Prognostic validation of a neural network unified physics parameterization
Prognostic validation of a neural network unified physics parameterization
Weather and climate models approximate diabatic and sub-grid-scale processes in terms of grid-scale variables using parameterizations. Current parameterizations are de- signed by humans based on physical understanding, observations and process modeling. As a result, they are numerically efficient and interpretable, but potentially over-simplified. However, the advent of global high-resolution simulations …