Articles | Volume 9, issue 1
https://doi.org/10.5194/wes-9-263-2024
© Author(s) 2024. 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-9-263-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Quantitative comparison of power production and power quality onshore and offshore: a case study from the eastern United States
Rebecca Foody
Department of Earth and Atmospheric Science, Cornell University, Ithaca, NY 14850, USA
Jacob Coburn
Department of Earth and Atmospheric Science, Cornell University, Ithaca, NY 14850, USA
Jeanie A. Aird
Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14850, USA
Rebecca J. Barthelmie
Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14850, USA
Department of Earth and Atmospheric Science, Cornell University, Ithaca, NY 14850, USA
Related authors
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Kelsey B. Thompson, Rebecca J. Barthelmie, and Sara C. Pryor
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-37, https://doi.org/10.5194/wes-2025-37, 2025
Revised manuscript accepted for WES
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Wind turbines in offshore lease areas (LA) along the eastern U.S. may be impacted by hurricanes. Regional simulations with atmosphere-only and coupled atmosphere-ocean-wave regional models well reproduce historical hurricanes and indicate no exceedance of 50 m s-1 wind speeds in the LA and only brief periods with low power production. Coupled simulations lead to more intense hurricanes possibly indicating that previous atmosphere-only simulations underestimate the risk to offshore wind turbines.
Tristan Shepherd, Frederick Letson, Rebecca J. Barthelmie, and Sara C. Pryor
Nat. Hazards Earth Syst. Sci., 24, 4473–4505, https://doi.org/10.5194/nhess-24-4473-2024, https://doi.org/10.5194/nhess-24-4473-2024, 2024
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A historic derecho in the USA is presented. The 29 June 2012 derecho caused more than 20 deaths and millions of US dollars of damage. We use a regional climate model to understand how model fidelity changes under different initial conditions. We find changes drive different convective conditions, resulting in large variation in the simulated hazards. The variation using different reanalysis data shows that framing these results in the context of contemporary and future climate is a challenge.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Ville Vakkari, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 9, 821–840, https://doi.org/10.5194/wes-9-821-2024, https://doi.org/10.5194/wes-9-821-2024, 2024
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Knowing the wind speed across the rotor of a wind turbine is key in making good predictions of the power production. However, models struggle to capture both the speed and the shape of the wind profile. Using machine learning methods based on the model data, we show that the predictions can be improved drastically. The work focuses on three coastal sites, spread over the Northern Hemisphere (the Baltic Sea, the North Sea, and the US Atlantic coast) with similar results for all sites.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 8, 1651–1658, https://doi.org/10.5194/wes-8-1651-2023, https://doi.org/10.5194/wes-8-1651-2023, 2023
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Low-level jets (LLJs) are special types of non-ideal wind profiles affecting both wind energy production and loads on a wind turbine. However, among LLJ researchers, there is no consensus regarding which definition to use to identify these profiles. In this work, we compare two different ways of identifying the LLJ – the falloff definition and the shear definition – and argue why the shear definition is better suited to wind energy applications.
Jeanie A. Aird, Rebecca J. Barthelmie, Tristan J. Shepherd, and Sara C. Pryor
Wind Energ. Sci., 6, 1015–1030, https://doi.org/10.5194/wes-6-1015-2021, https://doi.org/10.5194/wes-6-1015-2021, 2021
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Low-level jets (LLJs) are pronounced maxima in wind speed profiles affecting wind turbine performance and longevity. We present a climatology of LLJs over Iowa using output from the Weather Research and Forecasting (WRF) model and determine the rotor plane conditions when they occur. LLJ characteristics are highly sensitive to the identification criteria applied, and different (unique) LLJs are extracted with each criterion. LLJ characteristics also vary with different model output resolution.
Frederick W. Letson, Rebecca J. Barthelmie, Kevin I. Hodges, and Sara C. Pryor
Nat. Hazards Earth Syst. Sci., 21, 2001–2020, https://doi.org/10.5194/nhess-21-2001-2021, https://doi.org/10.5194/nhess-21-2001-2021, 2021
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Windstorms during the last 40 years in the US Northeast are identified and characterized using the spatial extent of extreme wind speeds at 100 m height from the ERA5 reanalysis. During all of the top 10 windstorms, wind speeds exceeding the local 99.9th percentile cover at least one-third of the land area in this high-population-density region. These 10 storms followed frequently observed cyclone tracks but have intensities 5–10 times the mean values for cyclones affecting this region.
