S-SOM v1.0: a structural self-organizing map algorithm for weather typing

Doan, Quang-Van; Kusaka, Hiroyuki; Sato, Takuto; Chen, Fei

doi:https://doi.org/10.5194/gmd-14-2097-2021

Articles | Volume 14, issue 4

https://doi.org/10.5194/gmd-14-2097-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-14-2097-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 4

Model description paper

|

22 Apr 2021

Model description paper |

| 22 Apr 2021

S-SOM v1.0: a structural self-organizing map algorithm for weather typing

Quang-Van Doan, Hiroyuki Kusaka, Takuto Sato, and Fei Chen

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Final revised paper (published on 22 Apr 2021)
Preprint (discussion started on 09 Oct 2020)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Comments on “A structural self-organizing map (S-SOM) algorithm for weather typing” by Doan et al., submitted for GMD.', Anonymous Referee #1, 26 Oct 2020
- AC2: 'Reply on RC1', Quang-Van Doan, 02 Jan 2021
RC2: 'Minor revision is needed', Anonymous Referee #2, 28 Oct 2020
- AC3: 'Reply on RC2', Quang-Van Doan, 02 Jan 2021
SC1: 'executive editor comment on gmd-2020-278', Astrid Kerkweg, 14 Nov 2020
- AC4: 'Reply on SC1', Quang-Van Doan, 02 Jan 2021
AC1: 'Comment on gmd-2020-278', Quang-Van Doan, 02 Jan 2021

Peer-review completion

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

AR by Quang-Van Doan on behalf of the Authors (15 Jan 2021) Author's response Manuscript

ED: Publish as is (17 Feb 2021) by David Topping

AR by Quang-Van Doan on behalf of the Authors (26 Feb 2021) Manuscript

Short summary

This study proposes a novel structural self-organizing map (S-SOM) algorithm. The superiority of S-SOM is that it can better recognize the difference (or similarity) among spatial (or temporal) data used for training and thus improve the clustering quality compared to traditional SOM algorithms.