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pro vyhledávání: '"Yacalis, Galen"'
Autor:
Mooers, Griffin, Pritchard, Mike, Beucler, Tom, Ott, Jordan, Yacalis, Galen, Baldi, Pierre, Gentine, Pierre
We explore the potential of feed-forward deep neural networks (DNNs) for emulating cloud superparameterization in realistic geography, using offline fits to data from the Super Parameterized Community Atmospheric Model. To identify the network archit
Externí odkaz:
http://arxiv.org/abs/2010.12996
Akademický článek
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Autor:
Yacalis, Galen
Publikováno v:
Yacalis, Galen. (2018). Artificial Neural Network Impact on Cloud Parameterization and Land-Atmosphere Interactions. UC Irvine: Biological Sciences. Retrieved from: http://www.escholarship.org/uc/item/16n7460w
Ecosystem dynamics are heavily dependent on atmospheric inputs such as rainfall, and are in turn an integral part of land-atmosphere coupling and the global carbon cycle. These global interactions and cycles are commonly modeled by Earth System Model
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______325::bbece6381a811db4461f4194458927c7
http://www.escholarship.org/uc/item/16n7460w
http://www.escholarship.org/uc/item/16n7460w
Modeling and representing moist convection in coarse-scale climate models remains one of the main bottlenecks of current climate simulations. Many of the biases present with parameterized convection are strongly reduced when convection is explicitly
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::83edb19beae4e6e7695a914bba7c618a
Autor:
Mooers, Griffin, Pritchard, Mike, Beucler, Tom, Ott, Jordan, Yacalis, Galen, Baldi, Pierre, Gentine, Pierre
We explore the potential of feed-forward deep neural networks (DNNs) for emulating cloud superparameterization in realistic geography, using offline fits to data from the Super Parameterized Community Atmospheric Model. To identify a network architec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fbb0b3d31cb1d47d39fc174b5f24f0b9