Articles | Volume 7, issue 4
https://doi.org/10.5194/wes-7-1693-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-1693-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Aug 2022
Research article |
| 11 Aug 2022
| 11 Aug 2022
Turbulence in a coastal environment: the case of Vindeby
Rieska Mawarni Putri
CORRESPONDING AUTHOR
Department of Mechanical and Structural Engineering and Materials Science, University of Stavanger, 4036 Stavanger, Norway
Etienne Cheynet
Geophysical Institute and Bergen Offshore Wind Centre (BOW), University of Bergen, 5007 Bergen, Norway
Charlotte Obhrai
Department of Mechanical and Structural Engineering and Materials Science, University of Stavanger, 4036 Stavanger, Norway
Jasna Bogunovic Jakobsen
Department of Mechanical and Structural Engineering and Materials Science, University of Stavanger, 4036 Stavanger, Norway
Related authors
No articles found.
Etienne Cheynet, Jan Markus Diezel, Hilde Haakenstad, Øyvind Breivik, Alfredo Peña, and Joachim Reuder
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-119, https://doi.org/10.5194/wes-2024-119, 2024
Revised manuscript accepted for WES
Short summary
Short summary
This study aims to help future large offshore wind turbines and airborne wind energy systems by providing insights into wind speeds at much higher altitudes than previously examined. We assessed three wind models (ERA5, NORA3, and NEWA) to predict wind speeds up to 500 m. Using lidar data from Norway and the North Sea, we found that ERA5 excels offshore, while NORA3 performs best onshore. However, the performance of the models depends on the locations and the evaluation criteria.
Mauro Ghirardelli, Stephan T. Kral, Etienne Cheynet, and Joachim Reuder
EGUsphere, https://doi.org/10.5194/egusphere-2024-1548, https://doi.org/10.5194/egusphere-2024-1548, 2024
Short summary
Short summary
The SAMURAI-S system is an innovative measurement tool combining a high accuracy wind sensor with a multi-rotor drone to improve atmospheric turbulence observations. While traditional methods lack flexibility and accuracy in dynamic environments, SAMURAI-S provides high manoeuvrability and precise 3D wind measurements. The research demonstrated the system's ability to match the data quality of conventional methods, with a slight overestimation in vertical turbulence under higher wind conditions.
Etienne Cheynet, Martin Flügge, Joachim Reuder, Jasna B. Jakobsen, Yngve Heggelund, Benny Svardal, Pablo Saavedra Garfias, Charlotte Obhrai, Nicolò Daniotti, Jarle Berge, Christiane Duscha, Norman Wildmann, Ingrid H. Onarheim, and Marte Godvik
Atmos. Meas. Tech., 14, 6137–6157, https://doi.org/10.5194/amt-14-6137-2021, https://doi.org/10.5194/amt-14-6137-2021, 2021
Short summary
Short summary
The COTUR campaign explored the structure of wind turbulence above the ocean to improve the design of future multi-megawatt offshore wind turbines. Deploying scientific instruments offshore is both a financial and technological challenge. Therefore, lidar technology was used to remotely measure the wind above the ocean from instruments located on the seaside. The experimental setup is tailored to the study of the spatial correlation of wind gusts, which governs the wind loading on structures.
Related subject area
Thematic area: Wind and the atmosphere | Topic: Wind and turbulence
Observations of wind farm wake recovery at an operating wind farm
Periods of constant wind speed: how long do they last in the turbulent atmospheric boundary layer?
Characterization of local wind profiles: a random forest approach for enhanced wind profile extrapolation
Simulations suggest offshore wind farms modify low-level jets
On the lidar-turbulence paradox and possible countermeasures
The actuator farm model for large eddy simulation (LES) of wind-farm-induced atmospheric gravity waves and farm–farm interaction
Understanding the impact of data gaps on long-term offshore wind resource estimates
Converging profile relationships for offshore wind speed and turbulence intensity
A simple steady-state inflow model of the neutral and stable atmospheric boundary layer applied to wind turbine wake simulations
Influences of lidar scanning parameters on wind turbine wake retrievals in complex terrain
Tall Wind Profile Validation Using Lidar Observations and Hindcast Data
Experimental evaluation of wind turbine wake turbulence impacts on a general aviation aircraft
Underestimation of strong wind speeds offshore in ERA5: evidence, discussion and correction
Brief communication: A simple axial induction modification to the Weather Research and Forecasting Fitch wind farm parameterization
Impact of swell waves on atmospheric surface turbulence: wave–turbulence decomposition methods
Flow acceleration statistics: a new paradigm for wind-driven loads, towards probabilistic turbine design
Machine-learning-based estimate of the wind speed over complex terrain using the long short-term memory (LSTM) recurrent neural network
Method to predict the minimum measurement and experiment durations needed to achieve converged and significant results in a wind energy field experiment
Evaluation of wind farm parameterizations in the WRF model under different atmospheric stability conditions with high-resolution wake simulations
Renewable Energy Complementarity (RECom) maps – a comprehensive visualisation tool to support spatial diversification
Control-oriented modelling of wind direction variability
Machine learning methods to improve spatial predictions of coastal wind speed profiles and low-level jets using single-level ERA5 data
Offshore low-level jet observations and model representation using lidar buoy data off the California coast
Measurement-driven large-eddy simulations of a diurnal cycle during a wake-steering field campaign
The fractal turbulent–non-turbulent interface in the atmosphere
TOSCA – an open-source, finite-volume, large-eddy simulation (LES) environment for wind farm flows
Quantitative comparison of power production and power quality onshore and offshore: a case study from the eastern United States
The wind farm pressure field
Realistic turbulent inflow conditions for estimating the performance of a floating wind turbine
Brief communication: On the definition of the low-level jet
A decision-tree-based measure–correlate–predict approach for peak wind gust estimation from a global reanalysis dataset
Revealing inflow and wake conditions of a 6 MW floating turbine
Stochastic gradient descent for wind farm optimization
Modelling the impact of trapped lee waves on offshore wind farm power output
Applying a random time mapping to Mann-modeled turbulence for the generation of intermittent wind fields
From shear to veer: theory, statistics, and practical application
Quantification and correction of motion influence for nacelle-based lidar systems on floating wind turbines
Gaussian mixture models for the optimal sparse sampling of offshore wind resource
Dependence of turbulence estimations on nacelle lidar scanning strategies
Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques
An investigation of spatial wind direction variability and its consideration in engineering models
From gigawatt to multi-gigawatt wind farms: wake effects, energy budgets and inertial gravity waves investigated by large-eddy simulations
Investigations of correlation and coherence in turbulence from a large-eddy simulation
Validation of turbulence intensity as simulated by the Weather Research and Forecasting model off the US northeast coast
On the laminar–turbulent transition mechanism on megawatt wind turbine blades operating in atmospheric flow
Brief communication: A momentum-conserving superposition method applied to the super-Gaussian wind turbine wake model
Turbulence structures and entrainment length scales in large offshore wind farms
Effect of different source terms and inflow direction in atmospheric boundary modeling over the complex terrain site of Perdigão
Comparison of large eddy simulations against measurements from the Lillgrund offshore wind farm
Adjusted spectral correction method for calculating extreme winds in tropical-cyclone-affected water areas
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.
