hazardous icing. We analyze the 21-year icing risk for US east coast wind areas based on numerical weather prediction simulations and further assess impacts from wind farm wakes over one winter season. Sea spray icing at 10 m can occur up to 67 h per month. However, turbine–atmosphere interactions reduce icing hours within wind plant areas.
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
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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.
Rachel Robey and Julie K. Lundquist
Wind Energ. Sci., 9, 1905–1922, https://doi.org/10.5194/wes-9-1905-2024, https://doi.org/10.5194/wes-9-1905-2024, 2024
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Measurements of wind turbine wakes with scanning lidar instruments contain complex errors. We model lidars in a simulated environment to understand how and why the measured wake may differ from the true wake and validate the results with observational data. The lidar smooths out the wake, making it seem more spread out and the slowdown of the winds less pronounced. Our findings provide insights into best practices for accurately measuring wakes with lidar and interpreting observational data.
Sara Müller, Xiaoli Guo Larsén, and David Robert Verelst
Wind Energ. Sci., 9, 1153–1171, https://doi.org/10.5194/wes-9-1153-2024, https://doi.org/10.5194/wes-9-1153-2024, 2024
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Tropical cyclone winds are challenging for wind turbines. We analyze a tropical cyclone before landfall in a mesoscale model. The simulated wind speeds and storm structure are sensitive to the boundary parametrization. However, independent of the boundary layer parametrization, the median change in wind speed and wind direction with height is small relative to wind turbine design standards. Strong spatial organization of wind shear and veer along the rainbands may increase wind turbine loads.
Nicola Bodini, Mike Optis, Stephanie Redfern, David Rosencrans, Alex Rybchuk, Julie K. Lundquist, Vincent Pronk, Simon Castagneri, Avi Purkayastha, Caroline Draxl, Raghavendra Krishnamurthy, Ethan Young, Billy Roberts, Evan Rosenlieb, and Walter Musial
Earth Syst. Sci. Data, 16, 1965–2006, https://doi.org/10.5194/essd-16-1965-2024, https://doi.org/10.5194/essd-16-1965-2024, 2024
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This article presents the 2023 National Offshore Wind data set (NOW-23), an updated resource for offshore wind information in the US. It replaces the Wind Integration National Dataset (WIND) Toolkit, offering improved accuracy through advanced weather prediction models. The data underwent regional tuning and validation and can be accessed at no cost.
Jana Fischereit, Henrik Vedel, Xiaoli Guo Larsén, Natalie E. Theeuwes, Gregor Giebel, and Eigil Kaas
Geosci. Model Dev., 17, 2855–2875, https://doi.org/10.5194/gmd-17-2855-2024, https://doi.org/10.5194/gmd-17-2855-2024, 2024
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Wind farms impact local wind and turbulence. To incorporate these effects in weather forecasting, the explicit wake parameterization (EWP) is added to the forecasting model HARMONIE–AROME. We evaluate EWP using flight data above and downstream of wind farms, comparing it with an alternative wind farm parameterization and another weather model. Results affirm the correct implementation of EWP, emphasizing the necessity of accounting for wind farm effects in accurate weather forecasting.
Shadan Mozafari, Paul Veers, Jennifer Rinker, and Katherine Dykes
Wind Energ. Sci., 9, 799–820, https://doi.org/10.5194/wes-9-799-2024, https://doi.org/10.5194/wes-9-799-2024, 2024
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Turbulence is one of the main drivers of fatigue in wind turbines. There is some debate on how to model the turbulence in normal wind conditions in the design phase. To address such debates, we study the fatigue load distribution and reliability following different models of the International Electrotechnical Commission 61400-1 standard. The results show the lesser importance of load uncertainty due to turbulence distribution compared to the uncertainty of material resistance and Miner’s rule.
David Rosencrans, Julie K. Lundquist, Mike Optis, Alex Rybchuk, Nicola Bodini, and Michael Rossol
Wind Energ. Sci., 9, 555–583, https://doi.org/10.5194/wes-9-555-2024, https://doi.org/10.5194/wes-9-555-2024, 2024
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The US offshore wind industry is developing rapidly. Using yearlong simulations of wind plants in the US mid-Atlantic, we assess the impacts of wind turbine wakes. While wakes are the strongest and longest during summertime stably stratified conditions, when New England grid demand peaks, they are predictable and thus manageable. Over a year, wakes reduce power output by over 35 %. Wakes in a wind plant contribute the most to that reduction, while wakes between wind plants play a secondary role.
