Exploring non-linear associations between atmospheric new-particle formation and ambient variables: a mutual information approach

Zaidan, Martha A.; Haapasilta, Ville; Relan, Rishi; Paasonen, Pauli; Kerminen, Veli-Matti; Junninen, Heikki; Kulmala, Markku; Foster, Adam S.

doi:https://doi.org/10.5194/acp-18-12699-2018

Articles | Volume 18, issue 17

https://doi.org/10.5194/acp-18-12699-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-18-12699-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 17

Research article

|

03 Sep 2018

Research article |

| 03 Sep 2018

Exploring non-linear associations between atmospheric new-particle formation and ambient variables: a mutual information approach

Martha A. Zaidan, Ville Haapasilta, Rishi Relan, Pauli Paasonen, Veli-Matti Kerminen, Heikki Junninen, Markku Kulmala, and Adam S. Foster

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Final revised paper (published on 03 Sep 2018)
Preprint (discussion started on 20 Apr 2018)

Interactive discussion

Status: closed

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

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RC1: 'Referee Comments', Anonymous Referee #1, 15 May 2018
- AC1: 'The author’s response to Anonymous Referee #1', Martha Arbayani Zaidan, 27 Jul 2018
RC2: 'Referee Comments', Anonymous Referee #2, 17 May 2018
- AC2: 'The author’s response to Anonymous Referee #2', Martha Arbayani Zaidan, 27 Jul 2018

Peer-review completion

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

AR by Martha Arbayani Zaidan on behalf of the Authors (06 Aug 2018) Author's response Manuscript

ED: Publish as is (15 Aug 2018) by Fangqun Yu

Short summary

This article promotes the use of the mutual information method for finding any non-linear associations among atmospheric variables. We demonstrate that the same results from previous studies are obtained by this method, which operates without supervision and without the need of understanding the physics deeply. This suggests that the method is suitable to be implemented widely in the atmospheric field to discover other interesting phenomena and their relevant variables.