Preprints
https://doi.org/10.5194/gmd-2021-349
https://doi.org/10.5194/gmd-2021-349

Submitted as: methods for assessment of models 22 Dec 2021

Submitted as: methods for assessment of models | 22 Dec 2021

Review status: this preprint is currently under review for the journal GMD.

TriCCo v1.0.0 – a cubulation-based method for computing connected components on triangular grids

Aiko Voigt1, Petra Schwer2, Noam von Rotberg2, and Nicole Knopf3 Aiko Voigt et al.
  • 1Department of Meteorology and Geophysics, University of Vienna, Austria
  • 2Institute for Algebra and Geometry, Department of Mathematics, Otto-von-Guericke University, Magdeburg, Germany
  • 3Institute of Meteorology and Climate Research - Department Troposphere Research Karlsruhe Institute of Technology, Germany

Abstract. We present a new method to identify connected components on a triangular grid. Triangular grids are, for example, used in atmosphere and climate models to discretize the horizontal dimension. Because they are unstructured, neighbor relations are not self-evident and identifying connected components is challenging. Our method addresses this challenge by involving the mathematical tool of cubulation. We show that cubulation allows one to map the 2-d cells of the triangular grid onto the vertices of the 3-d cells of a cubic grid. The latter is structured and so connected components can be readily identified on the cubic grid by previously developed software packages. An advantage is that the cubulation, i.e., the mapping between the triangular and cubic grids, needs to be computed only once, which should be benifical for analysing many data fields for the same grid.We further implement our method in a python package that we name TriCCo and that is made available via pypi and gitlab. We document the package, demonstrate its application using cloud data from the ICON atmosphere model, and characterize its computational performance. This shows that TriCCo is ready for triangular grids with 100,000 cells, but that its speed and memory requirements need to be improved to analyse larger grids.

Aiko Voigt et al.

Status: open (until 16 Feb 2022)

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 reply

Aiko Voigt et al.

Model code and software

TriCCo v1.0.0 python package Aiko Voigt, Petra Schwer, Noam von Rotberg, Nicole Knopf https://gitlab.phaidra.org/climate/tricco

Aiko Voigt et al.

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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 identifying coherent objects. We present a new method that solves this problem by moving model data from an unstrucutred 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.