Daniela Moreno, Jan Friedrich, Matthias Wächter, Jörg Schwarte, and Joachim Peinke
Wind Energ. Sci., 10, 347–360, https://doi.org/10.5194/wes-10-347-2025, https://doi.org/10.5194/wes-10-347-2025, 2025
Short summary
Short summary
Unexpected load events measured on operating wind turbines are not accurately predicted by numerical simulations. We introduce the periods of constant wind speed as a possible cause of such events. We measure and characterize their statistics from atmospheric data. Further comparisons to standard modelled data and experimental turbulence data suggest that such events are not intrinsic to small-scale turbulence and are not accurately described by current standard wind models.
Farkhondeh (Hanie) Rouholahnejad and Julia Gottschall
Wind Energ. Sci., 10, 143–159, https://doi.org/10.5194/wes-10-143-2025, https://doi.org/10.5194/wes-10-143-2025, 2025
Short summary
Short summary
In wind energy, precise wind speed prediction at hub height is vital. Our study in the Dutch North Sea reveals that the on-site-trained random forest model outperforms the global reanalysis data, ERA5, in accuracy and precision. Trained within a 200 km range, the model effectively extends the wind speed vertically but experiences bias. It also outperforms ERA5 corrected with measurements in capturing wind speed variations and fine wind patterns, highlighting its potential for site assessment.
Daphne Quint, Julie K. Lundquist, and David Rosencrans
Wind Energ. Sci., 10, 117–142, https://doi.org/10.5194/wes-10-117-2025, https://doi.org/10.5194/wes-10-117-2025, 2025
Short summary
Short summary
Offshore wind farms will be built along the East Coast of the United States. Low-level jets (LLJs) – layers of fast winds at low altitudes – also occur here. LLJs provide wind resources and also influence moisture and pollution transport, so it is important to understand how they might change. We develop and validate an automated tool to detect LLJs and compare 1 year of simulations with and without wind farms. Here, we describe LLJ characteristics and how they change with wind farms.
Alfredo Peña, Ginka G. Yankova, and Vasiliki Mallini
Wind Energ. Sci., 10, 83–102, https://doi.org/10.5194/wes-10-83-2025, https://doi.org/10.5194/wes-10-83-2025, 2025
Short summary
Short summary
Lidars are vastly used in wind energy, but most users struggle when interpreting lidar turbulence measures. Here, we explain the difficulty in converting them into standard measurements. We show two ways of converting lidar to in situ turbulence measurements, both using neural networks: one of them is based on physics, while the other is purely data-driven. They show promising results when compared to high-quality turbulence measurements from a tall mast.
Sebastiano Stipa, Arjun Ajay, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 2301–2332, https://doi.org/10.5194/wes-9-2301-2024, https://doi.org/10.5194/wes-9-2301-2024, 2024
Short summary
Short summary
This study presents the actuator farm model, a new method for modeling wind turbines within large wind farms. The model greatly reduces computational cost when compared to traditional actuator wind turbine models and is beneficial for studying flow around large wind farms as well as the interaction between multiple wind farms. Results obtained from numerical simulations show excellent agreement with past wind turbine models, demonstrating its utility for future large-scale wind farm simulations.
Martin Georg Jonietz Alvarez, Warren Watson, and Julia Gottschall
Wind Energ. Sci., 9, 2217–2233, https://doi.org/10.5194/wes-9-2217-2024, https://doi.org/10.5194/wes-9-2217-2024, 2024
Short summary
Short summary
Offshore wind measurements are often affected by gaps. We investigated how these gaps affect wind resource assessments and whether filling them reduces their effect. We find that the effect of gaps on the estimated long-term wind resource is lower than expected and that data gap filling does not significantly change the outcome. These results indicate a need to reduce current wind data availability requirements for offshore measurement campaigns.
Gus Jeans
Wind Energ. Sci., 9, 2001–2015, https://doi.org/10.5194/wes-9-2001-2024, https://doi.org/10.5194/wes-9-2001-2024, 2024
Short summary
Short summary
An extensive set of met mast data offshore northwestern Europe are used to reduce uncertainty in offshore wind speed and turbulence intensity. The performance of widely used industry standard relationships is quantified, while some new empirical relationships are derived for practical application. Motivations include encouraging appropriate convergence of traditionally separate technical disciplines within the rapidly growing offshore wind energy industry.
Maarten Paul van der Laan, Mark Kelly, Mads Baungaard, Antariksh Dicholkar, and Emily Louise Hodgson
Wind Energ. Sci., 9, 1985–2000, https://doi.org/10.5194/wes-9-1985-2024, https://doi.org/10.5194/wes-9-1985-2024, 2024
Short summary
Short summary
Wind turbines are increasing in size and operate more frequently above the atmospheric surface layer, which requires improved inflow models for numerical simulations of turbine interaction. In this work, a novel steady-state model of the atmospheric boundary layer (ABL) is introduced. Numerical wind turbine flow simulations subjected to shallow and tall ABLs are conducted, and the proposed model shows improved performance compared to other state-of-the-art steady-state models.
Rachel Robey and Julie K. Lundquist
Wind Energ. Sci., 9, 1905–1922, https://doi.org/10.5194/wes-9-1905-2024, https://doi.org/10.5194/wes-9-1905-2024, 2024
Short summary
Short summary
Measurements of wind turbine wakes with scanning lidar instruments contain complex errors. We model lidars in a simulated environment to understand how and why the measured wake may differ from the true wake and validate the results with observational data. The lidar smooths out the wake, making it seem more spread out and the slowdown of the winds less pronounced. Our findings provide insights into best practices for accurately measuring wakes with lidar and interpreting observational data.
Etienne Cheynet, Jan Markus Diezel, Hilde Haakenstad, Øyvind Breivik, Alfredo Peña, and Joachim Reuder
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-119, https://doi.org/10.5194/wes-2024-119, 2024
Revised manuscript accepted for WES
Short summary
Short summary
This study aims to help future large offshore wind turbines and airborne wind energy systems by providing insights into wind speeds at much higher altitudes than previously examined. We assessed three wind models (ERA5, NORA3, and NEWA) to predict wind speeds up to 500 m. Using lidar data from Norway and the North Sea, we found that ERA5 excels offshore, while NORA3 performs best onshore. However, the performance of the models depends on the locations and the evaluation criteria.
Jonathan D. Rogers
Wind Energ. Sci., 9, 1849–1868, https://doi.org/10.5194/wes-9-1849-2024, https://doi.org/10.5194/wes-9-1849-2024, 2024
Short summary
Short summary
This paper describes the results of a flight experiment to assess the existence of potential safety risks to a general aviation aircraft from added turbulence in the wake of a wind turbine. A general aviation aircraft was flown through the wake of an operating wind turbine at different downwind distances. Results indicated that there were small increases in disturbances to the aircraft due to added turbulence in the wake, but they never approached levels that would pose a safety risk.