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
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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
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The wind slows down as it approaches a wind plant; this phenomenon is called blockage. As a result, the turbines in the wind plant produce less power than initially anticipated. We investigate wind plant blockage for two atmospheric conditions. Blockage is larger for a wind plant compared to a stand-alone turbine. Also, blockage increases with atmospheric stability. Blockage is amplified by the vertical transport of horizontal momentum as the wind approaches the front-row turbines in the array.
Maciej M. Mroczek, Sanjay Raja Arwade, and Matthew A. Lackner
Wind Energ. Sci., 8, 807–817, https://doi.org/10.5194/wes-8-807-2023, https://doi.org/10.5194/wes-8-807-2023, 2023
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Benefits of orientating a three-legged offshore wind jacket relative to the metocean conditions for pile design are assessed considering the International Energy Agency 15 MW reference turbine and a reference site off the coast of Massachusetts. Results, based on the considered conditions, show that the pile design can be optimized by orientating the jacket relative to the dominant wave direction. This design optimization can be used on offshore wind projects to provide cost and risk reductions.
Y. Joseph Zhang, Tomas Fernandez-Montblanc, William Pringle, Hao-Cheng Yu, Linlin Cui, and Saeed Moghimi
Geosci. Model Dev., 16, 2565–2581, https://doi.org/10.5194/gmd-16-2565-2023, https://doi.org/10.5194/gmd-16-2565-2023, 2023
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Simulating global ocean from deep basins to coastal areas is a daunting task but is important for disaster mitigation efforts. We present a new 3D global ocean model on flexible mesh to study both tidal and nontidal processes and total water prediction. We demonstrate the potential for
seamlesssimulation, on a single mesh, from the global ocean to a few estuaries along the US West Coast. The model can serve as the backbone of a global tide surge and compound flooding forecasting framework.
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
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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.
Xiaoli Guo Larsén, Marc Imberger, Ásta Hannesdóttir, and Andrea N. Hahmann
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-102, https://doi.org/10.5194/wes-2022-102, 2023
Revised manuscript not accepted
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We study how climate change will impact extreme winds and choice of turbine class. We use data from 18 CMIP6 members from a historic and a future period to access the change in the extreme winds. The analysis shows an overall increase in the extreme winds in the North Sea and the southern Baltic Sea, but a decrease over the Scandinavian Peninsula and most of the Baltic Sea. The analysis is inconclusive to whether higher or lower classes of turbines will be installed in the future.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
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Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
Xiaoli Guo Larsén and Søren Ott
Wind Energ. Sci., 7, 2457–2468, https://doi.org/10.5194/wes-7-2457-2022, https://doi.org/10.5194/wes-7-2457-2022, 2022
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A method is developed for calculating the extreme wind in tropical-cyclone-affected water areas. The method is based on the spectral correction method that fills in the missing wind variability to the modeled time series, guided by best track data. The paper provides a detailed recipe for applying the method and the 50-year winds of equivalent 10 min temporal resolution from 10 to 150 m in several tropical-cyclone-affected regions.
Qiuyi Wu, Julie Bessac, Whitney Huang, Jiali Wang, and Rao Kotamarthi
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 205–224, https://doi.org/10.5194/ascmo-8-205-2022, https://doi.org/10.5194/ascmo-8-205-2022, 2022
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We study wind conditions and their potential future changes across the U.S. via a statistical conditional framework. We conclude that changes between historical and future wind directions are small, but wind speeds are generally weakened in the projected period, with some locations being intensified. Moreover, winter wind speeds are projected to decrease in the northwest, Colorado, and the northern Great Plains (GP), while summer wind speeds over the southern GP slightly increase in the future.
William J. Shaw, Larry K. Berg, Mithu Debnath, Georgios Deskos, Caroline Draxl, Virendra P. Ghate, Charlotte B. Hasager, Rao Kotamarthi, Jeffrey D. Mirocha, Paytsar Muradyan, William J. Pringle, David D. Turner, and James M. Wilczak
Wind Energ. Sci., 7, 2307–2334, https://doi.org/10.5194/wes-7-2307-2022, https://doi.org/10.5194/wes-7-2307-2022, 2022
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This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.
