On the intrinsic timescales of temporal variability in measurements of the surface solar radiation

Bengulescu, Marc; Blanc, Philippe; Wald, Lucien

doi:https://doi.org/10.5194/npg-25-19-2018

Articles | Volume 25, issue 1

Nonlin. Processes Geophys., 25, 19–37, 2018

https://doi.org/10.5194/npg-25-19-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Nonlin. Processes Geophys., 25, 19–37, 2018

https://doi.org/10.5194/npg-25-19-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 25, issue 1

Research article 26 Jan 2018

Research article | 26 Jan 2018

On the intrinsic timescales of temporal variability in measurements of the surface solar radiation

Marc Bengulescu et al.

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Final revised paper (published on 26 Jan 2018)
Preprint (discussion started on 17 Oct 2016)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'A methodological issue', Anonymous Referee #1, 28 Nov 2016
- AC2: 'ANSWER to Anonymous Referee #1', Lucien Wald, 28 Sep 2017
SC1: 'Review on "On the intrinsic..." by Bengulescu et al.', Tommaso Alberti, 29 Nov 2016
- AC1: 'Answer to Tommaso Alberti', Lucien Wald, 28 Sep 2017
RC2: 'A technically good and interesting paper with a few minor alterations to the interpretation of the results', Anonymous Referee #2, 13 Jul 2017
- AC3: 'ANSWER to Anonymous Referee #2', Lucien Wald, 28 Sep 2017

Peer-review completion

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

AR by Lucien Wald on behalf of the Authors (28 Sep 2017) Author's response Manuscript

ED: Referee Nomination & Report Request started (23 Oct 2017) by Ioulia Tchiguirinskaia

RR by Anonymous Referee #1 (03 Nov 2017)

ED: Publish as is (10 Dec 2017) by Ioulia Tchiguirinskaia

Short summary

We employ the Hilbert–Huang transform to study the temporal variability in time series of daily means of the surface solar irradiance (SSI) at different locations around the world. The data have a significant spectral peak corresponding to the yearly variability cycle and feature quasi-stochastic high-frequency "weather noise", irrespective of the geographical location or of the local climate. Our findings can improve models for estimating SSI from satellite images or forecasts of the SSI.