Session

DescriptionA promising application area for Machine Learning (ML) in the domain of Earth System Modelling (ESM) and Numerical Weather Prediction (NWP) revolves around scalable solutions that embed ML in running ESM/NWP code. For example, in order to ensure the long-term stability of ML models that can substitute existing model parameterizations, it may be required to tune an ML model against a live numerical model, in addition to the challenge of getting initial training data extracted from the model at possibly high spatial and temporal resolutions in the first place. Such setups are much more complex than offline training settings in terms of software integration and CPU-GPU data exchange in an HPC setup. The standard ML workflows will generate models for inference whose utility is currently limited to the Python ecosystem. Exporting them to work with ESMs in Fortran or C environments results in cumbersome workflows. Tackling such challenges is of key value to research and development efforts that aim to build ESM-ML hybrids. To encourage collaboration and formulation of best practices, the minisymposium will bring together ESM and ML developers, HPC experts as well as software engineering expertise revolving around building Python/Fortran/C bridges, and foster exchange with other application domains.