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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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Discussion papers
https://doi.org/10.5194/wes-2019-7
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2019-7
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 19 Mar 2019

Submitted as: research article | 19 Mar 2019

Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Wind Energy Science (WES) and is expected to appear here in due course.

Improving mid-altitude mesoscale wind speed forecasts using LiDAR-based observation nudging for Airborne Wind Energy Systems

Markus Sommerfeld1, Curran Crawford1, Gerald Steinfeld2, and Martin Dörenkämper3 Markus Sommerfeld et al.
  • 1Institute for Integrated Energy Systems, University of Victoria,British Columbia, Canada
  • 2Institute of Physics-Energy Meteorology, Carl von Ossietzky Universität Oldenburg, Germany
  • 3Fraunhofer Institute for Wind Energy Systems, Oldenburg, Germany

Abstract. Airborne wind energy systems (AWES) aim to operate at altitudes above conventional wind turbines where reliable high resolution wind data is scarce. Wind LiDAR measurements and mesoscale models both have their advantages and disadvantages when assessing the wind resource at such heights. This article investigates whether assimilating measurements into the mesoscale WRF model using observation nudging generates a more accurate, complete data set. The impact of continuous observation nudging at multiple altitudes on simulated wind conditions is compared to an unnudged reference run and to the LiDAR measurements themselves. We compare the impact on wind speed and direction for individual days, average diurnal variability and long term statistics. Finally, wind speed data is used to estimate optimal traction power and operating altitudes of AWES. Observation nudging improves the overall accuracy of WRF. Close to the surface the impact of nudging is limited as effects of the air-surface interaction dominate, but becomes more prominent at mid-altitudes and decreases towards high altitudes. The wind speed probability distribution shows a multi-modality caused by changing atmospheric stability conditions. Based on a simplified AWES model the most probable optimal altitude will be around 400 m. Such systems will benefit from dynamically adjusting their operating altitude.

Markus Sommerfeld et al.
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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Markus Sommerfeld et al.
Markus Sommerfeld et al.
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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 is scarce. Wind measurements and computational simulations both have their advantages and disadvantages when assessing the wind resource at such heights. This article estimates optimal operating altitudes and investigates whether assimilating measurements into the model generates a more accurate wind data set at heights up to 1100 m.
Airborne wind energy systems aim to operate at altitudes above conventional wind turbines where...
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