Articles | Volume 11, issue 3
https://doi.org/10.5194/wes-11-861-2026
© 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-11-861-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Mar 2026
Research article |
| 20 Mar 2026
| 20 Mar 2026
Alignment of scanning lidars in offshore campaigns – an extension of the sea surface levelling method
Kira Gramitzky
CORRESPONDING AUTHOR
Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), Joseph-Beuys-Str. 8, 34117 Kassel, Germany
Department of Integrated Energy Systems, University of Kassel, Wilhelmshöher Allee 71–73, 34121 Kassel, Germany
Florian Jäger
Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), Joseph-Beuys-Str. 8, 34117 Kassel, Germany
Department of Sustainable Electrical Energy Systems, University of Kassel, Wilhelmshöher Allee 71–73, 34121 Kassel, Germany
Doron Callies
Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), Joseph-Beuys-Str. 8, 34117 Kassel, Germany
Department of Sustainable Electrical Energy Systems, University of Kassel, Wilhelmshöher Allee 71–73, 34121 Kassel, Germany
Tabea Hildebrand
Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), Joseph-Beuys-Str. 8, 34117 Kassel, Germany
Department of Sustainable Electrical Energy Systems, University of Kassel, Wilhelmshöher Allee 71–73, 34121 Kassel, Germany
Julie K. Lundquist
Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD, USA
Lukas Pauscher
Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), Joseph-Beuys-Str. 8, 34117 Kassel, Germany
Department of Sustainable Electrical Energy Systems, University of Kassel, Wilhelmshöher Allee 71–73, 34121 Kassel, Germany
Acoustics and Vibrations Research Group (AVRG), Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium
Related authors
No articles found.
Valeria Vasquez-Barros, Nicola Bodini, Seth Zippel, Anthony Kirincich, and Julie K. Lundquist
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-115, https://doi.org/10.5194/wes-2026-115, 2026
Preprint under review for WES
Short summary
Short summary
Offshore wind turbines operate more than 100 meters above the ocean, but atmospheric conditions are often measured only near the sea surface. Using observations from an offshore research platform, we found that surface measurements frequently do not represent the conditions experienced by turbines because the lower atmosphere can become decoupled. These results highlight the need for measurements throughout the turbine height to improve wind energy forecasting and operations.
Nathan J. Agarwal, Julie K. Lundquist, Timothy W. Juliano, and Alex Rybchuk
Wind Energ. Sci., 11, 2369–2403, https://doi.org/10.5194/wes-11-2369-2026, https://doi.org/10.5194/wes-11-2369-2026, 2026
Short summary
Short summary
Models of wind behavior inform offshore wind farm site investment decisions. Here we compare a newly developed model to another, historically used model based on how these models represent winds and turbulence at two North Sea sites. The best model depends on the site. While the older model performs best at the site above a wind farm, the newer model performs best at the site that is at the same altitude as the wind farm. We support using the new model to represent winds at the turbine level.
Miguel Sanchez-Gomez, Georgios Deskos, Mike Optis, Julie K. Lundquist, Michael Sinner, Geng Xia, and Walter Musial
Wind Energ. Sci., 11, 2009–2036, https://doi.org/10.5194/wes-11-2009-2026, https://doi.org/10.5194/wes-11-2009-2026, 2026
Short summary
Short summary
Mesoscale simulations with the Fitch wind farm parameterization were compared to large-domain large-eddy simulations for three planned offshore wind farms under varied atmospheric conditions. Mesoscale runs captured key wake deficit patterns and stability effects in the wind farm wake evolution but underestimated power losses from internal wakes, especially in aligned winds or stable conditions. Results highlight mesoscale strengths for large-scale wakes and limits for turbine-level losses.
