Minisymposium Presentation

As hardware architectures and algorithmic approaches diversify, flexibility becomes a greater and greater virtue for weather and climate model developers. The approach of porting models to a domain-specific language can provide this flexibility; a Python frontend allows natural integration of ML components, and the compiler toolchain can optimize the code for target backends. However, not every task is trivial; challenges include training physics emulators, coupling and optimizing hybrid Fortran/dsl model configurations, and ensuring all algorithmic motifs are supported by the dsl. We discuss our experiences with these issues in the context of developing Pace, the GT4Py and DaCe implementation of the FV3GFS atmospheric model, and NDSL, the NOAA/NASA DSL middleware platform we use for model development. We explain which design decisions were made to address which obstacles, ongoing work on the modeling framework, and future development plans.