Articles | Volume 8, issue 4
https://doi.org/10.5194/wes-8-621-2023
© Author(s) 2023. 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-8-621-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Apr 2023
Research article |
| 28 Apr 2023
| 28 Apr 2023
Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques
Department of Wind and Energy Systems, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
Charlotte Bay Hasager
Department of Wind and Energy Systems, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
Ioanna Karagali
Danish Meteorological Institute, Lyngbyvej 100, 2100 Copenhagen Ø, Denmark
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Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Jonathan Baker, Clément Bricaud, Romain Bourdalle-Badie, Lluis Castrillo, Lijing Cheng, Frederic Chevallier, Daniele Ciani, Alvaro de Pascual-Collar, Vincenzo De Toma, Marie Drevillon, Claudia Fanelli, Gilles Garric, Marion Gehlen, Rianne Giesen, Kevin Hodges, Doroteaciro Iovino, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Thomas Lavergne, Simona Masina, Ronan McAdam, Audrey Minière, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Ad Stoffelen, Sulian Thual, Simon Van Gennip, Pierre Veillard, Chunxue Yang, and Hao Zuo
State Planet, 4-osr8, 1, https://doi.org/10.5194/sp-4-osr8-1-2024, https://doi.org/10.5194/sp-4-osr8-1-2024, 2024
Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Ali Aydogdu, Lluis Castrillo, Daniele Ciani, Andrea Cipollone, Emanuela Clementi, Gianpiero Cossarini, Alvaro de Pascual-Collar, Vincenzo De Toma, Marion Gehlen, Rianne Giesen, Marie Drevillon, Claudia Fanelli, Kevin Hodges, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Priidik Lagemaa, Vidar Lien, Leonardo Lima, Vladyslav Lyubartsev, Ilja Maljutenko, Simona Masina, Ronan McAdam, Pietro Miraglio, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Urmas Raudsepp, Roshin Raj, Ad Stoffelen, Simon Van Gennip, Pierre Veillard, and Chunxue Yang
State Planet, 4-osr8, 2, https://doi.org/10.5194/sp-4-osr8-2-2024, https://doi.org/10.5194/sp-4-osr8-2-2024, 2024
Guisella Gacitúa, Jacob L. Høyer, Sten Schmidl Søbjærg, Hoyeon Shi, Sotirios Skarpalezos, Ioanna Karagali, Emy Alerskans, and Craig Donlon
EGUsphere, https://doi.org/10.5194/egusphere-2024-542, https://doi.org/10.5194/egusphere-2024-542, 2024
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This study presents a shipborne intercomparison of sea surface temperature (SST) using thermal Infrared (TIR) and passive microwave (PMW) radiometers along the Denmark-Iceland route. Subskin SST was retrieved from PMW brightness temperatures. The investigation focuses on analyzing PMW data variability, quantifying uncertainty propagation, and comparing skin and subskin SSTs. The findings offer insights to optimize SST intercomparisons, enhancing the synergy between TIR and PMW observations.
Hugo Rubio, Daniel Hatfield, Charlotte Bay Hasager, Martin Kühn, and Julia Gottschall
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-11, https://doi.org/10.5194/amt-2024-11, 2024
Revised manuscript under review for AMT
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Unlocking offshore wind farms’ potential demands a precise understanding of available wind resources. Yet, limited in situ data in marine environments call for innovative solutions. This study delves into the world of satellite remote sensing and numerical models, exploring their capabilities and challenges in characterizing offshore wind dynamics. This investigation evaluates these tools against measurements from a floating ship-based lidar, collected through a novel campaign in the Baltic Sea.
Haichen Zuo and Charlotte Bay Hasager
Atmos. Meas. Tech., 16, 3901–3913, https://doi.org/10.5194/amt-16-3901-2023, https://doi.org/10.5194/amt-16-3901-2023, 2023
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Aeolus is a satellite equipped with a Doppler wind lidar to detect global wind profiles. This study evaluates the impact of Aeolus winds on surface wind forecasts over tropical oceans and high-latitude regions based on the ECMWF observing system experiments. We find that Aeolus can slightly improve surface wind forecasts for the region > 60° N, especially from day 5 onwards. For other study regions, the impact of Aeolus is nearly neutral or limited, which requires further investigation.