Nicola Bodini, Joseph Olson, Brian Gaudet, Giacomo Valerio Iungo, Mojtaba Shams Solari, Sayahnya Roy, Julie K. Lundquist, Nathan Agarwal, Timothy A. Myers, Bianca Adler, Jeffrey D. Mirocha, Eric James, Laura Bianco, James M. Wilczak, and David D. Turner
Wind Energ. Sci., 11, 1949–1961, https://doi.org/10.5194/wes-11-1949-2026, https://doi.org/10.5194/wes-11-1949-2026, 2026
Short summary
Short summary
To improve offshore wind forecasts, the Third Wind Forecast Improvement Project monitored the United States east coast for 18 months. We compiled a daily log of weather events using advanced scanners and expert notes. This public dataset identifies important wind patterns, helping scientists test computer models and choose specific cases to study.
Franziska Bär, Michael Borsche, Frank Kaspar, Thomas Spangehl, Dehong Yuan, David Geiger, and Lukas Pauscher
Adv. Sci. Res., 22, 149–164, https://doi.org/10.5194/asr-22-149-2026, https://doi.org/10.5194/asr-22-149-2026, 2026
Short summary
Short summary
COSMO-REA6 is widely used in energy applications but has known deficits. Its successor, COSMO-R6G2, is evaluated for Germany, focusing on Global Horizontal Irradiance (GHI) as well as wind speed. It shows slightly lower GHI and good wind quality in flat areas, but underestimates night-time wind in mountains. Overall, it is a useful successor, though users should consider remaining limitations.
David Geiger, Christoph Zink, Franziska Bär, Maximilian Pfennig, Doron Callies, Carsten Pape, Jaqueline Drücke, and Lukas Pauscher
Adv. Sci. Res., 22, 131–148, https://doi.org/10.5194/asr-22-131-2026, https://doi.org/10.5194/asr-22-131-2026, 2026
Short summary
Short summary
In this paper we analyse the suitability of different reanalyses and meso-scale models for simulating wind energy in Germany. We found that all datasets overestimate energy production, with errors ranging from 5 % to 45 %. This suggests that the underlying models may not accurately reflect average wind conditions. CERRA and ERA5 performed the best, but they also require regional adjustment. Understanding the cause of these differences is crucial for improving weather and wind energy modelling.
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
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.
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.
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
Revised manuscript accepted 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.
Anna Voss, Konrad B. Bärfuss, Beatriz Cañadillas, Maik Angermann, Mark Bitter, Matthias Cremer, Thomas Feuerle, Jonas Spoor, Julie K. Lundquist, Patrick Moriarty, and Astrid Lampert
Wind Energ. Sci., 11, 71–88, https://doi.org/10.5194/wes-11-71-2026, https://doi.org/10.5194/wes-11-71-2026, 2026
Short summary
Short summary
This study analyzes onshore wind farm wakes in a semi-complex terrain with data conducted with the research aircraft of TU Braunschweig during the American WAKE experimeNt (AWAKEN). Vertical profiles of temperature, humidity, and wind give insights into the stratification of the atmospheric boundary layer, while horizontal profiles downwind of wind farms reveal an amplification of the reduction in wind speed in a semi-complex terrain, in particular at a distance of 10 km.
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 accepted 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.
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.
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
Revised manuscript accepted 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.
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
Short summary
Short summary
This study examines energy losses from cluster wakes in offshore wind farms along the U.S. East Coast. Simulations based on real lease projects show that large wind speed deficits do not always cause equally large energy losses. The energy loss method revealed wake areas up to 30 % larger than traditional estimates, underscoring the need to consider both wind speed deficit and energy loss in planning offshore wind development.
Daphne Quint, Julie K. Lundquist, Nicola Bodini, and David Rosencrans
Wind Energ. Sci., 10, 1269–1301, https://doi.org/10.5194/wes-10-1269-2025, https://doi.org/10.5194/wes-10-1269-2025, 2025
Short summary
Short summary
Offshore wind farms along the US East Coast can have limited effects on local weather. To study these effects, we include wind farms near Massachusetts and Rhode Island, and we test different amounts of turbulence in our model. We analyze changes in wind, temperature, and turbulence. Simulated effects on surface temperature and turbulence change depending on how much turbulence is added to the model. The extent of the wind farm wake depends on how deep the atmospheric boundary layer is.
