Articles | Volume 7, issue 3
https://doi.org/10.5194/wes-7-1241-2022
© 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-7-1241-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Jun 2022
Research article |
| 20 Jun 2022
| 20 Jun 2022
Offshore wind farm cluster wakes as observed by long-range-scanning wind lidar measurements and mesoscale modeling
Beatriz Cañadillas
CORRESPONDING AUTHOR
Institute of Flight Guidance, Technische Universität Braunschweig, Braunschweig, Germany
Renewables, UL International GmbH, Oldenburg, Germany
Maximilian Beckenbauer
Institute of Flight Guidance, Technische Universität Braunschweig, Braunschweig, Germany
Juan J. Trujillo
Renewables, UL International GmbH, Oldenburg, Germany
Martin Dörenkämper
Fraunhofer Institute for Wind Energy Systems, Oldenburg, Germany
Richard Foreman
Renewables, UL International GmbH, Oldenburg, Germany
Thomas Neumann
Renewables, UL International GmbH, Oldenburg, Germany
Astrid Lampert
Institute of Flight Guidance, Technische Universität Braunschweig, Braunschweig, Germany
Related authors
Astrid Lampert, Konrad Bärfuss, Andreas Platis, Simon Siedersleben, Bughsin Djath, Beatriz Cañadillas, Robert Hunger, Rudolf Hankers, Mark Bitter, Thomas Feuerle, Helmut Schulz, Thomas Rausch, Maik Angermann, Alexander Schwithal, Jens Bange, Johannes Schulz-Stellenfleth, Thomas Neumann, and Stefan Emeis
Earth Syst. Sci. Data, 12, 935–946, https://doi.org/10.5194/essd-12-935-2020, https://doi.org/10.5194/essd-12-935-2020, 2020
Short summary
Short summary
With the research aircraft Do-128 of TU Braunschweig, meteorological measurements were performed in the wakes of offshore wind parks during the project WIPAFF. During stable atmospheric conditions, the areas of reduced wind speed and enhanced turbulence behind wind parks had an extension larger than 45 km downwind. The data set consisting of 41 measurement flights is presented. Parameters include wind vector, temperature, humidity and significant wave height.
Simon K. Siedersleben, Andreas Platis, Julie K. Lundquist, Bughsin Djath, Astrid Lampert, Konrad Bärfuss, Beatriz Cañadillas, Johannes Schulz-Stellenfleth, Jens Bange, Tom Neumann, and Stefan Emeis
Geosci. Model Dev., 13, 249–268, https://doi.org/10.5194/gmd-13-249-2020, https://doi.org/10.5194/gmd-13-249-2020, 2020
Short summary
Short summary
Wind farms affect local weather and microclimates. These effects can be simulated in weather models, usually by removing momentum at the location of the wind farm. Some debate exists whether additional turbulence should be added to capture the enhanced mixing of wind farms. By comparing simulations to measurements from airborne campaigns near offshore wind farms, we show that additional turbulence is necessary. Without added turbulence, the mixing is underestimated during stable conditions.
Bjarke Tobias Eisensøe Olsen, Andrea Noemi Hahmann, Nicolás González Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
EGUsphere, https://doi.org/10.5194/egusphere-2024-3123, https://doi.org/10.5194/egusphere-2024-3123, 2024
Short summary
Short summary
Low-level jets (LLJs) are strong winds in the lower atmosphere, important for wind energy as turbines get taller. This study compares a weather model (WRF) with real data across five North and Baltic Sea sites. Adjusting the model improved accuracy over the widely-used ERA5. In key offshore regions, LLJs occur 10–15 % of the time and significantly boost wind power, especially in spring and summer, contributing up to 30 % of total capacity in some areas.
Astrid Lampert, Rudolf Hankers, Thomas Feuerle, Thomas Rausch, Matthias Cremer, Maik Angermann, Mark Bitter, Jonas Füllgraf, Helmut Schulz, Ulf Bestmann, and Konrad B. Bärfuss
Earth Syst. Sci. Data, 16, 4777–4792, https://doi.org/10.5194/essd-16-4777-2024, https://doi.org/10.5194/essd-16-4777-2024, 2024
Short summary
Short summary
We conducted flights above the North Sea and investigated changes in the wind field. The research aircraft measured wind speed, wind direction, temperature, humidity and sea surface at high resolution. Wind parks reduce the wind speed, and the data help to determine how long it takes for the wind speed to recover. The coast also plays an important role, and the wind speed varies with distance from the coast. The results help in wind park planning and better estimating the energy yield.
Lukas Vollmer, Balthazar Arnoldus Maria Sengers, and Martin Dörenkämper
Wind Energ. Sci., 9, 1689–1693, https://doi.org/10.5194/wes-9-1689-2024, https://doi.org/10.5194/wes-9-1689-2024, 2024
Short summary
Short summary
This study proposes a modification to a well-established wind farm parameterization used in mesoscale models. The wind speed at the location of the turbine, which is used to calculate power and thrust, is corrected to approximate the free wind speed. Results show that the modified parameterization produces more accurate estimates of the turbine’s power curve.
Hassnae Erraji, Philipp Franke, Astrid Lampert, Tobias Schuldt, Ralf Tillmann, Andreas Wahner, and Anne Caroline Lange
EGUsphere, https://doi.org/10.5194/egusphere-2024-517, https://doi.org/10.5194/egusphere-2024-517, 2024
Short summary
Short summary
4D-var data assimilation allows for simultaneous adjustments of initial values and emission rates by applying trace gas profiles from drone observations within the regional model EURAD-IM. The drone data assimilation has a positive impact on the representation of air pollutants in the model by improving both their vertical distribution and ground concentrations. This case study reveals the potential of the drone observations to improve the air quality analyses and to assess emission corrections.
Barbara Harm-Altstädter, Konrad Bärfuss, Lutz Bretschneider, Martin Schön, Jens Bange, Ralf Käthner, Radovan Krejci, Mauro Mazzola, Kihong Park, Falk Pätzold, Alexander Peuker, Rita Traversi, Birgit Wehner, and Astrid Lampert
Aerosol Research, 1, 39–64, https://doi.org/10.5194/ar-1-39-2023, https://doi.org/10.5194/ar-1-39-2023, 2023
Short summary
Short summary
We present observations of aerosol particles and meteorological parameters in the horizontal and vertical distribution measured with uncrewed aerial systems in the Arctic. The field campaign was carried out during the snow melting season, when ultrafine aerosol particles (UFPs) with a size between 3 and 12 nm occurred frequently. A high variability of the measured UFPs was identified in the spatial scale, which was strongly associated with different atmospheric boundary layer properties.
Konrad B. Bärfuss, Holger Schmithüsen, and Astrid Lampert
Atmos. Meas. Tech., 16, 3739–3765, https://doi.org/10.5194/amt-16-3739-2023, https://doi.org/10.5194/amt-16-3739-2023, 2023
Short summary
Short summary
The first atmospheric soundings with an electrically powered small uncrewed aircraft system (UAS) up to an altitude of 10 km are presented and assessed for quality, revealing the potential to augment atmospheric observations and fill observation gaps for numerical weather prediction. This is significant because of the need for high-resolution meteorological data, in particular in remote areas with limited in situ measurements, and for reference data for satellite measurement calibration.
Markus Sommerfeld, Martin Dörenkämper, Jochem De Schutter, and Curran Crawford
Wind Energ. Sci., 8, 1153–1178, https://doi.org/10.5194/wes-8-1153-2023, https://doi.org/10.5194/wes-8-1153-2023, 2023
Short summary
Short summary
This study investigates the performance of pumping-mode ground-generation airborne wind energy systems by determining power-optimal flight trajectories based on realistic, k-means clustered, vertical wind velocity profiles. These profiles, derived from mesoscale weather simulations at an offshore and an onshore site in Europe, are incorporated into an optimal control model that maximizes average cycle power by optimizing the kite's trajectory.
Anna von Brandis, Gabriele Centurelli, Jonas Schmidt, Lukas Vollmer, Bughsin' Djath, and Martin Dörenkämper
Wind Energ. Sci., 8, 589–606, https://doi.org/10.5194/wes-8-589-2023, https://doi.org/10.5194/wes-8-589-2023, 2023
Short summary
Short summary
We propose that considering large-scale wind direction changes in the computation of wind farm cluster wakes is of high relevance. Consequently, we present a new solution for engineering modeling tools that accounts for the effect of such changes in the propagation of wakes. The new model is evaluated with satellite data in the German Bight area. It has the potential to reduce uncertainty in applications such as site assessment and short-term power forecasting.
Markus Sommerfeld, Martin Dörenkämper, Jochem De Schutter, and Curran Crawford
Wind Energ. Sci., 7, 1847–1868, https://doi.org/10.5194/wes-7-1847-2022, https://doi.org/10.5194/wes-7-1847-2022, 2022
Short summary
Short summary
This research explores the ground-generation airborne wind energy system (AWES) design space and investigates scaling effects by varying design parameters such as aircraft wing size, aerodynamic efficiency and mass. Therefore, representative simulated onshore and offshore wind data are implemented into an AWES trajectory optimization model. We estimate optimal annual energy production and capacity factors as well as a minimal operational lift-to-weight ratio.
