Articles | Volume 7, issue 4
https://doi.org/10.5194/wes-7-1575-2022
© 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-7-1575-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Jul 2022
Research article |
| 22 Jul 2022
| 22 Jul 2022
Spatiotemporal observations of nocturnal low-level jets and impacts on wind power production
Institute of Geophysics and Meteorology, University of Cologne, Cologne, Germany
Hans‐Ertel‐Centre for Weather Research, Climate Monitoring and Diagnostics, Cologne/Bonn, Germany
Stephanie Fiedler
Institute of Geophysics and Meteorology, University of Cologne, Cologne, Germany
Hans‐Ertel‐Centre for Weather Research, Climate Monitoring and Diagnostics, Cologne/Bonn, Germany
Related authors
No articles found.
Paul T. Griffiths, Laura J. Wilcox, Robert J. Allen, Vaishali Naik, Fiona M. O'Connor, Michael J. Prather, Alexander T. Archibald, Florence Brown, Makoto Deushi, William Collins, Stephanie Fiedler, Naga Oshima, Lee T. Murray, Christopher J. Smith, Steven T. Turnock, Duncan Watson-Parris, and Paul J. Young
EGUsphere, https://doi.org/10.5194/egusphere-2024-2528, https://doi.org/10.5194/egusphere-2024-2528, 2024
Short summary
Short summary
The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) aimed to quantify the climate and air quality impacts of aerosols and chemically reactive gases. In this paper, we review its contribution to AR6, and the wider understanding of the role of these species in climate and climate change. We identify remaining challenges concluding with recommendations aimed to improve the utility and uptake of climate model data to address the role of short-lived climate forcers in the Earth system.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
Short summary
Short summary
Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Mahdi Boroughani, Fahimeh Mirchooli, Mojtaba Hadavifar, and Stephanie Fiedler
SOIL, 9, 411–423, https://doi.org/10.5194/soil-9-411-2023, https://doi.org/10.5194/soil-9-411-2023, 2023
Short summary
Short summary
The present study used several different datasets, conducted a field survey, and paired the data with three different machine learning algorithms to construct spatial maps for areas at risk of land degradation for the Lut watershed in Iran. According to the land degradation map, almost the entire study region is at risk. A large fraction of 43 % of the area is prone to both high wind-driven and water-driven soil erosion.
Mark Reyers, Stephanie Fiedler, Patrick Ludwig, Christoph Böhm, Volker Wennrich, and Yaping Shao
Clim. Past, 19, 517–532, https://doi.org/10.5194/cp-19-517-2023, https://doi.org/10.5194/cp-19-517-2023, 2023
Short summary
Short summary
In this study we performed high-resolution climate model simulations for the hyper-arid Atacama Desert for the mid-Pliocene (3.2 Ma). The aim is to uncover the atmospheric processes that are involved in the enhancement of strong rainfall events during this period. We find that strong upper-level moisture fluxes (so-called moisture conveyor belts) originating in the tropical eastern Pacific are the main driver for increased rainfall in the mid-Pliocene.
Julian Steinheuer, Carola Detring, Frank Beyrich, Ulrich Löhnert, Petra Friederichs, and Stephanie Fiedler
Atmos. Meas. Tech., 15, 3243–3260, https://doi.org/10.5194/amt-15-3243-2022, https://doi.org/10.5194/amt-15-3243-2022, 2022
Short summary
Short summary
Doppler wind lidars (DWLs) allow the determination of wind profiles with high vertical resolution and thus provide an alternative to meteorological towers. We address the question of whether wind gusts can be derived since they are short-lived phenomena. Therefore, we compare different DWL configurations and develop a new method applicable to all of them. A fast continuous scanning mode that completes a full observation cycle within 3.4 s is found to be the best-performing configuration.
Robin D. Lamboll, Chris D. Jones, Ragnhild B. Skeie, Stephanie Fiedler, Bjørn H. Samset, Nathan P. Gillett, Joeri Rogelj, and Piers M. Forster
Geosci. Model Dev., 14, 3683–3695, https://doi.org/10.5194/gmd-14-3683-2021, https://doi.org/10.5194/gmd-14-3683-2021, 2021
Short summary
Short summary
Lockdowns to avoid the spread of COVID-19 have created an unprecedented reduction in human emissions. We can estimate the changes in emissions at a country level, but to make predictions about how this will affect our climate, we need more precise information about where the emissions happen. Here we combine older estimates of where emissions normally occur with very recent estimates of sector activity levels to enable different groups to make simulations of the climatic effects of lockdown.