Rémi Gandoin and Jorge Garza
Wind Energ. Sci., 9, 1727–1745, https://doi.org/10.5194/wes-9-1727-2024, https://doi.org/10.5194/wes-9-1727-2024, 2024
Short summary
Short summary
ERA5 has become the workhorse of most wind resource assessment applications, as it compares better with in situ measurements than other reanalyses. However, for design purposes, ERA5 suffers from a drawback: it underestimates strong wind speeds offshore (approx. from 10 m s−1). This is not widely discussed in the scientific literature. We address this bias and proposes a simple, robust correction. This article supports the growing need for use-case-specific validations of reanalysis datasets.
Lukas Vollmer, Balthazar Arnoldus Maria Sengers, and Martin Dörenkämper
Wind Energ. Sci., 9, 1689–1693, https://doi.org/10.5194/wes-9-1689-2024, https://doi.org/10.5194/wes-9-1689-2024, 2024
Short summary
Short summary
This study proposes a modification to a well-established wind farm parameterization used in mesoscale models. The wind speed at the location of the turbine, which is used to calculate power and thrust, is corrected to approximate the free wind speed. Results show that the modified parameterization produces more accurate estimates of the turbine’s power curve.
Mostafa Bakhoday Paskyabi
Wind Energ. Sci., 9, 1631–1645, https://doi.org/10.5194/wes-9-1631-2024, https://doi.org/10.5194/wes-9-1631-2024, 2024
Short summary
Short summary
The exchange of momentum and energy between the atmosphere and ocean depends on air–sea processes, especially wave-related ones. Precision in representing these interactions is vital for offshore wind turbine and farm design and operation. The development of a reliable wave–turbulence decomposition method to remove wave-induced interference from single-height wind measurements is essential for these applications and enhances our grasp of wind coherence within the wave boundary layer.
Mark Kelly
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-69, https://doi.org/10.5194/wes-2024-69, 2024
Revised manuscript accepted for WES
Short summary
Short summary
Industrial standards for wind turbine design are based on 10-minute statistics of wind speed at turbine hub-height, treating fluctuations as turbulence. But recent work shows the effect of strong transients is described by flow accelerations. We devise a method to measure the accelerations turbines encounter; the extremes offshore defy 10-minute statistics, due to various phenomena beyond turbulence. These findings are translated into a recipe supporting statistical turbine design.
Cássia Maria Leme Beu and Eduardo Landulfo
Wind Energ. Sci., 9, 1431–1450, https://doi.org/10.5194/wes-9-1431-2024, https://doi.org/10.5194/wes-9-1431-2024, 2024
Short summary
Short summary
Extrapolating the wind profile for complex terrain through the long short-term memory model outperformed the traditional power law methodology, which due to its universal nature cannot capture local features as the machine-learning methodology does. Moreover, considering the importance of investigating the wind potential and the need for alternative energy sources, it is motivating to find that a short observational campaign can produce better results than the traditional techniques.
Daniel R. Houck, Nathaniel B. de Velder, David C. Maniaci, and Brent C. Houchens
Wind Energ. Sci., 9, 1189–1209, https://doi.org/10.5194/wes-9-1189-2024, https://doi.org/10.5194/wes-9-1189-2024, 2024
Short summary
Short summary
Experiments offer incredible value to science, but results must come with an uncertainty quantification to be meaningful. We present a method to simulate a proposed experiment, calculate uncertainties, and determine the measurement duration (total time of measurements) and the experiment duration (total time to collect the required measurement data when including condition variability and time when measurement is not occurring) required to produce statistically significant and converged results.
Oscar García-Santiago, Andrea N. Hahmann, Jake Badger, and Alfredo Peña
Wind Energ. Sci., 9, 963–979, https://doi.org/10.5194/wes-9-963-2024, https://doi.org/10.5194/wes-9-963-2024, 2024
Short summary
Short summary
This study compares the results of two wind farm parameterizations (WFPs) in the Weather Research and Forecasting model, simulating a two-turbine array under three atmospheric stabilities with large-eddy simulations. We show that the WFPs accurately depict wind speeds either near turbines or in the far-wake areas, but not both. The parameterizations’ performance varies by variable (wind speed or turbulent kinetic energy) and atmospheric stability, with reduced accuracy in stable conditions.
Til Kristian Vrana and Harald G. Svendsen
Wind Energ. Sci., 9, 919–932, https://doi.org/10.5194/wes-9-919-2024, https://doi.org/10.5194/wes-9-919-2024, 2024
Short summary
Short summary
We developed new ways to plot comprehensive wind resource maps that show the revenue potential of different locations for future wind power developments. The relative capacity factor is introduced as an indicator showing the expected mean power output. The market value factor is introduced, which captures the expected mean market value relative to other wind parks. The Renewable Energy Complementarity (RECom) index combines the two into a single index, resulting in the RECom map.
Scott Dallas, Adam Stock, and Edward Hart
Wind Energ. Sci., 9, 841–867, https://doi.org/10.5194/wes-9-841-2024, https://doi.org/10.5194/wes-9-841-2024, 2024
Short summary
Short summary
This review presents the current understanding of wind direction variability in the context of control-oriented modelling of wind turbines and wind farms in a manner suitable to a wide audience. Motivation comes from the significant and commonly seen yaw error of horizontal axis wind turbines, which carries substantial negative impacts on annual energy production and the levellised cost of wind energy. Gaps in the literature are identified, and the critical challenges in this area are discussed.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Ville Vakkari, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 9, 821–840, https://doi.org/10.5194/wes-9-821-2024, https://doi.org/10.5194/wes-9-821-2024, 2024
Short summary
Short summary
Knowing the wind speed across the rotor of a wind turbine is key in making good predictions of the power production. However, models struggle to capture both the speed and the shape of the wind profile. Using machine learning methods based on the model data, we show that the predictions can be improved drastically. The work focuses on three coastal sites, spread over the Northern Hemisphere (the Baltic Sea, the North Sea, and the US Atlantic coast) with similar results for all sites.
Lindsay M. Sheridan, Raghavendra Krishnamurthy, William I. Gustafson Jr., Ye Liu, Brian J. Gaudet, Nicola Bodini, Rob K. Newsom, and Mikhail Pekour
Wind Energ. Sci., 9, 741–758, https://doi.org/10.5194/wes-9-741-2024, https://doi.org/10.5194/wes-9-741-2024, 2024
Short summary
Short summary
In 2020, lidar-mounted buoys owned by the US Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas and provided valuable year-long analyses of offshore low-level jet (LLJ) characteristics at heights relevant to wind turbines. In addition to the LLJ climatology, this work provides validation of LLJ representation in atmospheric models that are essential for assessing the potential energy yield of offshore wind farms.
Eliot Quon
Wind Energ. Sci., 9, 495–518, https://doi.org/10.5194/wes-9-495-2024, https://doi.org/10.5194/wes-9-495-2024, 2024
Short summary
Short summary
Engineering models used to design wind farms generally do not account for realistic atmospheric conditions that can rapidly evolve from minute to minute. This paper uses a first-principles simulation technique to predict the performance of five wind turbines during a wind farm control experiment. Challenges included limited observations and atypical conditions. The simulation accurately predicts the aerodynamics of a turbine when it is situated partially within the wake of an upstream turbine.