Andreas Rott, Jörge Schneemann, Frauke Theuer, Juan José Trujillo Quintero, and Martin Kühn
Wind Energ. Sci., 7, 283–297, https://doi.org/10.5194/wes-7-283-2022, https://doi.org/10.5194/wes-7-283-2022, 2022
Short summary
Short summary
We present three methods that can determine the alignment of a lidar placed on the transition piece of an offshore wind turbine based on measurements with the instrument: a practical implementation of hard targeting for north alignment, a method called sea surface levelling to determine the levelling of the system from water surface measurements, and a model that can determine the dynamic levelling based on the operating status of the wind turbine.
Jörge Schneemann, Frauke Theuer, Andreas Rott, Martin Dörenkämper, and Martin Kühn
Wind Energ. Sci., 6, 521–538, https://doi.org/10.5194/wes-6-521-2021, https://doi.org/10.5194/wes-6-521-2021, 2021
Short summary
Short summary
A wind farm can reduce the wind speed in front of it just by its presence and thus also slightly impact the available power. In our study we investigate this so-called global-blockage effect, measuring the inflow of a large offshore wind farm with a laser-based remote sensing method up to several kilometres in front of the farm. Our results show global blockage under a certain atmospheric condition and operational state of the wind farm; during other conditions it is not visible in our data.
Julia Gottschall and Martin Dörenkämper
Wind Energ. Sci., 6, 505–520, https://doi.org/10.5194/wes-6-505-2021, https://doi.org/10.5194/wes-6-505-2021, 2021
Bart M. Doekemeijer, Stefan Kern, Sivateja Maturu, Stoyan Kanev, Bastian Salbert, Johannes Schreiber, Filippo Campagnolo, Carlo L. Bottasso, Simone Schuler, Friedrich Wilts, Thomas Neumann, Giancarlo Potenza, Fabio Calabretta, Federico Fioretti, and Jan-Willem van Wingerden
Wind Energ. Sci., 6, 159–176, https://doi.org/10.5194/wes-6-159-2021, https://doi.org/10.5194/wes-6-159-2021, 2021
Short summary
Short summary
This article presents the results of a field experiment investigating wake steering on an onshore wind farm. The measurements show that wake steering leads to increases in power production of up to 35 % for two-turbine interactions and up to 16 % for three-turbine interactions. However, losses in power production are seen for various regions of wind directions. The results suggest that further research is necessary before wake steering will consistently lead to energy gains in wind farms.
Andrea N. Hahmann, Tija Sīle, Björn Witha, Neil N. Davis, Martin Dörenkämper, Yasemin Ezber, Elena García-Bustamante, J. Fidel González-Rouco, Jorge Navarro, Bjarke T. Olsen, and Stefan Söderberg
Geosci. Model Dev., 13, 5053–5078, https://doi.org/10.5194/gmd-13-5053-2020, https://doi.org/10.5194/gmd-13-5053-2020, 2020
Short summary
Short summary
Wind energy resource assessment routinely uses numerical weather prediction model output. We describe the evaluation procedures used for picking the suitable blend of model setup and parameterizations for simulating European wind climatology with the WRF model. We assess the simulated winds against tall mast measurements using a suite of metrics, including the Earth Mover's Distance, which diagnoses the performance of each ensemble member using the full wind speed and direction distribution.
Martin Dörenkämper, Bjarke T. Olsen, Björn Witha, Andrea N. Hahmann, Neil N. Davis, Jordi Barcons, Yasemin Ezber, Elena García-Bustamante, J. Fidel González-Rouco, Jorge Navarro, Mariano Sastre-Marugán, Tija Sīle, Wilke Trei, Mark Žagar, Jake Badger, Julia Gottschall, Javier Sanz Rodrigo, and Jakob Mann
Geosci. Model Dev., 13, 5079–5102, https://doi.org/10.5194/gmd-13-5079-2020, https://doi.org/10.5194/gmd-13-5079-2020, 2020
Short summary
Short summary
This is the second of two papers that document the creation of the New European Wind Atlas (NEWA). The paper includes a detailed description of the technical and practical aspects that went into running the mesoscale simulations and the microscale downscaling for generating the climatology. A comprehensive evaluation of each component of the NEWA model chain is presented using observations from a large set of tall masts located all over Europe.
Tuukka Petäjä, Ella-Maria Duplissy, Ksenia Tabakova, Julia Schmale, Barbara Altstädter, Gerard Ancellet, Mikhail Arshinov, Yurii Balin, Urs Baltensperger, Jens Bange, Alison Beamish, Boris Belan, Antoine Berchet, Rossana Bossi, Warren R. L. Cairns, Ralf Ebinghaus, Imad El Haddad, Beatriz Ferreira-Araujo, Anna Franck, Lin Huang, Antti Hyvärinen, Angelika Humbert, Athina-Cerise Kalogridis, Pavel Konstantinov, Astrid Lampert, Matthew MacLeod, Olivier Magand, Alexander Mahura, Louis Marelle, Vladimir Masloboev, Dmitri Moisseev, Vaios Moschos, Niklas Neckel, Tatsuo Onishi, Stefan Osterwalder, Aino Ovaska, Pauli Paasonen, Mikhail Panchenko, Fidel Pankratov, Jakob B. Pernov, Andreas Platis, Olga Popovicheva, Jean-Christophe Raut, Aurélie Riandet, Torsten Sachs, Rosamaria Salvatori, Roberto Salzano, Ludwig Schröder, Martin Schön, Vladimir Shevchenko, Henrik Skov, Jeroen E. Sonke, Andrea Spolaor, Vasileios K. Stathopoulos, Mikko Strahlendorff, Jennie L. Thomas, Vito Vitale, Sterios Vratolis, Carlo Barbante, Sabine Chabrillat, Aurélien Dommergue, Konstantinos Eleftheriadis, Jyri Heilimo, Kathy S. Law, Andreas Massling, Steffen M. Noe, Jean-Daniel Paris, André S. H. Prévôt, Ilona Riipinen, Birgit Wehner, Zhiyong Xie, and Hanna K. Lappalainen
Atmos. Chem. Phys., 20, 8551–8592, https://doi.org/10.5194/acp-20-8551-2020, https://doi.org/10.5194/acp-20-8551-2020, 2020
Short summary
Short summary
The role of polar regions is increasing in terms of megatrends such as globalization, new transport routes, demography, and the use of natural resources with consequent effects on regional and transported pollutant concentrations. Here we summarize initial results from our integrative project exploring the Arctic environment and pollution to deliver data products, metrics, and indicators for stakeholders.
Barbara Altstädter, Konrad Deetz, Bernhard Vogel, Karmen Babić, Cheikh Dione, Federica Pacifico, Corinne Jambert, Friederike Ebus, Konrad Bärfuss, Falk Pätzold, Astrid Lampert, Bianca Adler, Norbert Kalthoff, and Fabienne Lohou
Atmos. Chem. Phys., 20, 7911–7928, https://doi.org/10.5194/acp-20-7911-2020, https://doi.org/10.5194/acp-20-7911-2020, 2020
Short summary
Short summary
We present the high vertical variability of the black carbon (BC) mass concentration measured with the unmanned aerial system ALADINA during the field experiment of DACCIWA. The COSMO-ART model output was applied for the campaign period and is compared with the observational BC data during a case study on 14–15 July 2016. Enhanced BC concentrations were related to transport processes to the measurement site by maritime inflow and not to local emissions as initially expected.
Astrid Lampert, Konrad Bärfuss, Andreas Platis, Simon Siedersleben, Bughsin Djath, Beatriz Cañadillas, Robert Hunger, Rudolf Hankers, Mark Bitter, Thomas Feuerle, Helmut Schulz, Thomas Rausch, Maik Angermann, Alexander Schwithal, Jens Bange, Johannes Schulz-Stellenfleth, Thomas Neumann, and Stefan Emeis
Earth Syst. Sci. Data, 12, 935–946, https://doi.org/10.5194/essd-12-935-2020, https://doi.org/10.5194/essd-12-935-2020, 2020
Short summary
Short summary
With the research aircraft Do-128 of TU Braunschweig, meteorological measurements were performed in the wakes of offshore wind parks during the project WIPAFF. During stable atmospheric conditions, the areas of reduced wind speed and enhanced turbulence behind wind parks had an extension larger than 45 km downwind. The data set consisting of 41 measurement flights is presented. Parameters include wind vector, temperature, humidity and significant wave height.