Cited articles
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.
Aird, J. A., Barthelmie, R. J., Shepherd, T. J., and Pryor, S. C.: Occurrence of Low-Level Jets over the Eastern US Coastal Zone at Heights Relevant to Wind Energy, Energies 15, 445, https://doi.org/10.3390/en15020445, 2022.
American Clean Power: Clean Power Quarterly 2023 Q1, American Clean Power, https://cleanpower.org/resources/clean-power-quarterly-market-report-q1-2023/ (last access: 3 August 2023), 135, 2023.
Ayodele, T. R., Jimoh, A., Munda, J. L., and Tehile, A. J.: Challenges of grid integration of wind power on power system grid integrity: A review, International Journal of Renewable Energy Research, 2, 618–626, 2012.
Balsley, B. B., Frehlich, R. G., Jensen, M. L., Meillier, Y., and Muschinski, A.: Extreme gradients in the nocturnal boundary layer: Structure, evolution, and potential causes, J. Atmos. Sci., 60, 2496–2508, 2003.
Bamigbola, O. M., Ali, M. M., and Oke, M. O.: Mathematical modeling of electric power flow and the minimization of power losses on transmission lines, Appl. Math. Comput., 241, 214–221, 2014.
Barthelmie, R. J., Grisogono, B., and Pryor, S. C.: Observations and simulations of diurnal cycles of near-surface wind speeds over land and sea, J. Geophys. Res., 101, 21327–321337, https://doi.org/10.1029/96JD01520, 1996.
Barthelmie, R. J., Murray, F., and Pryor, S. C.: The economic benefit of short-term forecasting for wind energy in the UK electricity market, Energ. Policy, 36, 1687–1696, 2008.
Barthelmie, R. J., Doubrawa, P., Wang, H., Giroux, G., and Pryor, S. C.: Effects of an escarpment on flow parameters of relevance to wind turbines, Wind Energy, 19, 2271–2286, 2016.
Barthelmie, R. J., Shepherd, T. J., Aird, J. A., and Pryor, S. C.: Power and wind shear implications of large wind turbine scenarios in the U.S. Central Plains, Energies, 13, 4269 https://doi.org/10.3390/en13164269, 2020.
Barthelmie, R. J., Larsen, G. C., and Pryor, S. C.: Modeling Annual Electricity Production and Levelized Cost of Energy from the US East Coast Offshore Wind Energy Lease Areas, Energies, 16, 4550, https://doi.org/10.3390/en16124550, 2023.
Bingöl, F., Mann, J., and Larsen, G. C.: Light detection and ranging measurements of wake dynamics part I: one-dimensional scanning, Wind Energy, 13, 51–61, https://doi.org/10.1002/we.352, 2010.
Bischoff, O., Wurth, I., Gottschall, J., Gribben, B., Hughes, J., Stein, D., and Verhoef, H.: Floating Lidar Systems, IEA Expert Group Report on Recommended Practices, IEA Wind TCP RP 18 from Task 32, 89, https://iea-wind.org/portfolio-item/recommended-practice-18/ (last access: 3 August 2023), 2017.
Bistline, J. E. and Blanford, G. J.: The role of the power sector in net-zero energy systems, Energy and Climate Change, 2, 100045, https://doi.org/10.1016/j.egycc.2021.100045, 2021.
Blackadar, A. K.: Boundary layer wind maxima and their significance for the growth of nocturnal inversions, B. Am. Meteorol. Soc., 38, 283–290, 1957.
BOEM: Outer Continental Shelf Renewable Energy Leases Map Book, 33 pp., https://www.boem.gov/sites/default/files/
documents/renewable-energy/Outer%20Continental%20Shelf
%20Renewable%20Energy%20Leases%20January%202023b.
pdf (last access: 14 March 2023), 2023.
