Beck, Hauke and Kühn, Martin (2017) Dynamic data filtering of long-range Doppler LiDAR wind speed measurements. Remote sensing, 9 (6). p. 561. ISSN 2072-4292

- Published Version

Volltext (16Mb)


Doppler LiDARs have become flexible and versatile remote sensing devices for wind energy applications. The possibility to measure radial wind speed components contemporaneously at multiple distances is an advantage with respect to meteorological masts. However, these measurements must be filtered due to the measurement geometry, hard targets and atmospheric conditions. To ensure a maximum data availability while producing low measurement errors, we introduce a dynamic data filter approach that conditionally decouples the dependency of data availability with increasing range. The new filter approach is based on the assumption of self-similarity, that has not been used so far for LiDAR data filtering. We tested the accuracy of the dynamic data filter approach together with other commonly used filter approaches, from research and industry applications. This has been done with data from a long-range pulsed LiDAR installed at the offshore wind farm ‘alpha ventus’. There, an ultrasonic anemometer located approximately 2.8 km from the LiDAR was used as reference. The analysis of around 1.5 weeks of data shows, that the error of mean radial velocity can be minimised for wake and free stream conditions.

Item Type: Article
Additional Information: Publiziert mit Hilfe des DFG-geförderten Open Access-Publikationsfonds der Carl von Ossietzky Universität Oldenburg.
Uncontrolled Keywords: data density; spatial normalisation; temporal normalisation; carrier-to-noise-ratio; line-of-sight velocity; radial velocity; threshold filter
Subjects: Science and mathematics > Physics
Divisions: Faculty of Mathematics and Science > Institute of Physics (IfP)
Date Deposited: 25 Sep 2017 11:56
Last Modified: 01 Dec 2017 12:12
URN: urn:nbn:de:gbv:715-oops-33834
DOI: 10.3390/rs9060561

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...