paper
2019
Extremes & hazards
Application study
Classical ML (trees, GP, linear, kernels)6 h–3 days Journal
2019-05-17
022
May 2019
Bartosz Czernecki, Mateusz Taszarek, Michał Marosz et al.
Atmospheric Research
Applies machine learning with ERA5 convective parameters and radar reflectivity to predict large hail, highlighting which predictors matter most.
Application of machine learning to large hail prediction - The importance of radar reflectivity, lightning occurrence and convective parameters derived from ERA5
In one sentence. Applies machine learning with ERA5 convective parameters and radar reflectivity to predict large hail, highlighting which predictors matter most.
Labels
- Topic
- Extremes & hazards
- Type
- Application study
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- ERA5 / reanalysis, Radar
- Status
- Research
- Identifiers
- DOI:10.1016/j.atmosres.2019.05.010 OpenAlex:W2945435533
- Added
- 2026-10-22
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