Brotzge, J. A., Wang, J., Thorncroft, C., Joseph, E., Bain, N., Bassill, N., Farruggio, N., Freedman, J., Hemker Jr, K., and Johnston, D.: A technical overview of the New York State Mesonet standard network, J. Atmos. Ocean. Tech., 37, 1827–1845, 2020 (data available at: http://www.nysmesonet.org/, last access: 3 August 2023).
Burleyson, C. D., Rahman, A., Rice, J. S., Smith, A. D., and Voisin, N.: Multiscale effects masked the impact of the COVID-19 pandemic on electricity demand in the United States, Appl. Energ., 304, 117711, https://doi.org/10.1016/j.apenergy.2021.117711, 2021.
Castillo, V. Z., De Boer, H.-S., Muñoz, R. M., Gernaat, D. E., Benders, R., and van Vuuren, D.: Future global electricity demand load curves, Energy, 258, 124741, https://doi.org/10.1016/j.erss.2019.101337, 2022.
Coburn, J. J. and Pryor, S. C.: Projecting future energy production from operating wind farms in North America: Part 3: Variability, J. Appl. Meteorol. Clim., 62, 1523–1537, 2023.
DeMarco, A. and Basu, S.: On the tails of the wind ramp distributions, Wind Energy, 21, 892–905, 2018.
Diógenes, J. R. F., Claro, J., Rodrigues, J. C., and Loureiro, M. V.: Barriers to onshore wind energy implementation: A systematic review, Energy Research & Social Science, 60, 101337, https://doi.org/10.1016/j.tej.2020.106829, 2020.
Enevoldsen, P. and Jacobson, M. Z.: Data investigation of installed and output power densities of onshore and offshore wind turbines worldwide, Energy Sustain. Dev., 60, 40–51, https://doi.org/10.1016/j.esd.2020.11.004, 2021.
Eryilmaz, D., Patria, M., and Heilbrun, C.: Assessment of the COVID-19 pandemic effect on regional electricity generation mix in NYISO, MISO and PJM markets, Electricity Journal, 33, 106829, https://doi.org/10.1088/1742-6596/1934/1/012001, 2020.
Esteban, M. D., Diez, J. J., López, J. S., and Negro, V.: Why offshore wind energy?, Renew. Energ., 36, 444–450, 2011.
Frehlich, R.: Simulation of coherent Doppler lidar performance in the weak-signal regime, J. Atmos. Ocean. Tech., 13, 646–658, 1996.
Gadde, S. N., Liu, L., and Stevens, R. J.: Effect of low-level jet on turbine aerodynamic blade loading using large-eddy simulations, J. Phys. Conf. Ser., 2021, 012001, https://doi.org/10.1088/1742-6596/1934/1/012001, 2021.
Gaertner, E., Rinker, J., Sethuraman, L., Zahle, Z., Anderson, B., Barter, G., Abbas, B., Meng, F., Bortolotti, F., Skrzypinski, W., Scott, G., Feil, R., Bredmose, H., Dykes, K., Shields, M., Allen, C., and Viselli, A.: Definition of the IEA 15-Megawatt Offshore Reference Wind Turbine, National Renewable Energy Laboratory, Golden, CO, NREL/TP-5000-75698, https://www.nrel.gov/docs/fy20osti/75698.pdf (last access: 3 August 2023), 2020 (code available at: https://github.com/IEAWindTask37/IEA-15-240-RWT, last access: 31 January 2024).
Gramcianinov, C. B., Campos, R. M., de Camargo, R., Hodges, K. I., Guedes Soares, C., and da Silva Dias, P. L.: Analysis of Atlantic extratropical storm tracks characteristics in 41 years of ERA5 and CFSR/CFSv2 databases, Ocean Eng., 216, 108111, https://doi.org/10.1016/j.oceaneng.2020.108111, 2020.
Gryning, S.-E., and Floors, R.: Carrier-to-noise-threshold filtering on off-shore wind lidar measurements, Sensors, 19, 592, https://doi.org/10.3390/s19030592, 2019.
Gryning, S.-E., Floors, R., Peña, A., Batchvarova, E., and Brümmer, B.: Weibull wind-speed distribution parameters derived from a combination of wind-lidar and tall-mast measurements over land, coastal and marine sites, Bound.-Lay. Meteorol., 159, 329–348, 2016.
