Articles | Volume 7, issue 6
https://doi.org/10.5194/wes-7-2231-2022
© 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-7-2231-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
08 Nov 2022
Review article |
| 08 Nov 2022
| 08 Nov 2022
Research challenges and needs for the deployment of wind energy in hilly and mountainous regions
Andrew Clifton
CORRESPONDING AUTHOR
Stuttgart Wind Energy, University of Stuttgart, Stuttgart, Germany
now at: TGU enviConnect, TTI GmbH, Stuttgart, Germany
Sarah Barber
Institute for Energy Technology, Eastern Switzerland University of Applied Sciences, Oberseestrasse 10, 8640 Rapperswil, Switzerland
Alexander Stökl
Energiewerkstatt e.V., Heiligenstatt 24, 5211 Friedburg, Austria
Helmut Frank
FE13, Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, Germany
Timo Karlsson
VTT Technical Research Centre of Finland Ltd., Tekniikantie 21, P.O. Box 1000, 02044 VTT, Espoo, Finland
Related authors
Andrew Clifton, Sarah Barber, Andrew Bray, Peter Enevoldsen, Jason Fields, Anna Maria Sempreviva, Lindy Williams, Julian Quick, Mike Purdue, Philip Totaro, and Yu Ding
Wind Energ. Sci., 8, 947–974, https://doi.org/10.5194/wes-8-947-2023, https://doi.org/10.5194/wes-8-947-2023, 2023
Short summary
Short summary
Wind energy creates huge amounts of data, which can be used to improve plant design, raise efficiency, reduce operating costs, and ease integration. These all contribute to cheaper and more predictable energy from wind. But realising the value of data requires a digital transformation that brings
grand challengesaround data, culture, and coopetition. This paper describes how the wind energy industry could work with R&D organisations, funding agencies, and others to overcome them.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
Short summary
Short summary
Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
Vasilis Pettas, Matthias Kretschmer, Andrew Clifton, and Po Wen Cheng
Wind Energ. Sci., 6, 1455–1472, https://doi.org/10.5194/wes-6-1455-2021, https://doi.org/10.5194/wes-6-1455-2021, 2021
Short summary
Short summary
This study aims to quantify the effect of inter-farm interactions based on long-term measurement data from the Alpha Ventus (AV) wind farm and the nearby FINO1 platform. AV was initially the only operating farm in the area, but in subsequent years several farms were built around it. This setup allows us to quantify the farm wake effects on the microclimate of AV and also on turbine loads and operational characteristics depending on the distance and size of the neighboring farms.
Joseph C. Y. Lee, Peter Stuart, Andrew Clifton, M. Jason Fields, Jordan Perr-Sauer, Lindy Williams, Lee Cameron, Taylor Geer, and Paul Housley
Wind Energ. Sci., 5, 199–223, https://doi.org/10.5194/wes-5-199-2020, https://doi.org/10.5194/wes-5-199-2020, 2020
Short summary
Short summary
This work summarizes the results of the intelligence-sharing initiative of the Power Curve Working Group. Participants in this share exercise applied a handful of selected power curve modeling correction methods on their power performance test data, and they submitted the results for the coauthors to analyze. In this paper, we describe the share exercise, explain the analysis methodologies, and perform statistical tests to evaluate the correction methods in various inflow conditions.
Clara M. St. Martin, Julie K. Lundquist, Andrew Clifton, Gregory S. Poulos, and Scott J. Schreck
Wind Energ. Sci., 2, 295–306, https://doi.org/10.5194/wes-2-295-2017, https://doi.org/10.5194/wes-2-295-2017, 2017
Short summary
Short summary
We use upwind and nacelle-based measurements from a wind turbine and investigate the influence of atmospheric stability and turbulence regimes on nacelle transfer functions (NTFs) used to correct nacelle-mounted anemometer measurements. This work shows that correcting nacelle winds using NTFs results in similar energy production estimates to those obtained using upwind tower-based wind speeds. Further, stability and turbulence metrics have been found to have an effect on NTFs below rated speed.
Jennifer F. Newman and Andrew Clifton
Wind Energ. Sci., 2, 77–95, https://doi.org/10.5194/wes-2-77-2017, https://doi.org/10.5194/wes-2-77-2017, 2017
Short summary
Short summary
Remote-sensing devices such as lidars are often used for wind energy studies. Lidars measure mean wind speeds accurately but measure different values of turbulence than an instrument on a tower. In this paper, a model is described that improves lidar turbulence estimates. The model can be applied to commercially available lidars in real time or post-processing. Results indicate that the model performs well under most atmospheric conditions but retains some errors under daytime conditions.
Clara M. St. Martin, Julie K. Lundquist, Andrew Clifton, Gregory S. Poulos, and Scott J. Schreck
Wind Energ. Sci., 1, 221–236, https://doi.org/10.5194/wes-1-221-2016, https://doi.org/10.5194/wes-1-221-2016, 2016
Short summary
Short summary
We use turbine nacelle-based measurements and measurements from an upwind tower to calculate wind turbine power curves and predict the production of energy. We explore how different atmospheric parameters impact these power curves and energy production estimates. Results show statistically significant differences between power curves and production estimates calculated with turbulence and stability filters, and we suggest implementing an additional step in analyzing power performance data.
J. K. Lundquist, M. J. Churchfield, S. Lee, and A. Clifton
Atmos. Meas. Tech., 8, 907–920, https://doi.org/10.5194/amt-8-907-2015, https://doi.org/10.5194/amt-8-907-2015, 2015
Short summary
Short summary
Wind-profiling lidars are now regularly used in boundary-layer meteorology and in applications like wind energy, but their use often relies on assuming homogeneity in the wind. Using numerical simulations of stable flow past a wind turbine, we quantify the error expected because of the inhomogeneity of the flow. Large errors (30%) in winds are found near the wind turbine, but by three rotor diameters downwind, errors in the horizontal components have decreased to 15% of the inflow.
Philip Imanuel Franz, Imad Abdallah, Gregory Duthé, Julien Deparday, Ali Jafarabadi, Alexander Popp, Sarah Barber, and Eleni Chatzi
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-26, https://doi.org/10.5194/wes-2025-26, 2025
Revised manuscript under review for WES
Short summary
Short summary
New designs of large wind turbine blades have become increasingly flexible, and thus need cost-efficient monitoring solutions. Hence, we investigate if aerodynamic pressure measurements from a low-cost sensing system can be used to detect structural damage. Our research is based on a wind tunnel study, emulating a simplified wind turbine blade under various conditions. We show that using a convolutional neural network-based method, structural damage can indeed be detected and its severity rated.
Jonathan J. Day, Gunilla Svensson, Barbara Casati, Taneil Uttal, Siri-Jodha Khalsa, Eric Bazile, Elena Akish, Niramson Azouz, Lara Ferrighi, Helmut Frank, Michael Gallagher, Øystein Godøy, Leslie M. Hartten, Laura X. Huang, Jareth Holt, Massimo Di Stefano, Irene Suomi, Zen Mariani, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Teresa Remes, Rostislav Fadeev, Amy Solomon, Johanna Tjernström, and Mikhail Tolstykh
Geosci. Model Dev., 17, 5511–5543, https://doi.org/10.5194/gmd-17-5511-2024, https://doi.org/10.5194/gmd-17-5511-2024, 2024
Short summary
Short summary
The YOPP site Model Intercomparison Project (YOPPsiteMIP), which was designed to facilitate enhanced weather forecast evaluation in polar regions, is discussed here, focussing on describing the archive of forecast data and presenting a multi-model evaluation at Arctic supersites during February and March 2018. The study highlights an underestimation in boundary layer temperature variance that is common across models and a related inability to forecast cold extremes at several of the sites.
Yuriy Marykovskiy, Thomas Clark, Justin Day, Marcus Wiens, Charles Henderson, Julian Quick, Imad Abdallah, Anna Maria Sempreviva, Jean-Paul Calbimonte, Eleni Chatzi, and Sarah Barber
Wind Energ. Sci., 9, 883–917, https://doi.org/10.5194/wes-9-883-2024, https://doi.org/10.5194/wes-9-883-2024, 2024
Short summary
Short summary
This paper delves into the crucial task of transforming raw data into actionable knowledge which can be used by advanced artificial intelligence systems – a challenge that spans various domains, industries, and scientific fields amid their digital transformation journey. This article underscores the significance of cross-industry collaboration and learning, drawing insights from sectors leading in digitalisation, and provides strategic guidance for further development in this area.
Andrew Clifton, Sarah Barber, Andrew Bray, Peter Enevoldsen, Jason Fields, Anna Maria Sempreviva, Lindy Williams, Julian Quick, Mike Purdue, Philip Totaro, and Yu Ding
Wind Energ. Sci., 8, 947–974, https://doi.org/10.5194/wes-8-947-2023, https://doi.org/10.5194/wes-8-947-2023, 2023
Short summary
Short summary
Wind energy creates huge amounts of data, which can be used to improve plant design, raise efficiency, reduce operating costs, and ease integration. These all contribute to cheaper and more predictable energy from wind. But realising the value of data requires a digital transformation that brings
grand challengesaround data, culture, and coopetition. This paper describes how the wind energy industry could work with R&D organisations, funding agencies, and others to overcome them.