Jens Visbech, Tuhfe Göçmen, Charlotte Bay Hasager, Hristo Shkalov, Morten Handberg, and Kristian Pagh Nielsen
Wind Energ. Sci., 8, 173–191, https://doi.org/10.5194/wes-8-173-2023, https://doi.org/10.5194/wes-8-173-2023, 2023
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This paper presents a data-driven framework for modeling erosion damage based on real blade inspections and mesoscale weather data. The outcome of the framework is a machine-learning-based model that can predict and/or forecast leading-edge erosion damage based on weather data and user-specified wind turbine characteristics. The model output fits directly into the damage terminology used by the industry and can therefore support site-specific maintenance planning and scheduling of repairs.
Merete Badger, Haichen Zuo, Ásta Hannesdóttir, Abdalmenem Owda, and Charlotte Hasager
Wind Energ. Sci., 7, 2497–2512, https://doi.org/10.5194/wes-7-2497-2022, https://doi.org/10.5194/wes-7-2497-2022, 2022
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When wind turbine blades are exposed to strong winds and heavy rainfall, they may be damaged and their efficiency reduced. The problem is most pronounced offshore where turbines are tall and the climate is harsh. Satellites provide global half-hourly rain observations. We use these rain data as input to a model for blade lifetime prediction and find that the satellite-based predictions agree well with predictions based on observations from weather stations on the ground.
Ioanna Karagali, Magnus Barfod Suhr, Ruth Mottram, Pia Nielsen-Englyst, Gorm Dybkjær, Darren Ghent, and Jacob L. Høyer
The Cryosphere, 16, 3703–3721, https://doi.org/10.5194/tc-16-3703-2022, https://doi.org/10.5194/tc-16-3703-2022, 2022
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Ice surface temperature (IST) products were used to develop the first multi-sensor, gap-free Level 4 (L4) IST product of the Greenland Ice Sheet (GIS) for 2012, when a significant melt event occurred. For the melt season, mean IST was −15 to −1 °C, and almost the entire GIS experienced at least 1 to 5 melt days. Inclusion of the L4 IST to a surface mass budget (SMB) model improved simulated surface temperatures during the key onset of the melt season, where biases are typically large.
Haichen Zuo, Charlotte Bay Hasager, Ioanna Karagali, Ad Stoffelen, Gert-Jan Marseille, and Jos de Kloe
Atmos. Meas. Tech., 15, 4107–4124, https://doi.org/10.5194/amt-15-4107-2022, https://doi.org/10.5194/amt-15-4107-2022, 2022
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The Aeolus satellite was launched in 2018 for global wind profile measurement. After successful operation, the error characteristics of Aeolus wind products have not yet been studied over Australia. To complement earlier validation studies, we evaluated the Aeolus Level-2B11 wind product over Australia with ground-based wind profiling radar measurements and numerical weather prediction model equivalents. The results show that the Aeolus can detect winds with sufficient accuracy over Australia.
Anna-Maria Tilg, Charlotte Bay Hasager, Hans-Jürgen Kirtzel, and Poul Hummelshøj
Wind Energ. Sci., 5, 977–981, https://doi.org/10.5194/wes-5-977-2020, https://doi.org/10.5194/wes-5-977-2020, 2020
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Recently, there has been an increased awareness of leading-edge erosion of wind turbine blades. An option to mitigate the erosion at the leading edges is the deceleration of the wind turbine blades during severe precipitation events. This work shows that a vertically pointing radar can be used to nowcast precipitation events with the required spatial and temporal resolution. Furthermore, nowcasting allows a reduction in the rotational speed prior to the impact of precipitation on the blades.
Charlotte B. Hasager, Andrea N. Hahmann, Tobias Ahsbahs, Ioanna Karagali, Tija Sile, Merete Badger, and Jakob Mann
Wind Energ. Sci., 5, 375–390, https://doi.org/10.5194/wes-5-375-2020, https://doi.org/10.5194/wes-5-375-2020, 2020
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Europe's offshore wind resource mapping is part of the New European Wind Atlas (NEWA) international consortium effort. This study presents the results of analysis of synthetic aperture radar (SAR) ocean wind maps based on Envisat and Sentinel-1 with a brief description of the wind retrieval process and Advanced Scatterometer (ASCAT) ocean wind maps. Furthermore, the Weather Research and Forecasting (WRF) offshore wind atlas of NEWA is presented.