Gillian Thornhill, William Collins, Dirk Olivié, Ragnhild B. Skeie, Alex Archibald, Susanne Bauer, Ramiro Checa-Garcia, Stephanie Fiedler, Gerd Folberth, Ada Gjermundsen, Larry Horowitz, Jean-Francois Lamarque, Martine Michou, Jane Mulcahy, Pierre Nabat, Vaishali Naik, Fiona M. O'Connor, Fabien Paulot, Michael Schulz, Catherine E. Scott, Roland Séférian, Chris Smith, Toshihiko Takemura, Simone Tilmes, Kostas Tsigaridis, and James Weber
Atmos. Chem. Phys., 21, 1105–1126, https://doi.org/10.5194/acp-21-1105-2021, https://doi.org/10.5194/acp-21-1105-2021, 2021
Short summary
Short summary
We find that increased temperatures affect aerosols and reactive gases by changing natural emissions and their rates of removal from the atmosphere. Changing the composition of these species in the atmosphere affects the radiative budget of the climate system and therefore amplifies or dampens the climate response of climate models of the Earth system. This study found that the largest effect is a dampening of climate change as warmer temperatures increase the emissions of cooling aerosols.
Christopher J. Smith, Ryan J. Kramer, Gunnar Myhre, Kari Alterskjær, William Collins, Adriana Sima, Olivier Boucher, Jean-Louis Dufresne, Pierre Nabat, Martine Michou, Seiji Yukimoto, Jason Cole, David Paynter, Hideo Shiogama, Fiona M. O'Connor, Eddy Robertson, Andy Wiltshire, Timothy Andrews, Cécile Hannay, Ron Miller, Larissa Nazarenko, Alf Kirkevåg, Dirk Olivié, Stephanie Fiedler, Anna Lewinschal, Chloe Mackallah, Martin Dix, Robert Pincus, and Piers M. Forster
Atmos. Chem. Phys., 20, 9591–9618, https://doi.org/10.5194/acp-20-9591-2020, https://doi.org/10.5194/acp-20-9591-2020, 2020
Short summary
Short summary
The spread in effective radiative forcing for both CO2 and aerosols is narrower in the latest CMIP6 (Coupled Model Intercomparison Project) generation than in CMIP5. For the case of CO2 it is likely that model radiation parameterisations have improved. Tropospheric and stratospheric radiative adjustments to the forcing behave differently for different forcing agents, and there is still significant diversity in how clouds respond to forcings, particularly for total anthropogenic forcing.
Frank Kaspar, Deborah Niermann, Michael Borsche, Stephanie Fiedler, Jan Keller, Roland Potthast, Thomas Rösch, Thomas Spangehl, and Birger Tinz
Adv. Sci. Res., 17, 115–128, https://doi.org/10.5194/asr-17-115-2020, https://doi.org/10.5194/asr-17-115-2020, 2020
Short summary
Short summary
Reanalyses are long-term meteorological datasets that are based on numerical weather prediction models and the assimilation of historic observations. The regional model COSMO of Germany’s national meteorological service (Deutscher Wetterdienst) has been used to develop regional reanalyses with spatial resolution of up to 2 km. In this paper, we provide an overview of evaluation results and application examples at the European and national German level with a focus on renewable energy.
