Journal cover Journal topic
Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
Journal topic
WES | Articles | Volume 4, issue 4
Wind Energ. Sci., 4, 563–580, 2019
https://doi.org/10.5194/wes-4-563-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Wind Energ. Sci., 4, 563–580, 2019
https://doi.org/10.5194/wes-4-563-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 18 Oct 2019

Research article | 18 Oct 2019

Improving mesoscale wind speed forecasts using lidar-based observation nudging for airborne wind energy systems

Markus Sommerfeld et al.

Related authors

Surrogate models for unsteady aerodynamics using non-intrusive Polynomial Chaos Expansions
Rad Haghi and Curran Crawford
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2020-24,https://doi.org/10.5194/wes-2020-24, 2020
Preprint under review for WES
The Making of the New European Wind Atlas, Part 1: Model Sensitivity
Andrea N. Hahmann, Tija Sile, Björn Witha, Neil N. Davis, Martin Dörenkämper, Yasemin Ezber, Elena García-Bustamante, J. Fidel González Rouco, Jorge Navarro, Bjarke T. Olsen, and Stefan Söderberg
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-349,https://doi.org/10.5194/gmd-2019-349, 2020
Preprint under review for GMD
Short summary
Cluster wakes impact on a far-distant offshore wind farm's power
Jörge Schneemann, Andreas Rott, Martin Dörenkämper, Gerald Steinfeld, and Martin Kühn
Wind Energ. Sci., 5, 29–49, https://doi.org/10.5194/wes-5-29-2020,https://doi.org/10.5194/wes-5-29-2020, 2020
Short summary
Adjoint-based calibration of inlet boundary condition for atmospheric computational fluid dynamics solvers
Siamak Akbarzadeh, Hassan Kassem, Renko Buhr, Gerald Steinfeld, and Bernhard Stoevesandt
Wind Energ. Sci., 4, 619–632, https://doi.org/10.5194/wes-4-619-2019,https://doi.org/10.5194/wes-4-619-2019, 2019
Short summary
An active power control approach for wake-induced load alleviation in a fully developed wind farm boundary layer
Mehdi Vali, Vlaho Petrović, Gerald Steinfeld, Lucy Y. Pao, and Martin Kühn
Wind Energ. Sci., 4, 139–161, https://doi.org/10.5194/wes-4-139-2019,https://doi.org/10.5194/wes-4-139-2019, 2019
Short summary

Related subject area

Wind and turbulence
Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations
Paul Hulsman, Søren Juhl Andersen, and Tuhfe Göçmen
Wind Energ. Sci., 5, 309–329, https://doi.org/10.5194/wes-5-309-2020,https://doi.org/10.5194/wes-5-309-2020, 2020
Short summary
How to improve the state of the art in metocean measurement datasets
Erik Quaeghebeur and Michiel B. Zaaijer
Wind Energ. Sci., 5, 285–308, https://doi.org/10.5194/wes-5-285-2020,https://doi.org/10.5194/wes-5-285-2020, 2020
Short summary
The Power Curve Working Group's assessment of wind turbine power performance prediction methods
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
Aeroelastic response of a multi-megawatt upwind horizontal axis wind turbine (HAWT) based on fluid–structure interaction simulation
Yasir Shkara, Martin Cardaun, Ralf Schelenz, and Georg Jacobs
Wind Energ. Sci., 5, 141–154, https://doi.org/10.5194/wes-5-141-2020,https://doi.org/10.5194/wes-5-141-2020, 2020
Short summary
The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds
Nicola Bodini and Mike Optis
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2020-2,https://doi.org/10.5194/wes-2020-2, 2020
Revised manuscript accepted for WES
Short summary

Cited articles

Al-Yahyai, S., Charabi, Y., and Gastli, A.: Review of the use of numerical weather prediction (NWP) models for wind energy assessment, Renew. Sustain. Energy Rev., 14, 3192–3198, https://doi.org/10.1016/j.rser.2010.07.001, 2010. a
Archer, C. L. and Caldeira, K.: Global Assessment of High-Altitude Wind Power, Energies, 2, 307–319, https://doi.org/10.3390/en20200307, 2009. a
Arya, P. and Holton, J.: Introduction to Micrometeorology, in: International Geophysics, Elsevier Science, available at: https://www.elsevier.com/books/introduction-to-micrometeorology/arya/978-0-12-059354-5 (last access: 3 October 2019), 2001. a
Bastigkeit, I., Gottschall, J., Gambier, A., Sommerfeld, M., Wolken-Möhlmann, G., and Rudolph, C.: Abschlussbericht-OnKites-Juni 2017_Final-5, detailled report AP1-AP2-AP5, Fraunhofer-Institut für Windenergie und Energiesystemtechnik IWES Nordwest, Bremerhaven, 2017. a
Bechtle, P., Schelbergen, M., Schmehl, R., Zillmann, U., and Watson, S.: Airborne wind energy resource analysis, Renew. Energy, 141, 1103–1116, https://doi.org/10.1016/j.renene.2019.03.118, 2019. a
Publications Copernicus
Download
Short summary
Airborne wind energy systems aim to operate at altitudes above conventional wind turbines where reliable high-resolution wind data are scarce. Wind measurements and computational simulations both have advantages and disadvantages when assessing the wind resource at such heights. This article investigates whether assimilating measurements into the model generates a more accurate wind data set up to 1100 m. These wind data sets are used to estimate optimal AWES operating altitudes and power.
Airborne wind energy systems aim to operate at altitudes above conventional wind turbines where...
Citation