Lars Neuhaus, Matthias Wächter, and Joachim Peinke
Wind Energ. Sci., 9, 439–452, https://doi.org/10.5194/wes-9-439-2024, https://doi.org/10.5194/wes-9-439-2024, 2024
Short summary
Short summary
Future wind turbines reach unprecedented heights and are affected by wind conditions that have not yet been studied in detail. With increasing height, a transition to laminar conditions with a turbulent–non-turbulent interface (TNTI) becomes more likely. In this paper, the presence and fractality of this TNTI in the atmosphere are studied. Typical fractalities known from ideal laboratory and numerical studies and a frequent occurrence of the TNTI at heights of multi-megawatt turbines are found.
Sebastiano Stipa, Arjun Ajay, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 297–320, https://doi.org/10.5194/wes-9-297-2024, https://doi.org/10.5194/wes-9-297-2024, 2024
Short summary
Short summary
In the current study, we introduce TOSCA (Toolbox fOr Stratified Convective Atmospheres), an open-source computational fluid dynamics (CFD) tool, and demonstrate its capabilities by simulating the flow around a large wind farm, operating in realistic flow conditions. This is one of the grand challenges of the present decade and can yield better insight into physical phenomena that strongly affect wind farm operation but which are not yet fully understood.
Rebecca Foody, Jacob Coburn, Jeanie A. Aird, Rebecca J. Barthelmie, and Sara C. Pryor
Wind Energ. Sci., 9, 263–280, https://doi.org/10.5194/wes-9-263-2024, https://doi.org/10.5194/wes-9-263-2024, 2024
Short summary
Short summary
Using lidar-derived wind speed measurements at approx. 150 m height at onshore and offshore locations, we quantify the advantages of deploying wind turbines offshore in terms of the amount of electrical power produced and the higher reliability and predictability of the electrical power.
Ronald B. Smith
Wind Energ. Sci., 9, 253–261, https://doi.org/10.5194/wes-9-253-2024, https://doi.org/10.5194/wes-9-253-2024, 2024
Short summary
Short summary
Recent papers have investigated the impact of turbine drag on local wind patterns, but these studies have not given a full explanation of the induced pressure field. The pressure field blocks and deflects the wind and in other ways modifies farm efficiency. Current gravity wave models are complex and provide no estimation tools. We dig deeper into the cause of the pressure field and provide approximate closed-form expressions for pressure field effects.
Cédric Raibaudo, Jean-Christophe Gilloteaux, and Laurent Perret
Wind Energ. Sci., 8, 1711–1725, https://doi.org/10.5194/wes-8-1711-2023, https://doi.org/10.5194/wes-8-1711-2023, 2023
Short summary
Short summary
The work presented here proposes interfacing experimental measurements performed in a wind tunnel with simulations conducted with the aeroelastic code FAST and applied to a floating wind turbine model under wave-induced motion. FAST simulations using experiments match well with those obtained using the inflow generation method provided by TurbSim. The highest surge motion frequencies show a significant decrease in the mean power produced by the turbine and a mitigation of the flow dynamics.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 8, 1651–1658, https://doi.org/10.5194/wes-8-1651-2023, https://doi.org/10.5194/wes-8-1651-2023, 2023
Short summary
Short summary
Low-level jets (LLJs) are special types of non-ideal wind profiles affecting both wind energy production and loads on a wind turbine. However, among LLJ researchers, there is no consensus regarding which definition to use to identify these profiles. In this work, we compare two different ways of identifying the LLJ – the falloff definition and the shear definition – and argue why the shear definition is better suited to wind energy applications.
Serkan Kartal, Sukanta Basu, and Simon J. Watson
Wind Energ. Sci., 8, 1533–1551, https://doi.org/10.5194/wes-8-1533-2023, https://doi.org/10.5194/wes-8-1533-2023, 2023
Short summary
Short summary
Peak wind gust is a crucial meteorological variable for wind farm planning and operations. Unfortunately, many wind farms do not have on-site measurements of it. In this paper, we propose a machine-learning approach (called INTRIGUE, decIsioN-TRee-based wInd GUst Estimation) that utilizes numerous inputs from a public-domain reanalysis dataset, generating long-term, site-specific peak wind gust series.
Nikolas Angelou, Jakob Mann, and Camille Dubreuil-Boisclair
Wind Energ. Sci., 8, 1511–1531, https://doi.org/10.5194/wes-8-1511-2023, https://doi.org/10.5194/wes-8-1511-2023, 2023
Short summary
Short summary
This study presents the first experimental investigation using two nacelle-mounted wind lidars that reveal the upwind and downwind conditions relative to a full-scale floating wind turbine. We find that in the case of floating wind turbines with small pitch and roll oscillating motions (< 1°), the ambient turbulence is the main driving factor that determines the propagation of the wake characteristics.
Julian Quick, Pierre-Elouan Rethore, Mads Mølgaard Pedersen, Rafael Valotta Rodrigues, and Mikkel Friis-Møller
Wind Energ. Sci., 8, 1235–1250, https://doi.org/10.5194/wes-8-1235-2023, https://doi.org/10.5194/wes-8-1235-2023, 2023
Short summary
Short summary
Wind turbine positions are often optimized to avoid wake losses. These losses depend on atmospheric conditions, such as the wind speed and direction. The typical optimization scheme involves discretizing the atmospheric inputs, then considering every possible set of these discretized inputs in every optimization iteration. This work presents stochastic gradient descent (SGD) as an alternative, which randomly samples the atmospheric conditions during every optimization iteration.
Sarah J. Ollier and Simon J. Watson
Wind Energ. Sci., 8, 1179–1200, https://doi.org/10.5194/wes-8-1179-2023, https://doi.org/10.5194/wes-8-1179-2023, 2023
Short summary
Short summary
This modelling study shows that topographic trapped lee waves (TLWs) modify flow behaviour and power output in offshore wind farms. We demonstrate that TLWs can substantially alter the wind speeds at individual wind turbines and effect the power output of the turbine and whole wind farm. The impact on wind speeds and power is dependent on which part of the TLW wave cycle interacts with the wind turbines and wind farm. Positive and negative impacts of TLWs on power output are observed.
Khaled Yassin, Arne Helms, Daniela Moreno, Hassan Kassem, Leo Höning, and Laura J. Lukassen
Wind Energ. Sci., 8, 1133–1152, https://doi.org/10.5194/wes-8-1133-2023, https://doi.org/10.5194/wes-8-1133-2023, 2023
Short summary
Short summary
The current turbulent wind field models stated in the IEC 61400-1 standard underestimate the probability of extreme changes in wind velocity. This underestimation can lead to the false calculation of extreme and fatigue loads on the turbine. In this work, we are trying to apply a random time-mapping technique to one of the standard turbulence models to adapt to such extreme changes. The turbulent fields generated are compared with a standard wind field to show the effects of this new mapping.