Florian Hammer, Sarah Barber, Sebastian Remmler, Federico Bernardoni, Kartik Venkatraman, Gustavo A. Díez Sánchez, Alain Schubiger, Trond-Ola Hågbo, Sophia Buckingham, and Knut Erik Giljarhus
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-114, https://doi.org/10.5194/wes-2022-114, 2023
Preprint withdrawn
Short summary
Short summary
We further enhanced a knowledge base for choosing the most optimal wind resource assessment tool. For this, we compared different simulation tools for the Perdigão site in Portugal, in terms of accuracy and costs. In total five different simulation tools were compared. We found that with a high degree of automatisation and a high experience level of the modeller a cost effective and accurate prediction based on RANS could be achieved. LES simulations are still mainly reserved for academia.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
Short summary
Short summary
Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
Gianluca De Fezza and Sarah Barber
Wind Energ. Sci., 7, 1627–1640, https://doi.org/10.5194/wes-7-1627-2022, https://doi.org/10.5194/wes-7-1627-2022, 2022
Short summary
Short summary
As part of a master's thesis, this study analysed the aerodynamic performance of a multi-element airfoil using numerical flow simulations. The results show that these types of airfoil are very suitable for an upcoming wind energy generation concept. The parametric study of the wing led to a significant improvement of up to 46.6 % compared to the baseline design. The increased power output of the energy generation concept contributes substantially to today's energy transition.
Sarah Barber, Alain Schubiger, Sara Koller, Dominik Eggli, Alexander Radi, Andreas Rumpf, and Hermann Knaus
Wind Energ. Sci., 7, 1503–1525, https://doi.org/10.5194/wes-7-1503-2022, https://doi.org/10.5194/wes-7-1503-2022, 2022
Short summary
Short summary
In this work, a range of simulations are carried out with seven different wind modelling tools at five different complex terrain sites and the results compared to wind speed measurements at validation locations. This is then extended to annual energy production (AEP) estimations (without wake effects), showing that wind profile prediction accuracy does not translate directly or linearly to AEP accuracy. It is therefore vital to consider overall AEP when evaluating simulation accuracies.
Sarah Barber, Julien Deparday, Yuriy Marykovskiy, Eleni Chatzi, Imad Abdallah, Gregory Duthé, Michele Magno, Tommaso Polonelli, Raphael Fischer, and Hanna Müller
Wind Energ. Sci., 7, 1383–1398, https://doi.org/10.5194/wes-7-1383-2022, https://doi.org/10.5194/wes-7-1383-2022, 2022
Short summary
Short summary
Aerodynamic and acoustic field measurements on operating large-scale wind turbines are key for the further reduction in the costs of wind energy. In this work, a novel cost-effective MEMS (micro-electromechanical systems)-based aerodynamic and acoustic wireless measurement system that is thin, non-intrusive, easy to install, low power and self-sustaining is designed and tested.
Vasilis Pettas, Matthias Kretschmer, Andrew Clifton, and Po Wen Cheng
Wind Energ. Sci., 6, 1455–1472, https://doi.org/10.5194/wes-6-1455-2021, https://doi.org/10.5194/wes-6-1455-2021, 2021
Short summary
Short summary
This study aims to quantify the effect of inter-farm interactions based on long-term measurement data from the Alpha Ventus (AV) wind farm and the nearby FINO1 platform. AV was initially the only operating farm in the area, but in subsequent years several farms were built around it. This setup allows us to quantify the farm wake effects on the microclimate of AV and also on turbine loads and operational characteristics depending on the distance and size of the neighboring farms.
Jia Yi Jin, Timo Karlsson, and Muhammad S. Virk
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2021-55, https://doi.org/10.5194/wes-2021-55, 2021
Preprint withdrawn
Short summary
Short summary
In this manuscript, a numerical case study has been presented regarding ice detection and wind resource assessment in ice prone cold regions. Three years SCADA data from a wind park in Arctic region is used for this study. T19IceLossMethod based statistical analysis and Computational Fluid Dynamics (CFD) based numerical simulations are carried out for icing events classification and wind resource assessment, as well as estimation of resultant Annual Energy Production (AEP).
Alain Schubiger, Sarah Barber, and Henrik Nordborg
Wind Energ. Sci., 5, 1507–1519, https://doi.org/10.5194/wes-5-1507-2020, https://doi.org/10.5194/wes-5-1507-2020, 2020
Short summary
Short summary
A large-eddy simulation using the lattice Boltzmann method (LBM) Palabos framework was implemented to calculate the wind field over the complex terrain of Bolund Hill. The results were compared to Reynolds-averaged Navier–Stokes and detached-eddy simulation (DES) using Ansys Fluent and field measurements. A comparison of the three methods' computational costs has shown that the LBM, even though not yet fully optimised, can perform 5 times faster than DES and lead to reasonably accurate results.
Joseph C. Y. Lee, Peter Stuart, Andrew Clifton, M. Jason Fields, Jordan Perr-Sauer, Lindy Williams, Lee Cameron, Taylor Geer, and Paul Housley
Wind Energ. Sci., 5, 199–223, https://doi.org/10.5194/wes-5-199-2020, https://doi.org/10.5194/wes-5-199-2020, 2020
Short summary
Short summary
This work summarizes the results of the intelligence-sharing initiative of the Power Curve Working Group. Participants in this share exercise applied a handful of selected power curve modeling correction methods on their power performance test data, and they submitted the results for the coauthors to analyze. In this paper, we describe the share exercise, explain the analysis methodologies, and perform statistical tests to evaluate the correction methods in various inflow conditions.
Sarah Barber, Alain Schubiger, Natalie Wagenbrenner, Nicolas Fatras, and Henrik Nordborg
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-95, https://doi.org/10.5194/wes-2019-95, 2020
Publication in WES not foreseen
Short summary
Short summary
A new method for helping wind modellers choose the most cost-effective model for a given project is developed by applying six different Computational Fluid Dynamics tools to simulate the Bolund Hill experiment and studying appropriate comparison metrics in detail. The results show that this new method is successful, and that it is generally possible to apply it in order to choose the most appropriate model for a given project in advance.
Sarah Barber, Simon Boller, and Henrik Nordborg
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-97, https://doi.org/10.5194/wes-2019-97, 2019
Revised manuscript not accepted
Short summary
Short summary
The growing worldwide level of renewable power generation requires innovative solutions to maintain grid reliability and stability. In this work, twelve sites in Switzerland are chosen for a 100 % renewable energy microgrid feasibility study. For all of these sites, a combination of wind and PV performs consistently better than wind only and PV only. Five of the sites are found to be potentially economically viable, if investors would be prepared to make extra investments of 0.05–0.2 $/kWh.
Clara M. St. Martin, Julie K. Lundquist, Andrew Clifton, Gregory S. Poulos, and Scott J. Schreck
Wind Energ. Sci., 2, 295–306, https://doi.org/10.5194/wes-2-295-2017, https://doi.org/10.5194/wes-2-295-2017, 2017
Short summary
Short summary
We use upwind and nacelle-based measurements from a wind turbine and investigate the influence of atmospheric stability and turbulence regimes on nacelle transfer functions (NTFs) used to correct nacelle-mounted anemometer measurements. This work shows that correcting nacelle winds using NTFs results in similar energy production estimates to those obtained using upwind tower-based wind speeds. Further, stability and turbulence metrics have been found to have an effect on NTFs below rated speed.
Jennifer F. Newman and Andrew Clifton
Wind Energ. Sci., 2, 77–95, https://doi.org/10.5194/wes-2-77-2017, https://doi.org/10.5194/wes-2-77-2017, 2017
Short summary
Short summary
Remote-sensing devices such as lidars are often used for wind energy studies. Lidars measure mean wind speeds accurately but measure different values of turbulence than an instrument on a tower. In this paper, a model is described that improves lidar turbulence estimates. The model can be applied to commercially available lidars in real time or post-processing. Results indicate that the model performs well under most atmospheric conditions but retains some errors under daytime conditions.
Clara M. St. Martin, Julie K. Lundquist, Andrew Clifton, Gregory S. Poulos, and Scott J. Schreck
Wind Energ. Sci., 1, 221–236, https://doi.org/10.5194/wes-1-221-2016, https://doi.org/10.5194/wes-1-221-2016, 2016
Short summary
Short summary
We use turbine nacelle-based measurements and measurements from an upwind tower to calculate wind turbine power curves and predict the production of energy. We explore how different atmospheric parameters impact these power curves and energy production estimates. Results show statistically significant differences between power curves and production estimates calculated with turbulence and stability filters, and we suggest implementing an additional step in analyzing power performance data.
J. K. Lundquist, M. J. Churchfield, S. Lee, and A. Clifton
Atmos. Meas. Tech., 8, 907–920, https://doi.org/10.5194/amt-8-907-2015, https://doi.org/10.5194/amt-8-907-2015, 2015
Short summary
Short summary
Wind-profiling lidars are now regularly used in boundary-layer meteorology and in applications like wind energy, but their use often relies on assuming homogeneity in the wind. Using numerical simulations of stable flow past a wind turbine, we quantify the error expected because of the inhomogeneity of the flow. Large errors (30%) in winds are found near the wind turbine, but by three rotor diameters downwind, errors in the horizontal components have decreased to 15% of the inflow.
