Can A Machine Learning-enabled Numerical Model Help Extend Effective Forecast Range Through Consistently Trained Subgrid-scale Models?

Scientists are developing new parameterization schemes to more accurately simulate weather and climate processes. Traditional turbulence parameterization models are developed based on heuristic assumptions and may not be reliable. The new schemes incorporate resolved variables and the resulting tendencies into numerical integration in order to approximate subgrid-scale physical processes.