Mark Kelly and Maarten Paul van der Laan
Wind Energ. Sci., 8, 975–998, https://doi.org/10.5194/wes-8-975-2023, https://doi.org/10.5194/wes-8-975-2023, 2023
Short summary
Short summary
The turning of the wind with height, which is known as veer, can affect wind turbine performance. Thus far meteorology has only given idealized descriptions of veer, which has not yet been related in a way readily usable for wind energy. Here we derive equations for veer in terms of meteorological quantities and provide practically usable forms in terms of measurable shear (change in wind speed with height). Flow simulations and measurements at turbine heights support these developments.
Moritz Gräfe, Vasilis Pettas, Julia Gottschall, and Po Wen Cheng
Wind Energ. Sci., 8, 925–946, https://doi.org/10.5194/wes-8-925-2023, https://doi.org/10.5194/wes-8-925-2023, 2023
Short summary
Short summary
Inflow wind field measurements from nacelle-based lidar systems offer great potential for different applications including turbine control, load validation and power performance measurements. On floating wind turbines nacelle-based lidar measurements are affected by the dynamic behavior of the floating foundations. Therefore, the effects on lidar wind speed measurements induced by floater dynamics must be well understood. A new model for quantification of these effects is introduced in our work.
Robin Marcille, Maxime Thiébaut, Pierre Tandeo, and Jean-François Filipot
Wind Energ. Sci., 8, 771–786, https://doi.org/10.5194/wes-8-771-2023, https://doi.org/10.5194/wes-8-771-2023, 2023
Short summary
Short summary
A novel data-driven method is proposed to design an optimal sensor network for the reconstruction of offshore wind resources. Based on unsupervised learning of numerical weather prediction wind data, it provides a simple yet efficient method for the siting of sensors, outperforming state-of-the-art methods for this application. It is applied in the main French offshore wind energy development areas to provide guidelines for the deployment of floating lidars for wind resource assessment.
Wei Fu, Alessandro Sebastiani, Alfredo Peña, and Jakob Mann
Wind Energ. Sci., 8, 677–690, https://doi.org/10.5194/wes-8-677-2023, https://doi.org/10.5194/wes-8-677-2023, 2023
Short summary
Short summary
Nacelle lidars with different beam scanning locations and two types of systems are considered for inflow turbulence estimations using both numerical simulations and field measurements. The turbulence estimates from a sonic anemometer at the hub height of a Vestas V52 turbine are used as references. The turbulence parameters are retrieved using the radial variances and a least-squares procedure. The findings from numerical simulations have been verified by the analysis of the field measurements.
Daniel Hatfield, Charlotte Bay Hasager, and Ioanna Karagali
Wind Energ. Sci., 8, 621–637, https://doi.org/10.5194/wes-8-621-2023, https://doi.org/10.5194/wes-8-621-2023, 2023
Short summary
Short summary
Wind observations at heights relevant to the operation of modern offshore wind farms, i.e. 100 m and more, are required to optimize their positioning and layout. Satellite wind retrievals provide observations of the wind field over large spatial areas and extensive time periods, yet their temporal resolution is limited and they are only representative at 10 m height. Machine-learning models are applied to lift these satellite winds to higher heights, directly relevant to wind energy purposes.
Anna von Brandis, Gabriele Centurelli, Jonas Schmidt, Lukas Vollmer, Bughsin' Djath, and Martin Dörenkämper
Wind Energ. Sci., 8, 589–606, https://doi.org/10.5194/wes-8-589-2023, https://doi.org/10.5194/wes-8-589-2023, 2023
Short summary
Short summary
We propose that considering large-scale wind direction changes in the computation of wind farm cluster wakes is of high relevance. Consequently, we present a new solution for engineering modeling tools that accounts for the effect of such changes in the propagation of wakes. The new model is evaluated with satellite data in the German Bight area. It has the potential to reduce uncertainty in applications such as site assessment and short-term power forecasting.
Oliver Maas
Wind Energ. Sci., 8, 535–556, https://doi.org/10.5194/wes-8-535-2023, https://doi.org/10.5194/wes-8-535-2023, 2023
Short summary
Short summary
The study compares small vs. large wind farms regarding the flow and power output with a turbulence-resolving simulation model. It shows that a large wind farm (90 km length) significantly affects the wind direction and that the wind speed is higher in the large wind farm wake. Both wind farms excite atmospheric gravity waves that also affect the power output of the wind farms.
Regis Thedin, Eliot Quon, Matthew Churchfield, and Paul Veers
Wind Energ. Sci., 8, 487–502, https://doi.org/10.5194/wes-8-487-2023, https://doi.org/10.5194/wes-8-487-2023, 2023
Short summary
Short summary
We investigate coherence and correlation and highlight their importance for disciplines like wind energy structural dynamic analysis, in which blade loading and fatigue depend on turbulence structure. We compare coherence estimates to those computed using a model suggested by international standards. We show the differences and highlight additional information that can be gained using large-eddy simulation, further improving analytical coherence models used in synthetic turbulence generators.
Sheng-Lun Tai, Larry K. Berg, Raghavendra Krishnamurthy, Rob Newsom, and Anthony Kirincich
Wind Energ. Sci., 8, 433–448, https://doi.org/10.5194/wes-8-433-2023, https://doi.org/10.5194/wes-8-433-2023, 2023
Short summary
Short summary
Turbulence intensity is critical for wind turbine design and operation as it affects wind power generation efficiency. Turbulence measurements in the marine environment are limited. We use a model to derive turbulence intensity and test how sea surface temperature data may impact the simulated turbulence intensity and atmospheric stability. The model slightly underestimates turbulence, and improved sea surface temperature data reduce the bias. Error with unrealistic mesoscale flow is identified.
Brandon Arthur Lobo, Özge Sinem Özçakmak, Helge Aagaard Madsen, Alois Peter Schaffarczyk, Michael Breuer, and Niels N. Sørensen
Wind Energ. Sci., 8, 303–326, https://doi.org/10.5194/wes-8-303-2023, https://doi.org/10.5194/wes-8-303-2023, 2023
Short summary
Short summary
Results from the DAN-AERO and aerodynamic glove projects provide significant findings. The effects of inflow turbulence on transition and wind turbine blades are compared to computational fluid dynamic simulations. It is found that the transition scenario changes even over a single revolution. The importance of a suitable choice of amplification factor is evident from the simulations. An agreement between the power spectral density plots from the experiment and large-eddy simulations is seen.
Frédéric Blondel
Wind Energ. Sci., 8, 141–147, https://doi.org/10.5194/wes-8-141-2023, https://doi.org/10.5194/wes-8-141-2023, 2023
Short summary
Short summary
Accurate wind farm flow predictions based on analytical wake models are crucial for wind farm design and layout optimization. Wake superposition methods play a key role and remain a substantial source of uncertainty. In the present work, a momentum-conserving superposition method is extended to the superposition of super-Gaussian-type velocity deficit models, allowing the full wake velocity deficit estimation and design of closely packed wind farms.