Jakob Ilsted Bech, Charlotte Bay Hasager, and Christian Bak
Wind Energ. Sci., 3, 729–748, https://doi.org/10.5194/wes-3-729-2018, https://doi.org/10.5194/wes-3-729-2018, 2018
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Rain erosion on wind turbine blades is a severe challenge for wind energy today. It causes significant losses in power production, and large sums are spent on inspection and repair.
Blade life can be extended, power production increased and maintenance costs reduced by rotor speed reduction at extreme precipitation events. Combining erosion test results, meteorological data and models of blade performance, we show that a turbine control strategy is a promising new weapon against blade erosion.
Tobias Ahsbahs, Merete Badger, Patrick Volker, Kurt S. Hansen, and Charlotte B. Hasager
Wind Energ. Sci., 3, 573–588, https://doi.org/10.5194/wes-3-573-2018, https://doi.org/10.5194/wes-3-573-2018, 2018
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Satellites offer wind measurements offshore and can resolve the wind speed on scales of up to 500 m. To date, this data is not routinely used in the industry for planning wind farms. We show that this data can be used to predict local differences in the mean wind speed around the Anholt offshore wind farm. With satellite data, site-specific wind measurements can be introduced early in the planning phase of an offshore wind farm and help decision makers.
I. Karagali and J. L. Høyer
Ocean Sci., 10, 745–758, https://doi.org/10.5194/os-10-745-2014, https://doi.org/10.5194/os-10-745-2014, 2014
Related subject area
Thematic area: Wind and the atmosphere | Topic: Wind and turbulence
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
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
The Actuator Farm Model for LES of Wind Farm-Induced Atmospheric Gravity Waves and Farm-Farm Interaction
Machine-learning-based estimate of the wind speed over complex terrain using the long short-term memory (LSTM) recurrent neural network
Offshore wind farms modify low-level jets
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
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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
Understanding the impact of data gaps on long-term offshore wind resource estimates
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
Converging Profile Relationships for Offshore Wind Speed and Turbulence Intensity
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
The Jensen wind farm parameterization
Current and future wind energy resources in the North Sea according to CMIP6
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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
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
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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
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Measurements of wind turbine wakes with scanning lidar instruments contain complex errors. We model lidars in a simulated environment to understand how and why the measured wake may differ from the true wake and validate the results with observational data. The lidar smooths out the wake, making it seem more spread out and the slowdown of the winds less pronounced. Our findings provide insights into best practices for accurately measuring wakes with lidar and interpreting observational data.
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
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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
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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
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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
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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.
Sebastiano Stipa, Arjun Ajay, and Joshua Brinkerhoff
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-89, https://doi.org/10.5194/wes-2024-89, 2024
Revised manuscript accepted for WES
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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 showing its utility for future large-scale wind farm simulations.
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
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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.
Daphne Quint, Julie K. Lundquist, and David Rosencrans
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-48, https://doi.org/10.5194/wes-2024-48, 2024
Revised manuscript accepted for WES
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Offshore wind farms will be built along the east coast of the United States. Low-level jets (LLJs) – layers of fast winds at low altitudes – also occur here. LLJs provide wind resources and also influence moisture and pollution transport, so it is important to understand how they might change. We develop and validate an automated tool to detect LLJs, and compare one year of simulations with and without wind farms. Here, we describe LLJ characteristics and how they change with wind farms.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Martin Georg Jonietz Alvarez, Warren Watson, and Julia Gottschall
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-127, https://doi.org/10.5194/wes-2023-127, 2023
Revised manuscript accepted for WES
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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 gap effect 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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Gus Jeans
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-35, https://doi.org/10.5194/wes-2023-35, 2023
Revised manuscript accepted for WES
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An extensive set of met mast data offshore Northwestern Europe is 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.
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
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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
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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.
