Computationally Tractable High-Fidelity Representation of Global Hydrol-ogy in ESMs via Machine Learning Approaches to Scale-Bridging
暂无分享,去创建一个
Nathan Collier | Jitendra Kumar | Robert Jacob | Sarat Sreepathi | Forrest Hoffman | Zachary L. Langford | Zachary Langford | Richard Mills | F. Hoffman | R. Mills | R. Jacob | N. Collier | S. Sreepathi | J. Kumar
[1] Wojciech W. Grabowski,et al. An Improved Framework for Superparameterization. , 2004 .
[2] S. Alin,et al. Estimating the Surface Area of Small Rivers in the Southwestern Amazon and Their Role in CO2 Outgassing , 2008 .
[3] E. Sudicky,et al. Hyper‐resolution global hydrological modelling: what is next? , 2015 .
[4] L. Hess,et al. Outgassing from Amazonian rivers and wetlands as a large tropical source of atmospheric CO2 , 2002, Nature.
[5] V. Rich. Personal communication , 1989, Nature.
[6] M. Ek,et al. Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water , 2011 .
[7] Richard T. Mills,et al. Representativeness-based sampling network design for the State of Alaska , 2013, Landscape Ecology.
[8] Burr Settles,et al. Active Learning Literature Survey , 2009 .
[9] Akio Arakawa,et al. CLOUDS AND CLIMATE: A PROBLEM THAT REFUSES TO DIE. Clouds of many , 2022 .
[10] Ali Farhadi,et al. Unsupervised Deep Embedding for Clustering Analysis , 2015, ICML.