# GFDL Notebooks
(Previously MAR - Model Analysis Repository)
<p>
The Latin word <i>"mar"</i> translates to <i>"sea"</i>. This repository will contain a collection of (mainly) ocean-focused analyses to
inform next-generation ocean and climate model development.
</p>
## Ways to Run MAR
1. Interactively (clone the repository, edit the notebooks, and run)
2. Execute the batch script `run_mar.sh`
3. Visit https://dora.gfdl.noaa.gov/analysis/mar
## Contributing to MAR
Jupyter notebooks are the encapsulation of a particular analysis. There are relatively few constraints on how an analysis built, but there are
a few interfaces to be aware of:
### Configuration / Environment Variables
The batch and web engines for MAR (items 2 and 3 above) will set two runtime environment variables. Use one or both of these fields to
determine the top-level path to a model experiment to analyze:
* `MAR_DORA_ID`: The experiment ID in the dora database
* `MAR_PATHPP`: The top-level path to the post-processing experiment directory of the experiment (e.g. `/some/path/pp/`)
Each notebook should have a default set of model years to analyze (e.g. 1981-2010). The MAR engines will also provide two optional, additional variables,
`STARTYR` and `ENDYR`, that can be used to override the defaults in the notebook.
### Scalar Results / Metrics
If your notebook produces scalar metrics, it should write those results to a YAML file. See the `SST_bias_NOAA_OISSTv2.ipynb` notebook for an example of
how to construct a YAML file. Some examples of scalar fields might be RMSE and bias of a field, or the average depth of the Mediterranean outflow plume.
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"description": "# GFDL Notebooks\n(Previously MAR - Model Analysis Repository)\n\n<p>\nThe Latin word <i>\"mar\"</i> translates to <i>\"sea\"</i>. This repository will contain a collection of (mainly) ocean-focused analyses to \n inform next-generation ocean and climate model development.\n</p>\n\n## Ways to Run MAR\n\n1. Interactively (clone the repository, edit the notebooks, and run)\n2. Execute the batch script `run_mar.sh`\n3. Visit https://dora.gfdl.noaa.gov/analysis/mar\n\n## Contributing to MAR\nJupyter notebooks are the encapsulation of a particular analysis. There are relatively few constraints on how an analysis built, but there are \na few interfaces to be aware of:\n\n### Configuration / Environment Variables\nThe batch and web engines for MAR (items 2 and 3 above) will set two runtime environment variables. Use one or both of these fields to \ndetermine the top-level path to a model experiment to analyze:\n\n* `MAR_DORA_ID`: The experiment ID in the dora database\n* `MAR_PATHPP`: The top-level path to the post-processing experiment directory of the experiment (e.g. `/some/path/pp/`)\n\nEach notebook should have a default set of model years to analyze (e.g. 1981-2010). The MAR engines will also provide two optional, additional variables, \n`STARTYR` and `ENDYR`, that can be used to override the defaults in the notebook.\n\n### Scalar Results / Metrics\n\nIf your notebook produces scalar metrics, it should write those results to a YAML file. See the `SST_bias_NOAA_OISSTv2.ipynb` notebook for an example of \nhow to construct a YAML file. Some examples of scalar fields might be RMSE and bias of a field, or the average depth of the Mediterranean outflow plume.\n\n\n",
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