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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/amt-2019-450
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-450
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  20 Feb 2020

20 Feb 2020

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A revised version of this preprint was accepted for the journal AMT and is expected to appear here in due course.

Filtering of pulsed lidars data using spatial information and a clustering algorithm

Leonardo Alcayaga Leonardo Alcayaga
  • DTU Wind Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark

Abstract. Wind lidars present advantages over meteorological masts, including simultaneous multi-point observations, flexibility in measuring geometry, and reduced installation cost; but wind lidars come with the cost of increased complexity in terms of data quality and analysis. Carrier-to-noise ratio (CNR) has been the metric most commonly-used to recover reliable observations from lidar measurements, but with severely reduced data recovery. In this work we apply a clustering technique to identify unreliable measurements from pulsed lidars scanning a horizontal plane, taking advantage of all data available from the lidars–not only CNR, but also line-of-sight wind speed (VLOS), spatial position, and VLOS smoothness. The performance of this data filtering technique is evaluated in terms of data recovery and data quality, against both a median-like filter and a pure CNR-threshold filter. The results show that the clustering filter is capable of recovering more reliable data in noisy regions of the scans, increasing the data recovery up to 38 % and reducing by at least two thirds the acceptance of unreliable measurements, relative to the commonly used CNR-threshold. Along with this, the need for user intervention in the setup of data filtering is reduced considerably, which is a step towards a more automated and robust filter.

Leonardo Alcayaga

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Status: closed
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

Leonardo Alcayaga

Data sets

Østerild Balconies Experiment (Phase 2) E. Simon and N. Vasiljevic https://doi.org/10.11583/dtu.7306802.v1

Model code and software

Lidar data filtering L. Alcayaga https://doi.org/10.5281/zenodo.3676531

Leonardo Alcayaga

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Latest update: 21 Sep 2020
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Short summary
Wind lidars present advantages over meteorological masts, including simultaneous multi-point observations, flexibility in measuring geometry, and reduced installation cost; but wind lidars come with the cost of increased complexity in terms of data quality and analysis. The common Carrier-to-noise ratio and median filters are compared to DBSCAN clustering algorithm to find improved data quality and recovery rate.
Wind lidars present advantages over meteorological masts, including simultaneous multi-point...
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