Articles | Volume 4, issue 4
https://doi.org/10.5194/wes-4-563-2019
https://doi.org/10.5194/wes-4-563-2019
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, Martin Dörenkämper, Gerald Steinfeld, and Curran Crawford

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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
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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.
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