Articles | Volume 9, issue 11
https://doi.org/10.5194/wes-9-2147-2024
© 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-9-2147-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Nov 2024
Research article |
| 11 Nov 2024
| 11 Nov 2024
Estimating the technical wind energy potential of Kansas that incorporates the effect of regional wind resource depletion by wind turbines
Biospheric Theory and Modelling Group, Max Planck Institute of Biogeochemistry, Jena, Germany
Institute of Physics and Meteorology, University of Hohenheim, Stuttgart, Germany
Axel Kleidon
Biospheric Theory and Modelling Group, Max Planck Institute of Biogeochemistry, Jena, Germany
Nsilulu T. Mbungu
Research Institute of Sciences & Engineering (RISE), University of Sharjah, Sharjah, United Arab Emirates
Department of Electrical Engineering, Tshwane University of Technology, Pretoria, South Africa
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Pin-Hsin Hu, Christian H. Reick, Reiner Schnur, Axel Kleidon, and Martin Claussen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-111, https://doi.org/10.5194/gmd-2024-111, 2024
Preprint under review for GMD
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We introduce the new plant functional diversity model JeDi-BACH, a novel tool that integrates the Jena Diversity Model (JeDi) within the land component of the ICON Earth System Model. JeDi-BACH captures a richer set of plant trait variations based on environmental filtering and functional tradeoffs without a priori knowledge of the vegetation types. JeDi-BACH represents a significant advancement in modeling the complex interactions between plant functional diversity and climate.
Yinglin Tian, Deyu Zhong, Sarosh Alam Ghausi, Guangqian Wang, and Axel Kleidon
Earth Syst. Dynam., 14, 1363–1374, https://doi.org/10.5194/esd-14-1363-2023, https://doi.org/10.5194/esd-14-1363-2023, 2023
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Downward longwave radiation (Rld) is critical for the surface energy budget, but its climatological variation on a global scale is not yet well understood physically. We use a semi-empirical equation derived by Brutsaert (1975) to identify the controlling role that atmospheric heat storage plays in spatiotemporal variations of Rld. Our work helps us to better understand aspects of climate variability, extreme events, and global warming by linking these to the mechanistic contributions of Rld.
Axel Kleidon
Earth Syst. Dynam., 14, 861–896, https://doi.org/10.5194/esd-14-861-2023, https://doi.org/10.5194/esd-14-861-2023, 2023
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The second law of thermodynamics has long intrigued scientists, but what role does it play in the Earth system? This review shows that its main role is that it shapes the conversion of sunlight into work. This work can then maintain the dynamics of the physical climate system, the biosphere, and human societies. The relevance of it is that apparently many processes work at their limits, directly or indirectly, so they become predictable by simple means.
Axel Kleidon, Gabriele Messori, Somnath Baidya Roy, Ira Didenkulova, and Ning Zeng
Earth Syst. Dynam., 14, 241–242, https://doi.org/10.5194/esd-14-241-2023, https://doi.org/10.5194/esd-14-241-2023, 2023
Sarosh Alam Ghausi, Subimal Ghosh, and Axel Kleidon
Hydrol. Earth Syst. Sci., 26, 4431–4446, https://doi.org/10.5194/hess-26-4431-2022, https://doi.org/10.5194/hess-26-4431-2022, 2022
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The observed response of extreme precipitation to global warming remains unclear with significant regional variations. We show that a large part of this uncertainty can be removed when the imprint of clouds in surface temperatures is removed. We used a thermodynamic systems approach to remove the cloud radiative effect from temperatures. We then found that precipitation extremes intensified with global warming at positive rates which is consistent with physical arguments and model simulations.
Samuel Schroers, Olivier Eiff, Axel Kleidon, Ulrike Scherer, Jan Wienhöfer, and Erwin Zehe
Hydrol. Earth Syst. Sci., 26, 3125–3150, https://doi.org/10.5194/hess-26-3125-2022, https://doi.org/10.5194/hess-26-3125-2022, 2022
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In hydrology the formation of landform patterns is of special interest as changing forcings of the natural systems, such as climate or land use, will change these structures. In our study we developed a thermodynamic framework for surface runoff on hillslopes and highlight the differences of energy conversion patterns on two related spatial and temporal scales. The results indicate that surface runoff on hillslopes approaches a maximum power state.
Samuel Schroers, Olivier Eiff, Axel Kleidon, Jan Wienhöfer, and Erwin Zehe
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-79, https://doi.org/10.5194/hess-2021-79, 2021
Manuscript not accepted for further review
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In this study we ask the basic question why surface runoff forms drainage networks and confluences at all and how structural macro form and micro topography is a result of thermodynamic laws. We find that on a macro level hillslopes should tend from negative exponential towards exponential forms and that on a micro level the formation of rills goes hand in hand with drainage network formation of river basins. We hypothesize that we can learn more about erosion processes if we extend this theory.
