paper
2022
Local & station forecasting
Benchmarks, datasets & evaluation Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)0–6 h Journal
2022-04-01
012
Apr 2022
Michaela Vorndran, Adrian Schütz, Jörg Bendix et al.
Artificial Intelligence for the Earth Systems
XGBoost radiation fog nowcasts are evaluated with persistence and guessing baselines, showing high validation scores can hide operational failure and that blockwise CV inflates skill.
Current Training and Validation Weaknesses in Classification-Based Radiation Fog Nowcast Using Machine Learning Algorithms
In one sentence. XGBoost radiation fog nowcasts are evaluated with persistence and guessing baselines, showing high validation scores can hide operational failure and that blockwise CV inflates skill.
Labels
- Topic
- Local & station forecasting, Benchmarks, datasets & evaluation
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 0–6 h
- Domain
- Local / station
- Data
- Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1175/aies-d-21-0006.1 OpenAlex:W4389087004
- Added
- 2026-10-22
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