Astrid Lampert, Falk Pätzold, Magnus O. Asmussen, Lennart Lobitz, Thomas Krüger, Thomas Rausch, Torsten Sachs, Christian Wille, Denis Sotomayor Zakharov, Dominik Gaus, Stephan Bansmer, and Ellen Damm
Atmos. Meas. Tech., 13, 1937–1952, https://doi.org/10.5194/amt-13-1937-2020, https://doi.org/10.5194/amt-13-1937-2020, 2020
Short summary
Short summary
Methane has high climate warming potential. Sources of methane can be distinguished by the isotopic composition. To investigate the origin of methane, an airborne sampling system has been developed that can take air samples worldwide and at various altitudes. The article shows the performance of the overall system, from taking samples to laboratory analyses. As known methane source, a rewetted peatland site, was studied, and the vertical distribution of the isotopic composition is investigated.
Simon K. Siedersleben, Andreas Platis, Julie K. Lundquist, Bughsin Djath, Astrid Lampert, Konrad Bärfuss, Beatriz Cañadillas, Johannes Schulz-Stellenfleth, Jens Bange, Tom Neumann, and Stefan Emeis
Geosci. Model Dev., 13, 249–268, https://doi.org/10.5194/gmd-13-249-2020, https://doi.org/10.5194/gmd-13-249-2020, 2020
Short summary
Short summary
Wind farms affect local weather and microclimates. These effects can be simulated in weather models, usually by removing momentum at the location of the wind farm. Some debate exists whether additional turbulence should be added to capture the enhanced mixing of wind farms. By comparing simulations to measurements from airborne campaigns near offshore wind farms, we show that additional turbulence is necessary. Without added turbulence, the mixing is underestimated during stable conditions.
Jörge Schneemann, Andreas Rott, Martin Dörenkämper, Gerald Steinfeld, and Martin Kühn
Wind Energ. Sci., 5, 29–49, https://doi.org/10.5194/wes-5-29-2020, https://doi.org/10.5194/wes-5-29-2020, 2020
Short summary
Short summary
Offshore wind farm clusters cause reduced wind speeds in downstream regions which can extend over more than 50 km.
We analysed the impact of these so-called cluster wakes on a distant wind farm using remote-sensing wind measurements and power production data.
Cluster wakes caused power losses up to 55 km downstream in certain atmospheric states.
A better understanding of cluster wake effects reduces uncertainties in offshore wind resource assessment and improves offshore areal planning.
Markus Sommerfeld, Martin Dörenkämper, Gerald Steinfeld, and Curran Crawford
Wind Energ. Sci., 4, 563–580, https://doi.org/10.5194/wes-4-563-2019, https://doi.org/10.5194/wes-4-563-2019, 2019
Short summary
Short summary
Airborne wind energy systems aim to operate at altitudes above conventional wind turbines where reliable high-resolution wind data are scarce. Wind measurements and computational simulations both have advantages and disadvantages when assessing the wind resource at such heights. This article investigates whether assimilating measurements into the model generates a more accurate wind data set up to 1100 m. These wind data sets are used to estimate optimal AWES operating altitudes and power.
Barbara Altstädter, Andreas Platis, Michael Jähn, Holger Baars, Janine Lückerath, Andreas Held, Astrid Lampert, Jens Bange, Markus Hermann, and Birgit Wehner
Atmos. Chem. Phys., 18, 8249–8264, https://doi.org/10.5194/acp-18-8249-2018, https://doi.org/10.5194/acp-18-8249-2018, 2018
Short summary
Short summary
This article describes the appearance of ultrafine aerosol particles (size < 12 nm) within the atmospheric boundary layer under cloudy conditions. New particle formation (NPF) was observed with the ALADINA unmanned aerial system in relation to increased turbulence near the inversion layer. Fast mixing processes and rapid dilution of surrounding air led to an insufficient particle growth rate, seen in sporadic clusters at ground. These events might not have been classified as NPF by surface data.
Astrid Lampert, Jörg Hartmann, Falk Pätzold, Lennart Lobitz, Peter Hecker, Katrin Kohnert, Eric Larmanou, Andrei Serafimovich, and Torsten Sachs
Atmos. Meas. Tech., 11, 2523–2536, https://doi.org/10.5194/amt-11-2523-2018, https://doi.org/10.5194/amt-11-2523-2018, 2018
Short summary
Short summary
We compared two different fast-response humidity sensors simultaneously on different airborne platforms. One is a particular, well-establed Lyman-alpha hygrometer that has been used for decades as the standard for fast airborne humidity measurements. However, it is not available any more. The other one is a hygrometer based on the absorption of infrared radiation, from LI-COR. For an environment of low vibrations, the LI-COR sensor is suitable for fast airborne water vapour measurements.
Guylaine Canut, Fleur Couvreux, Marie Lothon, Dominique Legain, Bruno Piguet, Astrid Lampert, William Maurel, and Eric Moulin
Atmos. Meas. Tech., 9, 4375–4386, https://doi.org/10.5194/amt-9-4375-2016, https://doi.org/10.5194/amt-9-4375-2016, 2016
Short summary
Short summary
Turbulent processes of the atmospheric boundary layer contribute the most to transfers between the surface and the atmosphere. Typically, turbulent boundary layer parameters are measured by sonic anemometers on masts and by research aircraft. This is to measure in situ turbulent parameters in the planetary boundary layer (PBL) at altitudes above 50 m. For this purpose, our team have developed a system under a tethered balloon which has been in use since 2010.
Astrid Lampert, Falk Pätzold, Maria Antonia Jiménez, Lennart Lobitz, Sabrina Martin, Gerald Lohmann, Guylaine Canut, Dominique Legain, Jens Bange, Dani Martínez-Villagrasa, and Joan Cuxart
Atmos. Chem. Phys., 16, 8009–8021, https://doi.org/10.5194/acp-16-8009-2016, https://doi.org/10.5194/acp-16-8009-2016, 2016
Short summary
Short summary
For a large field experiment in summer 2011 in southern France (BLLAST campaign), the development of turbulence in the atmosphere was analysed during the afternoon and evening. Besides ground-based remote sensing and in situ observations, turbulence parameters were measured with an unmanned aerial vehicle and analysed by numerical simulation. Turbulence decreased during the afternoon, but increased after sunset due to local wind systems. Turbulent eddies lost symmetry during the transition.
Juan José Trujillo, Janna Kristina Seifert, Ines Würth, David Schlipf, and Martin Kühn
Wind Energ. Sci., 1, 41–53, https://doi.org/10.5194/wes-1-41-2016, https://doi.org/10.5194/wes-1-41-2016, 2016
Short summary
Short summary
We present the analysis of the trajectories followed by the wind, in the immediate vicinity, behind an offshore wind turbine and their dependence on its yaw misalignment. We apply wake tracking on wind fields measured with a lidar (light detection and ranging) system located at the nacelle of the wind turbine and pointing downstream. The analysis reveals discrepancies of the estimated mean wake paths against theoretical and wind tunnel experiments using different wake-tracking techniques.
B. Altstädter, A. Platis, B. Wehner, A. Scholtz, N. Wildmann, M. Hermann, R. Käthner, H. Baars, J. Bange, and A. Lampert
Atmos. Meas. Tech., 8, 1627–1639, https://doi.org/10.5194/amt-8-1627-2015, https://doi.org/10.5194/amt-8-1627-2015, 2015
Short summary
Short summary
The unmanned research aircraft Carolo P360 "ALADINA" is a flexible tool for investigating the horizontal and vertical distribution of freshly formed particles in the atmospheric boundary layer (ABL) combined with measurements of turbulent fluxes derived by fast meteorological sensors. First results of a feasibility study show, among others, events of particle bursts in an internal
layer of the ABL. Comparisons with ground-based instruments and a lidar present the reliability of the new system.
Related subject area
Thematic area: Wind and the atmosphere | Topic: Wind and turbulence
Converging profile relationships for offshore wind speed and turbulence intensity
A simple steady-state inflow model of the neutral and stable atmospheric boundary layer applied to wind turbine wake simulations
Influences of lidar scanning parameters on wind turbine wake retrievals in complex terrain
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
The fractal turbulent–non-turbulent interface in the atmosphere
TOSCA – an open-source, finite-volume, large-eddy simulation (LES) environment for wind farm flows
Characterization of Local Wind Profiles: A Random Forest Approach for Enhanced Wind Profile Extrapolation
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
Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques
An investigation of spatial wind direction variability and its consideration in engineering models
From gigawatt to multi-gigawatt wind farms: wake effects, energy budgets and inertial gravity waves investigated by large-eddy simulations
Investigations of correlation and coherence in turbulence from a large-eddy simulation
Validation of turbulence intensity as simulated by the Weather Research and Forecasting model off the US northeast coast
On the laminar–turbulent transition mechanism on megawatt wind turbine blades operating in atmospheric flow
Brief communication: A momentum-conserving superposition method applied to the super-Gaussian wind turbine wake model
Turbulence structures and entrainment length scales in large offshore wind farms
Effect of different source terms and inflow direction in atmospheric boundary modeling over the complex terrain site of Perdigão
Comparison of large eddy simulations against measurements from the Lillgrund offshore wind farm
Adjusted spectral correction method for calculating extreme winds in tropical-cyclone-affected water areas
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
Gus Jeans
Wind Energ. Sci., 9, 2001–2015, https://doi.org/10.5194/wes-9-2001-2024, https://doi.org/10.5194/wes-9-2001-2024, 2024
Short summary
Short summary
An extensive set of met mast data offshore northwestern Europe are used to reduce uncertainty in offshore wind speed and turbulence intensity. The performance of widely used industry standard relationships is quantified, while some new empirical relationships are derived for practical application. Motivations include encouraging appropriate convergence of traditionally separate technical disciplines within the rapidly growing offshore wind energy industry.