Regis Thedin, Eliot Quon, Matthew Churchfield, and Paul Veers
Wind Energ. Sci., 8, 487–502, https://doi.org/10.5194/wes-8-487-2023, https://doi.org/10.5194/wes-8-487-2023, 2023
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We investigate coherence and correlation and highlight their importance for disciplines like wind energy structural dynamic analysis, in which blade loading and fatigue depend on turbulence structure. We compare coherence estimates to those computed using a model suggested by international standards. We show the differences and highlight additional information that can be gained using large-eddy simulation, further improving analytical coherence models used in synthetic turbulence generators.
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
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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
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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
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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.
Cited articles
Ahsbahs, T., Nygaard, N. G., Newcombe, A., and Badger, M.: Wind farm wakes from sar and doppler radar, Remote Sens., 12, 462, https://doi.org/10.3390/rs12030462,
2020. a
Badger, M., Peña, A., Hahmann, A. N., Mouche, A. A., and Hasager, C. B.:
Extrapolating satellite winds to turbine operating heights, J.
Appl. Meteorol. Clim., 55, 975–991,
https://doi.org/10.1175/JAMC-D-15-0197.1, 2016. a, b
Barthelmie, R. J. and Pryor, S.: Can satellite sampling of offshore wind speeds
realistically represent wind speed distributions?, J. Appl.
Meteorol., 42, 83–94,
https://doi.org/10.1175/1520-0450(2003)042<0083:CSSOOW>2.0.CO;2, 2003. a
Belmonte Rivas, M. and Stoffelen, A.: Characterizing ERA-Interim and ERA5
surface wind biases using ASCAT, Ocean Sci., 15, 831–852,
https://doi.org/10.5194/os-15-831-2019, 2019. a
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001. a
Bundesamt für Seeschifffahrt und Hydrographie: FINO – Datenbankinformationen – Forschungsplattformen in Nord- und Ostsee, http://fino.bsh.de, last access: 27 April 2023. a
Chelton, D. B., Ries, J. C., Haines, B. J., Fu, L. L., and Callahan, P. S.:
Chapter 1 Satellite Altimetry, Int. Geophys., 69, 1–183,
https://doi.org/10.1016/S0074-6142(01)80146-7, 2001. a
Cheynet, E., Solbrekke, I. M., Diezel, J. M., and Reuder, J.: A one-year
comparison of new wind atlases over the North Sea, J. Phys.:
Conf. Ser., 2362, 012009,
https://doi.org/10.1088/1742-6596/2362/1/012009, 2022. a, b, c
Clifton, A., Clive, P., Gottschall, J., Schlipf, D., Simley, E., Simmons, L.,
Stein, D., Trabucchi, D., Vasiljevic, N., and Würth, I.: IEA Wind Task 32: Wind lidar identifying and mitigating barriers to the adoption of wind lidar, Remote Sens., 10, 406, https://doi.org/10.3390/rs10030406, 2018. a
CMEMS: Product User Manual for Baltic Sea SST Reprocessed products
SST_BAL_SST_L4_REP_OBSERVATIONS_010_016,
SST_BAL_PHY_L3S_MY_010_040, John Wiley & Sons, Ltd, https://doi.org/10.48670/moi-00156, 2022. a
de Kloe, J., Stoffelen, A., and Verhoef, A.: Improved Use of Scatterometer
Measurements by Using Stress-Equivalent Reference Winds, IEEE J.
