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Remote Sens. 2017, 9(6), 561; doi:10.3390/rs9060561

Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements

ForWind, Institute of Physics, University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, Germany
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Author to whom correspondence should be addressed.
Academic Editors: Xiaofeng Li and Prasad S. Thenkabail
Received: 24 December 2016 / Revised: 26 May 2017 / Accepted: 31 May 2017 / Published: 4 June 2017

Abstract

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. View Full-Text
Keywords: data density; spatial normalisation; temporal normalisation; carrier-to-noise-ratio; line-of-sight velocity; radial velocity; threshold filter data density; spatial normalisation; temporal normalisation; carrier-to-noise-ratio; line-of-sight velocity; radial velocity; threshold filter
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Beck, H.; Kühn, M. Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements. Remote Sens. 2017, 9, 561.

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