Abdul Haseeb Syed, Jakob Mann, Andreas Platis, and Jens Bange
Wind Energ. Sci., 8, 125–139, https://doi.org/10.5194/wes-8-125-2023, https://doi.org/10.5194/wes-8-125-2023, 2023
Short summary
Short summary
Wind turbines extract energy from the incoming wind flow, which needs to be recovered. In very large offshore wind farms, the energy is recovered mostly from above the wind farm in a process called entrainment. In this study, we analyzed the effect of atmospheric stability on the entrainment process in large offshore wind farms using measurements recorded by a research aircraft. This is the first time that in situ measurements are used to study the energy recovery process above wind farms.
Kartik Venkatraman, Trond-Ola Hågbo, Sophia Buckingham, and Knut Erik Teigen Giljarhus
Wind Energ. Sci., 8, 85–108, https://doi.org/10.5194/wes-8-85-2023, https://doi.org/10.5194/wes-8-85-2023, 2023
Short summary
Short summary
This paper is focused on the impact of modeling different effects, such as forest canopy and Coriolis forces, on the wind resource over a complex terrain site located near Perdigão, Portugal. A numerical model is set up and results are compared with field measurements. The results show that including a forest canopy improves the predictions close to the ground at some locations on the site, while the model with inflow from a precursor performed better at other locations.
Ishaan Sood, Elliot Simon, Athanasios Vitsas, Bart Blockmans, Gunner C. Larsen, and Johan Meyers
Wind Energ. Sci., 7, 2469–2489, https://doi.org/10.5194/wes-7-2469-2022, https://doi.org/10.5194/wes-7-2469-2022, 2022
Short summary
Short summary
In this work, we conduct a validation study to compare a numerical solver against measurements obtained from the offshore Lillgrund wind farm. By reusing a previously developed inflow turbulent dataset, the atmospheric conditions at the wind farm were recreated, and the general performance trends of the turbines were captured well. The work increases the reliability of numerical wind farm solvers while highlighting the challenges of accurately representing large wind farms using such solvers.
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
Short summary
Short summary
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.
Cited articles
Akaike, H.: Fitting autoregressive models for prediction, Ann. Inst. Stat. Math., 21, 243–247, 1969. a
Archer, C. L., Colle, B. A., Veron, D. L., Veron, F., and Sienkiewicz, M. J.:
On the predominance of unstable atmospheric conditions in the marine boundary
layer offshore of the US northeastern coast, J. Geophys. Res.-Atmos., 121, 8869–8885, 2016. a
Benasciutti, D. and Tovo, R.: Fatigue life assessment in non-Gaussian random
loadings, Int. J. Fatig., 28, 733–746, 2006. a
Benasciutti, D. and Tovo, R.: Frequency-based fatigue analysis of non-stationary switching random loads, Fatig. Fract. Eng. Mater.Struct., 30, 1016–1029, 2007. a
Benilov, A. Y., Kouznetsov, O., and Panin, G.: On the analysis of wind
wave-induced disturbances in the atmospheric turbulent surface layer,
Bound.-Lay. Meteorol., 6, 269–285, 1974. a
Cheynet, E., Jakobsen, J. B., and Obhrai, C.: Spectral characteristics of
surface-layer turbulence in the North Sea, Energy Proced., 137, 414–427,
2017. a
Cheynet, E., Jakobsen, J. B., and Snæbjörnsson, J.: Flow distortion
recorded by sonic anemometers on a long-span bridge: Towards a better
modelling of the dynamic wind load in full-scale, J. Sound Vibrat., 450, 214–230, 2019. a
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
Chougule, A., Mann, J., Kelly, M., Sun, J., Lenschow, D., and Patton, E.:
Vertical cross-spectral phases in neutral atmospheric flow, J. Turbulence, 36, 1–13, https://doi.org/10.1080/14685248.2012.711524, 2012. a
De Maré, M. and Mann, J.: Validation of the Mann spectral tensor for
offshore wind conditions at different atmospheric stabilities, J. Phys.: Conf. Ser., 524, 012106, https://doi.org/10.1088/1742-6596/524/1/012106, 2014. a
D'Errico, J.: inpaint_nans, MATLAB Central File Exchange, http://kr.mathworks.com/matlabcentral/fileexchange/4551-inpaint-nans (last access: November 2021), 2004. a
Dobson, F. W.: Review of reference height for and averaging time of surface
wind measurements at sea, World Meteorological Organization, https://library.wmo.int/doc_num.php?explnum_id=7561 (last access: 2 March 2021), 1981. a
Doubrawa, P., Churchfield, M. J., Godvik, M., and Sirnivas, S.: Load response
of a floating wind turbine to turbulent atmospheric flow, Appl. Energy, 242, 1588–1599, 2019. a
Edson, J. and Fairall, C.: Similarity relationships in the marine atmospheric
surface layer for terms in the TKE and scalar variance budgets, J. Atmos. Sci., 55, 2311–2328, 1998. a
Emeis, S. and Türk, M.: Wind-driven wave heights in the German Bight, Ocean Dynam., 59, 463–475, 2009. a
ESDU 85020: ESDU 85020 Characteristics of atmospheric turbulence near the
ground. Part II: single point data for strong winds (neutral atmosphere),
ESDU – Engineering Sciences Data Unit, https://www.osti.gov/etdeweb/biblio/10158377 (last access:
31 July 2022), 2002. a
Geernaert, G.: Measurements of the angle between the wind vector and wind
stress vector in the surface layer over the North Sea, J. Geophys.
