Towards Physically-Consistent, Data-Driven Models of Convection
Towards Physically-Consistent, Data-Driven Models of Convection
Data-driven algorithms, in particular neural networks, can emulate the effect of sub-grid scale processes in coarse-resolution climate models if trained on high-resolution climate simulations. However, they may violate key physical constraints and lack the ability to generalize outside of their training set. Here, we show that physical constraints can be …