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

Abstract

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.