Technical note: An improved Grassberger–Procaccia algorithm for analysis of climate system complexity

Di, Chongli; Wang, Tiejun; Yang, Xiaohua; Li, Siliang

doi:https://doi.org/10.5194/hess-22-5069-2018

Articles | Volume 22, issue 10

https://doi.org/10.5194/hess-22-5069-2018

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

https://doi.org/10.5194/hess-22-5069-2018

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

Articles | Volume 22, issue 10

Technical note

|

02 Oct 2018

Technical note |

| 02 Oct 2018

Technical note: An improved Grassberger–Procaccia algorithm for analysis of climate system complexity

Chongli Di, Tiejun Wang, Xiaohua Yang, and Siliang Li

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Final revised paper (published on 02 Oct 2018)
Preprint (discussion started on 13 Sep 2017)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Comments', Anonymous Referee #1, 12 Oct 2017
- AC1: 'Reply to referee＃1, Chongli Di, 13 Nov 2017', Chongli Di, 13 Nov 2017
- AC3: 'New reply to Referee #1', Chongli Di, 06 Sep 2018
RC2: 'Review of manuscript hess-2017-445', Anonymous Referee #2, 01 Sep 2018
- AC2: 'Reply to Referee #2', Chongli Di, 06 Sep 2018
EC1: 'Final editor's comment', Dimitri Solomatine, 06 Sep 2018

Peer-review completion

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

ED: Publish subject to revisions (further review by editor and referees) (06 Sep 2018) by Dimitri Solomatine

AR by Chongli Di on behalf of the Authors (09 Sep 2018) Author's response Manuscript

ED: Referee Nomination & Report Request started (19 Sep 2018) by Dimitri Solomatine

RR by Anonymous Referee #1 (19 Sep 2018)

RR by Anonymous Referee #2 (20 Sep 2018)

ED: Publish as is (20 Sep 2018) by Dimitri Solomatine

AR by Chongli Di on behalf of the Authors (20 Sep 2018) Manuscript

Short summary

The original Grassberger–Procaccia algorithm for complex analysis was modified by incorporating the normal-based K-means clustering technique and the RANSAC algorithm. The calculation accuracy of the proposed method was shown to outperform traditional algorithms. The proposed algorithm was used to diagnose climate system complexity in the Hai He basin. The spatial patterns of the complexity of precipitation and air temperature reflected the influence of the dominant climate system.