Non‐Local Parameterization of Atmospheric Subgrid Processes With Neural Networks
Non‐Local Parameterization of Atmospheric Subgrid Processes With Neural Networks
Subgrid processes in global climate models are represented by parameterizations which are a major source of uncertainties in simulations of climate. In recent years, it has been suggested that machine-learning (ML) parameterizations based on high-resolution model output data could be superior to traditional parameterizations. Currently, both traditional and ML parameterizations …