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
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
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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
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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
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
The Jensen wind farm parameterization
Current and future wind energy resources in the North Sea according to CMIP6
Optimization of wind farm portfolios for minimizing overall power fluctuations at selective frequencies – a case study of the Faroe Islands
Evaluating the mesoscale spatio-temporal variability in simulated wind speed time series over northern Europe
Gaussian mixture model for extreme wind turbulence estimation
The sensitivity of the Fitch wind farm parameterization to a three-dimensional planetary boundary layer scheme
Offshore reanalysis wind speed assessment across the wind turbine rotor layer off the United States Pacific coast
Statistical post-processing of reanalysis wind speeds at hub heights using a diagnostic wind model and neural networks
Investigations of Correlation and Coherence in Turbulence from a Large-Eddy Simulation
Computational-fluid-dynamics analysis of a Darrieus vertical-axis wind turbine installation on the rooftop of buildings under turbulent-inflow conditions
From gigawatt to multi-gigawatt wind farms: wake effects, energy budgets and inertial gravity waves investigated by large-eddy simulations
Spatiotemporal observations of nocturnal low-level jets and impacts on wind power production
Computational fluid dynamics studies on wind turbine interactions with the turbulent local flow field influenced by complex topography and thermal stratification
Brief communication: How does complex terrain change the power curve of a wind turbine?
The wide range of factors contributing to wind resource assessment accuracy in complex terrain
High-resolution offshore wind resource assessment at turbine hub height with Sentinel-1 synthetic aperture radar (SAR) data and machine learning
Impact of the wind field at the complex-terrain site Perdigão on the surface pressure fluctuations of a wind turbine
Surrogate models for the blade element momentum aerodynamic model using non-intrusive polynomial chaos expansions
Offshore wind farm cluster wakes as observed by long-range-scanning wind lidar measurements and mesoscale modeling
Classification and properties of non-idealized coastal wind profiles – an observational study
On the laminar-turbulent transition mechanism on Multi-Megawatt wind turbine blades operating in atmospheric flow
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
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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
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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
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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
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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
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A method is developed for calculating the extreme wind in tropical-cyclone-affected water areas. The method is based on the spectral correction method that fills in the missing wind variability to the modeled time series, guided by best track data. The paper provides a detailed recipe for applying the method and the 50-year winds of equivalent 10 min temporal resolution from 10 to 150 m in several tropical-cyclone-affected regions.
Yulong Ma, Cristina L. Archer, and Ahmadreza Vasel-Be-Hagh
Wind Energ. Sci., 7, 2407–2431, https://doi.org/10.5194/wes-7-2407-2022, https://doi.org/10.5194/wes-7-2407-2022, 2022
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Wind turbine wakes are important because they reduce the power production of wind farms and may cause unintended impacts on the weather around wind farms. Weather prediction models, like WRF and MPAS, are often used to predict both power and impacts of wind farms, but they lack an accurate treatment of wind farm wakes. We developed the Jensen wind farm parameterization, based on the existing Jensen model of an idealized wake. The Jensen parameterization is accurate and computationally efficient.
Andrea N. Hahmann, Oscar García-Santiago, and Alfredo Peña
Wind Energ. Sci., 7, 2373–2391, https://doi.org/10.5194/wes-7-2373-2022, https://doi.org/10.5194/wes-7-2373-2022, 2022
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We explore the changes in wind energy resources in northern Europe using output from simulations from the Climate Model Intercomparison Project (CMIP6) under the high-emission scenario. Our results show that climate change does not particularly alter annual energy production in the North Sea but could affect the seasonal distribution of these resources, significantly reducing energy production during the summer from 2031 to 2050.
Turið Poulsen, Bárður A. Niclasen, Gregor Giebel, and Hans Georg Beyer
Wind Energ. Sci., 7, 2335–2350, https://doi.org/10.5194/wes-7-2335-2022, https://doi.org/10.5194/wes-7-2335-2022, 2022
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Wind power is cheap and environmentally friendly, but it has a disadvantage: it is a variable power source. Because wind is not blowing everywhere simultaneously, optimal placement of wind farms can reduce the fluctuations.
This is explored for a small isolated area. Combining wind farms reduces wind power fluctuations for timescales up to 1–2 d. By optimally placing four wind farms, the hourly fluctuations are reduced by 15 %. These wind farms are located distant from each other.
Graziela Luzia, Andrea N. Hahmann, and Matti Juhani Koivisto
Wind Energ. Sci., 7, 2255–2270, https://doi.org/10.5194/wes-7-2255-2022, https://doi.org/10.5194/wes-7-2255-2022, 2022
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This paper presents a comprehensive validation of time series produced by a mesoscale numerical weather model, a global reanalysis, and a wind atlas against observations by using a set of metrics that we present as requirements for wind energy integration studies. We perform a sensitivity analysis on the numerical weather model in multiple configurations, such as related to model grid spacing and nesting arrangements, to define the model setup that outperforms in various time series aspects.
