Learning the Representations of Moist Convection with Convolutional Neural Networks
This addresses a critical problem in climate modeling for atmospheric scientists, offering an incremental improvement in simulation accuracy.
The study tackled the challenge of representing atmospheric moist convection in general circulation models by using convolutional neural networks to predict its effects, achieving more realistic predictions compared to other machine learning models and suggesting potential replacement of conventional cumulus parameterization.
The representations of atmospheric moist convection in general circulation models have been one of the most challenging tasks due to its complexity in physical processes, and the interaction between processes under different time/spatial scales. This study proposes a new method to predict the effects of moist convection on the environment using convolutional neural networks. With the help of considering the gradient of physical fields between adjacent grids in the grey zone resolution, the effects of moist convection predicted by the convolutional neural networks are more realistic compared to the effects predicted by other machine learning models. The result also suggests that the method proposed in this study has the potential to replace the conventional cumulus parameterization in the general circulation models.