Stephanie Fiedler, Stefan Kinne, Wan Ting Katty Huang, Petri Räisänen, Declan O'Donnell, Nicolas Bellouin, Philip Stier, Joonas Merikanto, Twan van Noije, Risto Makkonen, and Ulrike Lohmann
Atmos. Chem. Phys., 19, 6821–6841, https://doi.org/10.5194/acp-19-6821-2019, https://doi.org/10.5194/acp-19-6821-2019, 2019
Stephanie Fiedler, Bjorn Stevens, Matthew Gidden, Steven J. Smith, Keywan Riahi, and Detlef van Vuuren
Geosci. Model Dev., 12, 989–1007, https://doi.org/10.5194/gmd-12-989-2019, https://doi.org/10.5194/gmd-12-989-2019, 2019
Bjorn Stevens, Stephanie Fiedler, Stefan Kinne, Karsten Peters, Sebastian Rast, Jobst Müsse, Steven J. Smith, and Thorsten Mauritsen
Geosci. Model Dev., 10, 433–452, https://doi.org/10.5194/gmd-10-433-2017, https://doi.org/10.5194/gmd-10-433-2017, 2017
Short summary
Short summary
A simple analytic description of aerosol optical properties and their main effects on clouds is developed and described. The analytic description is easy to use and easy to modify and should aid experimentation to help understand how aerosol radiative and cloud interactions effect climate and circulation. The climatology is recommended for adoption by models participating in the sixth phase of the Coupled Model Intercomparison Project.
N. Huneeus, S. Basart, S. Fiedler, J.-J. Morcrette, A. Benedetti, J. Mulcahy, E. Terradellas, C. Pérez García-Pando, G. Pejanovic, S. Nickovic, P. Arsenovic, M. Schulz, E. Cuevas, J. M. Baldasano, J. Pey, S. Remy, and B. Cvetkovic
Atmos. Chem. Phys., 16, 4967–4986, https://doi.org/10.5194/acp-16-4967-2016, https://doi.org/10.5194/acp-16-4967-2016, 2016
Short summary
Short summary
Five dust models are evaluated regarding their performance in predicting an intense Saharan dust outbreak affecting western and northern Europe (NE). Models predict the onset and evolution of the event for all analysed lead times. On average, differences among the models are larger than differences in lead times for each model. The models tend to underestimate the long-range transport towards NE. This is partly due to difficulties in simulating the vertical dust distribution and horizontal wind.
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
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
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
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
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
Revised manuscript 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.
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
Short summary
Short summary
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
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
Abkar, M., Sharifi, A., and Porté-Agel, F.: Wake flow in a wind farm
during a diurnal cycle, J. Turbulence, 17, 420–441, https://doi.org/10.1080/14685248.2015.1127379, 2016. a
Aird, J. A., Barthelmie, R. J., Shepherd, T. J., and Pryor, S. C.: WRF-simulated low-level jets over Iowa: characterization and sensitivity studies, Wind Energ. Sci., 6, 1015–1030, https://doi.org/10.5194/wes-6-1015-2021, 2021. a, b
Andreas, E. L., Claffy, K. J., and Makshtas, A. P.: Low-Level Atmospheric Jets And Inversions Over The Western Weddell Sea, Bound.-Lay. Meteorol., 97,
459–486, https://doi.org/10.1023/A:1002793831076, 2000. a
Baas, P., Bosveld, F. C., Baltink, H. K., and Holtslag, A. A.: A climatology
of nocturnal low-level jets at Cabauw, J. Appl. Meteorol. Clim., 48, 1627–1642, https://doi.org/10.1175/2009JAMC1965.1, 2009. a, b
Banta, R., Newsom, R. K., Lundquist, J. K., Pichugina, Y. L., Coulter, R. L.,
and Mahrt, L.: Nocturnal Low-Level Jet Characteristics Over Kansas During
Cases-99, Bound.-Lay. Meteorol., 105, 221–252, https://doi.org/10.1023/A:1019992330866, 2002. a, b