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.
Daphne Quint, Julie K. Lundquist, and David Rosencrans
Wind Energ. Sci., 10, 117–142, https://doi.org/10.5194/wes-10-117-2025, https://doi.org/10.5194/wes-10-117-2025, 2025
Short summary
Short summary
Offshore wind farms will be built along the East Coast of the United States. Low-level jets (LLJs) – layers of fast winds at low altitudes – also occur here. LLJs provide wind resources and also influence moisture and pollution transport, so it is important to understand how they might change. We develop and validate an automated tool to detect LLJs and compare 1 year of simulations with and without wind farms. Here, we describe LLJ characteristics and how they change with wind farms.
David Rosencrans, Julie K. Lundquist, Mike Optis, and Nicola Bodini
Wind Energ. Sci., 10, 59–81, https://doi.org/10.5194/wes-10-59-2025, https://doi.org/10.5194/wes-10-59-2025, 2025
Short summary
Short summary
The US offshore wind industry is growing rapidly. Expansion into cold climates will subject turbines and personnel to hazardous icing. We analyze the 21-year icing risk for US east coast wind areas based on numerical weather prediction simulations and further assess impacts from wind farm wakes over one winter season. Sea spray icing at 10 m can occur up to 67 h per month. However, turbine–atmosphere interactions reduce icing hours within wind plant areas.
Rachel Robey and Julie K. Lundquist
Wind Energ. Sci., 9, 1905–1922, https://doi.org/10.5194/wes-9-1905-2024, https://doi.org/10.5194/wes-9-1905-2024, 2024
Short summary
Short summary
Measurements of wind turbine wakes with scanning lidar instruments contain complex errors. We model lidars in a simulated environment to understand how and why the measured wake may differ from the true wake and validate the results with observational data. The lidar smooths out the wake, making it seem more spread out and the slowdown of the winds less pronounced. Our findings provide insights into best practices for accurately measuring wakes with lidar and interpreting observational data.
Nicola Bodini, Mike Optis, Stephanie Redfern, David Rosencrans, Alex Rybchuk, Julie K. Lundquist, Vincent Pronk, Simon Castagneri, Avi Purkayastha, Caroline Draxl, Raghavendra Krishnamurthy, Ethan Young, Billy Roberts, Evan Rosenlieb, and Walter Musial
Earth Syst. Sci. Data, 16, 1965–2006, https://doi.org/10.5194/essd-16-1965-2024, https://doi.org/10.5194/essd-16-1965-2024, 2024
Short summary
Short summary
This article presents the 2023 National Offshore Wind data set (NOW-23), an updated resource for offshore wind information in the US. It replaces the Wind Integration National Dataset (WIND) Toolkit, offering improved accuracy through advanced weather prediction models. The data underwent regional tuning and validation and can be accessed at no cost.
David Rosencrans, Julie K. Lundquist, Mike Optis, Alex Rybchuk, Nicola Bodini, and Michael Rossol
Wind Energ. Sci., 9, 555–583, https://doi.org/10.5194/wes-9-555-2024, https://doi.org/10.5194/wes-9-555-2024, 2024
Short summary
Short summary
The US offshore wind industry is developing rapidly. Using yearlong simulations of wind plants in the US mid-Atlantic, we assess the impacts of wind turbine wakes. While wakes are the strongest and longest during summertime stably stratified conditions, when New England grid demand peaks, they are predictable and thus manageable. Over a year, wakes reduce power output by over 35 %. Wakes in a wind plant contribute the most to that reduction, while wakes between wind plants play a secondary role.
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.
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.
Alexander Basse, Doron Callies, Anselm Grötzner, and Lukas Pauscher
Wind Energ. Sci., 6, 1473–1490, https://doi.org/10.5194/wes-6-1473-2021, https://doi.org/10.5194/wes-6-1473-2021, 2021
Short summary
Short summary
This study investigates systematic, seasonal biases in the long-term correction of short-term wind measurements (< 1 year). Two popular measure–correlate–predict (MCP) methods yield remarkably different results. Six reanalysis data sets serve as long-term data. Besides experimental results, theoretical findings are presented which link the mechanics of the methods and the properties of the reanalysis data sets to the observations. Finally, recommendations for wind park planners are derived.