Axel Kleidon and Lee M. Miller
Geosci. Model Dev., 13, 4993–5005, https://doi.org/10.5194/gmd-13-4993-2020, https://doi.org/10.5194/gmd-13-4993-2020, 2020
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When winds are used as renewable energy by more and more wind turbines, one needs to account for the effect of wind turbines on the atmospheric flow. The Kinetic Energy Budget of the Atmosphere (KEBA) model provides a simple, physics-based approach to account for this effect very well when compared to much more detailed numerical simulations with an atmospheric model. KEBA should be useful to derive lower, more realistic wind energy resource potentials of different regions.
Annu Panwar, Maik Renner, and Axel Kleidon
Hydrol. Earth Syst. Sci., 24, 4923–4942, https://doi.org/10.5194/hess-24-4923-2020, https://doi.org/10.5194/hess-24-4923-2020, 2020
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Here we examine the effect of evaporative cooling across different vegetation types. Evaporation cools surface temperature significantly in short vegetation. In the forest, the high aerodynamic conductance explains 56 % of the reduced surface temperature. Therefore, the main cooling agent in the forest is the high aerodynamic conductance and not evaporation. Additionally, we propose the diurnal variation in surface temperature as being a potential indicator of evaporation in short vegetation.
Axel Kleidon, Erwin Zehe, and Ralf Loritz
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2019-52, https://doi.org/10.5194/esd-2019-52, 2019
Manuscript not accepted for further review
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Many fluxes in Earth systems are not homogeneously distributed across space, but occur highly concentrated in structures, such as turbulent eddies, river networks, vascular networks of plants, or human-made infrastructures. Yet, the highly-organized nature of these fluxes is typically only described at a rudimentary level, if at all. We propose that it requires a novel approach to describe these structures that focuses on the work done to build and maintain these structures, and the feedbacks.
Ralf Loritz, Axel Kleidon, Conrad Jackisch, Martijn Westhoff, Uwe Ehret, Hoshin Gupta, and Erwin Zehe
Hydrol. Earth Syst. Sci., 23, 3807–3821, https://doi.org/10.5194/hess-23-3807-2019, https://doi.org/10.5194/hess-23-3807-2019, 2019
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In this study, we develop a topographic index explaining hydrological similarity within a energy-centered framework, with the observation that the majority of potential energy is dissipated when rainfall becomes runoff.
Philipp Porada, Alexandra Tamm, Jose Raggio, Yafang Cheng, Axel Kleidon, Ulrich Pöschl, and Bettina Weber
Biogeosciences, 16, 2003–2031, https://doi.org/10.5194/bg-16-2003-2019, https://doi.org/10.5194/bg-16-2003-2019, 2019
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The trace gases NO and HONO are crucial for atmospheric chemistry. It has been suggested that biological soil crusts in drylands contribute substantially to global NO and HONO emissions, based on empirical upscaling of laboratory and field observations. Here we apply an alternative, process-based modeling approach to predict these emissions. We find that biological soil crusts emit globally significant amounts of NO and HONO, which also vary depending on the type of biological soil crust.
Erwin Zehe, Ralf Loritz, Conrad Jackisch, Martijn Westhoff, Axel Kleidon, Theresa Blume, Sibylle K. Hassler, and Hubert H. Savenije
Hydrol. Earth Syst. Sci., 23, 971–987, https://doi.org/10.5194/hess-23-971-2019, https://doi.org/10.5194/hess-23-971-2019, 2019
Martijn Westhoff, Axel Kleidon, Stan Schymanski, Benjamin Dewals, Femke Nijsse, Maik Renner, Henk Dijkstra, Hisashi Ozawa, Hubert Savenije, Han Dolman, Antoon Meesters, and Erwin Zehe
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2019-6, https://doi.org/10.5194/esd-2019-6, 2019
Publication in ESD not foreseen
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Even models relying on physical laws have parameters that need to be measured or estimated. Thermodynamic optimality principles potentially offer a way to reduce the number of estimated parameters by stating that a system evolves to an optimum state. These principles have been applied successfully within the Earth system, but it is often unclear what to optimize and how. In this review paper we identify commonalities between different successful applications as well as some doubtful applications.
Maik Renner, Claire Brenner, Kaniska Mallick, Hans-Dieter Wizemann, Luigi Conte, Ivonne Trebs, Jianhui Wei, Volker Wulfmeyer, Karsten Schulz, and Axel Kleidon
Hydrol. Earth Syst. Sci., 23, 515–535, https://doi.org/10.5194/hess-23-515-2019, https://doi.org/10.5194/hess-23-515-2019, 2019
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We estimate the phase lag of surface states and heat fluxes to incoming solar radiation at the sub-daily timescale. While evapotranspiration reveals a minor phase lag, the vapor pressure deficit used as input by Penman–Monteith approaches shows a large phase lag. The surface-to-air temperature gradient used by energy balance residual approaches shows a small phase shift in agreement with the sensible heat flux and thus explains the better correlation of these models at the sub-daily timescale.
Axel Kleidon and Maik Renner
Earth Syst. Dynam., 9, 1127–1140, https://doi.org/10.5194/esd-9-1127-2018, https://doi.org/10.5194/esd-9-1127-2018, 2018
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Turbulent fluxes represent an efficient way to transport heat and moisture from the surface into the atmosphere. Due to their inherently highly complex nature, they are commonly described by semiempirical relationships. What we show here is that these fluxes can also be predicted by viewing them as the outcome of a heat engine that operates between the warm surface and the cooler atmosphere and that works at its limit.
