TriCCo -- a cubulation-based method for computing connected components on triangular grids

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. 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.