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

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.