Select. Top. Appl. Earth Obs. Remote Sens., 10,
2340–2347, https://doi.org/10.1109/JSTARS.2017.2685242, 2017. a
de Montera, L., Berger, H., Husson, R., Appelghem, P., Guerlou, L., and
Fragoso, M.: High-resolution offshore wind resource assessment at turbine hub
height with Sentinel-1 synthetic aperture radar (SAR) data and machine
learning, Wind Energ. Sci., 7, 1441–1453, https://doi.org/10.5194/wes-7-1441-2022, 2022. a, b, c
Donlon, C., Robinson, I., Casey, K. S., Vazquez-Cuervo, J., Armstrong, E.,
Arino, O., Gentemann, C., May, D., LeBorgne, P., Piollé, J., Barton, I.,
Beggs, H., Poulter, D. J. S., Merchant, C. J., Bingham, A., Heinz, S.,
Harris, A., Wick, G., Emery, B., Minnett, P., Evans, R., Llewellyn-Jones, D.,
Mutlow, C., Reynolds, R. W., Kawamura, H., and Rayner, N.: The Global Ocean
Data Assimilation Experiment High-resolution Sea Surface Temperature Pilot
Project, B. Am. Meteorol. Soc., 88, 1197–1214,
https://doi.org/10.1175/BAMS-88-8-1197, 2007. a, b
Dörenkämper, M., Olsen, B. T., Witha, B., Hahmann, A. N., Davis, N. N., Barcons, J., Ezber, Y., García-Bustamante, E., González-Rouco, J. F., Navarro, J., Sastre-Marugán, M., Sīle, T., Trei, W., Žagar, M.,
Badger, J., Gottschall, J., Sanz Rodrigo, J., and Mann, J.: The Making of the
New European Wind Atlas – Part 2: Production and evaluation, Geoscientific
Model Development, 13, 5079–5102, https://doi.org/10.5194/gmd-13-5079-2020, 2020. a, b
Gottschall, J., Gribben, B., Stein, D., and Würth, I.: Floating lidar as an
advanced offshore wind speed measurement technique: current technology status
and gap analysis in regard to full maturity, WIREs Energ. Environ., 146, 1999–2049, https://doi.org/10.1002/wene.250, 2017. a
Gottschall, J., Catalano, E., Dörenkämper, M., and Witha, B.: The
NEWA Ferry Lidar Experiment: Measuring mesoscalewinds in the Southern Baltic
Sea, Remote Sens., 10, 1–13, https://doi.org/10.3390/rs10101620, 2018. a, b
Haakenstad, H., Breivik, Ø., Furevik, B. R., Reistad, M., Bohlinger, P., and
Aarnes, O. J.: NORA3: A Nonhydrostatic High-Resolution Hindcast of the North
Sea, the Norwegian Sea, and the Barents Sea, J. Appl. Meteorol.
Clim., 60, 1443–1464, https://doi.org/10.1175/JAMC-D-21-0029.1, 2021. a
Hahmann, A. N., Vincent, C. L., Peña, A., Lange, J., and Hasager, C. B.: Wind
climate estimation using WRF model output: method and model sensitivities
over the sea, Int. J. Climatol., 35, 3422–3439,
https://doi.org/10.1002/joc.4217, 2015. a
Hahmann, A. N., Sīle, T., Witha, B., Davis, N. N., Dörenkämper, M., Ezber, Y., García-Bustamante, E., González-Rouco, J. F., Navarro, J., Olsen, B. T., and Söderberg, S.: The making of the New European Wind Atlas – Part 1: Model sensitivity, Geosci. Model Dev., 13, 5053–5078,
https://doi.org/10.5194/gmd-13-5053-2020, 2020. a
Hasager, C. B., Hahmann, A. N., Ahsbahs, T., Karagali, I., Sile, T., Badger,
M., and Mann, J.: Europe's offshore winds assessed with synthetic aperture
radar, ASCAT and WRF, Wind Energ. Sci., 5, 375–390, https://doi.org/10.5194/wes-5-375-2020, 2020. a, b
Hastie, T., Tibshirani, R., Friedman, J. H., and Friedman, J. H.: The elements
of statistical learning: data mining, inference, and prediction, in: vol. 2,
Springer, https://doi.org/10.1007/978-0-387-21606-5, 2009. a
Hatfield, D., Gottschall, J., and Hasager, C. B.: Stability information derived
from a floating lidar system using bulk Richardson formulation, J.