Beyrich, F.: Sodar observations of the stable boundary layer height in relation to the nocturnal low-level jet, Meteorol. Z., 3, 29–34, 1994. a
Beyrich, F., Kalass, D., and Weisensee, U.: Influence of the nocturnal low-level-jet on the vertical and mesoscale structure of the stable boundary layer as revealed from doppler-sodar-observations, in: Acoustic remote sensing applications, Springer, 236–246, https://doi.org/10.1007/BFb0009568, 1997. a, b, c
Blackadar, A. K.: Boundary Layer Wind Maxima and Their Significance for the
Growth of Nocturnal Inversions, B. Am. Meteorol. Soc., 38, 283–290, https://doi.org/10.1175/1520-0477-38.5.283, 1957. a, b, c
Brown, A., Beljaars, A., Hersbach, H., Hollingsworth, A., Miller, M., and
Vasiljevic, D.: Wind turning across the marine atmospheric boundary layer, Q. J. Roy. Meteorol. Soc., 131, 1233–1250, 2005. a
Brown, A., Beare, R., Edwards, J., Lock, A., Keogh, S., Milton, S., and
Walters, D.: Upgrades to the boundary-layer scheme in the Met Office
numerical weather prediction model, Bound.-Lay. Meteorol., 128, 117–132, 2008. a
Debnath, M., Doubrawa, P., Optis, M., Hawbecker, P., and Bodini, N.: Extreme
wind shear events in US offshore wind energy areas and the role of induced
stratification, Wind Energ. Sci., 6, 1043–1059,
https://doi.org/10.5194/wes-6-1043-2021, 2021. a
Doosttalab, A., Siguenza-Alvarado, D., Pulletikurthi, V., Jin, Y., Bocanegra Evans, H., Chamorro, L. P., and Castillo, L.: Interaction of low-level jets with wind turbines: On the basic mechanisms for enhanced performance, J. Renew. Sustain. Energ., 12, 053301, https://doi.org/10.1063/5.0017230, 2020. a
Druecke, J., Borsche, M., James, P., Kaspar, F., Pfeifroth, U., Ahrens, B., and Trentmann, J.: Climatological analysis of solar and wind energy in Germany using the Grosswetterlagen classification, Renew. Energy, 164,
1254–1266, https://doi.org/10.1016/j.renene.2020.10.102, 2021. a
Emeis, S.: Wind speed and shear associated with low-level jets over Northern
Germany, Meteorol. Z., 23, 295–304, https://doi.org/10.1127/0941-2948/2014/0551, 2014. a, b, c, d
Englberger, A., Lundquist, J. K., and Dörnbrack, A.: Changing the rotational direction of a wind turbine under veering inflow: a parameter study, Wind Energ. Sci., 5, 1623–1644, https://doi.org/10.5194/wes-5-1623-2020, 2020. a
FESSTVal: FESSTVaL – Field Experiment on Submesoscale Spatio-Temporal
Variability in Lindenberg, https://fesstval.de/en/ (last access: 18 July 2022), 2020. a
Frank, C., Fiedler, S., and Crewell, S.: Balancing potential of natural
variability and extremes in photovoltaic and wind energy production for
European countries, Renew. Energy, 163, 674–684, 2021. a
Gualtieri, G.: A comprehensive review on wind resource extrapolation models
applied in wind energy, Renew. Sustain. Energ. Rev., 102, 215–233, 2019. a
Gutierrez, W., Araya, G., Kiliyanpilakkil, P., Ruiz-Columbie, A., Tutkun, M.,
and Castillo, L.: Structural impact assessment of low level jets over wind
turbines, J. Renew. Sustain. Energ., 8, 023308, https://doi.org/10.1063/1.4945359, 2016. a, b, c
Gutierrez, W., Ruiz-Columbie, A., Tutkun, M., and Castillo, L.: Impacts of the low-level jet's negative wind shear on the wind turbine, Wind Energ. Sci., 2, 533–545, https://doi.org/10.5194/wes-2-533-2017, 2017. a
Han, Y., Yang, Q., Liu, N., Zhang, K., Qing, C., Li, X., Wu, X., and Luo, T.:
Analysis of wind-speed profiles and optical turbulence above Gaomeigu and
the Tibetan Plateau using ERA5 data, Mon. Notic. Roy. Astron. Soc., 501, 4692–4702, https://doi.org/10.1093/mnras/staa2960, 2021. a
Heinold, B., Knippertz, P., Marsham, J., Fiedler, S., Dixon, N., Schepanski,
K., Laurent, B., and Tegen, I.: The role of deep convection and nocturnal
low-level jets for dust emission in summertime West Africa: Estimates from