Ralf Loritz, Hoshin Gupta, Conrad Jackisch, Martijn Westhoff, Axel Kleidon, Uwe Ehret, and Erwin Zehe
Hydrol. Earth Syst. Sci., 22, 3663–3684, https://doi.org/10.5194/hess-22-3663-2018, https://doi.org/10.5194/hess-22-3663-2018, 2018
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In this study we explore the role of spatially distributed information on hydrological modeling. For that, we develop and test an approach which draws upon information theory and thermodynamic reasoning. We show that the proposed set of methods provide a powerful framework for understanding and diagnosing how and when process organization and functional similarity of hydrological systems emerge in time and, hence, when which landscape characteristic is important in a model application.
Corina Buendía, Axel Kleidon, Stefano Manzoni, Björn Reu, and Amilcare Porporato
Biogeosciences, 15, 279–295, https://doi.org/10.5194/bg-15-279-2018, https://doi.org/10.5194/bg-15-279-2018, 2018
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Amazonia is highly biodiverse and of global importance for regulating the climate system. Because soils are highly weathered, phosphorus (P) is suggested to limit ecosystem productivity. Here, we evaluate the importance of P redistribution by animals using a simple mathematical model synthesizing the major processes of the Amazon P cycle. Our findings suggest that food web complexity plays an important role for sustaining the productivity of terra firme forests.
Axel Kleidon and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-674, https://doi.org/10.5194/hess-2017-674, 2017
Revised manuscript not accepted
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At larger scales, the flow of rivers can often be described by a relatively simple, exponential decay, and it is unclear how such simple behaviour can be explained given that river basins show such vast complexity. Here, we use a highly idealised model to show that such simple behaviour can be explained by viewing it as the emergent consequence of the groundwater system (which feeds river flow) minimising its energy dissipation.
Axel Kleidon and Maik Renner
Earth Syst. Dynam., 8, 849–864, https://doi.org/10.5194/esd-8-849-2017, https://doi.org/10.5194/esd-8-849-2017, 2017
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We provide an explanation why land temperatures respond more strongly to global warming than ocean temperatures, a robust finding in observations and models that has so far not been understood well. We explain it by the different ways by which ocean and land surfaces buffer the strong variation in solar radiation and demonstrate this with a simple, physically based model. Our explanation also illustrates why nighttime temperatures warm more strongly, another robust finding of global warming.
Philipp Porada, Ulrich Pöschl, Axel Kleidon, Christian Beer, and Bettina Weber
Biogeosciences, 14, 1593–1602, https://doi.org/10.5194/bg-14-1593-2017, https://doi.org/10.5194/bg-14-1593-2017, 2017
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Lichens and bryophytes have been shown to release nitrous oxide, which is a strong greenhouse gas and atmospheric ozone-depleting agent. Here we apply a process-based computer model of lichens and bryophytes at the global scale, to estimate growth and respiration of the organisms. By relating respiration to nitrous oxide release, we simulate global nitrous oxide emissions of 0.27 (0.19–0.35) Tg yr−1. Moreover, we quantify different sources of uncertainty in nitrous oxide emission rates.
Anke Hildebrandt, Axel Kleidon, and Marcel Bechmann
Hydrol. Earth Syst. Sci., 20, 3441–3454, https://doi.org/10.5194/hess-20-3441-2016, https://doi.org/10.5194/hess-20-3441-2016, 2016
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This theoretical paper describes the energy fluxes and dissipation along the flow paths involved in root water uptake, an approach that is rarely taken. We show that this provides useful additional insights for understanding the biotic and abiotic impediments to root water uptake. This approach shall be applied to explore efficient water uptake strategies and help locate the limiting processes in the complex soil–plant–atmosphere system.
Maik Renner, Sibylle K. Hassler, Theresa Blume, Markus Weiler, Anke Hildebrandt, Marcus Guderle, Stanislaus J. Schymanski, and Axel Kleidon
Hydrol. Earth Syst. Sci., 20, 2063–2083, https://doi.org/10.5194/hess-20-2063-2016, https://doi.org/10.5194/hess-20-2063-2016, 2016
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We estimated forest transpiration (European beech) along a steep valley cross section. Atmospheric demand, obtained by the thermodynamic limit of maximum power, is the dominant control of transpiration at all sites.
To our surprise we find that transpiration is rather similar across sites with different aspect (north vs. south) and different stand structure due to systematically varying sap velocities. Such a compensation effect is highly relevant for modeling and upscaling of transpiration.
