paper
2021
Extremes & hazards
Climate analysis & attribution Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels) Journal
2021-06-30
012
Jun 2021
부경대학교 지구환경시스템과학부 공간정보공학전공, Geunah Kim, Jonggu Kang et al.
Journal of Climate Research
A random forest trained on ERA5 variables reproduces a station-based heatwave index for South Korea (1979-2020) with MAE 0.365 and CC 0.873, and yields gridded maps.
Assessment of the Predictability of Heatwave Index Using ASOS and ERA5 Data with Machine Learning: Case Study of South Korea, 1979-2020
In one sentence. A random forest trained on ERA5 variables reproduces a station-based heatwave index for South Korea (1979-2020) with MAE 0.365 and CC 0.873, and yields gridded maps.
Labels
- Topic
- Extremes & hazards, Climate analysis & attribution
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Regional
- Data
- ERA5 / reanalysis, Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.14383/cri.2021.16.2.147 OpenAlex:W3191195662
- Added
- 2026-10-22
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