Gutierrez, W., Ruiz-Columbie, A., Tutkun, M., and Castillo, L.: The structural response of a wind turbine under operating conditions with a low-level jet, Renewable and Sustainable Energy Reviews, 108, 380–391, 2019.
Haghi, H. V., Bina, M. T., and Golkar, M. A.: Nonlinear modeling of temporal wind power variations, IEEE T. Sustain. Energ., 4, 838–848, 2013.
Hallgren, C., Arnqvist, J., Ivanell, S., Körnich, H., Vakkari, V., and Sahlée, E.: Looking for an offshore low-level jet champion among recent reanalyses: a tight race over the Baltic Sea, Energies, 13, 3670, https://doi.org/10.3390/en13143670, 2020.
Hallgren, C., Aird, J. A., Ivanell, S., Körnich, H., Barthelmie, R. J., Pryor, S. C., and Sahlée, E.: Brief communication: On the definition of the low-level jet, Wind Energ. Sci., 8, 1651–1658, https://doi.org/10.5194/wes-8-1651-2023, 2023.
Haslett, J. and Raftery, A. E.: Space-time modelling with long-memory dependence: Assessing Ireland's wind power resource, J. R. Stat. Soc. C-Appl., 38, 1–21, 1989.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Holton, J. R.: The diurnal boundary layer wind oscillation above sloping terrain, Tellus, 19, 200–205, 1967.
Hur, S., Recalde-Camacho, L., and Leithead, W.: Detection and compensation of anomalous conditions in a wind turbine, Energy, 124, 74–86, 2017.
IEA: Wind Electricity, International Energy Agency, Paris, https://www.iea.org/reports/wind-electricity (last access: 3 August 2023), 2022.
IEC: IEC 61400-1 Edition 4.0 2019-02 Wind turbines – Part 1: Design requirements, International Electrotechnical Commission, Geneva, Switzerland, ISBN 978-2-8322-6253-5, 168 pp., 2019.
Irwin, J. S.: A theoretical variation of the wind profile power-law exponent as a function of surface roughness and stability, Atmos. Environ., 13, 191–194, 1979.
Kallistratova, M., Kouznetsov, R. D., Kuznetsov, D. D., Kuznetsova, I. N., Nakhaev, M., and Chirokova, G.: Summertime low-level jet characteristics measured by sodars over rural and urban areas, Meteorol. Z., 18, 289–295, 2009.
Kalverla, P. C., Holtslag, A. A. M., Ronda, R. J., and Steeneveld, G. J.: Quality of wind characteristics in recent wind atlases over the North Sea, Q. J. Roy. Meteor. Soc., 146, 1498–1515, https://doi.org/10.1002/qj.3748, 2020.
Kariniotakis, G., Pinson, P., Siebert, N., Giebel, G., and Barthelmie, R.: The state of the art in short term prediction of wind power-from an offshore perspective, SeaTech Week-Ocean Energy Conference ADEME-IFREMER, Brest, France, October 2004 13, 2004.
Kelberlau, F. and Mann, J.: Quantification of motion-induced measurement error on floating lidar systems, Atmos. Meas. Tech., 15, 5323–5341, https://doi.org/10.5194/amt-15-5323-2022, 2022.
Kirkegaard, J. K., Rudolph, D. P., Nyborg, S., Solman, H., Gill, E., Cronin, T., and Hallisey, M.: Tackling grand challenges in wind energy through a socio-technical perspective, Nature Energy, 8, 655–664, https://doi.org/10.1038/s41560-41023-01266-z, 2023.
Kiviluoma, J., Holttinen, H., Weir, D., Scharff, R., Söder, L., Menemenlis, N., Cutululis, N. A., Danti Lopez, I., Lannoye, E., and Estanqueiro, A.: Variability in large-scale wind power generation, Wind Energy, 19, 1649–1665, 2016.
Knoop, S., Ramakrishnan, P., and Wijnant, I.: Dutch Offshore Wind Atlas Validation against Cabauw Meteomast Wind Measurements, Energies, 13, 6558, https://doi.org/10.3390/en13246558, 2020.
