Swiss Storm-Object Dataset for ML-Based Hail Detection: Polarimetric Radar Predictors and Crowdsourced Labels (2020–2024)

This dataset contains a set of object-based predictors derived from operational C-band polarimetric weather radar data in Switzerland, matched with ground-truth hail labels derived from crowdsourced reports. The predictors are calculated for storm cells identified and tracked by the Thunderstorms Radar Tracking (TRT) algorithm. This dataset was generated to support the development of machine learning models for hail detection and hail size estimation. The dataset covers convective storm activity over Switzerland and a 50-km buffer zone around its borders for the years 2020–2024.

Related Organizations

University of Bern
Switzerland
Federal Office of Meteorology and Climatology
Switzerland

Keywords

Machine Learning, Hail, Crowdsourced Data, Convective Storms

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average

Found an issue? Give us feedback

0

Average

Funded by

SNSF| Seamless coupling of kilometer-resolution weather predictions and climate simulations with hail impact assessments for multiple sectors (scClim)