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.

Abstract. We present a new method to identify connected components on triangular grids used in atmosphere and climate models to discretize the horizontal dimension. In contrast to structured latitude–longitude grids, triangular grids are unstructured and the neighbors of a grid cell do not simply follow from the grid cell index. This complicates the identification of connected components compared to structured grids. Here, we show that this complication can be addressed by involving the mathematical tool of cubulation, which allows one to map the 2-D cells of the triangular grid onto the vertices of the 3-D cells of a cubical grid. Because the latter is structured, connected components can be readily identified by previously developed software packages for cubical grids. Computing the cubulation can be expensive, but, importantly, needs to be done only once for a given grid. We implement our method in a Python package that we name TriCCo and make available via pypi, gitlab, and zenodo. We document the package and demonstrate its application using simulation output from the ICON atmosphere model. Finally, we characterize its computational performance and compare it to graph-based identifications of connected components using breadth-first search. The latter shows that TriCCo is ready for triangular grids with up to 500 000 cells, but that its speed and memory requirement should be improved for its application to larger grids.

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