Shingle 2.0: generalising self-consistent and automated domain discretisation for multi-scale geophysical models

Candy, Adam S.; Pietrzak, Julie D.

doi:https://doi.org/10.5194/gmd-11-213-2018

Articles | Volume 11, issue 1

https://doi.org/10.5194/gmd-11-213-2018

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

https://doi.org/10.5194/gmd-11-213-2018

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

Articles | Volume 11, issue 1

Model description paper

|

17 Jan 2018

Model description paper |

| 17 Jan 2018

Shingle 2.0: generalising self-consistent and automated domain discretisation for multi-scale geophysical models

Adam S. Candy and Julie D. Pietrzak

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Final revised paper (published on 17 Jan 2018)
Preprint (discussion started on 11 Apr 2017)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review of Shingle 2.0: generalising self-consistent and automated domain discretisation for multi-scale geophysical models', Anonymous Referee #1, 23 Jun 2017
- AC1: 'Response to Anonymous Referee #1', Adam Candy, 26 Sep 2017
RC2: 'Review of "Shingle 2.0: generalising self-consistent and automated domain discretisation for multi-scale geophysical models" by A. Candy and J. Pietrzak', Anonymous Referee #2, 27 Jun 2017
- AC2: 'Response to Anonymous Referee #2', Adam Candy, 26 Sep 2017

Peer-review completion

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

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

ED: Publish as is (09 Oct 2017) by Dan Goldberg

AR by Adam Candy on behalf of the Authors (19 Oct 2017) Manuscript

Short summary

Shingle is a new approach to describing and generating spatial mesh discretisations for multi-scale geophysical domains. Its novel use of an extendable, hierarchical formal grammar and natural language basis for geophysical features achieves robust reproduction and enables consistent comparison between models. This is designed to support the increase in complexity as models include a greater range of spatial scales and future-proof simulation set-up.