paper
2023
Post-processing & calibration
Local & station forecasting New model or method
Classical ML (trees, GP, linear, kernels)6 h–3 days Journal
2023-06-30
012
Jun 2023
Hyeong-Se Jeong, Sang-Sam Lee, Yong-Hee Lee et al.
Journal of Korean institute of intelligent systems
Sliding-window DNN bias correction of 100 m LDAPS forecasts at Pyeongchang reduces RMSE by 33% for temperature and 45% for wind speed.
Development of Real-Time Bias Correction Model for High-Resolution Weather Forecast Information of Mountainous Terrain Using Artificial Neural Network
In one sentence. Sliding-window DNN bias correction of 100 m LDAPS forecasts at Pyeongchang reduces RMSE by 33% for temperature and 45% for wind speed.
Labels
- Topic
- Post-processing & calibration, Local & station forecasting
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.5391/jkiis.2023.33.3.249 OpenAlex:W4383877464
- Added
- 2026-10-22
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