B. Brecht and H. Frank
Adv. Sci. Res., 11, 1–6, https://doi.org/10.5194/asr-11-1-2014, https://doi.org/10.5194/asr-11-1-2014, 2014
Related subject area
Thematic area: Wind and the atmosphere | Topic: Atmospheric physics
Modeling frontal low-level jets and associated extreme wind power ramps over the North Sea
Quantifying tropical-cyclone-generated waves in extreme-value-derived design for offshore wind
Estimating long-term annual energy production from shorter-time-series data: methods and verification with a 10-year large-eddy simulation of a large offshore wind farm
Evaluating the potential of short-term instrument deployment to improve distributed wind resource assessment
Brief communication: A note on the variance of wind speed and turbulence intensity
Investigating the relationship between simulation parameters and flow variables in simulating atmospheric gravity waves for wind energy applications
Large-eddy simulation of an atmospheric bore and associated gravity wave effects on wind farm performance in the southern Great Plains
Analyzing the performance of vertical wind profilers in rain events
How do convective cold pools influence the boundary-layer atmosphere near two wind turbines in northern Germany?
Linking large-scale weather patterns to observed and modeled turbine hub-height winds offshore of the US West Coast
The impact of far-reaching offshore cluster wakes on wind turbine fatigue loads
Improving wind and power predictions via four-dimensional data assimilation in the WRF model: case study of storms in February 2022 at Belgian offshore wind farms
Evaluating the ability of the operational High Resolution Rapid Refresh model version 3 (HRRRv3) and version 4 (HRRRv4) to forecast wind ramp events in the US Great Plains
Estimating the technical wind energy potential of Kansas that incorporates the effect of regional wind resource depletion by wind turbines
Mesoscale weather systems and associated potential wind power variations in a midlatitude sea strait (Kattegat)
A large-eddy simulation (LES) model for wind-farm-induced atmospheric gravity wave effects inside conventionally neutral boundary layers
Simulating low-frequency wind fluctuations
Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parametrization in the Weather Research and Forecasting model
The multi-scale coupled model: a new framework capturing wind farm–atmosphere interaction and global blockage effects
Seasonal variability of wake impacts on US mid-Atlantic offshore wind plant power production
Bayesian method for estimating Weibull parameters for wind resource assessment in a tropical region: a comparison between two-parameter and three-parameter Weibull distributions
Lessons learned in coupling atmospheric models across scales for onshore and offshore wind energy
Investigating the physical mechanisms that modify wind plant blockage in stable boundary layers
Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic
Lifetime prediction of turbine blades using global precipitation products from satellites
Evaluation of low-level jets in the southern Baltic Sea: a comparison between ship-based lidar observational data and numerical models
Predicting power ramps from joint distributions of future wind speeds
Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer
Observer-based power forecast of individual and aggregated offshore wind turbines
Sensitivity analysis of mesoscale simulations to physics parameterizations over the Belgian North Sea using Weather Research and Forecasting – Advanced Research WRF (WRF-ARW)
Harish Baki, Sukanta Basu, and George Lavidas
Wind Energ. Sci., 10, 1575–1609, https://doi.org/10.5194/wes-10-1575-2025, https://doi.org/10.5194/wes-10-1575-2025, 2025
Short summary
Short summary
Our study explores how frontal low-level jets (FLLJs) impact wind power production by causing ramp-down events. Using the Weather Research and Forecasting model, we analyzed various modeling configurations and found that initial and boundary conditions, domain configuration, and wind farm parameterization significantly influence simulations. Our findings show such extreme events can be forecasted 1 d in advance, helping manage wind power more efficiently for a stable, reliable energy supply.
Sarah McElman, Amrit Shankar Verma, and Andrew Goupee
Wind Energ. Sci., 10, 1529–1550, https://doi.org/10.5194/wes-10-1529-2025, https://doi.org/10.5194/wes-10-1529-2025, 2025
Short summary
Short summary
This paper investigates the treatment of tropical-cyclone-generated waves in metocean models and in statistical evaluation for offshore wind design. It provides recommendations for ensuring the accurate representation of extreme waves for the design and operation of offshore projects on the Atlantic coast of the United States.
Bernard Postema, Remco A. Verzijlbergh, Pim van Dorp, Peter Baas, and Harm J. J. Jonker
Wind Energ. Sci., 10, 1471–1484, https://doi.org/10.5194/wes-10-1471-2025, https://doi.org/10.5194/wes-10-1471-2025, 2025
Short summary
Short summary
Atmospheric large-eddy simulation is a technique that simulates weather conditions in high detail and is used to plan new wind farms. This research presents ways to estimate the long-term (10-year) power production of a wind farm without having to simulate 10 years of weather and instead simulating much less (1 year or less). The results show that the methods reduce the uncertainty in power production estimates by an order of magnitude and that wind observations can be included as well to add more insight.
Lindsay M. Sheridan, Dmitry Duplyakin, Caleb Phillips, Heidi Tinnesand, Raj K. Rai, Julia E. Flaherty, and Larry K. Berg
Wind Energ. Sci., 10, 1451–1470, https://doi.org/10.5194/wes-10-1451-2025, https://doi.org/10.5194/wes-10-1451-2025, 2025
Short summary
Short summary
A total of 12 months of onsite wind measurement is standard for correcting model-based long-term wind speed estimates for utility-scale wind farms; however, the time and capital investment involved in gathering onsite measurements must be reconciled with the energy needs and funding opportunities for distributed wind projects. This study aims to answer the question of how short you can go in terms of the observational time period needed to make impactful improvements to long-term wind speed estimates.
Cristina Lozej Archer
Wind Energ. Sci., 10, 1433–1438, https://doi.org/10.5194/wes-10-1433-2025, https://doi.org/10.5194/wes-10-1433-2025, 2025
Short summary
Short summary
Two approximate analytical expressions are derived, one for the variance of wind speed and the other for turbulence intensity, based on one simple assumption: that the turbulent fluctuations in wind are small with respect to the mean. The formulations perform well when applied to the observations from the American WAKE experimeNt (AWAKEN) field campaign conducted in 2023.
Mehtab Ahmed Khan, Dries Allaerts, Simon J. Watson, and Matthew J. Churchfield
Wind Energ. Sci., 10, 1167–1185, https://doi.org/10.5194/wes-10-1167-2025, https://doi.org/10.5194/wes-10-1167-2025, 2025
Short summary
Short summary
To guide realistic atmospheric gravity wave simulations, we study flow over a two-dimensional hill and through a wind farm canopy, examining optimal domain size and damping layer setup. Wave properties based on non-dimensional numbers determine the optimal domain and damping parameters. Accurate solutions require the domain length to exceed the effective horizontal wavelength, height, and damping thickness to equal the vertical wavelength and non-dimensional damping strength between 1 and 10.
Adam S. Wise, Robert S. Arthur, Aliza Abraham, Sonia Wharton, Raghavendra Krishnamurthy, Rob Newsom, Brian Hirth, John Schroeder, Patrick Moriarty, and Fotini K. Chow
Wind Energ. Sci., 10, 1007–1032, https://doi.org/10.5194/wes-10-1007-2025, https://doi.org/10.5194/wes-10-1007-2025, 2025
Short summary
Short summary
Wind farms can be subject to rapidly changing weather events. In the United States Great Plains, some of these weather events can result in waves in the atmosphere that ultimately affect how much power a wind farm can produce. We modeled a specific event of waves observed in Oklahoma. We determined how to accurately model the event and analyzed how it affected a wind farm’s power production, finding that the waves both decreased power and made it more variable.
Adriel J. Carvalho, Francisco L. Albuquerque Neto, and Denisson Q. Oliveira
Wind Energ. Sci., 10, 971–986, https://doi.org/10.5194/wes-10-971-2025, https://doi.org/10.5194/wes-10-971-2025, 2025
Short summary
Short summary
Wind profilers are important to the wind power industry since they capture wind velocity and direction at higher altitudes than meteorological masts. Although some studies have investigated their performance in different scenarios, this paper covers a gap in knowledge by investigating and comparing their performance under rain events. This investigation is important since the data collected support strategic decisions in the wind power industry, where high data availability in all situations is critical.
Jeffrey D. Thayer, Gerard Kilroy, and Norman Wildmann
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-38, https://doi.org/10.5194/wes-2025-38, 2025
Revised manuscript accepted for WES
Short summary
Short summary
With increasing wind energy in the German energy grid, it is crucial to better understand how different types of weather (including thunderstorms) can impact wind turbines and the surrounding atmosphere. We find rapid wind changes associated with the leading edge of thunderstorm outflows within the height range of wind turbines that would quickly increase wind power output, with longer-lasting changes in the near-surface atmosphere that would affect subsequent wind turbine operations.
