Using Machine Learning for Model Physics: an Overview
This is an incremental overview for researchers in computational physics and climate modeling.
The paper provides an overview of using machine learning tools to emulate, approximate, and develop parameterizations in model physics, including applications for ensuring physical constraints and controlling accuracy.
In the overview, a generic mathematical object (mapping) is introduced, and its relation to model physics parameterization is explained. Machine learning (ML) tools that can be used to emulate and/or approximate mappings are introduced. Applications of ML to emulate existing parameterizations, to develop new parameterizations, to ensure physical constraints, and control the accuracy of developed applications are described. Some ML approaches that allow developers to go beyond the standard parameterization paradigm are discussed.