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Filtering by: Depositor ssim jakecar@umich.edu Language English Discipline Engineering

Resources for Training Machine Learning Algorithms Using CAM6 Simple Physics Packages
User Collection

Creator:

Limon, Garrett

Description:

The collection contains the code and the data used to train machine learning algorithms to emulate simplified physical parameterizations within the Community Atmosphere Model (CAM6). CAM6 is the atmospheric general circulation model (GCM) within the Community Earth System Model (CESM) framework, developed by the National Center for Atmospheric Research (NCAR). GCMs are made up of a dynamical core, responsible for the geophysical fluid flow calculations, and physical parameterization schemes, which estimate various unresolved processes. Simple physics schemes were used to train both random forests and neural networks in the interest of exploring the feasibility of machine learning techniques being used in conjunction with the dynamical core for improved efficiency of future climate and weather models. The results of the research show that various physical forcing tendencies and precipitation rates can be effectively emulated by the machine learning models.

Keyword:

Machine Learning, Climate Modeling, and Physics Emulators

Discipline:

Science and Engineering

2Works
Complete Global Total Electron Content Database based on the VISTA Algorithm
User Collection

Creator:

Sun, Hu

Description:

Complete Global Total Electron Content Database based on the VISTA Algorithm

Keyword:

Total Electron Content, Matrix Completion, VISTA, Spherical Harmonics, and Spatial-Temporal Smoothing

Discipline:

Engineering

2Works

Resources for Training Machine Learning Algorithms Using CAM6 Simple Physics Packages

Complete Global Total Electron Content Database based on the VISTA Algorithm

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Resources for Training Machine Learning Algorithms Using CAM6 Simple Physics Packages

Complete Global Total Electron Content Database based on the VISTA Algorithm

Limit your search

Resource type

Creator

Discipline

Language