Kumer, V.-M., Reuder, J., Dorninger, M., Zauner, R., and Grubišić, V.: Turbulent kinetic energy estimates from profiling wind LiDAR measurements and their potential for wind energy applications, Renew. Energ., 99, 898–910, 2016.
Lazard: Lazard's Levelized Cost of Energy Analysis–Version 16.0, https://www.lazard.com/research-insights/levelized-cost-of-energyplus/ (last access: 3 August 2023), Zurich, Switzerland, 2023.
McCabe, E. J. and Freedman, J. M.: Development of an Objective Methodology for Identifying the Sea-Breeze Circulation and Associated Low-Level Jet in the New York Bight, Weather Forecast., 38, 571–589, 2023.
Meyer, P. J. and Gottschall, J.: How do NEWA and ERA5 compare for assessing offshore wind resources and wind farm siting conditions?, J. Phys. Conf. Ser., 2022, 012009,.
Musial, W., Heimiller, D., Beiter, P., Scott, G., and Draxl, C.: 2016 Offshore Wind Energy Resource Assessment for the United States, Technical Report NREL/TP-5000-66599, p. 88, https://www.nrel.gov/docs/fy16osti/66599.pdf (last access: 16 July 2020), 2016.
Optis, M., Bodini, N., Debnath, M., and Doubrawa, P.: New methods to improve the vertical extrapolation of near-surface offshore wind speeds, Wind Energ. Sci., 6, 935–948, https://doi.org/10.5194/wes-6-935-2021, 2021.
Pichault, M., Vincent, C., Skidmore, G., and Monty, J.: Characterisation of intra-hourly wind power ramps at the wind farm scale and associated processes, Wind Energ. Sci., 6, 131–147, https://doi.org/10.5194/wes-6-131-2021, 2021.
Pinson, P., Chevallier, C., and Kariniotakis, G. N.: Trading wind generation from short-term probabilistic forecasts of wind power, IEEE T. Power Syst., 22, 1148–1156, 2007.
Potisomporn, P. and Vogel, C. R.: Spatial and temporal variability characteristics of offshore wind energy in the United Kingdom, Wind Energy, 25, 537–552, 2022.
Pryor, S. C. and Barthelmie, R. J.: Comparison of potential power production at on- and off- shore sites, Wind Energy, 2001, 173–181, 2002.
Pryor, S. C., Nielsen, M., Barthelmie, R. J., and Mann, J.: Can satellite sampling of offshore wind speeds realistically represent wind speed distributions? Part II: Quantifying uncertainties associated with sampling strategy and distribution fitting methods, J. Appl. Meteorol., 43, 739–750, https://doi.org/10.1175/2096.1, 2004.
Pryor, S. C., Conrick, R., Miller, C., Tytell, J., and Barthelmie, R. J.: Intense and extreme wind speeds observed by anemometer and seismic networks: An Eastern US case study, J. Appl. Meteorol. Clim., 53, 2417–2429, https://doi.org/10.1175/jamc-d-14-0091.1, 2014.
Pryor, S. C., Letson, F. W., and Barthelmie, R. J.: Variability in wind energy generation across the contiguous USA, J. Appl. Meteorol. Clim., 59, 2021–2039, https://doi.org/10.1175/JAMC-D-20-0162.1, 2020a.
Pryor, S. C., Barthelmie, R. J., Bukovsky, M. S., Leung, L. R., and Sakaguchi, K.: Climate change impacts on wind power generation, Nature Reviews Earth & Environment, 1, 627–643, 2020b.
Pryor, S. C., Barthelmie, R. J., and Shepherd, T. J.: Wind power production from very large offshore wind farms, Joule, 5, 2663–2686, https://doi.org/10.1016/j.joule.2021.09.002, 2021.
Pryor, S. C., Coburn, J. J., Barthelmie, R. J., and Shepherd, T. J.: Projecting Future Energy Production from Operating Wind Farms in North America: Part 1: Dynamical Downscaling, J. Appl. Meteorol. Clim., 62, 63–80, https://doi.org/10.1175/jamc-d-22-0044.1, 2023.
