Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models With Real‐Geography Boundary Conditions-Reference-Cited by-同舟云学术

Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models With Real‐Geography Boundary Conditions

Author:

Mooers Griffin¹²^ORCID,Pritchard Michael¹^ORCID,Beucler Tom¹^ORCID,Ott Jordan¹²,Yacalis Galen³^ORCID,Baldi Pierre²,Gentine Pierre⁴^ORCID

Affiliation:

1. Department of Earth System Science University of California at Irvine Irvine CA USA

2. Department of Computer Science University of California at Irvine Irvine CA USA

3. Department of Mathematics University of California at Irvine Irvine CA USA

4. Department of Earth and Environmental Engineering Columbia University New York NY USA

Funder

National Science Foundation

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Environmental Chemistry,Global and Planetary Change

Link

Reference48 articles.

Cited by 29 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献