python

Processing data with Python (notebooks)

In this folder, all scripts and notebooks are stored that are used to pre-process data and to generate the maps in the Atlas.

Requirements

The dependencies of the notebooks and scripts can be installed in a Conda environment with

# From this directory
conda install mamba -n base -c conda-forge -y
mamba env create --file environment.yml
conda activate atlas

Preprocess NetCDF data

We provide some notebooks that check the original/raw data, fix/add the metadata using CF-conventions and save data in a NetCDF format. As the output of a method (i.e. original/raw data) is provided by a specific institute, there is one notebook per each institute-method:

• Preprocess CNRM-KCC data
• Preprocess EdinU-ASK data
• Preprocess ETHZ-ClimWIP data
• Preprocess ICTP-REA data
• Preprocess UKMO-UKCP data
• Preprocess UOxf-CALL data

To run a notebook, you only need to specify the path to raw data as datapath and a path to store the output as output_path. Defaults are:

• datapath = "./AtlasData/raw"

• output_path = "./AtlasData/preprocess"

The pre-processed data follows the following standards:

coordinates

• climatology_bounds (climatology_bounds) datetime64[ns] ['2050-06-01', '2050-09-01', '2050-12-01', '2051-03-01']
• time (time) (datetime64[ns]) [2050-07-16 2051-01-16] # "JJA", "DJF"
• latitude (lat) (float64) [30, ..., 75]
• longitude (lon) (float64) [-10, ..., 40]
• percentile (percentile) (int64) [10, 25, 50, 75, 90]

variables

• tas (time, latitude, longitude, percentile) (float64)
• pr (time, latitude, longitude, percentile) (float64)

attributes

The attributes of variables and coordinates are defined as:

• "tas": { "description": "Change in Air Temperature", "standard_name": "Change in Air Temperature", "long_name": "Change in Near-Surface Air Temperature", "units": "K", "cell_methods": "time: mean changes over 20 years 2041-2060 vs 1995-2014", },
• "pr": { "description": "Relative precipitation", "standard_name": "Relative precipitation", "long_name": "Relative precipitation", "units": "%",
  "cell_methods": "time: mean changes over 20 years 2041-2060 vs 1995-2014", },
• "latitude": {"units": "degrees_north", "long_name": "latitude", "axis": "Y"},
• "longitude": {"units": "degrees_east", "long_name": "longitude", "axis": "X"},
• "time": { "climatology": "climatology_bounds", "long_name": "time", "axis": "T", "climatology_bounds": ["2050-6-1", "2050-9-1", "2050-12-1", "2051-3-1"], "description": "mean changes over 20 years 2041-2060 vs 1995-2014. The mid point 2050 is chosen as the representative time.", },
• "percentile": {"units": "%", "long_name": "percentile", "axis": "Z"},

The attributes of the data is defined as:

• "description": "Contains modified institute method data used for Atlas in EUCP project.",
• "history": "original institute method data files ...",

output file names

output_file_name = prefix_activity_institution-id_source_method_sub-method_cmor-var

example: atlas_EUCP_CNRM_CMIP6_KCC_cons_tas.nc

Create maps

make sure that the conda environment atlas is activated.

Maps are created using pre-processed data and maps_creator_atlas_data.py script:

python ./atlas/python/maps_creator_atlas_data.py --inputdir "./AtlasData/preprocess" --outputdir "./atlas/assets/processed_figures"

Add new maps

If you want to add new maps to the Atlas, please publish the pre-processed data on Zenodo and add references to [FIXME].

Additional notebooks

These notebooks can be used to tweak plot settings and preview maps using raw model data.

• Preview map of relative precipitation
• Preview map of temperature