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.

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"
}

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

Copyright (C) 2020 Elco Koks. All versions released under the MIT license.