Determination of time-varying periodicities in unequally spaced time series of OH* temperatures using a moving Lomb–Scargle periodogram and a fast calculation of the false alarm probabilities

Kalicinsky, Christoph; Reisch, Robert; Knieling, Peter; Koppmann, Ralf

doi:https://doi.org/10.5194/amt-13-467-2020

Articles | Volume 13, issue 2

https://doi.org/10.5194/amt-13-467-2020

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

https://doi.org/10.5194/amt-13-467-2020

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

Articles | Volume 13, issue 2

Research article

|

05 Feb 2020

Research article |

| 05 Feb 2020

Determination of time-varying periodicities in unequally spaced time series of OH* temperatures using a moving Lomb–Scargle periodogram and a fast calculation of the false alarm probabilities

Christoph Kalicinsky, Robert Reisch, Peter Knieling, and Ralf Koppmann

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Final revised paper (published on 05 Feb 2020)
Preprint (discussion started on 30 Sep 2019)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'To editor', Anonymous Referee #1, 30 Oct 2019
- AC1: 'Reply to Referee #1', Christoph Kalicinsky, 18 Dec 2019
RC2: 'Review of amt-219-332', Anonymous Referee #2, 23 Nov 2019
- AC2: 'Reply to Referee #2', Christoph Kalicinsky, 18 Dec 2019

Peer-review completion

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

AR by Christoph Kalicinsky on behalf of the Authors (18 Dec 2019) Author's response Manuscript

ED: Publish as is (06 Jan 2020) by Lars Hoffmann

AR by Christoph Kalicinsky on behalf of the Authors (08 Jan 2020)

Short summary

This study presents an approach to analyse unequally spaced time series of OH* temperatures with respect to time-varying periodic fluctuations. The approach is based on the classical Lomb–Scargle periodogram and, additionally, the idea of a moving window is used. Furthermore, a fast and easy way to analyse the significance of the results is presented. The general performance of the approach is tested with artificially generated time series and results for real observations are presented.