Ye Liu, Timothy W. Juliano, Raghavendra Krishnamurthy, Brian J. Gaudet, and Jungmin Lee
Wind Energ. Sci., 10, 483–495, https://doi.org/10.5194/wes-10-483-2025, https://doi.org/10.5194/wes-10-483-2025, 2025
Short summary
Short summary
Our study reveals how different weather patterns influence wind conditions off the US West Coast. We identified key weather patterns affecting wind speeds at potential wind farm sites using advanced machine learning. This research helps improve weather prediction models, making wind energy production more reliable and efficient.
Arjun Anantharaman, Jörge Schneemann, Frauke Theuer, Laurent Beaudet, Valentin Bernard, Paul Deglaire, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-20, https://doi.org/10.5194/wes-2025-20, 2025
Revised manuscript accepted for WES
Short summary
Short summary
The offshore wind farm sector is expanding rapidly, and the interactions between wind farms are important to analyse for both existing and planned wind farms. We developed a new methodology to quantify how much the reductions in wind speed behind a farm can affect the loads on turbines which are tens of kilometers downstream. We found a 2.5 % increase in the turbine loads and discuss how further measurements could add to the design standards of future wind farms.
Tsvetelina Ivanova, Sara Porchetta, Sophia Buckingham, Gertjan Glabeke, Jeroen van Beeck, and Wim Munters
Wind Energ. Sci., 10, 245–268, https://doi.org/10.5194/wes-10-245-2025, https://doi.org/10.5194/wes-10-245-2025, 2025
Short summary
Short summary
This study explores how wind and power predictions can be improved by introducing local forcing of measurement data in a numerical weather model while taking into account the presence of neighboring wind farms. Practical implications for the wind energy industry include insights for informed offshore wind farm planning and decision-making strategies using open-source models, even under adverse weather conditions.
Laura Bianco, Reagan Mendeke, Jake Lindblom, Irina V. Djalalova, David D. Turner, and James M. Wilczak
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-133, https://doi.org/10.5194/wes-2024-133, 2024
Revised manuscript accepted for WES
Short summary
Short summary
Including more renewable energy into the electric grid is a critical part of the strategy to mitigate climate change. Reliable numerical weather prediction (NWP) models need to be able to predict the intrinsic nature of weather-dependent resources, such as wind ramp events, as wind energy could quickly be available in abundance or temporarily cease to exist. We assess the ability of the operational High Resolution Rapid Refresh NWP model to forecast wind ramp events in two most recent versions.
Jonathan Minz, Axel Kleidon, and Nsilulu T. Mbungu
Wind Energ. Sci., 9, 2147–2169, https://doi.org/10.5194/wes-9-2147-2024, https://doi.org/10.5194/wes-9-2147-2024, 2024
Short summary
Short summary
Estimates of power output from regional wind turbine deployments in energy scenarios assume that the impact of the atmospheric feedback on them is minimal. But numerical models show that the impact is large at the proposed scales of future deployment. We show that this impact can be captured by accounting only for the kinetic energy removed by turbines from the atmosphere. This can be easily applied to energy scenarios and leads to more physically representative estimates.
Jérôme Neirynck, Jonas Van de Walle, Ruben Borgers, Sebastiaan Jamaer, Johan Meyers, Ad Stoffelen, and Nicole P. M. van Lipzig
Wind Energ. Sci., 9, 1695–1711, https://doi.org/10.5194/wes-9-1695-2024, https://doi.org/10.5194/wes-9-1695-2024, 2024
Short summary
Short summary
In our study, we assess how mesoscale weather systems influence wind speed variations and their impact on offshore wind energy production fluctuations. We have observed, for instance, that weather systems originating over land lead to sea wind speed variations. Additionally, we noted that power fluctuations are typically more significant in summer, despite potentially larger winter wind speed variations. These findings are valuable for grid management and optimizing renewable energy deployment.
Sebastiano Stipa, Mehtab Ahmed Khan, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 1647–1668, https://doi.org/10.5194/wes-9-1647-2024, https://doi.org/10.5194/wes-9-1647-2024, 2024
Short summary
Short summary
We introduce a novel way to model the impact of atmospheric gravity waves (AGWs) on wind farms using high-fidelity simulations while significantly reducing computational costs. The proposed approach is validated across different atmospheric stability conditions, and implications of neglecting AGWs when predicting wind farm power are assessed. This work advances our understanding of the interaction of wind farms with the free atmosphere, ultimately facilitating cost-effective research.
Abdul Haseeb Syed and Jakob Mann
Wind Energ. Sci., 9, 1381–1391, https://doi.org/10.5194/wes-9-1381-2024, https://doi.org/10.5194/wes-9-1381-2024, 2024
Short summary
Short summary
Wind flow consists of swirling patterns of air called eddies, some as big as many kilometers across, while others are as small as just a few meters. This paper introduces a method to simulate these large swirling patterns on a flat grid. Using these simulations we can better figure out how these large eddies affect big wind turbines in terms of loads and forces.
Sara Müller, Xiaoli Guo Larsén, and David Robert Verelst
Wind Energ. Sci., 9, 1153–1171, https://doi.org/10.5194/wes-9-1153-2024, https://doi.org/10.5194/wes-9-1153-2024, 2024
Short summary
Short summary
Tropical cyclone winds are challenging for wind turbines. We analyze a tropical cyclone before landfall in a mesoscale model. The simulated wind speeds and storm structure are sensitive to the boundary parametrization. However, independent of the boundary layer parametrization, the median change in wind speed and wind direction with height is small relative to wind turbine design standards. Strong spatial organization of wind shear and veer along the rainbands may increase wind turbine loads.
Sebastiano Stipa, Arjun Ajay, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 1123–1152, https://doi.org/10.5194/wes-9-1123-2024, https://doi.org/10.5194/wes-9-1123-2024, 2024
Short summary
Short summary
This paper introduces the multi-scale coupled (MSC) model, an engineering framework aimed at modeling turbine–wake and wind farm–gravity wave interactions, as well as local and global blockage effects. Comparisons against large eddy simulations show that the MSC model offers a valid contribution towards advancing our understanding of the coupled wind farm–atmosphere interaction, helping refining power estimation methodologies for existing and future wind farm sites.
David Rosencrans, Julie K. Lundquist, Mike Optis, Alex Rybchuk, Nicola Bodini, and Michael Rossol
Wind Energ. Sci., 9, 555–583, https://doi.org/10.5194/wes-9-555-2024, https://doi.org/10.5194/wes-9-555-2024, 2024
Short summary
Short summary
The US offshore wind industry is developing rapidly. Using yearlong simulations of wind plants in the US mid-Atlantic, we assess the impacts of wind turbine wakes. While wakes are the strongest and longest during summertime stably stratified conditions, when New England grid demand peaks, they are predictable and thus manageable. Over a year, wakes reduce power output by over 35 %. Wakes in a wind plant contribute the most to that reduction, while wakes between wind plants play a secondary role.
Mohammad Golam Mostafa Khan and Mohammed Rafiuddin Ahmed
Wind Energ. Sci., 8, 1277–1298, https://doi.org/10.5194/wes-8-1277-2023, https://doi.org/10.5194/wes-8-1277-2023, 2023
Short summary
Short summary
A robust technique for wind resource assessment with a Bayesian approach for estimating Weibull parameters is proposed. Research conducted using seven sites' data in the tropical region from 1° N to 21° S revealed that the three-parameter (3-p) Weibull distribution with a non-zero shift parameter is a better fit for wind data that have a higher percentage of low wind speeds. Wind data with higher wind speeds are a special case of the 3-p distribution. This approach gives accurate results.
Sue Ellen Haupt, Branko Kosović, Larry K. Berg, Colleen M. Kaul, Matthew Churchfield, Jeffrey Mirocha, Dries Allaerts, Thomas Brummet, Shannon Davis, Amy DeCastro, Susan Dettling, Caroline Draxl, David John Gagne, Patrick Hawbecker, Pankaj Jha, Timothy Juliano, William Lassman, Eliot Quon, Raj K. Rai, Michael Robinson, William Shaw, and Regis Thedin
Wind Energ. Sci., 8, 1251–1275, https://doi.org/10.5194/wes-8-1251-2023, https://doi.org/10.5194/wes-8-1251-2023, 2023
Short summary
Short summary
The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models. Lessons learned and discerned best practices are described in the context of the cases studied for the purpose of enabling further deployment of wind energy. It also points to code, assessment tools, and data for testing the methods.
Miguel Sanchez Gomez, Julie K. Lundquist, Jeffrey D. Mirocha, and Robert S. Arthur
Wind Energ. Sci., 8, 1049–1069, https://doi.org/10.5194/wes-8-1049-2023, https://doi.org/10.5194/wes-8-1049-2023, 2023
Short summary
Short summary
The wind slows down as it approaches a wind plant; this phenomenon is called blockage. As a result, the turbines in the wind plant produce less power than initially anticipated. We investigate wind plant blockage for two atmospheric conditions. Blockage is larger for a wind plant compared to a stand-alone turbine. Also, blockage increases with atmospheric stability. Blockage is amplified by the vertical transport of horizontal momentum as the wind approaches the front-row turbines in the array.
