paper
2023
Climate analysis & attribution
Extremes & hazards Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels) Journal
2023-09-25
012
Sep 2023
Chanwoo Song, Sungsu Park, Siyun Kim et al.
Journal of Climate
Machine learning on GCM and ERA5 environmental variables identifies humidity convergence and CAPE as key predictors of tropical cyclone intensification rates.
Climate analysis & attribution
Lagrangian Analysis of Tropical Cyclone Intensification Simulated by General Circulation Models Compared with Observations
In one sentence. Machine learning on GCM and ERA5 environmental variables identifies humidity convergence and CAPE as key predictors of tropical cyclone intensification rates.
Labels
- Topic
- Climate analysis & attribution, Extremes & hazards
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Global
- Data
- CMIP / climate model output, ERA5 / reanalysis
- Status
- Research
- Identifiers
- DOI:10.1175/jcli-d-23-0070.1 OpenAlex:W4387018941
- Added
- 2026-10-22
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