E. Zehe, U. Ehret, L. Pfister, T. Blume, B. Schröder, M. Westhoff, C. Jackisch, S. J. Schymanski, M. Weiler, K. Schulz, N. Allroggen, J. Tronicke, L. van Schaik, P. Dietrich, U. Scherer, J. Eccard, V. Wulfmeyer, and A. Kleidon
Hydrol. Earth Syst. Sci., 18, 4635–4655, https://doi.org/10.5194/hess-18-4635-2014, https://doi.org/10.5194/hess-18-4635-2014, 2014
C. Buendía, S. Arens, T. Hickler, S. I. Higgins, P. Porada, and A. Kleidon
Biogeosciences, 11, 3661–3683, https://doi.org/10.5194/bg-11-3661-2014, https://doi.org/10.5194/bg-11-3661-2014, 2014
A. Kleidon, M. Renner, and P. Porada
Hydrol. Earth Syst. Sci., 18, 2201–2218, https://doi.org/10.5194/hess-18-2201-2014, https://doi.org/10.5194/hess-18-2201-2014, 2014
U. Ehret, H. V. Gupta, M. Sivapalan, S. V. Weijs, S. J. Schymanski, G. Blöschl, A. N. Gelfan, C. Harman, A. Kleidon, T. A. Bogaard, D. Wang, T. Wagener, U. Scherer, E. Zehe, M. F. P. Bierkens, G. Di Baldassarre, J. Parajka, L. P. H. van Beek, A. van Griensven, M. C. Westhoff, and H. C. Winsemius
Hydrol. Earth Syst. Sci., 18, 649–671, https://doi.org/10.5194/hess-18-649-2014, https://doi.org/10.5194/hess-18-649-2014, 2014
S. P. K. Bowring, L. M. Miller, L. Ganzeveld, and A. Kleidon
Earth Syst. Dynam., 5, 43–53, https://doi.org/10.5194/esd-5-43-2014, https://doi.org/10.5194/esd-5-43-2014, 2014
A. Kleidon and M. Renner
Earth Syst. Dynam., 4, 455–465, https://doi.org/10.5194/esd-4-455-2013, https://doi.org/10.5194/esd-4-455-2013, 2013
P. Porada, B. Weber, W. Elbert, U. Pöschl, and A. Kleidon
Biogeosciences, 10, 6989–7033, https://doi.org/10.5194/bg-10-6989-2013, https://doi.org/10.5194/bg-10-6989-2013, 2013
E. Zehe, U. Ehret, T. Blume, A. Kleidon, U. Scherer, and M. Westhoff
Hydrol. Earth Syst. Sci., 17, 4297–4322, https://doi.org/10.5194/hess-17-4297-2013, https://doi.org/10.5194/hess-17-4297-2013, 2013
E. Simoncini, N. Virgo, and A. Kleidon
Earth Syst. Dynam., 4, 317–331, https://doi.org/10.5194/esd-4-317-2013, https://doi.org/10.5194/esd-4-317-2013, 2013
A. Kleidon and M. Renner
Hydrol. Earth Syst. Sci., 17, 2873–2892, https://doi.org/10.5194/hess-17-2873-2013, https://doi.org/10.5194/hess-17-2873-2013, 2013
A. Kleidon, E. Zehe, U. Ehret, and U. Scherer
Hydrol. Earth Syst. Sci., 17, 225–251, https://doi.org/10.5194/hess-17-225-2013, https://doi.org/10.5194/hess-17-225-2013, 2013
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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
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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.
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
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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.
Mehtab Ahmed Khan, Dries Allaerts, Simon J. Watson, and Matthew J. Churchfield
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-138, https://doi.org/10.5194/wes-2024-138, 2024
Revised manuscript accepted for WES
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To guide realistic atmospheric gravity wave simulations, we conduct an LES study of flow over a 2D hill and through a wind farm canopy, examining optimal domain size and Rayleigh damping layer setup. Wave properties based on a Froude number determine optimal domain and damping parameters. Reasonably accurate solutions require the domain length exceed the effective horizontal wavelength, height and damping thickness equal a vertical wavelength, and normalized-damping coefficient between 1–10.
Adriel J. Carvalho, Francisco Albuquerque Leite Neto, and Denisson Q. Oliveira
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-132, https://doi.org/10.5194/wes-2024-132, 2024
Revised manuscript accepted for WES
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Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-84, https://doi.org/10.5194/wes-2024-84, 2024
Revised manuscript accepted for WES
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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When wind turbine blades are exposed to strong winds and heavy rainfall, they may be damaged and their efficiency reduced. The problem is most pronounced offshore where turbines are tall and the climate is harsh. Satellites provide global half-hourly rain observations. We use these rain data as input to a model for blade lifetime prediction and find that the satellite-based predictions agree well with predictions based on observations from weather stations on the ground.
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
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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
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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
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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.
Andrew Clifton, Sarah Barber, Alexander Stökl, Helmut Frank, and Timo Karlsson
Wind Energ. Sci., 7, 2231–2254, https://doi.org/10.5194/wes-7-2231-2022, https://doi.org/10.5194/wes-7-2231-2022, 2022
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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.
