WISC Windstorm Loss and Risk Model
Python implementation of the WISC (Windstorm Information Service) Loss and Risk model.
Please refer to the ReadTheDocs of this project for the full documentation of all functions.
Requirements: NumPy, pandas, geopandas, seaborn, matplotlib
Academic article Koks and Haer (2020)
Koks and Haer (2020) A high-resolution wind damage model for Europe. Scientific Reports. In Review
Project report of the model: Koks et al., 2017
Koks, E.E., Tiggeloven, T., Coumou D., Aerts, J.C.J.H., Whitelaw A. (2017)
WISC Risk and Loss Indicator Descriptions. Copernicus Climate Change Service.
Sensitivity Analysis of the model: Koks et al., 2017
Koks, E.E., Coumou D., Aerts, J.C.J.H.,(2017)
WISC Case Study: Tier 3 Indicators Sensitivity Analysis. Copernicus Climate Change Service.
Prepare data paths
Copy config.template.json
to config.json
and edit the paths for data and
figures, for example:
{
"data_path": "/home/<user>/projects/WISC/data",
"figures_path": "/home/<user>/projects/WISC/figures"
}
Python requirements
Recommended option is to use a miniconda environment to work in for this project, relying on conda to handle some of the trickier library dependencies.
# Add conda-forge channel for extra packages
conda config --add channels conda-forge
# Create a conda environment for the project and install packages
conda env create -f .environment.yml
activate WISC
License
Copyright (C) 2020 Elco Koks. All versions released under the MIT license.