Journal article
Improving Vertical Wind Speed Extrapolation Using Short-Term Lidar Measurements

Publication Details
Basse, A.; Pauscher, L.; Callies, D.
Publication year:
Remote Sensing
Pages range:
article number: 1091
Volume number:
Issue number:


This study investigates how short-term lidar measurements can be used in combination
with a mast measurement to improve vertical extrapolation of wind speed. Several methods are
developed and analyzed for their performance in estimating the mean wind speed, the wind
speed distribution, and the energy yield of an idealized wind turbine at the target height of
the extrapolation. These methods range from directly using the wind shear of the short-term
measurement to a classification approach based on commonly available environmental parameters
using linear regression. The extrapolation strategies are assessed using data of ten wind profiles up
to 200 m measured at different sites in Germany. Different mast heights and extrapolation distances
are investigated. The results show that, using an appropriate extrapolation strategy, even a very
short-term lidar measurement can significantly reduce the uncertainty in the vertical extrapolation of
wind speed. This observation was made for short as well as for very large extrapolation distances.
Among the investigated methods, the linear regression approach yielded better results than the other
methods. Integrating environmental variables into the extrapolation procedure further increased the
performance of the linear regression approach. Overall, the extrapolation error in (theoretical) energy
yield was decreased by around 50% to 70% on average for a lidar measurement of approximately one
to two months depending on the extrapolation height and distance. The analysis of seasonal patterns
revealed that appropriate extrapolation strategies can also significantly reduce the seasonal bias that
is connected to the season during which the short-term measurement is performed.

Last updated on 2020-06-04 at 09:20