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
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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
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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
Abkar, M., Sharifi, A., and Porté-Agel, F.: Large-eddy simulation of the diurnal variation of wake flows in a finite-size wind farm, J. Phys. Conf. Ser., 625, 012031, https://doi.org/10.1088/1742-6596/625/1/012031, 2015. a
Abkar, M., Sharifi, A., and Porté-Agel, F.: Wake flow in a wind farm during a diurnal cycle, J. Turbul., 17, 420–441, https://doi.org/10.1080/14685248.2015.1127379, 2016. a, b, c
Agora Energiewende, Agora Verkehrswende, Technical University of Denmark, and Max Planck Institute of Biogeochemistry: Making the Most of Offshore Wind: Re-Evaluating the Potential of Offshore Wind in the German North Sea, Tech. Rep. 176/01-S-2020/EN, Agora Energiewende, Agora Verkehrswende, Technical University of Denmark and Max-Planck-Institute for Biogeochemistry, https://static.agora-energiewende.de/fileadmin/Projekte/2019/Offshore_Potentials/176_A-EW_A-VW_Offshore-Potentials_Publication_WEB.pdf (last access: 10 September 2024), 2020. a, b, c
Ahsbahs, T., Nygaard, N., Newcombe, A., and Badger, M.: Wind Farm Wakes from SAR and Doppler Radar, Remote Sens.-Basel, 12, 462–484, https://doi.org/10.3390/rs12030462, 2020. a
Aitken, M. L., Kosović, B., Mirocha, J. D., and Lundquist, J. K.: Large eddy simulation of wind turbine wake dynamics in the stable boundary layer using the Weather Research and Forecasting Model, J. Renew. Sustain. Ener., 6, 033137-1–033137-13, https://doi.org/10.1063/1.4885111, 2014. a
Akhtar, N., Geyer, B., Rockel, B., Sommer, P. S., and Schrum, C.: Accelerating deployment of offshore wind energy alter wind climate and reduce future power generation potentials, Sci. Rep.-UK, 11, 11826, https://doi.org/10.1038/s41598-021-91283-3, 2021. a
AMS: Free atmosphere – Glossary of Meteorology, https://glossary.ametsoc.org/wiki/Free_atmosphere (last access: 27 March 2024), 2024. a
Archer, C. L. and Jacobson, M. Z.: Evaluation of global wind power, J. Geophys. Res.-Atmos., 110, D12110, https://doi.org/10.1029/2004JD005462, 2005. a, b
Archer, C. L., Wu, S., Ma, Y., and Jiménez, P. A.: Two Corrections for Turbulent Kinetic Energy Generated by Wind Farms in the WRF Model, Mon. Weather Rev., 148, 4823–4835, https://doi.org/10.1175/mwr-d-20-0097.1, 2020. a, b
Blahak, U. and Wetter-Jetzt: A Simple Parameterization of Drag Forces Induced by Large Wind Farms for Numerical Weather Prediction Models, https://api.semanticscholar.org/CorpusID:55966737 (last access: 9 July 2023), 2010. a
Blanco, M. I.: The economics of wind energy, Renewable and Sustainable Energy Reviews, 13, 1372–1382, https://doi.org/10.1016/j.rser.2008.09.004, 2009. a, b, c
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
Boettcher, M., Hoffmann, P., Lenhart, H.-J., Schlünzen, K. H., and Schoetter, R.: Influence of large offshore wind farms on North German climate, Meteorol. Z., 24, 465–480, https://doi.org/10.1127/metz/2015/0652, 2015. a
Brown, A., Beiter, P., Heimiller, D., Davidson, C., Denholm, P., Melius, J., Lopez, A., Hettinger, D., Mulcahy, D., and Porro, G.: Estimating Renewable Energy Economic Potential in the United States. Methodology and Initial Results, Tech. rep., OSTI.GOV, https://doi.org/10.2172/1215323, 2016. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Capps, S. B. and Zender, C. S.: Estimated global ocean wind power potential from QuikSCAT observations, accounting for turbine characteristics and siting, J. Geophys. Res.-Atmos., 115, D09101, https://doi.org/10.1029/2009JD012679, 2010. a
Cañadillas, B., Foreman, R., Barth, V., Siedersleben, S., Lampert, A., Platis, A., Djath, B., Schulz-Stellenfleth, J., Bange, J., Emeis, S., and Neumann, T.: Offshore wind farm wake recovery: Airborne measurements and its representation in engineering models, Wind Energy, 23, 1249–1265, https://doi.org/10.1002/we.2484, 2020. a, b
Christiansen, M. B. and Hasager, C. B.: Wake effects of large offshore wind farms identified from satellite SAR, Remote Sens. Environ., 98, 251–268, https://doi.org/10.1016/j.rse.2005.07.009, 2005. a
Cory, K. and Schwabe, P.: Wind Levelized Cost of Energy: A Comparison of Technical and Financing Input Variables, OSTI.GOV, https://doi.org/10.2172/966296, 2009. a
Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Seyboth, K., Matschoss, P., Kadner, S., Zwickel, T., Eickemeier, P., Hansen, G., Schlömer, S., and von Stechow, C.: IPCC special report on renewable energy sources and climate change mitigation, Prepared By Working Group III of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK, ISBN 978-1-107-02340-6 (hardback), ISBN 978-1-107-60710-1 (paperback), 2011. a
Enevoldsen, P., Permien, F.-H., Bakhtaoui, I., von Krauland, A.-K., Jacobson, M. Z., Xydis, G., Sovacool, B. K., Valentine, S. V., Luecht, D., and Oxley, G.: How much wind power potential does europe have? Examining european wind power potential with an enhanced socio-technical atlas, Energ. Policy, 132, 1092–1100, https://doi.org/10.1016/j.enpol.2019.06.064, 2019. a, b
Eurek, K., Sullivan, P., Gleason, M., Hettinger, D., Heimiller, D., and Lopez, A.: An improved global wind resource estimate for integrated assessment models, Energ. Econ., 64, 552–567, https://doi.org/10.1016/j.eneco.2016.11.015, 2017. a, b, c, d
Fischereit, J., Brown, R., Larsén, X. G., Badger, J., and Hawkes, G.: Review of Mesoscale Wind-Farm Parametrizations and Their Applications, Bound.-Lay. Meteorol., 182, 175–224, https://doi.org/10.1007/s10546-021-00652-y, 2021. a, b, c
Fitch, A. C., Olson, J. B., Lundquist, J. K., Dudhia, J., Gupta, A. K., Michalakes, J., and Barstad, I.: Local and Mesoscale Impacts of Wind Farms as Parameterized in a Mesoscale NWP Model, Mon. Weather Rev., 140, 3017–3038, https://doi.org/10.1175/mwr-d-11-00352.1, 2012. a
Fitch, A. C., Lundquist, J. K., and Olson, J. B.: Mesoscale Influences of Wind Farms throughout a Diurnal Cycle, Mon. Weather Rev., 141, 2173–2198, https://doi.org/10.1175/mwr-d-12-00185.1, 2013a. a, b, c, d
Fitch, A. C., Olson, J. B., and Lundquist, J. K.: Parameterization of Wind Farms in Climate Models, J. Climate, 26, 6439–6458, https://doi.org/10.1175/JCLI-D-12-00376.1, 2013b. a, b
Fraedrich, K., Kirk, E., Luksch, U., Lunkeit, F., and Jansen, H.: The planet simulator: towards a user friendly model, Meteorol. Z., 14, 299–304, https://doi.org/10.1127/0941-2948/2005/0043, 2005. a
Frandsen, S., Barthelmie, R., Pryor, S., Rathmann, O., Larsen, S., Højstrup, J., and Thøgersen, M.: Analytical modelling of wind speed deficit in large offshore wind farms, Wind Energy, 9, 39–53, 2006. a
GEA: Global Energy Assessment: Toward a Sustainable Future, Cambridge University Press, https://doi.org/10.1017/CBO9780511793677, 2012. a
Gustavson, M. R.: Limits to Wind Power Utilization, Science, 204, 13–17, https://doi.org/10.1126/science.204.4388.13, 1979. a, b, c
Hasager, C., Vincent, P., Badger, J., Badger, M., Bella, A. D., Peña, A., Husson, R., and Volker, P.: Using Satellite SAR to Characterize the Wind Flow around Offshore Wind Farms, Energies, 8, 5413–5439, https://doi.org/10.3390/en8065413, 2015. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Hoogwijk, M., de Vries, B., and Turkenburg, W.: Assessment of the global and regional geographical, technical and economic potential of onshore wind energy, Energ. Econ., 26, 889–919, https://doi.org/10.1016/j.eneco.2004.04.016, 2004. a, b, c, d
IEA: World energy outlook 2021 – analysis, https://www.iea.org/reports/world-energy-outlook-2021 (last access: 6 July 2023), 2021. a
Jacobson, M. Z.: GATOR-GCMM: A global- through urban-scale air pollution and weather forecast model: 1. Model design and treatment of subgrid soil, vegetation, roads, rooftops, water, sea ice, and snow, J. Geophys. Res.-Atmos., 106, 5385–5401, https://doi.org/10.1029/2000JD900560, 2001. a
Jacobson, M. Z. and Delucchi, M. A.: Providing all global energy with wind, water, and solar power, Part I: Technologies, energy resources, quantities and areas of infrastructure, and materials, Energ. Policy, 39, 1154–1169, https://doi.org/10.1016/j.enpol.2010.11.040, 2011. a
Katic, I., Højstrup, J., and Jensen, N. O.: A simple model for cluster efficiency, in: European wind energy association conference and exhibition, vol. 1, A. Raguzzi Rome, Italy, pp. 407–410, 1986. a
Kleidon, A.: Physical limits of wind energy within the atmosphere and its use as renewable energy: From the theoretical basis to practical implications, Meteorol. Z., 30, 203–225, https://doi.org/10.1127/metz/2021/1062, 2021. a, b
Kleidon, A. and Miller, L. M.: The Kinetic Energy Budget of the Atmosphere (KEBA) model 1.0: a simple yet physical approach for estimating regional wind energy resource potentials that includes the kinetic energy removal effect by wind turbines, Geosci. Model Dev., 13, 4993–5005, https://doi.org/10.5194/gmd-13-4993-2020, 2020. a, b, c, d, e, f, g, h, i, j, k