Sharmar, V. and Markina, M.: Validation of global wind wave hindcasts using ERA5, MERRA2, ERA-Interim and CFSRv2 reanalyzes, IOP C. Ser. Earth Env., 606, 012056, https://doi.org/10.1088/1755-1315/606/1/012056, 2020.
Shrestha, B., Brotzge, J. A., and Wang, J.: Evaluation of the New York State Mesonet Profiler Network data, Atmos. Meas. Tech., 15, 6011–6033, https://doi.org/10.5194/amt-15-6011-2022, 2022.
Simão, H., Powell, W., Archer, C., and Kempton, W.: The challenge of integrating offshore wind power in the US electric grid. Part II: Simulation of electricity market operations, Renew. Energ., 103, 418–431, 2017.
Smith, D. A., Harris, M., Coffey, A. S., Mikkelsen, T., Jørgensen, H. E., Mann, J., and Danielian, R.: Wind lidar evaluation at the Danish wind test site in Høvsøre, Wind Energy, 9, 87–93, 2006.
Solbrekke, I. M., Kvamstø, N. G., and Sorteberg, A.: Mitigation of offshore wind power intermittency by interconnection of production sites, Wind Energ. Sci., 5, 1663–1678, https://doi.org/10.5194/wes-5-1663-2020, 2020.
Staffell, I. and Pfenninger, S.: The increasing impact of weather on electricity supply and demand, Energy, 145, 65–78, 2018.
Stehly, T. and Duffy, P.: 2020 Cost of Wind Energy Review, NREL/TP-5000-81209, National Renewable Energy Laboratory, Golden, CO, https://www.nrel.gov/docs/fy22osti/81209.pdf (last access: 3 August 2023), 77, 2022.
Stensrud, D. J.: Importance of low-level jets to climate: A review, J. Climate, 9, 1698–1711, 1996.
Stone, K. M., Leiter, S. M., Kenney, R. D., Wikgren, B. C., Thompson, J. L., Taylor, J. K., and Kraus, S. D.: Distribution and abundance of cetaceans in a wind energy development area offshore of Massachusetts and Rhode Island, J. Coast. Conserv., 21, 527–543, https://doi.org/10.1007/s11852-017-0526-4, 2017.
Stull, R. B.: Practical Meteorology: An Algebra-based Survey of Atmospheric Science, Univ. of British Columbia, ISBN: 978-0-88865-283-6, 940 pp., 2017.
St. Martin, C. M., Lundquist, J. K., and Handschy, M. A.: Variability of interconnected wind plants: correlation length and its dependence on variability time scale, Environ. Res. Lett., 10, 044004, https://doi.org/10.1088/1748-9326/10/4/044004, 2015.
Troen, I. and Lundtang Petersen, E.: European Wind Atlas, Risø National Laboratory, Roskilde, 656 pp., 1989.
U.S. Department of the Interior: Clean Energy Future, https://www.doi.gov/priorities/clean-energy-future (last access: 3 August 2023), 2021.
U.S. White House: FACT SHEET: President Biden to Catalyze Global Climate Action through the Major Economies Forum on Energy and Climate, https://www.
whitehouse.gov/briefing-room/statements-releases/2023/04/20/
fact-sheet-president-biden-to-catalyze-global-climate-action-through-the-major-economies-forum-on-energy-and-climate/ (last access: 3 August 2023), 2023.
Wilks, D. S.: Statistical methods in the atmospheric sciences, International geophysics series, Academic Press, Oxford, UK, ISBN: 9780123850225, 2011.
Wiser, R. H., Bolinger, M., Hoen, B., Millstein, D., Rand, J., Barbose, G. L., Darghouth, N. R., Gorman, W., Jeong, S., and Mills, A. D.: Land-Based Wind Market Report: 2021 Edition, Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States), https://www.osti.gov/biblio/1818277 (last access: 3 August 2023), 88, 2021.
Zeng, J. and Qiao, W.: Support vector machine-based short-term wind power forecasting, 2011 IEEE/PES power systems conference and exposition, 20–23 March 2011, Phoenix, AZ, USA, 8, https://doi.org/10.1109/PSCE.2011.5772573, 2011.
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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.
Using lidar-derived wind speed measurements at approx. 150 m height at onshore and offshore...
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