Improved real-time hail damage estimates leveraging dense crowdsourced observations

The scripts here reproduce the main results of the paper: Schmid T., Gebhart, V., Bresch D. N. (2025) Improved real-time hail damage estimates leveraging dense crowdsourced observations. Meteorological Applications, DOI:10.1002/met.70059

Publication status: accepted

Contact: Timo Schmid

Content

./notebooks/

Jupyter notebooks to reproduce figures that appear in the paper.

./scClim/

Contains functions which are called in other scripts for data pre-processing, calibration, visualizing, as well as utility functions and constants. These function represent a subset of all utility functions used in the damage modelling part of the scClim project

Requirements

Requires:

• Python 3.9+ environment (best to use conda for CLIMADA repository)
• CLIMADA repository version 5.0: https://wcr.ethz.ch/research/climada.html https://github.com/CLIMADA-project/climada_python
• Exposure and damage data for the calibration. The hail damage data used in the paper are not public and only available within the scClim project.