Data from: Training data from SPCAM for machine learning in moist physics

Guang Zhang, Yilun Han, Xiaomeng Huang & Yong Wang
Current moist physics parameterization schemes in general circulation models (GCMs) are the main source of biases in simulated precipitation and atmospheric circulation. Recent advances in machine learning make it possible to explore data-driven approaches to developing parameterization for moist physics processes such as convection and clouds. This study aims to develop a new moist physics parameterization scheme based on deep learning. We use a residual convolutional neural network (ResNet) for this purpose. It is trained...
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3,360 downloads reported since publication in 2020.

These counts follow the COUNTER Code of Practice, meaning that Internet robots and repeats within a certain time frame are excluded.
What does this mean?