1. Clone this git project on your computer
2. Install required python packages in a virtualenv
This project relies on Python3.7, if you do not have it, install it and rely on this python version for the following.
This project only requires to install R with a version >= 4.0 (R 4.2.1 was used to test everything)
To create this virtualenv from the requirements.txt files, you can either use:
-the integrated tool of the pycharm IDE
-or the following command in a terminal located at the root of the project:
$ virtualenv <env_name>
$ source <env_name>/bin/activate
(<env_name>)$ pip install -r requirements.txt
3. Download files
Several metadata need to be downloaded. You can find this metadata in the following google drive folder ( https://drive.google.com/drive/folders/1bZmmYhyvSqlrgAYXnsF_J2hHdgR41ayl?usp=sharing ). Download all the zip files, unzip them, and put them in the "data" folder.
4. Generate plots from the data section
run main_data.py to obtain the plot with the 21 time series with many colors run main_temperature.py to obtain the plot with the global mean temperatures with respect to the years
These two scripts are located in the folder projected_extremes/section_data
5. Generate plots from the results section
Activate the virtualenv $ source <env_name>/bin/activate
First step: Select the setting
In each script, you have to specify two arguments "fast" and "snowfall".
- "fast=False" considers all ensemble members and all elevations, while "fast=True" considers only 6 ensemble mmebers and 1 elevation
- "snowfall=True" corresponds to daily snowfall, while "snowfall=False" corresponds to accumulated ground snow load, "snowfall=None" corresponds to daily winter precipitation
Second step: The validation experiment
- run main_model_as_truth_experiment.py for the model as truth experiment (to select the optimal number of linear pieces)
- run main_calibration_validation_experiment_optimized.py for the calibration validation experiment (to select the parameterization for the adjustment coefficients)
These two scripts are located in the folder projected_extremes/section_results/validation_experiment
Third step: Create some plots
- main_simple_visualization_with_adjustments.py to create the Figure 4 for the ESD article
- main_projections_map.py to create maps of return levels, and changes of return levels
- main_projections_elevation_plot.py to create almost all the Figures for the chapter on extreme snowfall in the PhD manuscript
These three scripts are located in the folder projected_extremes/section_results