Maarten Paul van der Laan, Mark Kelly, Mads Baungaard, Antariksh Dicholkar, and Emily Louise Hodgson
Wind Energ. Sci., 9, 1985–2000, https://doi.org/10.5194/wes-9-1985-2024, https://doi.org/10.5194/wes-9-1985-2024, 2024
Short summary
Short summary
Wind turbines are increasing in size and operate more frequently above the atmospheric surface layer, which requires improved inflow models for numerical simulations of turbine interaction. In this work, a novel steady-state model of the atmospheric boundary layer (ABL) is introduced. Numerical wind turbine flow simulations subjected to shallow and tall ABLs are conducted, and the proposed model shows improved performance compared to other state-of-the-art steady-state models.
Rachel Robey and Julie K. Lundquist
Wind Energ. Sci., 9, 1905–1922, https://doi.org/10.5194/wes-9-1905-2024, https://doi.org/10.5194/wes-9-1905-2024, 2024
Short summary
Short summary
Measurements of wind turbine wakes with scanning lidar instruments contain complex errors. We model lidars in a simulated environment to understand how and why the measured wake may differ from the true wake and validate the results with observational data. The lidar smooths out the wake, making it seem more spread out and the slowdown of the winds less pronounced. Our findings provide insights into best practices for accurately measuring wakes with lidar and interpreting observational data.
Jonathan D. Rogers
Wind Energ. Sci., 9, 1849–1868, https://doi.org/10.5194/wes-9-1849-2024, https://doi.org/10.5194/wes-9-1849-2024, 2024
Short summary
Short summary
This paper describes the results of a flight experiment to assess the existence of potential safety risks to a general aviation aircraft from added turbulence in the wake of a wind turbine. A general aviation aircraft was flown through the wake of an operating wind turbine at different downwind distances. Results indicated that there were small increases in disturbances to the aircraft due to added turbulence in the wake, but they never approached levels that would pose a safety risk.
Rémi Gandoin and Jorge Garza
Wind Energ. Sci., 9, 1727–1745, https://doi.org/10.5194/wes-9-1727-2024, https://doi.org/10.5194/wes-9-1727-2024, 2024
Short summary
Short summary
ERA5 has become the workhorse of most wind resource assessment applications, as it compares better with in situ measurements than other reanalyses. However, for design purposes, ERA5 suffers from a drawback: it underestimates strong wind speeds offshore (approx. from 10 m s−1). This is not widely discussed in the scientific literature. We address this bias and proposes a simple, robust correction. This article supports the growing need for use-case-specific validations of reanalysis datasets.
Lukas Vollmer, Balthazar Arnoldus Maria Sengers, and Martin Dörenkämper
Wind Energ. Sci., 9, 1689–1693, https://doi.org/10.5194/wes-9-1689-2024, https://doi.org/10.5194/wes-9-1689-2024, 2024
Short summary
Short summary
This study proposes a modification to a well-established wind farm parameterization used in mesoscale models. The wind speed at the location of the turbine, which is used to calculate power and thrust, is corrected to approximate the free wind speed. Results show that the modified parameterization produces more accurate estimates of the turbine’s power curve.
Mostafa Bakhoday Paskyabi
Wind Energ. Sci., 9, 1631–1645, https://doi.org/10.5194/wes-9-1631-2024, https://doi.org/10.5194/wes-9-1631-2024, 2024
Short summary
Short summary
The exchange of momentum and energy between the atmosphere and ocean depends on air–sea processes, especially wave-related ones. Precision in representing these interactions is vital for offshore wind turbine and farm design and operation. The development of a reliable wave–turbulence decomposition method to remove wave-induced interference from single-height wind measurements is essential for these applications and enhances our grasp of wind coherence within the wave boundary layer.
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
Short summary
Short summary
This study presents the actuator farm model, a new method for modeling wind turbines within large wind farms. The model greatly reduces computational cost when compared to traditional actuator wind turbine models and is beneficial for studying flow around large wind farms as well as the interaction between multiple wind farms. Results obtained from numerical simulations show excellent agreement with past wind turbine models 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
Short summary
Short summary
Extrapolating the wind profile for complex terrain through the long short-term memory model outperformed the traditional power law methodology, which due to its universal nature cannot capture local features as the machine-learning methodology does. Moreover, considering the importance of investigating the wind potential and the need for alternative energy sources, it is motivating to find that a short observational campaign can produce better results than the traditional techniques.
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
Short summary
Short summary
Offshore wind farms will be built along the east coast of the United States. Low-level jets (LLJs) – layers of fast winds at low altitudes – also occur here. LLJs provide wind resources and also influence moisture and pollution transport, so it is important to understand how they might change. We develop and validate an automated tool to detect LLJs, and compare 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
Short summary
Short summary
Experiments offer incredible value to science, but results must come with an uncertainty quantification to be meaningful. We present a method to simulate a proposed experiment, calculate uncertainties, and determine the measurement duration (total time of measurements) and the experiment duration (total time to collect the required measurement data when including condition variability and time when measurement is not occurring) required to produce statistically significant and converged results.
Oscar García-Santiago, Andrea N. Hahmann, Jake Badger, and Alfredo Peña
Wind Energ. Sci., 9, 963–979, https://doi.org/10.5194/wes-9-963-2024, https://doi.org/10.5194/wes-9-963-2024, 2024
Short summary
Short summary
This study compares the results of two wind farm parameterizations (WFPs) in the Weather Research and Forecasting model, simulating a two-turbine array under three atmospheric stabilities with large-eddy simulations. We show that the WFPs accurately depict wind speeds either near turbines or in the far-wake areas, but not both. The parameterizations’ performance varies by variable (wind speed or turbulent kinetic energy) and atmospheric stability, with reduced accuracy in stable conditions.
Til Kristian Vrana and Harald G. Svendsen
Wind Energ. Sci., 9, 919–932, https://doi.org/10.5194/wes-9-919-2024, https://doi.org/10.5194/wes-9-919-2024, 2024
Short summary
Short summary
We developed new ways to plot comprehensive wind resource maps that show the revenue potential of different locations for future wind power developments. The relative capacity factor is introduced as an indicator showing the expected mean power output. The market value factor is introduced, which captures the expected mean market value relative to other wind parks. The Renewable Energy Complementarity (RECom) index combines the two into a single index, resulting in the RECom map.
Scott Dallas, Adam Stock, and Edward Hart
Wind Energ. Sci., 9, 841–867, https://doi.org/10.5194/wes-9-841-2024, https://doi.org/10.5194/wes-9-841-2024, 2024
Short summary
Short summary
This review presents the current understanding of wind direction variability in the context of control-oriented modelling of wind turbines and wind farms in a manner suitable to a wide audience. Motivation comes from the significant and commonly seen yaw error of horizontal axis wind turbines, which carries substantial negative impacts on annual energy production and the levellised cost of wind energy. Gaps in the literature are identified, and the critical challenges in this area are discussed.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Ville Vakkari, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 9, 821–840, https://doi.org/10.5194/wes-9-821-2024, https://doi.org/10.5194/wes-9-821-2024, 2024
Short summary
Short summary
Knowing the wind speed across the rotor of a wind turbine is key in making good predictions of the power production. However, models struggle to capture both the speed and the shape of the wind profile. Using machine learning methods based on the model data, we show that the predictions can be improved drastically. The work focuses on three coastal sites, spread over the Northern Hemisphere (the Baltic Sea, the North Sea, and the US Atlantic coast) with similar results for all sites.
Lindsay M. Sheridan, Raghavendra Krishnamurthy, William I. Gustafson Jr., Ye Liu, Brian J. Gaudet, Nicola Bodini, Rob K. Newsom, and Mikhail Pekour
Wind Energ. Sci., 9, 741–758, https://doi.org/10.5194/wes-9-741-2024, https://doi.org/10.5194/wes-9-741-2024, 2024
Short summary
Short summary
In 2020, lidar-mounted buoys owned by the US Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas and provided valuable year-long analyses of offshore low-level jet (LLJ) characteristics at heights relevant to wind turbines. In addition to the LLJ climatology, this work provides validation of LLJ representation in atmospheric models that are essential for assessing the potential energy yield of offshore wind farms.
