paper
2019
Sub-seasonal to seasonal
Applications & impactsPost-processing & calibration Application study
Classical ML (trees, GP, linear, kernels)Seasonal to annualKorea Meteorological Administration (KMA) Journal
2019-11-29
018
Nov 2019
Ju‐Young Shin, Kyu Rang Kim, Jong-Chul Ha
Agricultural and Forest Meteorology
Korea Meteorological Administration (KMA)
Machine learning corrects a coupled climate model's seasonal forecasts of daily air temperature for field-scale agricultural management.
Seasonal forecasting of daily mean air temperatures using a coupled global climate model and machine learning algorithm for field-scale agricultural management
In one sentence. Machine learning corrects a coupled climate model's seasonal forecasts of daily air temperature for field-scale agricultural management.
Labels
- Topic
- Sub-seasonal to seasonal, Applications & impacts, Post-processing & calibration
- Type
- Application study
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Seasonal to annual
- Domain
- Local / station
- Data
- CMIP / climate model output
- Status
- Research
- Identifiers
- DOI:10.1016/j.agrformet.2019.107858 OpenAlex:W2990957319
- Added
- 2026-10-22
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