Articles | Volume 15, issue 19
Geosci. Model Dev., 15, 7489–7504, 2022
https://doi.org/10.5194/gmd-15-7489-2022
Geosci. Model Dev., 15, 7489–7504, 2022
https://doi.org/10.5194/gmd-15-7489-2022
Methods for assessment of models
11 Oct 2022
Methods for assessment of models | 11 Oct 2022

TriCCo v1.1.0 – a cubulation-based method for computing connected components on triangular grids

Aiko Voigt et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-349', Anonymous Referee #1, 23 Dec 2021
  • RC2: 'Comment on gmd-2021-349', Anonymous Referee #2, 03 Apr 2022
  • AC1: 'Comment on gmd-2021-349', Aiko Voigt, 19 Jun 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Aiko Voigt on behalf of the Authors (20 Jun 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (15 Jul 2022) by Paul Ullrich
RR by Anonymous Referee #1 (19 Jul 2022)
ED: Publish as is (03 Sep 2022) by Paul Ullrich
AR by Aiko Voigt on behalf of the Authors (09 Sep 2022)  Author's response    Manuscript
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Short summary
In climate science, it is helpful to identify coherent objects, for example, those formed by clouds. However, many models now use unstructured grids, which makes it harder to identify coherent objects. We present a new method that solves this problem by moving model data from an unstructured triangular grid to a structured cubical grid. We implement the method in an open-source Python package and show that the method is ready to be applied to climate model data.