Eliot Quon
Wind Energ. Sci., 9, 495–518, https://doi.org/10.5194/wes-9-495-2024, https://doi.org/10.5194/wes-9-495-2024, 2024
Short summary
Short summary
Engineering models used to design wind farms generally do not account for realistic atmospheric conditions that can rapidly evolve from minute to minute. This paper uses a first-principles simulation technique to predict the performance of five wind turbines during a wind farm control experiment. Challenges included limited observations and atypical conditions. The simulation accurately predicts the aerodynamics of a turbine when it is situated partially within the wake of an upstream turbine.
Lars Neuhaus, Matthias Wächter, and Joachim Peinke
Wind Energ. Sci., 9, 439–452, https://doi.org/10.5194/wes-9-439-2024, https://doi.org/10.5194/wes-9-439-2024, 2024
Short summary
Short summary
Future wind turbines reach unprecedented heights and are affected by wind conditions that have not yet been studied in detail. With increasing height, a transition to laminar conditions with a turbulent–non-turbulent interface (TNTI) becomes more likely. In this paper, the presence and fractality of this TNTI in the atmosphere are studied. Typical fractalities known from ideal laboratory and numerical studies and a frequent occurrence of the TNTI at heights of multi-megawatt turbines are found.
Sebastiano Stipa, Arjun Ajay, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 297–320, https://doi.org/10.5194/wes-9-297-2024, https://doi.org/10.5194/wes-9-297-2024, 2024
Short summary
Short summary
In the current study, we introduce TOSCA (Toolbox fOr Stratified Convective Atmospheres), an open-source computational fluid dynamics (CFD) tool, and demonstrate its capabilities by simulating the flow around a large wind farm, operating in realistic flow conditions. This is one of the grand challenges of the present decade and can yield better insight into physical phenomena that strongly affect wind farm operation but which are not yet fully understood.
Farkhondeh Rouholahnejad and Julia Gottschall
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-178, https://doi.org/10.5194/wes-2023-178, 2024
Preprint under review for WES
Short summary
Short summary
In wind energy, precise wind speed prediction at hub-height is vital. Our study in the Dutch North Sea reveals that the on-site trained random forest model outperforms the global reanalysis data, ERA5, in accuracy and precision. Trained within a 200 km range, the model effectively extends the wind speed vertically but experiences bias. It also outperforms corrected ERA5 in capturing wind speed variations and fine wind patterns, highlighting its potential for offshore wind resource assessment.
Rebecca Foody, Jacob Coburn, Jeanie A. Aird, Rebecca J. Barthelmie, and Sara C. Pryor
Wind Energ. Sci., 9, 263–280, https://doi.org/10.5194/wes-9-263-2024, https://doi.org/10.5194/wes-9-263-2024, 2024
Short summary
Short summary
Using lidar-derived wind speed measurements at approx. 150 m height at onshore and offshore locations, we quantify the advantages of deploying wind turbines offshore in terms of the amount of electrical power produced and the higher reliability and predictability of the electrical power.
Ronald B. Smith
Wind Energ. Sci., 9, 253–261, https://doi.org/10.5194/wes-9-253-2024, https://doi.org/10.5194/wes-9-253-2024, 2024
Short summary
Short summary
Recent papers have investigated the impact of turbine drag on local wind patterns, but these studies have not given a full explanation of the induced pressure field. The pressure field blocks and deflects the wind and in other ways modifies farm efficiency. Current gravity wave models are complex and provide no estimation tools. We dig deeper into the cause of the pressure field and provide approximate closed-form expressions for pressure field effects.
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
Short summary
Short summary
Offshore wind measurements are often affected by gaps. We investigated how these gaps affect wind resource assessments and whether filling them reduces their effect. We find that the 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
Short summary
Short summary
The work presented here proposes interfacing experimental measurements performed in a wind tunnel with simulations conducted with the aeroelastic code FAST and applied to a floating wind turbine model under wave-induced motion. FAST simulations using experiments match well with those obtained using the inflow generation method provided by TurbSim. The highest surge motion frequencies show a significant decrease in the mean power produced by the turbine and a mitigation of the flow dynamics.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 8, 1651–1658, https://doi.org/10.5194/wes-8-1651-2023, https://doi.org/10.5194/wes-8-1651-2023, 2023
Short summary
Short summary
Low-level jets (LLJs) are special types of non-ideal wind profiles affecting both wind energy production and loads on a wind turbine. However, among LLJ researchers, there is no consensus regarding which definition to use to identify these profiles. In this work, we compare two different ways of identifying the LLJ – the falloff definition and the shear definition – and argue why the shear definition is better suited to wind energy applications.
Serkan Kartal, Sukanta Basu, and Simon J. Watson
Wind Energ. Sci., 8, 1533–1551, https://doi.org/10.5194/wes-8-1533-2023, https://doi.org/10.5194/wes-8-1533-2023, 2023
Short summary
Short summary
Peak wind gust is a crucial meteorological variable for wind farm planning and operations. Unfortunately, many wind farms do not have on-site measurements of it. In this paper, we propose a machine-learning approach (called INTRIGUE, decIsioN-TRee-based wInd GUst Estimation) that utilizes numerous inputs from a public-domain reanalysis dataset, generating long-term, site-specific peak wind gust series.
Nikolas Angelou, Jakob Mann, and Camille Dubreuil-Boisclair
Wind Energ. Sci., 8, 1511–1531, https://doi.org/10.5194/wes-8-1511-2023, https://doi.org/10.5194/wes-8-1511-2023, 2023
Short summary
Short summary
This study presents the first experimental investigation using two nacelle-mounted wind lidars that reveal the upwind and downwind conditions relative to a full-scale floating wind turbine. We find that in the case of floating wind turbines with small pitch and roll oscillating motions (< 1°), the ambient turbulence is the main driving factor that determines the propagation of the wake characteristics.
Julian Quick, Pierre-Elouan Rethore, Mads Mølgaard Pedersen, Rafael Valotta Rodrigues, and Mikkel Friis-Møller
Wind Energ. Sci., 8, 1235–1250, https://doi.org/10.5194/wes-8-1235-2023, https://doi.org/10.5194/wes-8-1235-2023, 2023
Short summary
Short summary
Wind turbine positions are often optimized to avoid wake losses. These losses depend on atmospheric conditions, such as the wind speed and direction. The typical optimization scheme involves discretizing the atmospheric inputs, then considering every possible set of these discretized inputs in every optimization iteration. This work presents stochastic gradient descent (SGD) as an alternative, which randomly samples the atmospheric conditions during every optimization iteration.
Sarah J. Ollier and Simon J. Watson
Wind Energ. Sci., 8, 1179–1200, https://doi.org/10.5194/wes-8-1179-2023, https://doi.org/10.5194/wes-8-1179-2023, 2023
Short summary
Short summary
This modelling study shows that topographic trapped lee waves (TLWs) modify flow behaviour and power output in offshore wind farms. We demonstrate that TLWs can substantially alter the wind speeds at individual wind turbines and effect the power output of the turbine and whole wind farm. The impact on wind speeds and power is dependent on which part of the TLW wave cycle interacts with the wind turbines and wind farm. Positive and negative impacts of TLWs on power output are observed.
Khaled Yassin, Arne Helms, Daniela Moreno, Hassan Kassem, Leo Höning, and Laura J. Lukassen
Wind Energ. Sci., 8, 1133–1152, https://doi.org/10.5194/wes-8-1133-2023, https://doi.org/10.5194/wes-8-1133-2023, 2023
Short summary
Short summary
The current turbulent wind field models stated in the IEC 61400-1 standard underestimate the probability of extreme changes in wind velocity. This underestimation can lead to the false calculation of extreme and fatigue loads on the turbine. In this work, we are trying to apply a random time-mapping technique to one of the standard turbulence models to adapt to such extreme changes. The turbulent fields generated are compared with a standard wind field to show the effects of this new mapping.
Mark Kelly and Maarten Paul van der Laan
Wind Energ. Sci., 8, 975–998, https://doi.org/10.5194/wes-8-975-2023, https://doi.org/10.5194/wes-8-975-2023, 2023
Short summary
Short summary
The turning of the wind with height, which is known as veer, can affect wind turbine performance. Thus far meteorology has only given idealized descriptions of veer, which has not yet been related in a way readily usable for wind energy. Here we derive equations for veer in terms of meteorological quantities and provide practically usable forms in terms of measurable shear (change in wind speed with height). Flow simulations and measurements at turbine heights support these developments.
Moritz Gräfe, Vasilis Pettas, Julia Gottschall, and Po Wen Cheng
Wind Energ. Sci., 8, 925–946, https://doi.org/10.5194/wes-8-925-2023, https://doi.org/10.5194/wes-8-925-2023, 2023
Short summary
Short summary
Inflow wind field measurements from nacelle-based lidar systems offer great potential for different applications including turbine control, load validation and power performance measurements. On floating wind turbines nacelle-based lidar measurements are affected by the dynamic behavior of the floating foundations. Therefore, the effects on lidar wind speed measurements induced by floater dynamics must be well understood. A new model for quantification of these effects is introduced in our work.
