paper
2021
Extremes & hazards
Evaluation or intercomparison of existing models
CNN / U-NetClassical ML (trees, GP, linear, kernels)6 h–3 daysMassachusetts Institute of Technology (MIT) Journal
2021-05-27
022
May 2021
Xiang Gao, Shray Mathur
Journal of Climate
Massachusetts Institute of Technology (MIT)
Compares CNNs with analogue methods for predicting US regional extreme precipitation from large-scale patterns, finding CNNs more skillful and integrated vapor transport the most predictive variable.
Predictability of U.S. Regional Extreme Precipitation Occurrence Based on Large-Scale Meteorological Patterns (LSMPs)
In one sentence. Compares CNNs with analogue methods for predicting US regional extreme precipitation from large-scale patterns, finding CNNs more skillful and integrated vapor transport the most predictive variable.
Labels
- Topic
- Extremes & hazards
- Type
- Evaluation or intercomparison of existing models
- Method
- CNN / U-Net, Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- ERA5 / reanalysis
- Status
- Research
- Identifiers
- DOI:10.1175/jcli-d-21-0137.1 OpenAlex:W3165364839
- Added
- 2026-10-22
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