Larsén, X. G. and Fischereit, J.: A case study of wind farm effects using two wake parameterizations in the Weather Research and Forecasting (WRF) model (V3.7.1) in the presence of low-level jets, Geosci. Model Dev., 14, 3141–3158, https://doi.org/10.5194/gmd-14-3141-2021, 2021. a
Lu, X., McElroy, M. B., and Kiviluoma, J.: Global potential for wind-generated electricity, P. Natl. Acad. Sci. USA, 106, 10933–10938, https://doi.org/10.1073/pnas.0904101106, 2009. a, b
Lundquist, J. K., Takle, E. S., Boquet, M., Kosovic, B., Rhodes, M. E., Rajewski, D., Doorenbos, R., Irvin, S., Aitken, M. L., Friedrich, K., Quelet, P. T., Rana, J., Martin, C. S., Vanderwende, B., and Worsnop, R.: Lidar observations of interacting wind turbine wakes in an onshore wind farm, in: EWEA meeting proceedings, EWEA – European Wind Energy Agency, 10–13, https://www.nrgsystems.com/assets/resources/Lidar-observations-of-interacting-wind-turbine-wakes-Whitepaper.pdf (last access: 27 January 2023), 2014. a
Lundquist, J. K., DuVivier, K. K., Kaffine, D., and Tomaszewski, J. M.: Costs and consequences of wind turbine wake effects arising from uncoordinated wind energy development, Nature Energy, 4, 26–34, https://doi.org/10.1038/s41560-018-0281-2, 2018. a
Lütkehus, I., Salecker, H., and Adlunger, K.: Potenzial der Windenergie an Land: Studie zur Ermittlung des Bundesweiten Flächen- und Leistungspotenzials der Windenergienutzung an Land, Tech. rep., UBA – Umweltbundesamt, https://www.umweltbundesamt.de/sites/default/files/medien/378/publikationen/potenzial_der_windenergie.pdf (last access: 14 April 2023), 2013. a
Maas, O. and Raasch, S.: Wake properties and power output of very large wind farms for different meteorological conditions and turbine spacings: a large-eddy simulation case study for the German Bight, Wind Energ. Sci., 7, 715–739, https://doi.org/10.5194/wes-7-715-2022, 2022. a
Manwell, J. F., McGowan, J. G., and Rogers, A. L.: Wind energy explained, 2 edn., John Wiley & Sons, Nashville, TN, https://doi.org/10.1002/9781119994367, 2010. a
Marvel, K., Kravitz, B., and Caldeira, K.: Geophysical limits to global wind power, Nat. Clim. Change, 3, 118–121, https://doi.org/10.1038/nclimate1683, 2012. a, b, c, d
McKenna, R., Pfenninger, S., Heinrichs, H., Schmidt, J., Staffell, I., Bauer, C., Gruber, K., Hahmann, A. N., Jansen, M., Klingler, M., Landwehr, N., Larsén, X. G., Lilliestam, J., Pickering, B., Robinius, M., Tröndle, T., Turkovska, O., Wehrle, S., Weinand, J. M., and Wohland, J.: High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs, Renew. Energ., 182, 659–684, https://doi.org/10.1016/j.renene.2021.10.027, 2022. a, b
Méchali, M., Barthelmie, R., Frandsen, S., Jensen, L., and Réthoré, P.-E.: Wake effects at Horns Rev and their influence on energy production, EWEA – European Wind Energy Association, https://api.semanticscholar.org/CorpusID:14985777 (last access: 15 December 2021), 2006. a
Miller, L., Brunsell, N. A., Mechem, D. B., Gans, F., Monaghan, A. J., Vautard, R., Keith, D. W., and Kleidon, A.: Two methods for estimating limits to large-scale wind power generation, P. Natl. Acad. Sci. USA, 112, 11169–11174, https://doi.org/10.1073/pnas.1408251112, 2015. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, aa, ab, ac, ad, ae
Miller, L. M. and Keith, D. W.: Observation-based solar and wind power capacity factors and power densities, Environ. Res. Lett., 13, 104008, https://doi.org/10.1088/1748-9326/aae102, 2018. a, b
Miller, L. M., Gans, F., and Kleidon, A.: Estimating maximum global land surface wind power extractability and associated climatic consequences, Earth Syst. Dynam., 2, 1–12, https://doi.org/10.5194/esd-2-1-2011, 2011. a, b, c, d
Minz, J., Kleidon, A., and Mbungu, N. T.: Supplementary material and KEBA Model used in the evaluation of Kansas wind energy potential, EDMOND [code and data set], https://doi.org/10.17617/3.78, 2024. a
Mirocha, J. D., Rajewski, D. A., Marjanovic, N., Lundquist, J. K., Kosović, B., Draxl, C., and Churchfield, M. J.: Investigating wind turbine impacts on near-wake flow using profiling lidar data and large-eddy simulations with an actuator disk model, J. Renew. Sustain. Ener., 7, 043143, https://doi.org/10.1063/1.4928873, 2015. a
Nygaard, N. G. and Newcombe, A. C.: Wake behind an offshore wind farm observed with dual-Doppler radars, J. Phys. Conf. Ser., 1037, 072008, https://doi.org/10.1088/1742-6596/1037/7/072008, 2018. a
