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

Alcayaga, Leonardo

doi:https://doi.org/10.5194/amt-13-6237-2020

Articles | Volume 13, issue 11

https://doi.org/10.5194/amt-13-6237-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-13-6237-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 11

Research article

|

20 Nov 2020

Research article |

| 20 Nov 2020

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

Leonardo Alcayaga

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Final revised paper (published on 20 Nov 2020)
Preprint (discussion started on 20 Feb 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Reviewer comment', Anonymous Referee #1, 20 Mar 2020
- AC1: 'Answer to reviewer comments', Leonardo Alcayaga, 19 Jun 2020
RC2: 'Review', Anonymous Referee #2, 12 Apr 2020
- AC2: 'Answer to reviewer comments', Leonardo Alcayaga, 19 Jun 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Leonardo Alcayaga on behalf of the Authors (18 Jul 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (06 Aug 2020) by Murray Hamilton

RR by Anonymous Referee #1 (18 Aug 2020)

ED: Publish as is (25 Aug 2020) by Murray Hamilton

AR by Leonardo Alcayaga on behalf of the Authors (04 Sep 2020)

Short summary

Wind lidars present advantages over meteorological masts, including simultaneous multipoint 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 the DBSCAN clustering algorithm to find improved data quality and recovery rate.