Xiaodong Zhang and Anand Natarajan
Wind Energ. Sci., 7, 2135–2148, https://doi.org/10.5194/wes-7-2135-2022, https://doi.org/10.5194/wes-7-2135-2022, 2022
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Joint probability distribution of 10 min mean wind speed and the standard deviation is proposed using the Gaussian mixture model and has been shown to agree well with 15 years of measurements. The environmental contour with a 50-year return period (extreme turbulence) is estimated. The results from the model could be taken as inputs for structural reliability analysis and uncertainty quantification of wind turbine design loads.
Alex Rybchuk, Timothy W. Juliano, Julie K. Lundquist, David Rosencrans, Nicola Bodini, and Mike Optis
Wind Energ. Sci., 7, 2085–2098, https://doi.org/10.5194/wes-7-2085-2022, https://doi.org/10.5194/wes-7-2085-2022, 2022
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Numerical weather prediction models are used to predict how wind turbines will interact with the atmosphere. Here, we characterize the uncertainty associated with the choice of turbulence parameterization on modeled wakes. We find that simulated wind speed deficits in turbine wakes can be significantly sensitive to the choice of turbulence parameterization. As such, predictions of future generated power are also sensitive to turbulence parameterization choice.
Lindsay M. Sheridan, Raghu Krishnamurthy, Gabriel García Medina, Brian J. Gaudet, William I. Gustafson Jr., Alicia M. Mahon, William J. Shaw, Rob K. Newsom, Mikhail Pekour, and Zhaoqing Yang
Wind Energ. Sci., 7, 2059–2084, https://doi.org/10.5194/wes-7-2059-2022, https://doi.org/10.5194/wes-7-2059-2022, 2022
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Using observations from lidar buoys, five reanalysis and analysis models that support the wind energy community are validated offshore and at rotor-level heights along the California Pacific coast. The models are found to underestimate the observed wind resource. Occasions of large model error occur in conjunction with stable atmospheric conditions, wind speeds associated with peak turbine power production, and mischaracterization of the diurnal wind speed cycle in summer months.
Sebastian Brune and Jan D. Keller
Wind Energ. Sci., 7, 1905–1918, https://doi.org/10.5194/wes-7-1905-2022, https://doi.org/10.5194/wes-7-1905-2022, 2022
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A post-processing of the wind speed of the regional reanalysis COSMO-REA6 in Central Europe is performed based on a combined physical and statistical approach. The physical basis is provided by downscaling wind speeds with the help of a diagnostic wind model, which reduces the horizontal grid point spacing by a factor of 8. The statistical correction using a neural network based on different variables of the reanalysis leads to an improvement of 30 % in RMSE compared to COSMO-REA6.
Regis Thedin, Eliot Quon, Matthew Churchfield, and Paul Veers
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-71, https://doi.org/10.5194/wes-2022-71, 2022
Revised manuscript under review for WES
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We investigate coherence and correlation and highlight their importance for disciplines like wind energy structural dynamic analysis, in which blade loading and fatigue depend on turbulence structure. We compare coherence estimates to those computed using a model suggested by international standards. We show the differences and highlight additional information that can be gained using LES, supporting its use to further improve analytical coherence models used in synthetic turbulence generators.
Pradip Zamre and Thorsten Lutz
Wind Energ. Sci., 7, 1661–1677, https://doi.org/10.5194/wes-7-1661-2022, https://doi.org/10.5194/wes-7-1661-2022, 2022
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To get more insight into the influence of the urban-terrain flow on the performance of the rooftop-mounted two-bladed Darrieus vertical-axis wind turbine, scale resolving simulations are performed for a generic wind turbine in realistic terrain under turbulent conditions. It is found that the turbulence and skewed nature of the flow near rooftop locations have a positive impact on the performance of the wind turbine.
Oliver Maas
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-63, https://doi.org/10.5194/wes-2022-63, 2022
Revised manuscript under review for WES
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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.
Eduardo Weide Luiz and Stephanie Fiedler
Wind Energ. Sci., 7, 1575–1591, https://doi.org/10.5194/wes-7-1575-2022, https://doi.org/10.5194/wes-7-1575-2022, 2022
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This work analyses a meteorological event, called nocturnal low-level jets (NLLJs), defined as high wind speeds relatively close to the surface. There were positive and negative impacts from NLLJs. While NLLJs increased the mean power production, they also increased the variability in the wind with height. Our results imply that long NLLJ events are also larger, affecting many wind turbines at the same time. Short NLLJ events are more local, having stronger effects on power variability.
Patrick Letzgus, Giorgia Guma, and Thorsten Lutz
Wind Energ. Sci., 7, 1551–1573, https://doi.org/10.5194/wes-7-1551-2022, https://doi.org/10.5194/wes-7-1551-2022, 2022
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The research article presents the results of a study of highly resolved numerical simulations of a wind energy test site in complex terrain that is currently under construction in the Swabian Alps in southern Germany. The numerical results emphasised the importance of considering orography, vegetation, and thermal stratification in numerical simulations to resolve the wind field decently. In this way, the effects on loads, power, and wake of the wind turbine can also be predicted well.