convection-permitting simulations, J. Geophys. Res.-Atmos., 118, 4385–4400, 2013. 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., 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., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, 2020. a, b
IRENA: Future of wind: Deployment, investment, technology, grid integration
and socio-economic aspects (A Global Energy Transformation paper), Tech. rep., International Renewable Energy Agency, Abu Dhabi, https://www.irena.org/publications/2019/Oct/Future-of-wind (last access: 18 July 2022), 2019. a
Kalverla, P. C., Duncan, J. B., Steeneveld, G. J., and Holtslag, A. A.:
Low-level jets over the north sea based on ERA5 and observations: Together
they do better, Wind Energ. Sci., 4, 193–209, https://doi.org/10.5194/wes-4-193-2019, 2019. a, b
Lampert, A., Bernalte Jimenez, B., Gross, G., Wulff, D., and Kenull, T.:
One-year observations of the wind distribution and low-level jet occurrence
at Braunschweig, North German Plain, Wind Energy, 19, 1807–1817,
https://doi.org/10.1002/we.1951, 2016. a
Lupo, A. R.: Atmospheric blocking events: a review, Ann. NY. Acad. Sci., 1504, 5–24, 2021. a
Marke, T., Crewell, S., Schemann, V., Schween, J. H., and Tuononen, M.:
Long-term observations and high-resolution modeling of midlatitude nocturnal
boundary layer processes connected to low-level jets, J. Appl. Meteorol. Clim., 57, 1155–1170, https://doi.org/10.1175/JAMC-D-17-0341.1, 2018. a
Mirocha, J. D., Simpson, M. D., Fast, J. D., Berg, L. K., and Baskett, R. L.:
Investigation of boundary-layer wind predictions during nocturnal low-level
jet events using the Weather Research and Forecasting model, Wind Energy, 19, 739–762, https://doi.org/10.1002/we.1862, 2016. a
Murphy, P., Lundquist, J. K., and Fleming, P.: How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine, Wind Energ. Sci., 5, 1169–1190, https://doi.org/10.5194/wes-5-1169-2020, 2020. a, b
Päschke, E., Leinweber, R., and Lehmann, V.: An assessment of the
performance of a 1.5 µm Doppler lidar for operational vertical wind
profiling based on a 1-year trial, Atmos. Meas. Tech., 8, 2251–2266, https://doi.org/10.5194/amt-8-2251-2015, 2015. a
Pearson, G., Davies, F., and Collier, C.: An Analysis of the Performance of
the UFAM Pulsed Doppler Lidar for Observing the Boundary Layer, J. Atmos. Ocean. Tech., 26, 240–250, https://doi.org/10.1175/2008JTECHA1128.1, 2009. a
Peña Diaz, A., Mikkelsen, T., Gryning, S.-E., Hasager, C. B., Hahmann,
A. N., Badger, M., Karagali, I., and Courtney, M.: Offshore vertical wind
shear: Final report on NORSEWInD's work task 3.1, Tech. rep., DTU Wind
Energy, https://orbit.dtu.dk/en/publications/offshore-vertical-wind-shear-final-report-on-norsewinds-work-task (last access: 18 July 2022), 2012. a
Pichugina, Y. L., Brewer, W. A., Banta, R. M., Choukulkar, A., Clack, C. T. M., Marquis, M. C., McCarty, B. J., Weickmann, A. M., Sandberg, S. P.,
Marchbanks, R. D., and Hardesty, R. M.: Properties of the offshore low level
jet and rotor layer wind shear as measured by scanning Doppler Lidar, Wind
Energy, 20, 987–1002, https://doi.org/10.1002/we.2075, 2017. a, b
Rohrig, K., Berkhout, V., Callies, D., Durstewitz, M., Faulstich, S., Hahn, B., Jung, M., Pauscher, L., Seibel, A., Shan, M., Siefert, M., Steffen, J.,
Collmann, M., Czichon, S., Dörenkämper, M., Gottschall, J., Lange, B., Ruhle, A., Sayer, F., Stoevesandt, B., and Wenske, J.: Powering the 21st century by wind energy – Options, facts, figures, Appl. Phys. Rev., 6, 031303, https://doi.org/10.1063/1.5089877, 2019. a, b
Sadorsky, P.: Wind energy for sustainable development: Driving factors and
future outlook, J. Clean. Product., 289, 125779, https://doi.org/10.1016/j.jclepro.2020.125779, 2021. a
Shapiro, A. and Fedorovich, E.: Analytical description of a nocturnal low-level jet, Q. J. Roy. Meteorol. Soc., 136, 1255–1262, https://doi.org/10.1002/qj.628, 2010. a, b
