Ontology highlight
ABSTRACT:
SUBMITTER: Rasp S
PROVIDER: S-EPMC6166853 | biostudies-literature | 2018 Sep
REPOSITORIES: biostudies-literature
Proceedings of the National Academy of Sciences of the United States of America 20180906 39
The representation of nonlinear subgrid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for short-term simulations of at most a few years because of computational limitations. Here we demonstrate that deep learning can be used to capture many advantages of cloud-resolving modeling at a fraction of the computational cost. We train a deep neural n ...[more]