1 code implementation • 14 Dec 2021 • Tom Beucler, Michael Pritchard, Janni Yuval, Ankitesh Gupta, Liran Peng, Stephan Rasp, Fiaz Ahmed, Paul A. O'Gorman, J. David Neelin, Nicholas J. Lutsko, Pierre Gentine
Data-driven algorithms, in particular neural networks, can emulate the effects of unresolved processes in coarse-resolution climate models when trained on high-resolution simulation data; however, they often make large generalization errors when evaluated in conditions they were not trained on.
Numerical weather prediction has traditionally been based on physical models of the atmosphere.
Atmospheric and Oceanic Physics
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.
Data-driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains.
Neural networks can emulate nonlinear physical systems with high accuracy, yet they may produce physically-inconsistent results when violating fundamental constraints.
Computational Physics Atmospheric and Oceanic Physics
Here, I propose online learning as a way to combat these issues.
Atmospheric and Oceanic Physics Computational Physics
Artificial neural-networks have the potential to emulate cloud processes with higher accuracy than the semi-empirical emulators currently used in climate models.
In particular, the focus is on the organization of shallow cumulus convection in the trade wind regions.
We train a deep neural network to represent all atmospheric sub-grid processes in a climate model by learning from a multi-scale model in which convection is treated explicitly.