paper
2021
Climate analysis & attribution
Extremes & hazards New model or method
CNN / U-NetClassical ML (trees, GP, linear, kernels) Preprint
2021-01-01
012
Jan 2021
Grzegorz Muszyński
University of Liverpool
Thesis develops learned pattern recognition for atmospheric rivers (topological data analysis plus ML) and blocking (deep CNNs) in climate simulations and reanalysis.
Climate analysis & attribution
Pattern Recognition for Weather Phenomena in Climate Data
In one sentence. Thesis develops learned pattern recognition for atmospheric rivers (topological data analysis plus ML) and blocking (deep CNNs) in climate simulations and reanalysis.
Labels
- Topic
- Climate analysis & attribution, Extremes & hazards
- Type
- New model or method
- Method
- CNN / U-Net, Classical ML (trees, GP, linear, kernels)
- Domain
- Global
- Data
- ERA5 / reanalysis, CMIP / climate model output
- Status
- Research
- Identifiers
- DOI:10.17638/03116349 OpenAlex:W3157100220
- Added
- 2026-10-22
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