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.
Cited articles
Abacus Laser GmbH: Abacus Laser LiTra S/S+ Module, Abacus Laser GmbH, datasheet, Revision 001, http://abacus-laser.com/wp-content/uploads/LiTra-S-S-Module-Specs_Rev001_QR.pdf (last access: 18 August 2025), 2023. a
Anantharaman, A., Centurelli, G., Schneemann, J., Bot, E., and Kühn, M.: Comparison of near wind farm wake measurements from scanning lidar with engineering models, J. Phys. Conf. Ser., 2265, 022034, https://doi.org/10.1088/1742-6596/2265/2/022034, 2022. a
Bodini, N., Zardi, D., and Lundquist, J. K.: Three-dimensional structure of wind turbine wakes as measured by scanning lidar, Atmos. Meas. Tech., 10, 2881–2896, https://doi.org/10.5194/amt-10-2881-2017, 2017. a
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
Champneys, D., Frühmann, R., Menke, R., Sathiyanarayanan, D., and Steger, M.: Guidelines on dual scanning lidar measurements for wind resource assessments, Tech. Rep. Rev. 0, DNV and Vaisala, https://www.dnv.com/publications/guidelines-on-dual-scanning-lidar-measurements-for-wind-resource-assessments (last access: 19 March 2024), 2024. a, b
Cheynet, E., Flügge, M., Reuder, J., Jakobsen, J. B., Heggelund, Y., Svardal, B., Saavedra Garfias, P., Obhrai, C., Daniotti, N., Berge, J., Duscha, C., Wildmann, N., Onarheim, I. H., and Godvik, M.: The COTUR project: remote sensing of offshore turbulence for wind energy application, Atmos. Meas. Tech., 14, 6137–6157, https://doi.org/10.5194/amt-14-6137-2021, 2021. a
Gebraad, P. M., Teeuwisse, F. W., Van Wingerden, J., Fleming, P. A., Ruben, S. D., Marden, J. R., and Pao, L. Y.: Wind plant power optimization through yaw control using a parametric model for wake effects – a CFD simulation study, Wind Energy, 19, 95–114, https://doi.org/10.1002/we.1822, 2016. a
Global Wind Energy Council: Global Wind Report 2025, Tech. rep., Global Wind Energy Council, Lisbon, Portugal, https://www.gwec.net/reports/globalwindreport (last access: 22 August 2025), 2025. a
Goit, J. P., Yamaguchi, A., and Ishihara, T.: Measurement and prediction of wind fields at an offshore site by scanning Doppler LiDAR and WRF, Atmosphere-Basel, 11, 442, https://doi.org/10.3390/atmos11050442, 2020. a, b
Gómez, P., Simon, E., and Courtney, M.: Scanning LiDAR Best Practice for Power Curve Testing, Carbon Trust, https://www.carbontrust.com/our-work-and-impact/guides-reports-and-tools/guidelines-for-scanning-lidars-for-power-curve-testing (last access: 20 August 2025), 2023. a
Gramitzky, K., Jäger, F., Hildebrand, T., Gloria, N., Riechert, J., Steger, M., and Pauscher, L.: Alignment calibration and correction for offshore wind measurements using scanning lidars, J. Phys. Conf. Ser., 2767, 042014, https://doi.org/10.1088/1742-6596/2767/4/042014, 2024. a, b, c, d, e, f, g, h
Gramitzky, K. and Michaelis, L.: xssl – eXtended Sea Surface Levelling Method for Scanning Lidar Calibration, Zenodo [code], https://doi.org/10.5281/zenodo.19056854 (last access: 17 March 2026), 2026. a
Heineke, D. and Verhagen, H. J.: On the use of the fictitious wave steepness and related surf-similarity parameter in methods that describe the hydraulic and structural response to waves, in: Coastal Structures 2007: (In 2 Volumes), 1057–1066, World Scientific, https://doi.org/10.1142/9789814282024_0093, 2009. a, b
