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  • Simple Physics CAM6 Dataset for Training Machine Learning Algorithms

    work
    Creator:
    Limon, Garrett C.
    Description:
    The data represents weekly output from three 60-year CAM6 model runs. The output includes state (.h0. files) and tendency (.h1. files) fields for three difference model configurations of increasing complexity. State fields include temperature, surface pressure, specific humidity, among others; while tendencies include temperature tendencies, specific humidity tendencies, as well as precipitation rates. Using the state variables at a given time step, machine learning techniques can be trained to predict the following tendency field, which can then be applied to the state variables to provide the state at the next physics time step of the model.
    Keyword:
    Machine Learning, Climate Modeling, and Physics Emulation
    Citation to related publication:
    Limon, G. C., Jablonowski, C. (2022) Probing the Skill of Random Forest Emulators for Physical Parameterizations via a Hierarchy of Simple CAM6 Configurations [Preprint]. ESSOAr.  https://10.1002/essoar.10512353.1
    Discipline:
    Engineering and Science

  • Simple Physics CAM6 Codebase for Training Machine Learning Algorithms

    work
    Creator:
    Limon, Garrett C.
    Description:
    The work guides the processing of CAM6 data for use in machine learning applications. We also provide workflow scripts for training both random forests and neural networks to emulate physic s schemes from the data, as well as analysis scripts written in both Python and NCL in order to process our results.
    Keyword:
    Machine Learning, Climate Modeling, and Physics Emulation
    Citation to related publication:
    Limon, G. C., Jablonowski, C. (2022) Probing the Skill of Random Forest Emulators for Physical Parameterizations via a Hierarchy of Simple CAM6 Configurations [Pre Print]. ESSOAr.  https://10.1002/essoar.10512353.1
    Discipline:
    Engineering and Science