paper
2025
Extremes & hazards
Nowcasting Evaluation or intercomparison of existing models
CNN / U-Net0–6 hMétéo-France (incl. CNRM) Journal
2025-01-01
051
Jan 2025
Gabriel Arnould, Thibaut Montmerle, Lucie Rottner et al.
Quarterly Journal of the Royal Meteorological Society
Météo-France (incl. CNRM)
Deep-learning segmentation of radar and satellite images tracks MCS life cycles in AROME-France, identifying three predictors for MCS nowcasting.
An object‐based method to study the life cycle of mesoscale convective systems and their environment from cloud‐resolving AROME‐France simulations
In one sentence. Deep-learning segmentation of radar and satellite images tracks MCS life cycles in AROME-France, identifying three predictors for MCS nowcasting.
Labels
- Topic
- Extremes & hazards, Nowcasting
- Type
- Evaluation or intercomparison of existing models
- Method
- CNN / U-Net
- Time scale
- 0–6 h
- Domain
- Regional
- Data
- Radar, Satellite, NWP analysis or forecasts (IFS, GFS, ...), km-scale or high-res simulation
- Status
- Research
- Identifiers
- DOI:10.1002/qj.4919 OpenAlex:W4406682573
- Added
- 2026-10-22
Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.