Robin Marcille, Maxime Thiébaut, Pierre Tandeo, and Jean-François Filipot
Wind Energ. Sci., 8, 771–786, https://doi.org/10.5194/wes-8-771-2023, https://doi.org/10.5194/wes-8-771-2023, 2023
Short summary
Short summary
A novel data-driven method is proposed to design an optimal sensor network for the reconstruction of offshore wind resources. Based on unsupervised learning of numerical weather prediction wind data, it provides a simple yet efficient method for the siting of sensors, outperforming state-of-the-art methods for this application. It is applied in the main French offshore wind energy development areas to provide guidelines for the deployment of floating lidars for wind resource assessment.
Wei Fu, Alessandro Sebastiani, Alfredo Peña, and Jakob Mann
Wind Energ. Sci., 8, 677–690, https://doi.org/10.5194/wes-8-677-2023, https://doi.org/10.5194/wes-8-677-2023, 2023
Short summary
Short summary
Nacelle lidars with different beam scanning locations and two types of systems are considered for inflow turbulence estimations using both numerical simulations and field measurements. The turbulence estimates from a sonic anemometer at the hub height of a Vestas V52 turbine are used as references. The turbulence parameters are retrieved using the radial variances and a least-squares procedure. The findings from numerical simulations have been verified by the analysis of the field measurements.
Daniel Hatfield, Charlotte Bay Hasager, and Ioanna Karagali
Wind Energ. Sci., 8, 621–637, https://doi.org/10.5194/wes-8-621-2023, https://doi.org/10.5194/wes-8-621-2023, 2023
Short summary
Short summary
Wind observations at heights relevant to the operation of modern offshore wind farms, i.e. 100 m and more, are required to optimize their positioning and layout. Satellite wind retrievals provide observations of the wind field over large spatial areas and extensive time periods, yet their temporal resolution is limited and they are only representative at 10 m height. Machine-learning models are applied to lift these satellite winds to higher heights, directly relevant to wind energy purposes.
Anna von Brandis, Gabriele Centurelli, Jonas Schmidt, Lukas Vollmer, Bughsin' Djath, and Martin Dörenkämper
Wind Energ. Sci., 8, 589–606, https://doi.org/10.5194/wes-8-589-2023, https://doi.org/10.5194/wes-8-589-2023, 2023
Short summary
Short summary
We propose that considering large-scale wind direction changes in the computation of wind farm cluster wakes is of high relevance. Consequently, we present a new solution for engineering modeling tools that accounts for the effect of such changes in the propagation of wakes. The new model is evaluated with satellite data in the German Bight area. It has the potential to reduce uncertainty in applications such as site assessment and short-term power forecasting.
Oliver Maas
Wind Energ. Sci., 8, 535–556, https://doi.org/10.5194/wes-8-535-2023, https://doi.org/10.5194/wes-8-535-2023, 2023
Short summary
Short summary
The study compares small vs. large wind farms regarding the flow and power output with a turbulence-resolving simulation model. It shows that a large wind farm (90 km length) significantly affects the wind direction and that the wind speed is higher in the large wind farm wake. Both wind farms excite atmospheric gravity waves that also affect the power output of the wind farms.
Regis Thedin, Eliot Quon, Matthew Churchfield, and Paul Veers
Wind Energ. Sci., 8, 487–502, https://doi.org/10.5194/wes-8-487-2023, https://doi.org/10.5194/wes-8-487-2023, 2023
Short summary
Short summary
We investigate coherence and correlation and highlight their importance for disciplines like wind energy structural dynamic analysis, in which blade loading and fatigue depend on turbulence structure. We compare coherence estimates to those computed using a model suggested by international standards. We show the differences and highlight additional information that can be gained using large-eddy simulation, further improving analytical coherence models used in synthetic turbulence generators.
Sheng-Lun Tai, Larry K. Berg, Raghavendra Krishnamurthy, Rob Newsom, and Anthony Kirincich
Wind Energ. Sci., 8, 433–448, https://doi.org/10.5194/wes-8-433-2023, https://doi.org/10.5194/wes-8-433-2023, 2023
Short summary
Short summary
Turbulence intensity is critical for wind turbine design and operation as it affects wind power generation efficiency. Turbulence measurements in the marine environment are limited. We use a model to derive turbulence intensity and test how sea surface temperature data may impact the simulated turbulence intensity and atmospheric stability. The model slightly underestimates turbulence, and improved sea surface temperature data reduce the bias. Error with unrealistic mesoscale flow is identified.
Brandon Arthur Lobo, Özge Sinem Özçakmak, Helge Aagaard Madsen, Alois Peter Schaffarczyk, Michael Breuer, and Niels N. Sørensen
Wind Energ. Sci., 8, 303–326, https://doi.org/10.5194/wes-8-303-2023, https://doi.org/10.5194/wes-8-303-2023, 2023
Short summary
Short summary
Results from the DAN-AERO and aerodynamic glove projects provide significant findings. The effects of inflow turbulence on transition and wind turbine blades are compared to computational fluid dynamic simulations. It is found that the transition scenario changes even over a single revolution. The importance of a suitable choice of amplification factor is evident from the simulations. An agreement between the power spectral density plots from the experiment and large-eddy simulations is seen.
Frédéric Blondel
Wind Energ. Sci., 8, 141–147, https://doi.org/10.5194/wes-8-141-2023, https://doi.org/10.5194/wes-8-141-2023, 2023
Short summary
Short summary
Accurate wind farm flow predictions based on analytical wake models are crucial for wind farm design and layout optimization. Wake superposition methods play a key role and remain a substantial source of uncertainty. In the present work, a momentum-conserving superposition method is extended to the superposition of super-Gaussian-type velocity deficit models, allowing the full wake velocity deficit estimation and design of closely packed wind farms.
Abdul Haseeb Syed, Jakob Mann, Andreas Platis, and Jens Bange
Wind Energ. Sci., 8, 125–139, https://doi.org/10.5194/wes-8-125-2023, https://doi.org/10.5194/wes-8-125-2023, 2023
Short summary
Short summary
Wind turbines extract energy from the incoming wind flow, which needs to be recovered. In very large offshore wind farms, the energy is recovered mostly from above the wind farm in a process called entrainment. In this study, we analyzed the effect of atmospheric stability on the entrainment process in large offshore wind farms using measurements recorded by a research aircraft. This is the first time that in situ measurements are used to study the energy recovery process above wind farms.
Kartik Venkatraman, Trond-Ola Hågbo, Sophia Buckingham, and Knut Erik Teigen Giljarhus
Wind Energ. Sci., 8, 85–108, https://doi.org/10.5194/wes-8-85-2023, https://doi.org/10.5194/wes-8-85-2023, 2023
Short summary
Short summary
This paper is focused on the impact of modeling different effects, such as forest canopy and Coriolis forces, on the wind resource over a complex terrain site located near Perdigão, Portugal. A numerical model is set up and results are compared with field measurements. The results show that including a forest canopy improves the predictions close to the ground at some locations on the site, while the model with inflow from a precursor performed better at other locations.
Ishaan Sood, Elliot Simon, Athanasios Vitsas, Bart Blockmans, Gunner C. Larsen, and Johan Meyers
Wind Energ. Sci., 7, 2469–2489, https://doi.org/10.5194/wes-7-2469-2022, https://doi.org/10.5194/wes-7-2469-2022, 2022
Short summary
Short summary
In this work, we conduct a validation study to compare a numerical solver against measurements obtained from the offshore Lillgrund wind farm. By reusing a previously developed inflow turbulent dataset, the atmospheric conditions at the wind farm were recreated, and the general performance trends of the turbines were captured well. The work increases the reliability of numerical wind farm solvers while highlighting the challenges of accurately representing large wind farms using such solvers.