Nygaard, N. G., Steen, S. T., Poulsen, L., and Pedersen, J. G.: Modelling cluster wakes and wind farm blockage, J. Phys. Conf. Ser., 1618, 062072, https://doi.org/10.1088/1742-6596/1618/6/062072, 2020. a
Pedersen, J. G., Svensson, E., Poulsen, L., and Nygaard, N. G.: Turbulence Optimized Park model with Gaussian wake profile, J. Phys. Conf. Ser., 2265, 022063, https://doi.org/10.1088/1742-6596/2265/2/022063, 2022. a
Peixoto, J. P. and Oort, A. H.: Physics of climate, Springer, ISBN 978-0-88318-712-8, 1992. a
Platis, A., Siedersleben, S. K., Bange, J., Lampert, A., Bärfuss, K., Hankers, R., Cañadillas, B., Foreman, R., Schulz-Stellenfleth, J., Djath, B., Neumann, T., and Emeis, S.: First in situ evidence of wakes in the far field behind offshore wind farms, Sci. Rep.-UK, 8, 2163, https://doi.org/10.1038/s41598-018-20389-y, 2018. a
Prakash, G., Anuta, H., Gielen, D., Gorini, R., Wagner, N., and Gallina, G.: Future of wind:Deployment, investment, technology, grid integration and socio-economic aspects (A Global Energy Transformation paper), Tech. Rep., ISBN 978-92-926-155-3, International Renewable Energy Agency, https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/Oct/IRENA_Future_of_wind_2019.pdf (last access: 2 January 2022), 2019. a, b
Ragheb, M.: Chapter 25 – Economics of Wind Power Generation, in: Wind Energy Engineering, edited by: Letcher, T. M., Academic Press, https://doi.org/10.1016/B978-0-12-809451-8.00025-4, pp. 537–555, 2017. a, b, c, d
Rajewski, D. A., Takle, E. S., Lundquist, J. K., Oncley, S., Prueger, J. H., Horst, T. W., Rhodes, M. E., Pfeiffer, R., Hatfield, J. L., Spoth, K. K., and Doorenbos, R. K.: Crop Wind Energy Experiment (CWEX): Observations of Surface-Layer, Boundary Layer, and Mesoscale Interactions with a Wind Farm, B. Am. Meteorol. Soc., 94, 655–672, https://doi.org/10.1175/bams-d-11-00240.1, 2013. a
Ruijgrok, E. C. M., Bulder, B. H., and van Druten, E. J.: Cost Evaluation of North Sea Offshore Wind, Wittveen+Bos, https://northseawindpowerhub.eu/sites/northseawindpowerhub.eu/files/media/document/Cost-Evaluation-of-North-Sea-Offshore-Wind-1.pdf (last access: 7 November 2024), 2019. a
Schallenberg-Rodriguez, J.: A methodological review to estimate techno-economical wind energy production, Renewable and Sustainable Energy Reviews, 21, 272–287, https://doi.org/10.1016/j.rser.2012.12.032, 2013. a, b
Schneemann, J., Rott, A., Dörenkämper, M., Steinfeld, G., and Kühn, M.: Cluster wakes impact on a far-distant offshore wind farm's power, Wind Energ. Sci., 5, 29–49, https://doi.org/10.5194/wes-5-29-2020, 2020. a
Siedersleben, S. K., Platis, A., Lundquist, J. K., Lampert, A., Bärfuss, K., Cañadillas, B., Djath, B., Schulz-Stellenfleth, J., Bange, J., Neumann, T., and Emeis, S.: Evaluation of a wind farm parametrization for mesoscale atmospheric flow models with aircraft measurements, Meteorol. Z., 27, 401–415, 2018. a
Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Barker, D., Wang, W., Huang, X.-Y., and Duda, M.: A Description of the Advanced Research WRF Version 3, Tech. rep., UCAR, https://doi.org/10.5065/D68S4MVH, 2008. a
Staffell, I. and Pfenninger, S.: Using bias-corrected reanalysis to simulate current and future wind power output, Energy, 114, 1224–1239, https://doi.org/10.1016/j.energy.2016.08, 2016. a, b, c
Volker, P. J. H., Badger, J., Hahmann, A. N., and Ott, S.: The Explicit Wake Parametrisation V1.0: a wind farm parametrisation in the mesoscale model WRF, Geosci. Model Dev., 8, 3715–3731, https://doi.org/10.5194/gmd-8-3715-2015, 2015. a, b, c, d
Wang, C. and Prinn, R. G.: Potential climatic impacts and reliability of very large-scale wind farms, Atmos. Chem. Phys., 10, 2053–2061, https://doi.org/10.5194/acp-10-2053-2010, 2010. a, b, c, d
Wang, C. and Prinn, R. G.: Potential climatic impacts and reliability of large-scale offshore wind farms, Environ. Res. Lett., 6, 025101, https://doi.org/10.1088/1748-9326/6/2/025101, 2011. a, b, c, d
Wiser, R., Jenni, K., Seel, J., Baker, E., Hand, M., Lantz, E., and Smith, A.: Expert elicitation survey on future wind energy costs, Nature Energy, 1, 16135, https://doi.org/10.1038/nenergy.2016.135, 2016. a, b
Wu, Y.-T. and Porté-Agel, F.: Modeling turbine wakes and power losses within a wind farm using LES: An application to the Horns Rev offshore wind farm, Renew. Energ., 75, 945–955, https://doi.org/10.1016/j.renene.2014.06.019, 2015. a
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.
Estimates of power output from regional wind turbine deployments in energy scenarios assume that...
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