Hildebrand, T., Jäger, F., Gloria, N., Michaelis, L., Krasnov, A., Callies, D., Riechert, J., and Pauscher, L.: Offshore wakes measured by an adaptive dual-Doppler scanning lidar system, J. Phys. Conf. Ser., 3025, 012005, https://doi.org/10.1088/1742-6596/3025/1/012005, 2025. a
Holthuijsen, L. H.: Waves in oceanic and coastal waters, Cambridge University Press, https://doi.org/10.1017/CBO9780511618536, 2010. a, b, c, d
Kahmen, H.: Angewandte Geodäsie: Vermessungskunde, Walter de Gruyter, ISBN 3110911140, 2011. a
Käsler, Y., Rahm, S., Simmet, R., and Kühn, M.: Wake measurements of a multi-MW wind turbine with coherent long-range pulsed Doppler wind lidar, J. Atmos. Ocean. Tech., 27, 1529–1532, https://doi.org/10.1175/2010JTECHA1483.1, 2010. a
Krishnamurthy, R., Reuder, J., Svardal, B., Fernando, H. J. S., and Jakobsen, J. B.: Offshore wind turbine wake characteristics using scanning Doppler lidar, Energy Proced., 137, 428–442, https://doi.org/10.1016/j.egypro.2017.10.367, 2017. a, b
Leosphere (Vaisala): WINDCUBE 100S/200S/400S 3D Wind Doppler Lidar – Datasheet, Sartelco Sistemi S.r.l. (Distributor), Vimercate (MB), Italy, datasheet retrieved from Sartelco Sistemi website, https://www.sartelco.it/contenuti/download/EOLICO_WIND/Windcube100S-200S-300S_Wind-Power.pdf (last access: 18 August 2025), 2015. a
Mann, J., Cariou, J.-P. C., Parmentier, R. M., Wagner, R., Lindelöw, P., Sjöholm, M., and Enevoldsen, K.: Comparison of 3D turbulence measurements using three staring wind lidars and a sonic anemometer, Meteorol. Z., 18, 135–140, https://doi.org/10.1127/0941-2948/2009/0370, 2009. a, b
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
Meyer, P., Rott, A., Schneemann, J., Pauscher, L., Gramitzky, K., and Kühn, M.: Experimental validation of the Sea Surface Calibration for scanning lidar static elevation offset determination, Torque Conference, submitted, 2026. a
Newsom, R., Calhoun, R., Ligon, D., and Allwine, J.: Linearly organized turbulence structures observed over a suburban area by dual-Doppler lidar, Bound.-Lay. Meteorol., 127, 111–130, https://doi.org/10.1007/s10546-007-9243-0, 2008. a
Oldroyd, A., Young, M., Docherty, M., Bost, J., Redford, S., Haize, J., Royle, J., and Gottschall, J.: Comparison of classical and drone based hard-target methodologies applied to scanning lidar for offshore wind, presented at EERA DeepWind Conference 2024, Trondheim, Norway, 17–19 January 2024, J. Phys. Conf. Ser., 2875, 012041, https://doi.org/10.1088/1742-6596/2875/1/012041, 2024. a, b, c
Pauscher, L., Vasiljević, N., Callies, D., Lea, G., Mann, J., Klaas, T., Hieronimus, J., Gottschall, J., Schwesig, A., Kühn, M., and Courtney, M.: An inter-comparison study of multi-and DBS lidar measurements in complex terrain, Remote Sens.-Basel, 8, 782, https://doi.org/10.3390/rs8090782, 2016. a, b, c, d
Peña, A. and Mann, J.: Turbulence measurements with dual-Doppler scanning lidars, Remote Sens.-Basel, 11, 2444, https://doi.org/10.3390/rs11202444, 2019. a, b