Sharma, V., Parlange, M. B., and Calaf, M.: Perturbations to the Spatial and
Temporal Characteristics of the Diurnally-Varying Atmospheric Boundary Layer
Due to an Extensive Wind Farm, Bound.-Lay. Meteorol., 162, 255–282,
https://doi.org/10.1007/s10546-016-0195-0, 2017. a
Sisterson, D. L. and Frenzen, P.: Nocturnal boundary-layer wind maxima and the problem of wind power assessment, Environ. Sci. Technol., 12, 218–221, https://doi.org/10.1021/es60138a014, 1978. a
Smalikho, I. N. and Banakh, V. A.: Measurements of wind turbulence parameters by a conically scanning coherent Doppler lidar in the atmospheric boundary layer, Atmos. Meas. Tech., 10, 4191–4208, https://doi.org/10.5194/amt-10-4191-2017, 2017. a
Steinheuer, J., Detring, C., Kayser, M., and Leinweber, R.: Doppler wind lidar wind and gust data from FESTVAL 2019/2020, Falkenberg, data version 01, Universität Hamburg [data set],
https://doi.org/10.25592/UHHFDM.9758, 2021. a
Steinheuer, J., Detring, C., Beyrich, F., Löhnert, U., Friederichs, P., and Fiedler, S.: A new scanning scheme and flexible retrieval for mean winds and gusts from Doppler lidar measurements, Atmos. Meas. Tech., 15, 3243–3260, https://doi.org/10.5194/amt-15-3243-2022, 2022. a
Stensrud, D. J.: Importance of low-level jets to climate: A review, J. Climate, 9, 1698–1711, https://doi.org/10.1175/1520-0442(1996)009<1698:IOLLJT>2.0.CO;2, 1996. a
Suomi, I., Gryning, S. E., O'Connor, E. J., and Vihma, T.: Methodology for
obtaining wind gusts using Doppler lidar, Q. J. Roy. Meteorol. Soc., 143, 2061–2072, https://doi.org/10.1002/qj.3059, 2017. a, b
Svensson, G. and Holtslag, A. A.: Analysis of model results for the turning of the wind and related momentum fluxes in the stable boundary layer,
Bound.-Lay. Meteorol., 132, 261–277, 2009. a
Svensson, N., Bergström, H., Rutgersson, A., and Sahlée, E.:
Modification of the Baltic Sea wind field by land‐sea interaction, Wind
Energy, 22, 764–779, https://doi.org/10.1002/we.2320, 2019. a
Tibaldi, S. and Molteni, F.: On the operational predictability of blocking,
Tellus A, 42, 343–365, 1990. a
Tuononen, M., O'Connor, E. J., Sinclair, V. A., and Vakkari, V.: Low-level
jets over Utö, Finland, based on Doppler lidar observations, J. Appl. Meteorol. Clim., 56, 2577–2594, 2017. a
Vanderwende, B. J. and Lundquist, J. K.: The modification of wind turbine
performance by statistically distinct atmospheric regimes, Environ. Res. Lett., 7, 034035, https://doi.org/10.1088/1748-9326/7/3/034035, 2012. a
Van de Wiel, B. J. H., Moene, A. F., Steeneveld, G. J., Baas, P., Bosveld, F. C., and Holtslag, A. A. M.: A Conceptual View on Inertial Oscillations and Nocturnal Low-Level Jets, J. Atmos. Sci., 67, 2679–2689, https://doi.org/10.1175/2010JAS3289.1, 2010. a, b, c
Wagner, D., Steinfeld, G., Witha, B., Wurps, H., and Reuder, J.: Low level
jets over the southern North Sea, Meteorol. Z., 28, 389–415,
https://doi.org/10.1127/metz/2019/0948, 2019. a
Wharton, S. and Lundquist, J. K.: Atmospheric stability affects wind turbine
power collection, Environ. Res. Lett., 7, 014005, https://doi.org/10.1088/1748-9326/7/1/014005, 2012. a
Short summary
This work analyses a meteorological event, called nocturnal low-level jets (NLLJs), defined as high wind speeds relatively close to the surface. There were positive and negative impacts from NLLJs. While NLLJs increased the mean power production, they also increased the variability in the wind with height. Our results imply that long NLLJ events are also larger, affecting many wind turbines at the same time. Short NLLJ events are more local, having stronger effects on power variability.
This work analyses a meteorological event, called nocturnal low-level jets (NLLJs), defined as...
Altmetrics
Final-revised paper
Preprint