Xiaoli Guo Larsén and Søren Ott
Wind Energ. Sci., 7, 2457–2468, https://doi.org/10.5194/wes-7-2457-2022, https://doi.org/10.5194/wes-7-2457-2022, 2022
Short summary
Short summary
A method is developed for calculating the extreme wind in tropical-cyclone-affected water areas. The method is based on the spectral correction method that fills in the missing wind variability to the modeled time series, guided by best track data. The paper provides a detailed recipe for applying the method and the 50-year winds of equivalent 10 min temporal resolution from 10 to 150 m in several tropical-cyclone-affected regions.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Ahsbahs, T., Badger, M., Volker, P., Hansen, K. S., and Hasager, C. B.: Applications of satellite winds for the offshore wind farm site Anholt, Wind Energ. Sci., 3, 573–588, https://doi.org/10.5194/wes-3-573-2018, 2018. a
Ahsbahs, T., Nygaard, N., Newcombe, A., and Badger, M.: Wind Farm Wakes from
SAR and Doppler Radar, Remote Sensing, 12, 462, https://doi.org/10.3390/rs12030462, 2020. a, b
Aitken, M. L., Banta, R. M., Pichugina, Y. L., and Lundquist, J. K.:
Quantifying Wind Turbine Wake Characteristics from Scanning Remote Sensor
Data, J. Atmos. Ocean. Tech., 31, 765–787,
https://doi.org/10.1175/JTECH-D-13-00104.1, 2014. a
Akhtar, N., Geyer, B., Rockel, B., Sommer, P. S., and Schrum, C.: Accelerating
deployment of offshore wind energy alter wind climate and reduce future power
generation potentials, Sci. Rep., 11, 11826,
https://doi.org/10.1038/s41598-021-91283-3, 2021. a, b
Archer, C., Wu, S., ma, Y., and Jimenez, P.: Two Corrections for Turbulent
Kinetic Energy Generated by Wind Farms in the WRF Model, Mon. Weather
Rev., 148, 4823–4835, https://doi.org/10.1175/MWR-D-20-0097.1, 2020. a
Bastine, D., Wächter, M., Peinke, J., Trabucchi, D., and Kühn, M.:
Characterizing Wake Turbulence with Staring Lidar Measurements, Journal of Physics: Conference Series, 625,
012006, https://doi.org/10.1088/1742-6596/625/1/012006, 2015. a
Cañadillas, B.: Figures wes-2021-159 manuscript, figshare [data set],
https://doi.org/10.6084/m9.figshare.19747252.v1, 2022. a
Bingöl, F., Mann, J., and Larsen, G. C.: Light detection and ranging
measurements of wake dynamics part I: one-dimensional scanning, Wind Energy,
13, 51–61, https://doi.org/10.1002/we.352, 2010. a
Borraccino, A., Courtney, M., and Wagner, R.: Generic Methodology for Field
Calibration of Nacelle-Based Wind Lidars, Remote Sensing, 8, 6588–6596,
https://doi.org/10.3390/rs8110907, 2016. a
Brower, M. and Robinson, N.: The OpenWind deep-array wake model: development
and validation, Tech. rep., AWS Truepower, Albany, NY, USA, 2012. a
BSH: Flächenentwicklungsplan 2020 für die deutsche Nord- und Ostsee,
Tech. Rep. 7608, BSH – Bundesamt für Seeschiffahrt und Hydrographie,
https://www.bsh.de/DE/THEMEN/Offshore/Meeresfachplanung/Fortschreibung/_Anlagen/Downloads/FEP_2020_Flaechenentwicklungsplan_2020.pdf?__blob=publicationFile&v=6, last access: 1 April 2021. a
Cañadillas, B., Foreman, R., Barth, V., Siedersleben, S., Lampert, A.,
Platis, A., Djath, B., Schulz-Stellenfleth, J., Bange, J., Emeis, S., and
Neumann, T.: Offshore wind farm wake recovery: Airborne measurements and its
representation in engineering models, Wind Energy, 23, 1249–1265,
https://doi.org/10.1002/we.2484, 2020. a, b, c, d, e, f
Christiansen, M. and Hasager, C.: Wake effects of large offshore wind farms
identified from satellite SAR, Remote Sens. Environ., 98, 251–268,
https://doi.org/10.1016/j.rse.2005.07.009, 2005. a
Corsmeier, U., Hankers, R., and Wieser, A.: Airborne turbulence measurements in
the lower troposphere onboard the research aircraft Dornier 128-6, D-IBUF,
Meteorol. Z., 10, 315–329,
https://doi.org/10.1127/0941-2948/2001/0010-0315, 2001. a
Djath, B., Schulz-Stellenfleth, J., and Cañadillas, B.: Impact of
atmospheric stability on X-band and C-band synthetic aperture radar imagery
of offshore windpark wakes, J. Renew. Sustain. Energ., 10, 043301,
https://doi.org/10.1063/1.5020437, 2018. a
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and
Wimmer, W.: The Operational Sea Surface Temperature and Sea Ice Analysis
(OSTIA) system, Remote Sens. Environ., 116, 140–158,
https://doi.org/10.1016/j.rse.2010.10.017, 2012. a
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, Geosci. Model Dev., 13, 5079–5102, https://doi.org/10.5194/gmd-13-5079-2020, 2020. a, b
Emeis, S.: A simple analytical wind park model considering atmospheric
stability, Wind Energy, 13, 459–469, https://doi.org/10.1002/we.367, 2010. a
Emeis, S.: Wind Energy Meteorology:Atmospheric Physics for Wind Power
Generation, Springer International Publishing, 2 edn.,
https://doi.org/10.1007/978-3-319-72859-9, 2018. a
Fischler, M. A. and Bolles, R.: Random sample consensus: a paradigm for model
fitting with applications to image analysis and automated cartography,
Communications of the ACM, 24, 381–395, https://doi.org/10.1145/358669.358692,
1981. a
Fitch, A. C., Olson, J. B., Lundquist, J. K., Dudhia, J., Gupta, A. K.,
Michalakes, J., and Barstad, I.: Local and mesoscale impacts of wind farms as
parameterized in a mesoscale nwp model, Mon. Weather Rev., 140, 3017–3038,
https://doi.org/10.1175/MWR-D-11-00352.1, 2012. a, b, c, d
Franke, K.: Summary of classification of remote sensing device, type: Leosphere
WINDCUBE, techreport PP18030.A1, Deutsche WindGuard Consulting GmbH, Varel,
DE, 2018. a
Frühmann, R., Neumann, T., and Decker, H.: Platform based infrared sea
surface temperature measurement: experiences from a one year trial in the
North Sea, in: Deutsche Windenergie-Konferenz (DEWEK), 2018. a
Goit, J., Yamaguchi, A., and Ishihara, T.: Measurement and Prediction of Wind
Fields at an Offshore Site by Scanning Doppler LiDAR and WRF, Atmosphere, 11,
1–20, https://doi.org/10.3390/atmos11050442, 2020. a
Gómez Arranz, P. and Courtney, M.: WP1 – Literature Review: Scanning
Lidar For Wind Turbine Power Performance Testing, 2021. a
Gottschall, J. and Dörenkämper, M.: Understanding and mitigating the impact of data gaps on offshore wind resource estimates, Wind Energ. Sci., 6, 505–520, https://doi.org/10.5194/wes-6-505-2021, 2021. a, b
Gottschall, J., Catalano, E., Dörenkämper, M., and Witha, B.: The NEWA Ferry
Lidar Experiment: Measuring Mesoscale Winds in the Southern Baltic Sea,
Remote Sensing, 10, 1620, https://doi.org/10.3390/rs10101620, 2018. a, b
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, b
Herges, T. G., Maniaci, D. C., Naughton, B. T., Mikkelsen, T., and
Sjöholm, M.: High resolution wind turbine wake measurements with a
scanning lidar, in: Journal of Physics Conference Series, vol. 854 of Journal of Physics Conference Series, 854, 012021,
https://doi.org/10.1088/1742-6596/854/1/012021, 2017. a
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
Huang, H.-P., Giannakopoulou, E.-M., and Nhili, R.: WRF Model Methodology for
Offshore Wind Energy Applications, Adv. Meteorol., 2014, 1687–9309,
https://doi.org/10.1155/2014/319819, 2014. a
IEC-61400-12-1: Wind energy generation systems – Part 12-1: Power performance
measurements of electricity producing wind turbines, International Standard
IEC TC 29110-1:2016, International Electrotechnical Commission, Geneve,
Switzerland, https://webstore.iec.ch/publication/26603 (last access: 10 December 2021), 2017. a, b
Iungo, G. V. and Porté-Agel, F.: Volumetric Lidar Scanning of Wind Turbine
Wakes under Convective and Neutral Atmospheric Stability Regimes, J.