Rösner, B., Egli, S., Thies, B., Beyer, T., Callies, D., Pauscher, L., and Bendix, J.: Fog and Low Stratus Obstruction of Wind Lidar Observations in Germany – A Remote Sensing-Based Data Set for Wind Energy Planning, Energies, 13, 3859, https://doi.org/10.3390/en13153859, 2020. a
Rott, A., Schneemann, J., and Theuer, F.: AndreasRott/Alignment_of_scanning_lidars_in_offshore_wind_farms: Version 1.0, Zenodo [code], https://doi.org/10.5281/zenodo.5654919, 2021a. a
Rott, A., Schneemann, J., and Theuer, F.: Data supplement for “Alignment of scanning lidars in offshore wind farms”, Zenodo [data set], https://doi.org/10.5281/zenodo.5654866, 2021b. 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
Shimada, S., Goit, J. P., Ohsawa, T., Kogaki, T., and Nakamura, S.: Coastal wind measurements using a single scanning LiDAR, Remote Sens.-Basel, 12, 1347, https://doi.org/10.3390/rs12081347, 2020. a
Shimada, S., Kogaki, T., Konagaya, M., Mito, T., Araki, R., Ueda, Y., and Ohsawa, T.: Validation of near-shore wind measurements using a dual scanning light detection and ranging system, Wind Energy, 25, 1555–1572, https://doi.org/10.1002/we.2757, 2022. a
Shimada, S., Kogaki, T., Konagaya, M., Mito, T., Misaki, T., Hamada, K., Araki, R., Ohhata, S., Ueda, Y., and Ohsawa, T.: Mutsu 2020 Scanning LiDAR Experiment: Comparison of Dual and Single Scanning LiDAR Systems for Near-Shore Wind Measurement, Wind Energy, 28, e70003, https://doi.org/10.1002/we.70003, 2025. a
Theuer, F., Rott, A., Schneemann, J., von Bremen, L., and Kühn, M.: Observer-based power forecast of individual and aggregated offshore wind turbines, Wind Energ. Sci., 7, 2099–2116, https://doi.org/10.5194/wes-7-2099-2022, 2022. a
Thorsen, G. R., Simon, E., and Clausen, E. H.: Drone-based scanning lidar pointing calibration (D4.4), Tech. rep., DTU Wind and Energy Systems, https://orbit.dtu.dk/en/publications/drone-based-scanning-lidar-pointing-calibration-d44/ (last access: 14 January 2026), 2023. a
Vasiljević, N.: A time-space synchronization of coherent Doppler scanning lidars for 3D measurements of wind fields, Ph.D. thesis, Danmarks Tekniske Universitet, https://findit.dtu.dk/en/catalog/547c5c7b7fa1857b0900010f (last access: 20 August 2025), 2014. a
Vasiljević, N., Lea, G., Courtney, M., Cariou, J.-P., Mann, J., and Mikkelsen, T.: Long-range WindScanner system, Remote Sens.-Basel, 8, 896, https://doi.org/10.3390/rs8110896, 2016. a, b, c, d
Vinje, T.: The statistical distribution of wave heights in a random seaway, Appl. Ocean Res., 11, 143–152, https://doi.org/10.1016/0141-1187(89)90024-2, 1989. a
Wildmann, N., Vasiljevic, N., and Gerz, T.: Wind turbine wake measurements with automatically adjusting scanning trajectories in a multi-Doppler lidar setup, Atmos. Meas. Tech., 11, 3801–3814, https://doi.org/10.5194/amt-11-3801-2018, 2018. a
Williams, R., Zhao, F., Melkonyan, N., Hutchinson, M., and Cheong, J.: Global Offshore Wind Report 2025, Tech. rep., Global Wind Energy Council, Lissabon, Portugal, https://gwec.net/reports/globaloffshorewindreport (last access: 18 August 2025), 2025. a
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.
This study introduces an extended sea surface levelling method for the accurate offshore...
Altmetrics
Final-revised paper
Preprint