GFDL Notebooks
(Previously MAR - Model Analysis Repository)
The Latin word "mar" translates to "sea". This repository will contain a collection of (mainly) ocean-focused analyses to inform next-generation ocean and climate model development.
Ways to Run MAR
- Interactively (clone the repository, edit the notebooks, and run)
- Execute the batch script
run_mar.sh - 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 databaseMAR_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.
Release files for gfdlnb 0.0.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| gfdlnb-0.0.4.tar.gz | 5.4 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| gfdlnb-0.0.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 10.7 MB
Release files / gfdlnb-0.0.4.tar.gz
| Download URL | gfdlnb-0.0.4.tar.gz |
|---|---|
| Size | 5.4 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
b207b4188ba4002398f89ae4aab619e1af726bd05970a8a28318e65eeebef6f6
|
|
BLAKE2b-256 checksum How to use checksums |
b56bfd9e6f94ac2951fbf16dde8c5b92e7c0dc4aa47c07bd11e649fcf6056956
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.9.20
|
Release files / gfdlnb-0.0.4-py3-none-any.whl
| Download URL | gfdlnb-0.0.4-py3-none-any.whl |
|---|---|
| Size | 5.4 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
68f37a1eac071f6ed6f350a70e3124b7299f2ff212187b3cc11309f504a3bce0
|
|
BLAKE2b-256 checksum How to use checksums |
9741637bbcd139739497bbb25a28c5e24c1c8953d80eaf4fc73e8570649f5ed7
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.9.20
|