Phys.: Conf. Ser., 2265, 042024,
https://doi.org/10.1088/1742-6596/2265/4/042024, 2022a. a
Hatfield, D., Hasager, C. B., and Karagali, I.: Comparing Offshore Ferry LidarMeasurements in the Southern Baltic Sea with ASCAT, FINO2 and WRF, Remote Sens., 14, 1427, https://doi.org/10.3390/rs14061427, 2022b. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Høyer, J. L. and Karagali, I.: Sea Surface Temperature Climate Data Record for
the North Sea and Baltic Sea, J. Climate, 29, 2529–2541,
https://doi.org/10.1175/JCLI-D-15-0663.1, 2016. a
Høyer, J. L. and She, J.: Optimal interpolation of sea surface temperature for
the North Sea and Baltic Sea, J. Mar. Syst., 65, 176–189,
https://doi.org/10.1016/j.jmarsys.2005.03.008, 2007. a
Karagali, I., Peña, A., Badger, M., and Hasager, C. B.: Wind characteristics
in the North and Baltic Seas from the QuikSCAT satellite, Wind Energy, 17,
123–140, https://doi.org/10.1002/we.1565, 2014. a
Karagali, I., Badger, M., and Hasager, C. B.: ASCAT winds used for offshore
wind energy applications, in: Proceedings for the 2018 EUMETSAT Meteorological Satellite Conference, 17–21 September 2018, Tallinn, Estonia, 17–21,
https://www.eumetsat.int/eumetsat-meteorological-satellite-conference-2018
(last access: 27 April 2023), 2018a. a
Karagali, I., Hahmann, A. N., Badger, M., Hasager, C., and Mann, J.: Offshore
new European wind atlas, J. Phys.: Conf. Ser., 1037, 052007, https://doi.org/10.1088/1742-6596/1037/5/052007, 2018b. a
Kelly, M. and Gryning, S.-E.: Long-Term Mean Wind Profiles Based on Similarity
Theory, Bound.-Lay. Meteorol., 136, 377–390,
https://doi.org/10.1007/s10546-010-9509-9, 2010. a
Leiding, T., Tinz, B., Gates, L., Rosenhagen, G., Herklotz, K., Senet, C.,
Outzen, O., Lindenthal, A., Neumann, T., Frühmann, R., Wilts, F.,
Bégué, F., Schwenk, P., Stein, D., Bastigkeit, I., Bernhard,
Hagemann, L. S., Müller, S., and Schwabe, J.: Standardisierung und
vergleichende Analyse der meteorologischen FINO-Messdaten (FINO123), Tech.
rep., Deutscher Wetterdienst,
https://www.dwd.de/DE/klimaumwelt/klimaforschung/klimaueberwachung/finowind/finodoku/abschlussbericht_pdf.pdf?__blob=publicationFile&v=3
(last access: 27 April 2023), 2016. a
MacAskill, A. and Mitchell, P.: Offshore wind – an overview, WIREs Energ.
Environ., 2, 374–383, https://doi.org/10.1002/wene.30, 2013. a
Martin, S.: An Introduction to Ocean Remote Sensing, in: 2nd Edn., Cambridge University Press, https://doi.org/10.1017/CBO9781139094368, 2014. a
Misra, T., Chakraborty, P., Lad, C., Gupta, P., Rao, J., Upadhyay, G., Kumar,
S., Kumar, B., Gangele, S., Sinha, S., Tolani, H., Vithani, V., Raman, B.,
N Rao, C., Dave, D., Jyoti, R., and Desai, N.: SCATSAT-1 Scatterometer:An
Improved Successor of OSCAT, Current Sci., 117, 941, https://doi.org/10.18520/cs/v117/i6/941-949, 2019. a
Mohandes, M. A. and Rehman, S.: Wind speed extrapolation using machine learning
methods and LiDAR measurements, IEEE Access, 6, 77634–77642,
https://doi.org/10.1109/ACCESS.2018.2883677, 2018. a, b, c
Norwegian Meteorological Institute: Catalog, https://thredds.met.no/thredds/catalog/nora3/catalog.html, last access: 27 April 2023. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, É.: Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011. a
Pena Diaz, A., Hahmann, A., Hasager, C., Bingöl, F., Karagali, I.,
Badger, J., Badger, M., and Clausen, N.-E.: South Baltic Wind Atlas: South
Baltic Offshore Wind Energy Regions Project, no. 1775(EN) in Denmark,
Forskningscenter Risoe, Risoe-R, Danmarks Tekniske Universitet, Risø
Nationallaboratoriet for Bæredygtig Energi, ISBN 978-87-550-3899-8,
https://backend.orbit.dtu.dk/ws/portalfiles/portal/5578113/ris-r-1775.pdf
(last access: 27 April 2023), 2011. a
Remmers, T., Cawkwell, F., Desmond, C., Murphy, J., and Politi, E.: The
potential of advanced scatterometer (ASCAT) 12.5 km coastal observations for
offshore wind farm site selection in Irish waters, Energies, 12, 206,
https://doi.org/10.3390/en12020206, 2019. a
Rivas, M. B., Stoffelen, A., Verspeek, J., Verhoef, A., Neyt, X., and Anderson,
C.: Cone Metrics: A New Tool for the Intercomparison of Scatterometer
Records, IEEE J. Select. Top. Appl. Earth Obs.