Niels Troldborg, Søren J. Andersen, Emily L. Hodgson, and Alexander Meyer Forsting
Wind Energ. Sci., 7, 1527–1532, https://doi.org/10.5194/wes-7-1527-2022, https://doi.org/10.5194/wes-7-1527-2022, 2022
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This article shows that the power performance of a wind turbine may be very different in flat and complex terrain. This is an important finding because it shows that the power output of a given wind turbine is governed by not only the available wind at the position of the turbine but also how the ambient flow develops in the region behind the turbine.
Sarah Barber, Alain Schubiger, Sara Koller, Dominik Eggli, Alexander Radi, Andreas Rumpf, and Hermann Knaus
Wind Energ. Sci., 7, 1503–1525, https://doi.org/10.5194/wes-7-1503-2022, https://doi.org/10.5194/wes-7-1503-2022, 2022
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In this work, a range of simulations are carried out with seven different wind modelling tools at five different complex terrain sites and the results compared to wind speed measurements at validation locations. This is then extended to annual energy production (AEP) estimations (without wake effects), showing that wind profile prediction accuracy does not translate directly or linearly to AEP accuracy. It is therefore vital to consider overall AEP when evaluating simulation accuracies.
Louis de Montera, Henrick Berger, Romain Husson, Pascal Appelghem, Laurent Guerlou, and Mauricio Fragoso
Wind Energ. Sci., 7, 1441–1453, https://doi.org/10.5194/wes-7-1441-2022, https://doi.org/10.5194/wes-7-1441-2022, 2022
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A novel method for estimating offshore wind resources at turbine hub height with synthetic aperture radar (SAR) satellites is presented. The machine learning algorithm uses as input geometrical parameters of the SAR sensors and parameters related to atmospheric stability. It is trained with Doppler wind lidar vertical profiles. The extractable wind power accuracy up to 200 m is within 3 %, and SAR can resolve the coastal wind gradient, unlike the Weather Research and Forecasting numerical mode.
Florian Wenz, Judith Langner, Thorsten Lutz, and Ewald Krämer
Wind Energ. Sci., 7, 1321–1340, https://doi.org/10.5194/wes-7-1321-2022, https://doi.org/10.5194/wes-7-1321-2022, 2022
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To get a better understanding of the influence of the terrain flow on the unsteady pressure distributions on the wind turbine surface, a fully resolved turbine was simulated in the complex terrain of Perdigão, Portugal. It was found that the pressure fluctuations at the tower caused by vortex shedding are significantly hampered by the terrain flow, while the pressure fluctuations caused by the blade–tower interaction are hardly changed.
Rad Haghi and Curran Crawford
Wind Energ. Sci., 7, 1289–1304, https://doi.org/10.5194/wes-7-1289-2022, https://doi.org/10.5194/wes-7-1289-2022, 2022
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Based on the IEC standards, a limited number of simulations is sufficient to calculate the extreme and fatigue loads on a wind turbine. However, this means inaccuracy in the output statistics. This paper aims to build a surrogate model on blade element momentum aerodynamic model simulation output employing non-intrusive polynomial chaos expansion. The surrogate model is then used in a large number of Monte Carlo simulations to provide an accurate statistical estimate of the loads.
Beatriz Cañadillas, Maximilian Beckenbauer, Juan J. Trujillo, Martin Dörenkämper, Richard Foreman, Thomas Neumann, and Astrid Lampert
Wind Energ. Sci., 7, 1241–1262, https://doi.org/10.5194/wes-7-1241-2022, https://doi.org/10.5194/wes-7-1241-2022, 2022
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Scanning lidar measurements combined with meteorological sensors and mesoscale simulations reveal the strong directional and stability dependence of the wake strength in the direct vicinity of wind farm clusters.
Christoffer Hallgren, Johan Arnqvist, Erik Nilsson, Stefan Ivanell, Metodija Shapkalijevski, August Thomasson, Heidi Pettersson, and Erik Sahlée
Wind Energ. Sci., 7, 1183–1207, https://doi.org/10.5194/wes-7-1183-2022, https://doi.org/10.5194/wes-7-1183-2022, 2022
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Non-idealized wind profiles with negative shear in part of the profile (e.g., low-level jets) frequently occur in coastal environments and are important to take into consideration for offshore wind power. Using observations from a coastal site in the Baltic Sea, we analyze in which meteorological and sea state conditions these profiles occur and study how they alter the turbulence structure of the boundary layer compared to idealized profiles.
Brandon Arthur Lobo, Özge Sinem Özçakmak, Helge Aagaard Madsen, Alois Peter Schaffarczyk, Michael Breuer, and Niels N. Sørensen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-42, https://doi.org/10.5194/wes-2022-42, 2022
Revised manuscript accepted for WES
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Results from the DAN-AERO and aerodynamic Glove projects provide significant findings. The effect of inflow turbulence on transition on wind turbine blades are compared to CFD simulations. It is found that the transition scenario changes even over a single revolution. The importance of a suitable choice of the amplification factor is evident from both the RANS and URANS simulations. A good agreement between the power spectral density plots from the experiment and LES is seen.
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...