Stephanie Redfern, Mike Optis, Geng Xia, and Caroline Draxl
Wind Energ. Sci., 8, 1–23, https://doi.org/10.5194/wes-8-1-2023, https://doi.org/10.5194/wes-8-1-2023, 2023
Short summary
Short summary
As wind farm developments expand offshore, accurate forecasting of winds above coastal waters is rising in importance. Weather models rely on various inputs to generate their forecasts, one of which is sea surface temperature (SST). In this study, we evaluate how the SST data set used in the Weather Research and Forecasting model may influence wind characterization and find meaningful differences between model output when different SST products are used.
Merete Badger, Haichen Zuo, Ásta Hannesdóttir, Abdalmenem Owda, and Charlotte Hasager
Wind Energ. Sci., 7, 2497–2512, https://doi.org/10.5194/wes-7-2497-2022, https://doi.org/10.5194/wes-7-2497-2022, 2022
Short summary
Short summary
When wind turbine blades are exposed to strong winds and heavy rainfall, they may be damaged and their efficiency reduced. The problem is most pronounced offshore where turbines are tall and the climate is harsh. Satellites provide global half-hourly rain observations. We use these rain data as input to a model for blade lifetime prediction and find that the satellite-based predictions agree well with predictions based on observations from weather stations on the ground.
Hugo Rubio, Martin Kühn, and Julia Gottschall
Wind Energ. Sci., 7, 2433–2455, https://doi.org/10.5194/wes-7-2433-2022, https://doi.org/10.5194/wes-7-2433-2022, 2022
Short summary
Short summary
A proper development of offshore wind farms requires the accurate description of atmospheric phenomena like low-level jets. In this study, we evaluate the capabilities and limitations of numerical models to characterize the main jets' properties in the southern Baltic Sea. For this, a comparison against ship-mounted lidar measurements from the NEWA Ferry Lidar Experiment has been implemented, allowing the investigation of the model's capabilities under different temporal and spatial constraints.
Thomas Muschinski, Moritz N. Lang, Georg J. Mayr, Jakob W. Messner, Achim Zeileis, and Thorsten Simon
Wind Energ. Sci., 7, 2393–2405, https://doi.org/10.5194/wes-7-2393-2022, https://doi.org/10.5194/wes-7-2393-2022, 2022
Short summary
Short summary
The power generated by offshore wind farms can vary greatly within a couple of hours, and failing to anticipate these ramp events can lead to costly imbalances in the electrical grid. A novel multivariate Gaussian regression model helps us to forecast not just the means and variances of the next day's hourly wind speeds, but also their corresponding correlations. This information is used to generate more realistic scenarios of power production and accurate estimates for ramp probabilities.
William J. Shaw, Larry K. Berg, Mithu Debnath, Georgios Deskos, Caroline Draxl, Virendra P. Ghate, Charlotte B. Hasager, Rao Kotamarthi, Jeffrey D. Mirocha, Paytsar Muradyan, William J. Pringle, David D. Turner, and James M. Wilczak
Wind Energ. Sci., 7, 2307–2334, https://doi.org/10.5194/wes-7-2307-2022, https://doi.org/10.5194/wes-7-2307-2022, 2022
Short summary
Short summary
This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.
Frauke Theuer, Andreas Rott, Jörge Schneemann, Lueder von Bremen, and Martin Kühn
Wind Energ. Sci., 7, 2099–2116, https://doi.org/10.5194/wes-7-2099-2022, https://doi.org/10.5194/wes-7-2099-2022, 2022
Short summary
Short summary
Remote-sensing-based approaches have shown potential for minute-scale forecasting and need to be further developed towards an operational use. In this work we extend a lidar-based forecast to an observer-based probabilistic power forecast by combining it with a SCADA-based method. We further aggregate individual turbine power using a copula approach. We found that the observer-based forecast benefits from combining lidar and SCADA data and can outperform persistence for unstable stratification.
Adithya Vemuri, Sophia Buckingham, Wim Munters, Jan Helsen, and Jeroen van Beeck
Wind Energ. Sci., 7, 1869–1888, https://doi.org/10.5194/wes-7-1869-2022, https://doi.org/10.5194/wes-7-1869-2022, 2022
Short summary
Short summary
The sensitivity of the WRF mesoscale modeling framework in accurately representing and predicting wind-farm-level environmental variables for three extreme weather events over the Belgian North Sea is investigated in this study. The overall results indicate highly sensitive simulation results to the type and combination of physics parameterizations and the type of the weather phenomena, with indications that scale-aware physics parameterizations better reproduce wind-related variables.
Cited articles
Antoniou, I., Pedersen, S. M., and Enevoldsen, P. B.: Wind shear and
uncertainties in power curve measurement and wind resources, Wind Eng., 33, 449–468, 2009. a
Arbez, C., Clément, M., Godreau, C., Swytink-Binnema, N., Tete, K., and
Wadham-Gagnon, M.: Development and Validation of an Ice Prediction Model for
Wind Farms, Tech. rep., TechnoCenter éolien,
https://nergica.com/en/development-and-validation-of-an-ice-prediction-model-for-wind (last access: 1 October 2022), 2016. a
Barber, S. and Nordborg, H.: Improving site-dependent power curve prediction
accuracy using regression trees, J. Phys.: Conf. Ser., 1618, 062003, https://doi.org/10.1088/1742-6596/1618/6/062003, 2020. a, b
Barber, S., Buehler, M., and Nordborg, H.: IEA Wind Task 31: Design of a new
comparison metrics simulation challenge for wind resource assessment in
complex terrain Stage 1, J. Phys.: Conf. Ser., 1618, 062013, https://doi.org/10.1088/1742-6596/1618/6/062013, 2020a. a
Barber, S., Schubiger, A., Koller, S., Rumpf, A., Knaus, H., and Nordborg, H.: Actual Total Cost reduction of commercial CFD modelling tools for Wind
Resource Assessment in complex terrain, J. Phys.: Conf. Ser., 1618, 062012, https://doi.org/10.1088/1742-6596/1618/6/062012, 2020b.
a
Barber, S., Schubiger, A., Koller, S., Rumpf, A., and Knaus, H.: The Pragmatic Choice of Wind Model in Complex Terrain – Decision Tool Development, Zenodo [code], https://doi.org/10.5281/zenodo.4876982, 2021. a
Barber, S., Hammer, F., and Tica, A.: Tools for predicting site-specific performance, ASME J. Risk Uncertain., 8, 021102, https://doi.org/10.1115/1.4053513, 2022. a
Bell, T. M., Klein, P., Wildmann, N., and Menke, R.: Analysis of flow in
complex terrain using multi-Doppler lidar retrievals, Atmos. Meas. Tech., 13, 1357–1371, https://doi.org/10.5194/amt-13-1357-2020, 2020. a
Berg, J., Mann, J., Bechmann, A., Courtney, M. S., and Jørgensen, H. E.: The Bolund Experiment, Part I: Flow Over a Steep, Three-Dimensional Hill,
Bound.-Lay. Meteorol., 141, 219, https://doi.org/10.1007/s10546-011-9636-y, 2011. a
Bingöl, F., Mann, J., and Foussekis, D.: Conically scanning lidar error in complex terrain, Meteorol. Z., 18, 189–195, https://doi.org/10.1127/0941-2948/2009/0368, 2009. a
Black, A., Mazoyer, P., Wylie, S., Debnath, M., Lammers, A., Spalding, T., and Schultz, R.: Survey of Correction Techniques for Remote Sensing Devices in Complex Flow, Zenodo [data set], https://doi.org/10.5281/zenodo.4302363, 2020. a
Bodini, N., Zardi, D., and Lundquist, J. K.: Three-dimensional structure of
wind turbine wakes as measured by scanning lidar, Atmos. Meas. Tech., 10, 2881–2896, https://doi.org/10.5194/amt-10-2881-2017, 2017. a
Borraccino, A., Schlipf, D., Haizmann, F., and Wagner, R.: Wind field
reconstruction from nacelle-mounted lidar short-range measurements, Wind
Energ. Sci., 2, 269–283, https://doi.org/10.5194/wes-2-269-2017, 2017. a
