Application of Machine Learning to Parameterization Emulation and Development
Vladimir M. Krasnopolsky, Alexei A. Belochitski · Geophysical monograph · 2023
In this chapter, 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 to control the accuracy and stability of developed applications are described. Some ML approaches that allow developers to go beyond the standard parameterization paradigm are discussed.