Res.-Oceans, 93, 8215–8220, 1988. a
Geernaert, G., Hansen, F., Courtney, M., and Herbers, T.: Directional attributes of the ocean surface wind stress vector, J. Geophys. Res.-Oceans, 98, 16571–16582, 1993. a
GE Renewable Energy: Haliade-X offshore wind turbine,
https://www.ge.com/renewableenergy/wind-energy/offshore-wind/haliade-x-offshore-turbine,
last access: 8 April 2021. a
Grachev, A., Fairall, C., Hare, J., Edson, J., and Miller, S.: Wind stress
vector over ocean waves, J. Phys. Oceanogr., 33, 2408–2429, 2003. a
Grare, L., Lenain, L., and Melville, W. K.: Wave-coherent airflow and critical layers over ocean waves, J. Phys. Oceanogr., 43, 2156–2172, 2013. a
Hansen, A. and Butterfield, C.: Aerodynamics of horizontal-axis wind turbines, Annu. Rev. Fluid Mech., 25, 115–149, 1993. a
Hansen, K. S., Larsen, G. C., and Ott, S.: Dependence of offshore wind turbine fatigue loads on atmospheric stratification, J. Phys.: Conf. Ser., 524, 012165, https://doi.org/10.1088/1742-6596/524/1/012165, 2014. a
Hansen, K. S., Vasiljevic, N., and Sørensen, S. A.: Resource data and
turbulence array measurements from the 3 Vindeby masts, DTU,
https://doi.org/10.11583/DTU.14387480.v1, 2021. a
Högström, U.: Von Karman's constant in atmospheric boundary layer flow: Reevaluated, J. Atmos. Sci., 42, 263–270, 1985. a
Högström, U.: Non-dimensional wind and temperature profiles in the
atmospheric surface layer: A re-evaluation, in: Topics in Micrometeorology. A
Festschrift for Arch Dyer, Springer, 55–78, https://doi.org/10.1007/978-94-009-2935-7_6, 1988. a
Hojstrup, J.: A statistical data screening procedure, Meas. Sci. Technol., 4, 153–157, https://doi.org/10.1088/0957-0233/4/2/003/, 1993. a
Holtslag, M. C., Bierbooms, W. A. A. M., and Van Bussel, G. J. W.: Wind turbine fatigue loads as a function of atmospheric conditions offshore, Wind Energy, 19, 1917–1932, 2016. a
Hristov, T., Friehe, C., and Miller, S.: Wave-coherent fields in air flow over ocean waves: Identification of cooperative behavior buried in turbulence, Phys. Rev. Lett., 81, 5245, https://doi.org/10.1103/PhysRevLett.81.5245, 1998. a
Janssen, P. A.: Wave-induced stress and the drag of air flow over sea waves,
J. Phys. Oceanogr., 19, 745–754, 1989. a
Jiang, Q.: Influence of Swell on Marine Surface-Layer Structure, J. Atmos. Sci., 77, 1865–1885, 2020. a
Jiang, Q., Wang, Q., Wang, S., and Gaberšek, S.: Turbulence adjustment
and scaling in an offshore convective internal boundary layer: A CASPER case
study, J. Atmos. Sci., 77, 1661–1681, 2020. a
Jonkman, J. and Musial, W.: Offshore code comparison collaboration (OC3) for
IEA Wind Task 23 offshore wind technology and deployment, Tech. rep., NREL – National Renewable Energy Lab., Golden, CO, USA, https://www.nrel.gov/docs/fy11osti/48191.pdf (last access:
21 December 2020), 2010. a
Kelly, M.: From standard wind measurements to spectral characterization: turbulence length scale and distribution, Wind Energ. Sci., 3, 533–543, https://doi.org/10.5194/wes-3-533-2018, 2018. a
Kristensen, L. and Jensen, N.: Lateral coherence in isotropic turbulence and in the natural wind, Bound.-Lay. Meteorol., 17, 353–373, 1979. a
Kristensen, L., Panofsky, H. A., and Smith, S. D.: Lateral coherence of
longitudinal wind components in strong winds, Bound.-Lay. Meteorol., 21,
199–205, 1981. a
Larsén, X. G., Petersen, E. L., and Larsen, S. E.: Variation of
boundary-layer wind spectra with height, Q. J. Roy. Meteorol. Soc., 144, 2054–2066, 2018. a
Leys, C., Ley, C., Klein, O., Bernard, P., and Licata, L.: Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median, J. Exp. Social Psychol., 49, 764–766, 2013. a
Lumley, J. and Panofsky, H.: The Structure of Atmospheric Turbulence,
Interscience monographs and texts in physics and astronomy, Interscience
Publishers, ISBN 9780470553657, 1964. a
Mahrt, L., Vickers, D., Howell, J., Højstrup, J., Wilczak, J. M., Edson, J.,
and Hare, J.: Sea surface drag coefficients in the Risø Air Sea Experiment, J. Geophys. Res.-Oceans, 101, 14327–14335, 1996. a
Mahrt, L., Vickers, D., Edson, J., Wilczak, J. M., Hare, J., and Højstrup, J.: Vertical structure of turbulence in offshore flow during RASEX, J. Geophys. Res.-Oceans, 101, 14327–14335, 2001. a
Mikkelsen, T., Larsen, S. E., Jørgensen, H. E., Astrup, P., and Larsén, X. G.: Scaling of turbulence spectra measured in strong shear flow near the Earth's surface, Physica Scripta, 92, 124002, https://doi.org/10.1088/1402-4896/aa91b2, 2017. a
Monin, A. S.: The structure of atmospheric turbulence, Theor. Probabil. Appl., 3, 266–296, 1958. a
Monin, A. S. and Obukhov, A. M.: Basic laws of turbulent mixing in the
atmosphere near the ground, Tr. Geofiz. Inst., Akad. Nauk SSSR, 24, 163–187,
1954. a
Mouzakis, F., Morfiadakis, E., and Dellaportas, P.: Fatigue loading parameter
identification of a wind turbine operating in complex terrain, J. Wind Eng. Indust. Aerodynam., 82, 69–88, 1999. a
Naito, G.: Spatial structure of surface wind over the ocean, J. Wind Eng. Indust. Aerodynam., 13, 67–76, 1983. a
Nielsen, F. G., Hanson, T. D., and Skaare, B.: Integrated dynamic analysis of
floating offshore wind turbines, in: vol. 47462, 25th International Conference on Offshore Mechanics and Arctic Engineering, 4–9 June 2006, Hamburg, Germany, 671–679, https://doi.org/10.1115/OMAE2006-92291, 2006. a
Nybø, A., Nielsen, F. G., Reuder, J., Churchfield, M. J., and Godvik, M.:
Evaluation of different wind fields for the investigation of the dynamic
response of offshore wind turbines, Wind Energy, 23, 1810–1830, 2020. a
Peña, A. and Gryning, S.-E.: Charnock’s roughness length model and
non-dimensional wind profiles over the sea, Bound.-Lay. Meteorol., 128,
191–203, 2008. a
Peña, A., Dellwik, E., and Mann, J.: A method to assess the accuracy of sonic anemometer measurements, Atmos. Meas. Tech., 12, 237–252, https://doi.org/10.5194/amt-12-237-2019, 2019. a
Power Technology: Full circle: decommissioning the first ever offshore
windfarm,
https://www.power-technology.com/features/full-circle-decommissioning-first-ever-offshore-windfarm/
(last access: 28 March 2021), 2020. a
Putri, R., Obhrai, C., Jakobsen, J., and Ong, M.: Numerical Analysis of the
Effect of Offshore Turbulent Wind Inflow on the Response of a Spar Wind
Turbine, Energies, 13, 2506, https://doi.org/10.3390/en13102506, 2020. a
Robertson, A., Jonkman, J., Vorpahl, F., Popko, W., Qvist, J., Frøyd, L.,
