This notebook is part of the tutorials in the ASP summer school.
These notebooks involve preparing the data for use in the [S2S verification tutorial
]
In the S2S verification tutorial,
we use climpred https://climpred.readthedocs.io/en/stable/ to verify subseasonal-to-seasonal (S2S) forecasts against observations.
- Evaluate metrics across models and model versions
- Predictability limits
- State-dependent predictability
- contribute to climpred
- 'ASP_data_catalog.yml':
intakecatalog, seedata_access_with_intake.ipynb s2s-asp.yml: conda environment file. Needed to create the data.s2s-asp.yml: conda environment filecluster.ipynb: Start aPBS-Cluster oncasper, needed for big data (10GB+)metrics_example.ipynb: Jupyter notebooks aboutclimpredand student projects
It is recommented to use the enviroment s2s-asp on NCAR_casper.
Else create your own environment:
conda activate
conda env create -f s2s-asp.yml
# update existing
# conda env update -f s2s-asp.yml
conda activate s2s-aspxarray: working horse for geospatial data in python- documentation: xarray.pydata.org/
- tutorial: https://xarray-contrib.github.io/xarray-tutorial/
xskillscore: is built on top ofxarrayand providesmetrics toclimpredclimpred:- documentation: https://climpred.readthedocs.io/en/stable/
- data model: https://climpred.readthedocs.io/en/stable/setting-up-data.html
- classes: https://climpred.readthedocs.io/en/stable/prediction-ensemble-object.html
- list of initialized public datasets to work with: https://climpred.readthedocs.io/en/stable/initialized-datasets.html
- terminology: https://climpred.readthedocs.io/en/stable/terminology.html
- alignment: https://climpred.readthedocs.io/en/stable/alignment.html
Consider...
- Aaron Spring
- Judith Berner
- Abby Jaye