Alex Rybchuk, Timothy W. Juliano, Julie K. Lundquist, David Rosencrans, Nicola Bodini, and Mike Optis
Wind Energ. Sci., 7, 2085–2098, https://doi.org/10.5194/wes-7-2085-2022, https://doi.org/10.5194/wes-7-2085-2022, 2022
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Numerical weather prediction models are used to predict how wind turbines will interact with the atmosphere. Here, we characterize the uncertainty associated with the choice of turbulence parameterization on modeled wakes. We find that simulated wind speed deficits in turbine wakes can be significantly sensitive to the choice of turbulence parameterization. As such, predictions of future generated power are also sensitive to turbulence parameterization choice.
Rachel Robey and Julie K. Lundquist
Atmos. Meas. Tech., 15, 4585–4622, https://doi.org/10.5194/amt-15-4585-2022, https://doi.org/10.5194/amt-15-4585-2022, 2022
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Our work investigates the behavior of errors in remote-sensing wind lidar measurements due to turbulence. Using a virtual instrument, we measured winds in simulated atmospheric flows and decomposed the resulting error. Dominant error mechanisms, particularly vertical velocity variations and interactions with shear, were identified in ensemble data over three test cases. By analyzing the underlying mechanisms, the response of the error behavior to further varying flow conditions may be projected.
Chuxuan Li, Alexander L. Handwerger, Jiali Wang, Wei Yu, Xiang Li, Noah J. Finnegan, Yingying Xie, Giuseppe Buscarnera, and Daniel E. Horton
Nat. Hazards Earth Syst. Sci., 22, 2317–2345, https://doi.org/10.5194/nhess-22-2317-2022, https://doi.org/10.5194/nhess-22-2317-2022, 2022
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In January 2021 a storm triggered numerous debris flows in a wildfire burn scar in California. We use a hydrologic model to assess debris flow susceptibility in pre-fire and postfire scenarios. Compared to pre-fire conditions, postfire conditions yield dramatic increases in peak water discharge, substantially increasing debris flow susceptibility. Our work highlights the hydrologic model's utility in investigating and potentially forecasting postfire debris flows at regional scales.
Jana Fischereit, Kurt Schaldemose Hansen, Xiaoli Guo Larsén, Maarten Paul van der Laan, Pierre-Elouan Réthoré, and Juan Pablo Murcia Leon
Wind Energ. Sci., 7, 1069–1091, https://doi.org/10.5194/wes-7-1069-2022, https://doi.org/10.5194/wes-7-1069-2022, 2022
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Wind turbines extract kinetic energy from the flow to create electricity. This induces a wake of reduced wind speed downstream of a turbine and consequently downstream of a wind farm. Different types of numerical models have been developed to calculate this effect. In this study, we compared models of different complexity, together with measurements over two wind farms. We found that higher-fidelity models perform better and the considered rapid models cannot fully capture the wake effect.
Romit Maulik, Vishwas Rao, Jiali Wang, Gianmarco Mengaldo, Emil Constantinescu, Bethany Lusch, Prasanna Balaprakash, Ian Foster, and Rao Kotamarthi
Geosci. Model Dev., 15, 3433–3445, https://doi.org/10.5194/gmd-15-3433-2022, https://doi.org/10.5194/gmd-15-3433-2022, 2022
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In numerical weather prediction, data assimilation is frequently utilized to enhance the accuracy of forecasts from equation-based models. In this work we use a machine learning framework that approximates a complex dynamical system given by the geopotential height. Instead of using an equation-based model, we utilize this machine-learned alternative to dramatically accelerate both the forecast and the assimilation of data, thereby reducing need for large computational resources.
Vincent Pronk, Nicola Bodini, Mike Optis, Julie K. Lundquist, Patrick Moriarty, Caroline Draxl, Avi Purkayastha, and Ethan Young
Wind Energ. Sci., 7, 487–504, https://doi.org/10.5194/wes-7-487-2022, https://doi.org/10.5194/wes-7-487-2022, 2022
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In this paper, we have assessed to which extent mesoscale numerical weather prediction models are more accurate than state-of-the-art reanalysis products in characterizing the wind resource at heights of interest for wind energy. The conclusions of our work will be of primary importance to the wind industry for recommending the best data sources for wind resource modeling.
