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.