Bortolotti, P., Tarres, H. C., Dykes, K. L., Merz, K., Sethuraman, L., Verelst, D., and Zahle, F.: IEA Wind TCP Task 37: Systems Engineering in Wind Energy – WP2.1 Reference Wind Turbines, Tech. Rep. NREL/TP-5000-73492, National Renewable Energy Laboratory, Golden, CO, https://doi.org/10.2172/1529216, 2019. a
Bowen, A. J. and Mortensen, N. G.: Exploring the limits of WAsP: the Wind Atlas Analysis and Application Program, in: Proceedings of the 1996 European Union Wind Energy Conference, 20–24 May 1996, Göteborg, Sweden, 584–587, Paper O15.2, https://backend.orbit.dtu.dk/ws/portalfiles/portal/116681565/Exploring_the_limits.pdf (last access: 1 October 2022), 1996. a
Bradley, S., Strehz, A., and Emeis, S.: Remote sensing winds in complex terrain – a review, Meteorol. Z., 24, 547–555, https://doi.org/10.1127/metz/2015/0640, 2015. a
Bredesen, R. E., Cattin, R., Clausen, N.-E., Davis, N., Jordaens, P. J.,
Khadiri-Yazami, Z., Klintström, R., Krenn, A., Lehtomäki, V.,
Ronsten, G., Wadham-Gagnon, M., and Wickman, H.: IEA Wind TCP Recommended
Practice 13 2nd Edition: Wind Energy in Cold Climates, Tech. rep., IEA Wind
Task 19, https://iea-wind.org/task19/t19-publications/ (last access: 1 October 2022), 2017. a
Clifton, A., Kilcher, L., Lundquist, J. K., and Fleming, P.: Using machine
learning to predict wind turbine power output, Environ. Res. Lett., 8, 024009, https://doi.org/10.1088/1748-9326/8/2/024009, 2013. a, b
Clifton, A., Daniels, M. H., and Lehning, M.: Effect of winds in a mountain
pass on turbine performance, Wind Energy, 17, 1543–1562, https://doi.org/10.1002/we.1650, 2014. a, b
Clifton, A., Boquet, M., Burin Des Roziers, E., Westerhellweg, A., Hofsass, M., Klaas, T., Vogstad, K., Clive, P., Harris, M., Wylie, S., Osler, E., Banta, B., Choukulkar, A., Lundquist, J., and Aitken, M.: Remote Sensing of Complex Flows by Doppler Wind Lidar: Issues and Preliminary Recommendations, Tech. Rep. NREL/TP-5000-64634, National Renewable Energy Laboratory, Golden, CO, USA, https://doi.org/10.2172/1351595, 2015. a
Clifton, A., Smith, A., and Fields, M.: Wind Plant Preconstruction Energy
Estimates. Current Practice and Opportunities, Tech. Rep. NREL/TP-5000-64735,
National Renewable Energy Laboratory, Golden, CO, USA, https://doi.org/10.2172/1248798, 2016. a
Clifton, A., Hodge, B.-M., Draxl, C., Badger, J., and Habte, A.: Wind and solar resource data sets, WIREs Energ. Environ., 7, e276,
https://doi.org/10.1002/wene.276, 2018. a, b, c
Draxl, C., Clifton, A., Hodge, B.-M., and McCaa, J.: The Wind Integration
National Dataset (WIND) Toolkit, Appl. Energy, 151, 355–366, 2015. a
ECMWF: C3S Copernicus Climate Change Service,
https://cds.climate.copernicus.eu/about-c3s (last access: 31 May 2022), 2020. a
Elliott, D., Holladay, C., Barchet, W., Foote, H., and Sandusky, W.: Wind Energy Resource Atlas of the United States, Technical Report DOE/CH 10093-4, Pacific Northwest National Laboratory, https://www.nrc.gov/docs/ML0609/ML060940383.pdf (last access: 1 October 2022), 1986. 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
European Commission: In-Depth Analysis In Support Of The Commission Communication COM(2018) 773 A Clean Planet for all: A European long-term
strategic vision for a prosperous, modern, competitive and climate neutral
economy, Tech. rep., European Commission,
https://ec.europa.eu/clima/system/files/2018-11/com_2018_733_analysis_in_support_en.pdf
(last access: 1 October 2022), 2018. a
Feng, Y., Miranda-Fuentes, J., Guo, S., Jacob, J., and Sagaut, P.: ProLB: A
Lattice Boltzmann Solver of Large-Eddy Simulation for Atmospheric Boundary
Layer Flows, J. Adv. Model.Earth Syst., 13, e2020MS002107, https://doi.org/10.1029/2020MS002107, 2021. a
Foresti, L., Tuia, D., and Kanevski, M.: Learning wind fields with multiple
kernels, Stoch. Environ. Res. Risk A., 25, 51–66, https://doi.org/10.1007/s00477-010-0405-0, 2011. a
Frediani, M. E. B., Hopson, T. M., Hacker, J. P., Anagnostou, E. N., Monache,
L. D., and Vandenberghe, F.: Object-Based Analog Forecasts for Surface Wind
Speed, Mon. Weather Rev., 145, 5083–5102, https://doi.org/10.1175/MWR-D-17-0012.1, 2017. a
Friis Pedersen, T.: Development of a Classification System for Cup
Anemometers-CLASSCUP, Tech. Rep. 1348(EN), Risø National Laboratory,
Roskilde, ISBN 87-550-3076-9, https://backend.orbit.dtu.dk/ws/portalfiles/portal/7711662/ris_r_1348.pdf (last access: 1 October 2022), 2003. a
Godreau, C. and Tete, K.: Ice protection systems and retrofits: Performance
and experiences, Winterwind 2020,
https://windren.se/WW2020/13_4_39_Godreau_Ice_protection_systems_and_retrofits_Performance_and_experiences_Pub.pdf
(last access: 1 October 2022), 2020. a
Hansen, C. and Hansen, K.: Recent Advances in Wind Turbine Noise Research,
Acoustics, 2, 171–206, https://doi.org/10.3390/acoustics2010013, 2020. a
Hedevang, E.: Wind turbine power curves incorporating turbulence intensity,
Wind Energy, 17, 173–195, 2014. a
Hofsäß, M., Clifton, A., and Cheng, P. W.: Reducing the Uncertainty of Lidar Measurements in Complex Terrain Using a Linear Model Approach, Remote Sens., 10, 1465, https://doi.org/10.3390/rs10091465, 2018. a
Hofsäß, M., Bergmann, D., Denzel, J., and Cheng, P. W.: Flying UltraSonic – A new way to measure the wind, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2019-81, 2019. a
Holleran, S., Roscheck, F., Fields, J., Kersting, G., Bohara, A., Purdue, M.,
and Lee, J.: IEA-Task-43/digital_wra_data_standard: 0.1.1-2021.04,
Zenodo [code], https://doi.org/10.5281/zenodo.4710169, 2021. a
Hübner, G., Pohl, J., Hoen, B., Firestone, J., Rand, J., Elliott, D., and
Haac, R.: Monitoring annoyance and stress effects of wind turbines on nearby
residents: A comparison of U.S. and European samples, Environ. Int., 132, 105090, https://doi.org/10.1016/j.envint.2019.105090, 2019. a
IEC 61400-13:2015: Wind energy generation systems – Part 13: Measurement of mechanical loads, https://webstore.iec.ch/publication/72669 (last access: 1 October 2022), 2015. a
IEC 61400-24:2019: Wind energy generation systems – Part 24: Lightning Protection, https://webstore.iec.ch/publication/32050 (last access: 1 October 2022), 2019b. a
IEC 61400-50-3:2019: Wind energy generation systems – Part 50-3: Use of nacelle mounted lidars for wind measurements, https://webstore.iec.ch/publication/59587 (last access: 1 October 2022), 2019c. a
Karagulle, D., Frye, C., Sayre, R., Breyer, S., Aniello, P., Vaughan, R., and
Wright, D.: Modeling global Hammond landform regions from 250-m elevation
data, T. GIS, 21, 1040–1060, https://doi.org/10.1111/tgis.12265, 2017. a, b
Karlsson, T.: Cold climate wind market study 2020–2025, Winterwind 2021,
https://windren.se/WW2021/14_2_21_Karlsson_IEA_Wind_Task_19_Cold_climate_wind_market_study_Public.pdf
(last access: 1 October 2022), 2021. a
Kelberlau, F. and Mann, J.: Cross-contamination effect on turbulence spectra
from Doppler beam swinging wind lidar, Wind Energ. Sci., 5, 519–541,
https://doi.org/10.5194/wes-5-519-2020, 2020. a
Kilpatrick, R. J., Hildebrandt, S., Swytink-Binnema, N., and Clément, M.:
Advances in wind power forecasting and power loss mitigation for cold climate operation, J. Phys.: Conf. Ser., 1452, 012079, https://doi.org/10.1088/1742-6596/1452/1/012079, 2020. a
Klaas-Witt, T. and Emeis, S.: The five main influencing factors for lidar errors in complex terrain, Wind Energ. Sci., 7, 413–431, https://doi.org/10.5194/wes-7-413-2022, 2022. a, b
Komusanac, I., Brindley, G., Fraile, D., and Ramirez, L.: Wind energy in
Europe: 2021 Statistics and the outlook for 2022–2026, Tech. rep.,