Atmos. Ocean. Tech., 31, 2035–2048,
https://doi.org/10.1175/JTECH-D-13-00252.1, 2014. a
Käsler, Y., Rahm, S., Simmet, R., and Kühn, M.: Wake Measurements of a
Multi-MW Wind Turbine with Coherent Long-Range Pulsed Doppler Wind Lidar,
J. Atmos. Ocean. Tech., 27, 1529–1532,
https://doi.org/10.1175/2010JTECHA1483.1, 2010. a
Kibona, T. E.: Application of WRF mesoscale model for prediction of wind energy
resources in Tanzania, Scientific African, 7, e00302,
https://doi.org/10.1016/j.sciaf.2020.e00302, 2020. a
Krishnamurthy, R., Reuder, J., Svardal, B., Fernando, H., and Jakobsen, J.:
Offshore Wind Turbine Wake characteristics using Scanning Doppler Lidar,
Energy Procedia, 137, 428–442,
https://doi.org/10.1016/j.egypro.2017.10.367, 2017. a, b
Lampert, A., Bärfuss, K., Platis, A., Siedersleben, S., Djath, B., Cañadillas, B., Hunger, R., Hankers, R., Bitter, M., Feuerle, T., Schulz, H., Rausch, T., Angermann, M., Schwithal, A., Bange, J., Schulz-Stellenfleth, J., Neumann, T., and Emeis, S.: In situ airborne measurements of atmospheric and sea surface parameters related to offshore wind parks in the German Bight, Earth Syst. Sci. Data, 12, 935–946, https://doi.org/10.5194/essd-12-935-2020, 2020. a, b, c
Larsén, X. G. and Fischereit, J.: A case study of wind farm effects using two wake parameterizations in the Weather Research and Forecasting (WRF) model (V3.7.1) in the presence of low-level jets, Geosci. Model Dev., 14, 3141–3158, https://doi.org/10.5194/gmd-14-3141-2021, 2021. a, b
Lee, J. and Zhao, F.: Global wind report 2021, Tech. rep., Global Wind Energy
Council (GWEC), Brussels, Belgium, 2021. a
Lundquist, J. K., Churchfield, M. J., Lee, S., and Clifton, A.: Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics, Atmos. Meas. Tech., 8, 907–920, https://doi.org/10.5194/amt-8-907-2015, 2015. a, b
METEK-GmbH: Doppler Lidar Stream Line XR, Brochure,
https://metek.de/de/wp-content/uploads/sites/6/2016/01/20150410_DataSheet_StreamLineXR.pdf (last access: 7 February 2022),
2021. a
NCAR Users Page: WRF Model User’s Page, WRF Version 4.0.1,
https://doi.org/10.5065/D6MK6B4K, 2021. a
Newsom, R. K., Brewer, W. A., Wilczak, J. M., Wolfe, D. E., Oncley, S. P., and Lundquist, J. K.: Validating precision estimates in horizontal wind measurements from a Doppler lidar, Atmos. Meas. Tech., 10, 1229–1240, https://doi.org/10.5194/amt-10-1229-2017, 2017. a
Nygaard, N. and Newcombe, A.: Wake behind an offshore wind farm observed with
dual-Doppler radars, Journal of Physics: Conference Series, 1037, 072008,
https://doi.org/10.1088/1742-6596/1037/7/072008, 2018. a
Patrick, V., Hahmann, A. N., and Badger, J.: Wake Effects of Large Offshore
Wind Farms – a study of the Mesoscale Atmosphere, Ph.D. thesis, DTU Wind
Energy, Denmark, 2014. a
Platis, A., Bange, J., Bärfuss, K., Cañadillas, B., Hundhausen, M.,
Djath, B., Lampert, A., Schulz-Stellenfleth, J., Siedersleben, S., Neumann,
T., and Emeis, S.: Long-range modifications of the wind field by offshore
wind parks – results of the project WIPAFF, Meteorol.
Z., 29, 355–376, https://doi.org/10.1127/metz/2020/1023,
2020. a
Platis, A., Hundhausen, M., Mauz, M., Siedersleben, S., Lampert, A., Emeis, S.,
and Bange, J.: The Role of Atmospheric Stability and Turbulence in Offshore
Wind-Farm Wakes in the German Bight, Bound.-Lay. Meteorol., 182,
1573–1472, https://doi.org/10.1007/s10546-021-00668-4, 2021. a
Pryor, S. C., Shepherd, T. J., Barthelmie, R. J., Hahmann, A. N., and Volker,
P.: Wind Farm Wakes Simulated Using WRF, J. Phys.: Conf. Ser., 1256,
012025, https://doi.org/10.1088/1742-6596/1256/1/012025, 2019. a
Rettenmeier, A., Schlipf, D., Würth, I., and Cheng, P. W.: Power Performance
Measurements of the NREL CART-2 Wind Turbine Using a Nacelle-Based Lidar
Scanner, J. Atmos. Ocean. Tech., 31, 2029–2034,
https://doi.org/10.1175/JTECH-D-13-00154.1, 2014. a
Rott, A., Schneemann, J., Theuer, F., Trujillo Quintero, J. J., and Kühn, M.: Alignment of scanning lidars in offshore wind farms, Wind Energ. Sci., 7, 283–297, https://doi.org/10.5194/wes-7-283-2022, 2022. a
Schneemann, J., Rott, A., Dörenkämper, M., Steinfeld, G., and Kühn, M.: Cluster wakes impact on a far-distant offshore wind farm's power, Wind Energ. Sci., 5, 29–49, https://doi.org/10.5194/wes-5-29-2020, 2020. a, b
Schneemann, J., Theuer, F., Rott, A., Dörenkämper, M., and Kühn, M.: Offshore wind farm global blockage measured with scanning lidar, Wind Energ. Sci., 6, 521–538, https://doi.org/10.5194/wes-6-521-2021, 2021. a
Siedersleben, S. K., Lundquist, J. K., Platis, A., Bange, J., Bärfuss, K.,
Lampert, A., Cañadillas, B., Neumann, T., and Emeis, S.:
Micrometeorological impacts of offshore wind farms as seen in observations
and simulations, Environ. Res. Lett., 13, 1–12,
https://doi.org/10.1088/1748-9326/aaea0b, 2018a. a, b
Siedersleben, S. K., Platis, A., Lundquist, J. K., Lampert, A., Bärfuss,
K., Canadillas, B., Djath, B., Schulz-Stellenfleth, J., Bange, J., Neumann,
T., and Emeis, S.: Evaluation of a Wind Farm Parametrization for Mesoscale
Atmospheric Flow Models with Aircraft Measurements, Meteorol.
Z., 27, 401–415, https://doi.org/10.1127/metz/2018/0900, 2018b. a, b, c
Siedersleben, S. K., Platis, A., Lundquist, J. K., Djath, B., Lampert, A., Bärfuss, K., Cañadillas, B., Schulz-Stellenfleth, J., Bange, J., Neumann, T., and Emeis, S.: Turbulent kinetic energy over large offshore wind farms observed and simulated by the mesoscale model WRF (3.8.1), Geosci. Model Dev., 13, 249–268, https://doi.org/10.5194/gmd-13-249-2020, 2020. a
Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Liu, Z., Berner, J., Wang, W.,
Power, J., Duda, M., Barker, D., and Huang, X.-Y.: A description of the
advanced research WRF version 3, Technical Report, 162 pages
NCAR/TN-556+STR, NCAR - National Center for Atmospheric Research, Boulder,
Colorado, USA, https://doi.org/10.5065/1dfh-6p97, 2019. a
Smalikho, I. N., Banakh, V. A., Pichugina, Y. L., Brewer, W. A., Banta, R. M.,
Lundquist, J. K., and Kelley, N. D.: Lidar Investigation of Atmosphere Effect
on a Wind Turbine Wake, J. Atmos. Ocean. Tech., 30,
2554–2570, https://doi.org/10.1175/JTECH-D-12-00108.1, 2013. a
Sørensen, T. L., Thøgersen, M. L., and Nielsen, P. M.: Adapting and
calibration of existing wake models to meet the conditions inside offshore
wind farms, EMD International A/S, 2008. a
Trujillo, J., Bingöl, F., Mann, J., and Larsen, G. C.: Light detection and
ranging measurements of wake dynamics part I: one-dimensional scanning, Wind
Energy, 13, 51–61, https://doi.org/10.1002/we.352, 2010. a
Volker, P. J. H., Badger, J., Hahmann, A. N., and Ott, S.: The Explicit Wake Parametrisation V1.0: a wind farm parametrisation in the mesoscale model WRF, Geosci. Model Dev., 8, 3715–3731, https://doi.org/10.5194/gmd-8-3715-2015, 2015.
a
Wagner, R., Courtney, M. S., Pedersen, T. F., and Davoust, S.: Uncertainty of
power curve measurement with a two-beam nacelle-mounted lidar, Wind Energy,
19, 1269–1287, https://doi.org/10.1002/we.1897, 2016. a
Wang, H. and Barthelmie, R.: Wind turbine wake detection with a single Doppler
wind lidar, Journal of Physics: Conference Series, 625, 012017,
https://doi.org/10.1088/1742-6596/625/1/012017, 2015. a
Werner, C.: Lidar – Range-Resolved Optical Remote Sensing of the Atmosphere,
chap. Doppler Wind Lidar, pp. 325–354, Springer-Verlag New York, 2005. a
WRF Users Page: WRF Model Physics Options and References,
https://www2.mmm.ucar.edu/wrf/users/phys_references.html
(last access: 13 July 2020), 2020. a
Zhan, L., Letizia, S., and Valerio Iungo, G.: LiDAR measurements for an onshore
wind farm: Wake variability for different incoming wind speeds and
atmospheric stability regimes, Wind Energy, 23, 501–527,
https://doi.org/10.1002/we.2430, 2020. a
Short summary
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.
Scanning lidar measurements combined with meteorological sensors and mesoscale simulations...
Altmetrics
Final-revised paper
Preprint