Towards Physically-consistent, Data-driven Models of Convection
This work addresses the challenge of improving the physical consistency and generalizability of neural networks for sub-grid scale processes in climate modeling, which is incremental as it builds on existing data-driven approaches.
The authors tackled the problem of data-driven models violating physical constraints and lacking generalization in climate simulations by enforcing physical constraints in neural networks and rescaling training data, achieving generalization to unseen climates.
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 enforced in neural networks, either approximately by adapting the loss function or to within machine precision by adapting the architecture. As these physical constraints are insufficient to guarantee generalizability, we additionally propose to physically rescale the training and validation data to improve the ability of neural networks to generalize to unseen climates.