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Peer-reviewedCC BYJournal of Hydrology X

Precipitation-driven typology of storms in the Alps

  • Georgia Papacharalampous1,2
  • Eleonora Dallan1,2
  • Moshe Armon3
  • Joydeb Saha1,4
  • Colin Price5
  • Marco Borga1,2
  • Francesco Marra6,2
6 affiliations
  1. 1Department of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro, Italy
  2. 2Research Center on Climate Change Impacts, University of Padova, Rovigo, Italy
  3. 3The Fredy and Nadine Herrmann Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
  4. 4National Centre for Medium Range Weather Forecasting, Noida, India
  5. 5Porter School of the Environment and Earth Sciences, Tel Aviv University, Tel Aviv, Israel
  6. 6Department of Geosciences, University of Padova, Padova, Italy
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Abstract

Advances in precipitation science often depend on categorizing storms into homogeneous classes, particularly convective- and stratiform-like. To address this need, this study introduces an Alpine storm typology derived from the pairing of a vast sub-hourly gauge dataset, comprising over 790,000 independent storms, with an objective method, driven solely by precipitation features and optimized for big data clustering. Five dominant classes were identified from this partition, with distinct clustering fingerprints in maximum intensity, total volume, total duration and temporal variability, alongside clear spatial organization. Additional traits (initiation month, peak solar time, lightning count) and comparisons with radar-based benchmarks suggest that one class is likely associated with convective-like extremes (high intensity, summer occurrence, afternoon peaks, high lightning) and another with stratiform-like behaviors (medium intensity, large volume, long duration, low lightning), while the remaining classes gather moderate and minor storms. The typology could support applications such as class-specific stochastic simulation, class-informed bias-adjustment of climate projections or multi-class extreme value analyses. Climatological investigations revealed, among others, higher convective-like activity in recent years and specific regions, offering direct evidence on evolving hazard risks. We provide the historical occurrences of the classes as open data to facilitate further investigation of Alpine storm dynamics and their implications.

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Cite

BibTeXRIS
BibTeX
@article{papacharalampous2026precipitationdriven,
  title = {{Precipitation-driven typology of storms in the Alps}},
  author = {Georgia Papacharalampous and Eleonora Dallan and Moshe Armon and Joydeb Saha and Colin Price and Marco Borga and Francesco Marra},
  journal = {Journal of Hydrology X},
  year = {2026},
  month = aug,
  doi = {10.1016/j.hydroa.2026.100223},
  url = {https://doi.org/10.1016/j.hydroa.2026.100223},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Precipitation-driven typology of storms in the Alps
AU  - Georgia Papacharalampous
AU  - Eleonora Dallan
AU  - Moshe Armon
AU  - Joydeb Saha
AU  - Colin Price
AU  - Marco Borga
AU  - Francesco Marra
PY  - 2026
DA  - 2026/08/11
JO  - Journal of Hydrology X
DO  - 10.1016/j.hydroa.2026.100223
UR  - https://doi.org/10.1016/j.hydroa.2026.100223
AB  - Advances in precipitation science often depend on categorizing storms into homogeneous classes, particularly convective- and stratiform-like. To address this need, this study introduces an Alpine storm typology derived from the pairing of a vast sub-hourly gauge dataset, comprising over 790,000 independent storms, with an objective method, driven solely by precipitation features and optimized for big data clustering. Five dominant classes were identified from this partition, with distinct clustering fingerprints in maximum intensity, total volume, total duration and temporal variability, alongside clear spatial organization. Additional traits (initiation month, peak solar time, lightning count) and comparisons with radar-based benchmarks suggest that one class is likely associated with convective-like extremes (high intensity, summer occurrence, afternoon peaks, high lightning) and another with stratiform-like behaviors (medium intensity, large volume, long duration, low lightning), while the remaining classes gather moderate and minor storms. The typology could support applications such as class-specific stochastic simulation, class-informed bias-adjustment of climate projections or multi-class extreme value analyses. Climatological investigations revealed, among others, higher convective-like activity in recent years and specific regions, offering direct evidence on evolving hazard risks. We provide the historical occurrences of the classes as open data to facilitate further investigation of Alpine storm dynamics and their implications.
SN  - 2589-9155
ER  -