Learning the Representations of Moist Convection with Convolutional Neural Networks
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 effect...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
23.05.2019
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.1905.09614 |