WindEurope,
https://windeurope.org/intelligence-platform/product/wind-energy-in-europe-2021-statistics-and-the-outlook-for, last access: 1 October 2022. a
Krenn, A., Stökl, A., Weber, N., Barup, S., Weidl, T., Hoffmann, A.,
Bredesen, R. E., Lannic, M., Müller, S., Stoffels, N., Hahm, T., Storck, F., and Lautenschlager, F.: International Recommendations for Ice Fall and Ice Throw Risk Assessments, https://iea-wind.org/task19/t19-publications/ (last access: 1 October 2022), 2018. a
Lange, J., Mann, J., Berg, J., Parvu, D., Kilpatrick, R., Costache, A.,
Jubayer, C., Siddiqui, K., and Hangan, H.: For wind turbines in complex
terrain, the devil is in the detail, Environ. Res. Lett., 12, 094020, https://doi.org/10.1088/1748-9326/aa81db, 2017. a
Lee, J., Zhao, F., Dutton, A., Backwell, B., Fiestas, R., Qiao, L.,
Balachandran, N., Lim, S., Liang, W., Clarke, E., Lathigara, A., and Younger,
D. R.: Global Wind Report 2021, Tech. rep., Global Wind Energy Council, https://gwec.net/global-wind-report-2021/ (last access: 1 October 2022), 2021. a
Lee, J. C. Y., Stuart, P., Clifton, A., Fields, M. J., Perr-Sauer, J.,
Williams, L., Cameron, L., Geer, T., and Housley, P.: The Power Curve Working
Group's assessment of wind turbine power performance prediction methods, Wind
Energ. Sci., 5, 199–223, https://doi.org/10.5194/wes-5-199-2020, 2020. a
Letson, F., Shepherd, T. J., Barthelmie, R. J., and Pryor, S. C.: Modelling
Hail and Convective storms with WRF for Wind Energy Applications, J. Phys.: Conf. Ser., 1452, 012051, https://doi.org/10.1088/1742-6596/1452/1/012051, 2020. a
Macdonald, H., Infield, D., Nash, D. H., and Stack, M. M.: Mapping hail
meteorological observations for prediction of erosion in wind turbines, Wind
Energy, 19, 777–784, https://doi.org/10.1002/we.1854, 2016. a
Mann, J., Angelou, N., Arnqvist, J., Callies, D., Cantero, E., Arroyo, R. C.,
Courtney, M., Cuxart, J., Dellwik, E., Gottschall, J., Ivanell, S., Kühn,
P., Lea, G., Matos, J. C., Palma, J. M. L. M., Pauscher, L., Peña, A.,
Rodrigo, J. S., Söderberg, S., Vasiljevic, N., and Rodrigues, C. V.:
Complex terrain experiments in the New European Wind Atlas, Philos. T. Roy. Soc. Lond. A, 375, 20160101, https://doi.org/10.1098/rsta.2016.0101, 2017. a
Menke, R., Vasiljević, N., Hansen, K. S., Hahmann, A. N., and Mann, J.: Does the wind turbine wake follow the topography? A multi-lidar study in complex terrain, Wind Energy Science, 3, 681–691, https://doi.org/10.5194/wes-3-681-2018, 2018. a, b
Menke, R., Vasiljević, N., Wagner, J., Oncley, S. P., and Mann, J.:
Multi-lidar wind resource mapping in complex terrain, Wind Energ. Sci., 5,
1059–1073, https://doi.org/10.5194/wes-5-1059-2020, 2020. a
Mickle, R. E., Cook, N. J., Hoff, A. M., Jensen, N., Salmon, J. R., Taylor,
P. A., Tetzlaff, G., and Teunissen, H.: The Askervein Hill Project: Vertical
profiles of wind and turbulence, Bound.-Lay. Meteorol., 43, 143–169, 1988. a
Möhrlen, C., Zack, J., and Giebel, G.: IEA Wind Recommended Practice for the Implementation of Renewable Energy Forecasting Solutions, Elsevier Academic Press, 270 pp., ISBN 9780443186813,
https://www.elsevier.com/books/iea-wind-recommended-practice-for-the-implementation-of, last access: 1 October 2022. a
Molinder, J., Scher, S., Nilsson, E., Körnich, H., Bergström, H.,
and Sjöblom, A.: Probabilistic Forecasting of Wind Turbine Icing Related Production Losses Using Quantile Regression Forests, Energies, 14, 158, https://doi.org/10.3390/EN14010158, 2020. a
Molter, C. and Cheng, P. W.: ANDroMeDA – A Novel Flying Wind Measurement
System, J. Phys.: Conf. Ser., 1618, 032049, https://doi.org/10.1088/1742-6596/1618/3/032049, 2020. a
Mortensen, N., Nielsen, M., and Ejsing Jørgensen, H.: Comparison of
Resource and Energy Yield Assessment Procedures 2011–2015: What have we
learned and what needs to be done?, in: Proceedings of the EWEA Annual Event
and Exhibition 2015, EWEA – European Wind Energy Association, https://backend.orbit.dtu.dk/ws/portalfiles/portal/118434032/Comparison_of_Resource_and_Energy_Yield_paper.pdf (last access: 1 October 2022), 2015. a
Newman, J. F. and Clifton, A.: An error reduction algorithm to improve lidar
turbulence estimates for wind energy, Wind Energ. Sci., 2, 77–95,
https://doi.org/10.5194/wes-2-77-2017, 2017. a
Olson, J. B., Kenyon, J. S., Djalalova, I., Bianco, L., Turner, D. D.,
Pichugina, Y., Choukulkar, A., Toy, M. D., Brown, J. M., Angevine, W. M.,
Akish, E., Bao, J.-W., Jimenez, P., Kosovic, B., Lundquist, K. A., Draxl, C.,
Lundquist, J. K., McCaa, J., McCaffrey, K., Lantz, K., Long, C., Wilczak, J.,
Banta, R., Marquis, M., Redfern, S., Berg, L. K., Shaw, W., and Cline, J.:
The Second Wind Forecast Improvement Project (WFIP2): General Overview, B. Am. Meteorol. Soc., 100, 2201–2220, https://doi.org/10.1175/BAMS-D-18-0040.1, 2019. a
Onodera, N., Idomura, Y., and Hasegawa, Y.: Real-Time Tracer Dispersion
Simulations in Oklahoma City Using the Locally Mesh-Refined Lattice Boltzmann
Method, Bound.-Lay. Meteorol., 179, 187–208, https://doi.org/10.1007/s10546-020-00594-x, 2021. a
Papadopoulos, K. H., Stefantos, N. C., Paulsen, U. S., and Morfiadakis, E.:
Effects of Turbulence and Flow Inclination on the Performance of Cup
Anemometers in the Field, Bound.-Lay. Meteorol., 101, 77–107,
https://doi.org/10.1023/A:1019254020039, 2001. a
Pohl, J., Gabriel, J., and Hübner, G.: Understanding stress effects of wind turbine noise – The integrated approach, Energy Policy, 112, 119–128,
https://doi.org/10.1016/j.enpol.2017.10.007, 2018. a
Pryor, S. C., Barthelmie, R. J., Bukovsky, M. S., Leung, L. R., and Sakaguchi, K.: Climate change impacts on wind power generation, Nat. Rev. Earth Environ., 1, 627–643, https://doi.org/10.1038/s43017-020-0101-7, 2020. a, b
Rasp, S. and Lerch, S.: Neural Networks for Postprocessing Ensemble Weather
Forecasts, Mon. Weather Rev., 146, 3885–3900, https://doi.org/10.1175/MWR-D-18-0187.1, 2018. a
Rautenberg, A., Schön, M., zum Berge, K., Mauz, M., Manz, P., Platis, A.,
van Kesteren, B., Suomi, I., Kral, S. T., and Bange, J.: The Multi-Purpose
Airborne Sensor Carrier MASC-3 for Wind and Turbulence Measurements in the
Atmospheric Boundary Layer, Sensors, 19, 2292, https://doi.org/10.3390/s19102292, 2019. a
Reinert, D., Prill, F., Frank, H., Denhard, M., Baldauf, M., Schraff, C.,
Gebhardt, C., Marsigli, C., and Zängl, G.: DWD Database Reference for the
Global and Regional ICON and ICON-EPS Forecasting System, Tech. rep.,
Deutscher Wetterdienst, Offenbach am Main, Germany,
https://www.dwd.de/SharedDocs/downloads/DE/modelldokumentationen/nwv/icon/icon_dbbeschr_aktuell.pdf (last access: 1 October 2022), 2021. a
RISE: Wind power in cold climates,
https://www.ri.se/en/press/wind-power-in-cold-climates (last access: 19 April 2022), 2020. a
Roberge, P., Lemay, J., Ruel, J., and Bégin-Drolet, A.: Field analysis,
modeling and characterization of wind turbine hot air ice protection systems, Cold Reg. Sci. Technol., 163, 19–26, https://doi.org/10.1016/j.coldregions.2019.04.001, 2019. a
Sandu, I., Beljaars, A., Bechtold, P., Mauritsen, T., and Balsamo, G.: Why is
it so difficult to represent stably stratified conditions in numerical
weather prediction (NWP) models?, J. Adv. Model. Earth Syst., 5, 117–133, https://doi.org/10.1002/jame.20013, 2013. a, b
Sanz Rodrigo, J., Chávez Arroyo, R. A., Moriarty, P., Churchfield, M.,
Kosović, B., Réthoré, P.-E., Hansen, K. S., Hahmann, A., Mirocha, J. D., and Rife, D.: Mesoscale to microscale wind farm flow modeling and evaluation, WIREs Energ. Environ., 6, e214, https://doi.org/10.1002/wene.214, 2017. a