Remote Sens., 10, 2195–2204, https://doi.org/10.1109/JSTARS.2017.2647842, 2017. a
Rubio, H., Kühn, M., and Gottschall, J.: Evaluation of low-level jets in the southern Baltic Sea: a comparison between ship-based lidar observational data and numerical models, Wind Energ. Sci., 7, 2433–2455,
https://doi.org/10.5194/wes-7-2433-2022, 2022. a
Solbrekke, I. M., Sorteberg, A., and Haakenstad, H.: The 3 km Norwegian
reanalysis (NORA3) – a validation of offshore wind resources in the North
Sea and the Norwegian Sea, Wind Energ. Sci., 6, 1501–1519,
https://doi.org/10.5194/wes-6-1501-2021, 2021. a
Stoffelen, A., Verspeek, J. A., Vogelzang, J., and Verhoef, A.: The CMOD7
Geophysical Model Function for ASCAT and ERS Wind Retrievals, IEEE J.
of Select. Top. Appl. Earth Obs. Remote Sens., 10,
2123–2134, https://doi.org/10.1109/JSTARS.2017.2681806, 2017. a
Stoffelen, A. C. M.: Error modelling of scatterometer, in-situ, and ECMWF model winds: A calibration refinement, KNMI, https://cdn.knmi.nl/knmi/pdf/bibliotheek/knmipubTR/TR193.pdf (last access: 27 April 2023), 1996. a
Türkan, Y. S., Aydoğmuş, H. Y., and Erdal, H.: The prediction
of the wind speed at different heights by machine learning methods,
Int. J. Optimiz. Control: Theor. Appl., 6, 179–187, https://doi.org/10.11121/ijocta.01.2016.00315, 2016. a, b
Verhoef, A., Vogelzang, J., Verspeek, J., and Stoffelen, A.: Long-Term
Scatterometer Wind Climate Data Records, IEEE J. Select. Top.
Appl. Earth Obs. Remote Sens., 10, 2186–2194,
https://doi.org/10.1109/JSTARS.2016.2615873, 2017.
a
Vogelzang, J., Stoffelen, A., Lindsley, R. D., Verhoef, A., and Verspeek, J.:
The ASCAT 6.25-km Wind Product, IEEE J. Select. Top. Appl.
Earth Obs. Remote Sens., 10, 2321–2331,
https://doi.org/10.1109/JSTARS.2016.2623862, 2017. a
Wang, Z., Stoffelen, A., Zhang, B., He, Y., Lin, W., and Li, X.:
Inconsistencies in scatterometer wind products based on ASCAT and OSCAT-2
collocations, Remote Sens. Environ., 225, 207–216,
https://doi.org/10.1016/j.rse.2019.03.005, 2019. a
Witha, B., Hahmann, A., Sile, T., Dörenkämper, M., Ezber, Y.,
García-Bustamante, E., González-Rouco, J. F., Leroy, G., and Navarro, J.:
WRF model sensitivity studies and specifications for the NEWA mesoscale wind
atlas production runs, Zenodo [data set], https://doi.org/10.5281/zenodo.2682604, 2019. a, b
Zhao, K., Zhao, C., and Chen, G.: Evaluation of Chinese Scatterometer Ocean
Surface Wind Data: Preliminary Analysis, Earth Space Sci., 8, e2020EA001482, https://doi.org/10.1029/2020EA001482, 2021. a
Zhu, B., Chen, J., Xu, Y., Zheng, Q., and Li, X.: Validation of the CFOSAT
Scatterometer Data With Buoy Observations and Tests of Operational Application to Extreme Weather Forecasts in Taiwan Strait, Earth Space Sci., 9, e2021EA001865, https://doi.org/10.1029/2021EA001865, 2022. a
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.
Wind observations at heights relevant to the operation of modern offshore wind farms, i.e. 100 m...
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