# Simple Physics CAM6 Data set for Training Machine Learning Algorithms

## Creator

Garrett Limon (glimon@umich.edu)

## Funding

NSF GRFP

## Research and Description

This is a collection of CAM6 output to be used for training machine learning emulators for physical parameterization schemes. Three configurations of physical parameterizations in CAM6 are included, the Dry Held Suarez (HS), Moist Held Suarez (TJ) and the TJ scheme with a Betts-Miller convection scheme coupled. Intent is to be used with machine learning techniques to emulate the tendency calculations. An example workflow that utilizes this data is provided here: https://github.com/gclimon/simplePhysicsML. 

## Format

Directory is organized as follows.
Dry case: ml_data/HS_19L/
Moist case: ml_data/TJ_19L/
Convection case: ml_data/TJ_convection_19L/

Within each directory there is a collection of CAM6 output files. The 'h0' files indicate containing state variables (T, ps, q, etc.), while the 'h1' files contain the tendencies (dT/dt, dq/dt) and precipitation rates.

## Data Citation

Limon, G. C. (2022) Simple Physics CAM6 Dataset for Training Machine Learning Algorithms [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/r6v3-s064

## Associated Work

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