Adam S. Wise, James M. T. Neher, Robert S. Arthur, Jeffrey D. Mirocha, Julie K. Lundquist, and Fotini K. Chow
Wind Energ. Sci., 7, 367–386, https://doi.org/10.5194/wes-7-367-2022, https://doi.org/10.5194/wes-7-367-2022, 2022
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Wind turbine wake behavior in hilly terrain depends on various atmospheric conditions. We modeled a wind turbine located on top of a ridge in Portugal during typical nighttime and daytime atmospheric conditions and validated these model results with observational data. During nighttime conditions, the wake deflected downwards following the terrain. During daytime conditions, the wake deflected upwards. These results can provide insight into wind turbine siting and operation in hilly regions.
Anna Rutgersson, Erik Kjellström, Jari Haapala, Martin Stendel, Irina Danilovich, Martin Drews, Kirsti Jylhä, Pentti Kujala, Xiaoli Guo Larsén, Kirsten Halsnæs, Ilari Lehtonen, Anna Luomaranta, Erik Nilsson, Taru Olsson, Jani Särkkä, Laura Tuomi, and Norbert Wasmund
Earth Syst. Dynam., 13, 251–301, https://doi.org/10.5194/esd-13-251-2022, https://doi.org/10.5194/esd-13-251-2022, 2022
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A natural hazard is a naturally occurring extreme event with a negative effect on people, society, or the environment; major events in the study area include wind storms, extreme waves, high and low sea level, ice ridging, heavy precipitation, sea-effect snowfall, river floods, heat waves, ice seasons, and drought. In the future, an increase in sea level, extreme precipitation, heat waves, and phytoplankton blooms is expected, and a decrease in cold spells and severe ice winters is anticipated.
Marcus Reckermann, Anders Omstedt, Tarmo Soomere, Juris Aigars, Naveed Akhtar, Magdalena Bełdowska, Jacek Bełdowski, Tom Cronin, Michał Czub, Margit Eero, Kari Petri Hyytiäinen, Jukka-Pekka Jalkanen, Anders Kiessling, Erik Kjellström, Karol Kuliński, Xiaoli Guo Larsén, Michelle McCrackin, H. E. Markus Meier, Sonja Oberbeckmann, Kevin Parnell, Cristian Pons-Seres de Brauwer, Anneli Poska, Jarkko Saarinen, Beata Szymczycha, Emma Undeman, Anders Wörman, and Eduardo Zorita
Earth Syst. Dynam., 13, 1–80, https://doi.org/10.5194/esd-13-1-2022, https://doi.org/10.5194/esd-13-1-2022, 2022
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As part of the Baltic Earth Assessment Reports (BEAR), we present an inventory and discussion of different human-induced factors and processes affecting the environment of the Baltic Sea region and their interrelations. Some are naturally occurring and modified by human activities, others are completely human-induced, and they are all interrelated to different degrees. The findings from this study can largely be transferred to other comparable marginal and coastal seas in the world.
Marc Imberger, Xiaoli Guo Larsén, and Neil Davis
Adv. Geosci., 56, 77–87, https://doi.org/10.5194/adgeo-56-77-2021, https://doi.org/10.5194/adgeo-56-77-2021, 2021
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Events like mid-latitude storms with their high winds have an impact on wind energy production and forecasting of such events is crucial. This study investigates the capabilities of a global weather prediction model MPAS and looks at how key parameters like storm intensity, arrival time and duration are represented compared to measurements and traditional methods. It is found that storm intensity is represented well while model drifts negatively influence estimation of arrival time and duration.
Jiali Wang, Zhengchun Liu, Ian Foster, Won Chang, Rajkumar Kettimuthu, and V. Rao Kotamarthi
Geosci. Model Dev., 14, 6355–6372, https://doi.org/10.5194/gmd-14-6355-2021, https://doi.org/10.5194/gmd-14-6355-2021, 2021
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Downscaling, the process of generating a higher spatial or time dataset from a coarser observational or model dataset, is a widely used technique. Two common methodologies for performing downscaling are to use either dynamic (physics-based) or statistical (empirical). Here we develop a novel methodology, using a conditional generative adversarial network (CGAN), to perform the downscaling of a model's precipitation forecasts and describe the advantages of this method compared to the others.
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Short summary
Wind energy is expanding into regions exposed to tropical cyclones, where extreme winds and waves can damage turbines and disrupt energy production. We reviewed observations, simulations, engineering standards, and risk quantification frameworks to identify the most important gaps. Today's design rules, originally built for milder storms, do not capture cyclone hazards. Closing these gaps requires better offshore measurements, improved models, and risk-informed design standards.
Wind energy is expanding into regions exposed to tropical cyclones, where extreme winds and...
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