Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 19
Articles | Volume 15, issue 19
https://doi.org/10.5194/gmd-15-7489-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-7489-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 19
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, Petra Schwer, Noam von Rotberg, and Nicole Knopf

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Ardila, F., Baker, T., and Yatchak, R.: Moving Robots Efficiently Using the Combinatorics of CAT(0) Cubical Complexes, SIAM J. Discrete Math., 28, 986–1007, https://doi.org/10.1137/120898115, 2014. a, b
Baumgart, M., Ghinassi, P., Wirth, V., Selz, T., Craig, G. C., and Riemer, M.: Quantitative View on the Processes Governing the Upscale Error Growth up to the Planetary Scale Using a Stochastic Convection Scheme, Mon. Weather Rev., 147, 1713–1731, https://doi.org/10.1175/MWR-D-18-0292.1, 2019. a
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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.
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