Sathe, A., Mann, J., Gottschall, J., and Courtney, M. S.: Can Wind Lidars
Measure Turbulence?, J. Atmos. Ocean. Tech., 28, 853–868, https://doi.org/10.1175/JTECH-D-10-05004.1, 2011. a, b
Sayre, R., Frye, C., Karagulle, D., Krauer, J., Breyer, S., Aniello, P.,
Wright, D. J., Payne, D., Adler, C., Warner, H., VanSistine, D. P., and
Cress, J.: A New High-Resolution Map of World Mountains and an Online Tool for Visualizing and Comparing Characterizations of Global Mountain Distributions, Mount. Res. Dev., 38, 240–249, https://doi.org/10.1659/MRD-JOURNAL-D-17-00107.1, 2018. a, b, c
Schubiger, A., Barber, S., and Nordborg, H.: Evaluation of the lattice
Boltzmann method for wind modelling in complex terrain, Wind Energ. Sci., 5, 1507–1519, https://doi.org/10.5194/wes-5-1507-2020, 2020. a
Shaw, W. J., Berg, L. K., Cline, J., Draxl, C., Djalalova, I., Grimit, E. P.,
Lundquist, J. K., Marquis, M., McCaa, J., Olson, J. B., Sivaraman, C., Sharp,
J., and Wilczak, J. M.: The Second Wind Forecast Improvement Project (WFIP2):
General Overview, B. Am. Meteorol. Soc., 100, 1687–1699, https://doi.org/10.1175/BAMS-D-18-0036.1, 2019. a
Son, C. and Kim, T.: Development of an icing simulation code for rotating wind turbines, J. Wind Eng. Indust. Aerodynam., 203, 104239, https://doi.org/10.1016/j.jweia.2020.104239, 2020. a
Stawiarski, C., Träumner, K., Knigge, C., and Calhoun, R.: Scopes and
Challenges of Dual-Doppler Lidar Wind Measurements – An Error Analysis, J. Atmos. Ocean. Tech., 30, 2044–2062, https://doi.org/10.1175/JTECH-D-12-00244.1, 2013. a
Straka, T. M., Fritze, M., and Voigt, C. C.: The human dimensions of a
green–green-dilemma: Lessons learned from the wind energy – wildlife conflict in Germany, Energ. Rep., 6, 1768–1777, https://doi.org/10.1016/j.egyr.2020.06.028, 2020. a
Strauss, L., Serafin, S., and Dorninger, M.: Skill and Potential Economic
Value of Forecasts of Ice Accretion on Wind Turbines, J. Appl. Meteorol. Clim., 59, 1845–1864, https://doi.org/10.1175/JAMC-D-20-0025.1, 2020. a
Swytink-Binnema, N., Godreau, C., and Arbez, C.: Detecting instrumental icing
using automated double anemometry, Wind Energy, 22, 80–88, https://doi.org/10.1002/we.2271, 2019. a
Tabas, D., Fang, J., and Porté-Agel, F.: Wind Energy Prediction in Highly
Complex Terrain by Computational Fluid Dynamics, Energies, 12, 1311, https://doi.org/10.3390/en12071311, 2019. a
Thompson, G.: High Resolution Numerical Weather Model Forecasts of Icing at the Ground and in the Air, in: Proc. of the Int. Workshop on Atmospheric Icing of Structures, IWAIS 2019, https://iwais2019.is/images/Papers/042_iwais_thompson.pdf (last access: 1 October 2022), 2019. a
Tong, D., Farnham, D. J., Duan, L., Zhang, Q., Lewis, N. S., Caldeira, K., and Davis, S. J.: Geophysical constraints on the reliability of solar and wind power worldwide, Nat. Commun., 12, 6146, https://doi.org/10.1038/s41467-021-26355-z, 2021. a
USGS: Geosciences and Environmental Change Science Center, https://rmgsc.cr.usgs.gov/gme/gme. shtml, last access: 1 October 2022. 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
Vanderwende, B. J., Lundquist, J. K., Rhodes, M. E., Takle, E. S., and Irvin,
S. L.: Observing and Simulating the Summertime Low-Level Jet in Central Iowa,
Mon. Weather Rev., 143, 2319–2336, https://doi.org/10.1175/MWR-D-14-00325.1, 2015. a
van Kuik, G. A. M., Peinke, J., Nijssen, R., Lekou, D., Mann, J., Sørensen, J. N., Ferreira, C., van Wingerden, J. W., Schlipf, D., Gebraad, P., Polinder, H., Abrahamsen, A., van Bussel, G. J. W., Sørensen, J. D.,
Tavner, P., Bottasso, C. L., Muskulus, M., Matha, D., Lindeboom, H. J.,
Degraer, S., Kramer, O., Lehnhoff, S., Sonnenschein, M., Sørensen, P. E.,
Künneke, R. W., Morthorst, P. E., and Skytte, K.: Long-term research
challenges in wind energy – a research agenda by the European Academy of Wind Energy, Wind Energ. Sci., 1, 1–39, https://doi.org/10.5194/wes-1-1-2016, 2016. a, b
Vasiljević, N., L. M. Palma, J. M., Angelou, N., Carlos Matos, J., Menke, R., Lea, G., Mann, J., Courtney, M., Frölen Ribeiro, L., and M. G. C. Gomes, V. M.: Perdigão 2015: methodology for atmospheric multi-Doppler lidar experiments, Atmos. Meas. Tech., 10, 3463–3483,
https://doi.org/10.5194/amt-10-3463-2017, 2017. a
Vasiljevic, N., Klaas, T., Pauscher, L., Lopes, J. C., Gomes, D. F., Abreuand, R., and Bardal, L. M.: e-WindLidar: making wind lidar data FAIR,
Zenodo [data set], https://doi.org/10.5281/zenodo.2478051, 2018.
a
Vasiljević, N., Harris, M., Tegtmeier Pedersen, A., Rolighed Thorsen, G.,
Pitter, M., Harris, J., Bajpai, K., and Courtney, M.: Wind sensing with
drone-mounted wind lidars: proof of concept, Atmos. Meas. Tech., 13, 521–536, https://doi.org/10.5194/amt-13-521-2020, 2020a. a
Vasiljević, N., Vignaroli, A., Bechmann, A., and Wagner, R.: Digitalization of scanning lidar measurement campaign planning, Wind Energ. Sci., 5, 73–87, https://doi.org/10.5194/wes-5-73-2020, 2020b. a
Veers, P., Dykes, K., Lantz, E., Barth, S., Bottasso, C. L., Carlson, O.,
Clifton, A., Green, J., Green, P., Holttinen, H., Laird, D., Lehtomäki,
V., Lundquist, J. K., Manwell, J., Marquis, M., Meneveau, C., Moriarty, P.,
Munduate, X., Muskulus, M., Naughton, J., Pao, L., Paquette, J., Peinke, J.,
Robertson, A., Rodrigo, J. S., Sempreviva, A. M., Smith, J. C., Tuohy, A.,
and Wiser, R.: Grand challenges in the science of wind energy, Science, 366,
6464, https://doi.org/10.1126/science.aau2027, 2019. a, b
Wagenbrenner, N. S., Forthofer, J. M., Lamb, B. K., Shannon, K. S., and Butler, B. W.: Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja, Atmos. Chem. Phys., 16, 5229–5241, https://doi.org/10.5194/acp-16-5229-2016, 2016. a
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
Wagner, R., Courtney, M., Gottschall, J., and Lindelow-Marsden, P.: Accounting for the speed shear in wind turbine power performance measurement, Wind Energy, 14, 993–1004, https://doi.org/10.1002/we.509, 2011. 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
Wilczak, J., Finley, C., Freedman, J., Cline, J., Bianco, L., Olson, J.,
Djalalova, I., Sheridan, L., Ahlstrom, M., Manobianco, J., Zack, J., Carley,
J. R., Benjamin, S., Coulter, R., Berg, L. K., Mirocha, J., Clawson, K.,
Natenberg, E., and Marquis, M.: The Wind Forecast Improvement Project (WFIP):
A Public-Private Partnership Addressing Wind Energy Forecast Needs, B. Am. Meteorol. Soc., 96, 1699–1718, https://doi.org/10.1175/BAMS-D-14-00107.1, 2015. a
Würth, I., Valldecabres, L., Simon, E., Möhrlen, C., Uzunoğlu, B., Gilbert, C., Giebel, G., Schlipf, D., and Kaifel, A.: Minute-Scale
Forecasting of Wind Power – Results from the Collaborative Workshop of IEA
Wind Task 32 and 36, Energies, 12, 712, https://doi.org/10.3390/en12040712, 2019. a
Zängl, G.: Extending the Numerical Stability Limit of Terrain-Following
Coordinate Models over Steep Slopes, Mon. Weather Rev., 140, 3722–3733,
https://doi.org/10.1175/MWR-D-12-00049.1, 2012. a
Short summary
The transition to low-carbon sources of energy means that wind turbines will need to be built in hilly or mountainous regions or in places affected by icing. These locations are called
complexand are hard to develop. This paper sets out the research and development (R&D) needed to make it easier and cheaper to harness wind energy there. This includes collaborative R&D facilities, improved wind and weather models, frameworks for sharing data, and a clear definition of site complexity.
The transition to low-carbon sources of energy means that wind turbines will need to be built in...
Altmetrics
Final-revised paper
Preprint