Chen, X., Azcona, J., Uzunoglu, E., Guedes Soares, C., Luan, C., Yutong, H., Pengcheng, F., Yde, A., Larsen, T., Nichols, J., Buils, R., Lei, L., Anders Nygard, T., Manolas, D., Heege, A., Ringdalen Vatne, S., Ormberg, H., Duarte, T., Godreau, C., Fabricius Hansen, H., Wedel Nielsen, A., Riber, H., Le Cunff, C., Abele, R., Beyer, F., Yamaguchi, A., Jin Jung, K., Shin, H., Shi, W., Park, H., Alves, M., and Guérinel, M.: Offshore code comparison collaboration continuation within IEA wind task 30: phase II results regarding a floating semisubmersible wind system, in: vol. 45547, International Conference on Offshore Mechanics and Arctic Engineering,
American Society of Mechanical Engineers, V09BT09A012, https://www.nrel.gov/docs/fy14osti/61154.pdf (last access:
21 December 2020), 2014. a
Robertson, A. N., Shaler, K., Sethuraman, L., and Jonkman, J.: Sensitivity analysis of the effect of wind characteristics and turbine properties on wind turbine loads, Wind Energ. Sci., 4, 479–513, https://doi.org/10.5194/wes-4-479-2019, 2019. a
Rosner, B.: Percentage points for a generalized ESD many-outlier procedure,
Technometrics, 25, 165–172, 1983. a
Sacré, C. and Delaunay, D.: Structure spatiale de la turbulence au cours de vents forts sur differents sites, J. Wind Eng. Indust. Aerodynam., 41, 295–303, 1992. a
Sanchez Gomez, M. and Lundquist, J. K.: The effect of wind direction shear on turbine performance in a wind farm in central Iowa, Wind Energ. Sci., 5, 125–139, https://doi.org/10.5194/wes-5-125-2020, 2020. a
Saranyasoontorn, K., Manuel, L., and Veers, P. S.: A comparison of standard
coherence models for inflow turbulence with estimates from field measurements, J. Sol. Energy Eng., 126, 1069–1082, 2004. a
Sathe, A. and Bierbooms, W.: Influence of different wind profiles due to
varying atmospheric stability on the fatigue life of wind turbines, J. Phys.: Conf. Ser., 75, 012056, https://doi.org/10.1088/1742-6596/75/1/012056, 2007. a
Sathe, A., Mann, J., Barlas, T., Bierbooms, W., and van Bussel, G.: Influence
of atmospheric stability on wind turbine loads, Wind Energy, 16, 1013–1032,
2013. a
Schotanus, P., Nieuwstadt, F., and De Bruin, H.: Temperature measurement with a sonic anemometer and its application to heat and moisture fluxes, Bound.-Lay. Meteorol., 26, 81–93, 1983. a
Sempreviva, A. M. and Gryning, S. E.: Humidity fluctuations in the marine
boundary layer measured at a coastal site with an infrared humidity sensor,
Bound.-Lay. Meteorol., 77, 331–352, 1996. a
Sheinman, Y. and Rosen, A.: A dynamic model of the influence of turbulence on
the power output of a wind turbine, J. Wind Eng. Indust. Aerodynam., 39, 329–341, 1992. a
Sjöblom, A. and Smedman, A.-S.: Vertical structure in the marine
atmospheric boundary layer and its implication for the inertial dissipation
method, Bound.-Lay. Meteorol., 109, 1–25, 2003a. a
Sjöblom, A. and Smedman, A.-S.: Vertical structure in the marine
atmospheric boundary layer and its implication for the inertial dissipation
method, Bound.-Lay. Meteorol., 109, 1–25, 2003b. a
Smedman-Högström, A.-S. and Högström, U.: Spectral gap in
surface-layer measurements, J. Atmos. Sci., 32, 340–350, 1975. a
Soucy, R., Woodward, R., and Panofsky, H.: Vertical cross-spectra of horizontal velocity components at the Boulder observatory, Bound.-Lay. Meteorol., 24, 57–66, 1982. a
Stiperski, I. and Rotach, M. W.: On the measurement of turbulence over complex mountainous terrain, Bound.-Lay. Meteorol., 159, 97–121, 2016. a
Stull, R. B.: An Introduction to Boundary Layer Meteorology, in: 1st Edn., Kluwer Academic Publishers, ISBN 978-90-277-2768-8, https://doi.org/10.1007/978-94-009-3027-8, 1988. a
Tamura, H., Drennan, W. M., Collins, C. O., and Graber, H. C.: Turbulent
airflow and wave-induced stress over the ocean, Bound.-Lay. Meteorol., 169, 47–66, 2018. a
Taylor, G. I.: The spectrum of turbulence, P. Roy. Soc. Lond. A, 164, 476–490, 1938. a
Van der Hoven, I.: Power spectrum of horizontal wind speed in the frequency
range from 0.0007 to 900 cycles per hour, J. Atmos. Sci., 14, 160–164, 1957. a
Veers, P., Dykes, K., Lantz, E., Barth, S., Bottasso, C. L., Carlson, O.,
Clifton, A., Green, J., Green, P., Holttinen, H., Laird, D., Lehtomäki, V., Lundquist, J. K., Manwell, J., Marquis, M., Meneveau, C., Moriarty, P.,
Munduate, X., Muskulus, M., Naughton, J., Pao, L., Paquette, J., Peinke, J., Robertson, A., Sanz Rodrigo, J., Sempreviva, A. M., Smith, J. C., Tuohy, A., and Wiser, R.: Grand challenges in the science of wind energy, Science, 366, eaau2027, https://doi.org/10.1126/science.aau2027, 2019. a, b
Vickers, D. and Mahrt, L.: Quality control and flux sampling problems for tower and aircraft data, J. Atmos. Ocean. Tech., 14, 512–526, 1997. a
Vickers, D. and Mahrt, L.: The cospectral gap and turbulent flux calculations, J. Atmos. Ocean. Tech., 20, 660–672, 2003. a
Weber, R. O.: Remarks on the definition and estimation of friction velocity,
Bound.-Lay. Meteorol., 93, 197–209, 1999. a
Weiler, H. S. and Burling, R.: Direct measurements of stress and spectra of
turbulence in the boundary layer over the sea, J. Atmos. Sci., 24, 653–664, 1967. a
Welch, P.: The use of fast Fourier transform for the estimation of power
spectra: a method based on time averaging over short, modified periodograms,
IEEE T. Audio Electroacoust., 15, 70–73, 1967. a
Wendell, L., Gower, G., Morris, V., and Tomich, S.: Wind turbulence
characterization for wind energy development, Tech. rep., Pacific Northwest
Lab., Richland, WA, USA, https://www.osti.gov/servlets/purl/602073 (last access: 3 November 2021), 1991. a
WMO: Guide to meteorological instruments and methods of observation,
Secretariat of the World Meteorological Organization, ISBN 978-92-63-10008-5, 2008. a
Wyngaard, J. C.: On the surface-layer turbulence, in: Workshop on Micrometeorology, edited by: Haugen, D. A., American Meteorological Society, 101–149, https://cir.nii.ac.jp/crid/1570291225953095424 (last access:
1 March 2021), 1973. a
Wyngaard, J. C. and Coté, O. R.: The budgets of turbulent kinetic energy and temperature variance in the atmospheric surface layer, J. Atmos. Sci., 28, 190–201, 1971. a
Short summary
As offshore wind turbines' sizes are increasing, thorough knowledge of wind characteristics in the marine atmospheric boundary layer (MABL) is becoming crucial to help improve offshore wind turbine design and reliability. The present study discusses the wind characteristics at the first offshore wind farm, Vindeby, and compares them with the wind measurements at the FINO1 platform. Consistent wind characteristics are found between Vindeby measurements and the FINO1 measurements.
As offshore wind turbines' sizes are increasing, thorough knowledge of wind characteristics in...
Altmetrics
Final-revised paper
Preprint