This github repository contains material for an introductory workshop on using the Python scientific 'ecosystem' to access and analyse climate data, given for the He Kaupapa Hononga and Otago Climate Change Postgraduate Network (OCCPN).
The introductory slides can be found here
In the notebooks folder, you will find 6 Jupyter notebooks:
- Jupyter_notebooks.ipynb provides a very brief overview of the Jupyter notebook (and Jupyter lab) development environment
- Pandas_NZ_station_data.ipynb shows how to read a csv file using pandas and perform some basic operations on pandas's DataFrames
- reanalyses.ipynb shows how to access NCEP/NCAR and ERA5 reanalysis data 'remotely' (i.e. without having to download the data) using xarray
- global_average_temp_NCEP.ipynb shows how to calculate global area-weigthed temperature anomalies, using the NCEP/NCAR reanalysis product (1948-2021)
- CMIP6_in_the_cloud.ipynb shows how to access a subset of the CMIP6 climate change model's simulations on the Google cloud
- downscaled_CMIP6.ipynb provides some pointers towards bias-corrected and downscaled CMIP6 global datasets
The first step is to download and install either the anaconda or the mambaforge Python distribution.
You can then download (or git clone) the content of this repository (e.g. 'code / Download ZIP` in the upper right)
The environment.yml file provides the list of packages used or relied upon for this workshop.
You can create a climatedata environment using conda or mamba by typing:
conda env create -f environment.yml
or
mamba env create -f environment.yml
in a terminal
The environment is activated using
conda activate climatedata
or
mamba activate climatedata
Then navigate to the notebooks folder and type
jupyter notebook
for the classic notebook interface, or
jupyter lab
For jupyterlab
Any questions: Nicolas Fauchereau