paper
2021
Post-processing & calibration
New model or method
Classical ML (trees, GP, linear, kernels)Seasonal to annual Journal
2021-10-12
012
Oct 2021
Sourabh Shrivastava, Ram Avtar, Prasanta Kumar Bal
International Journal of Big Data Mining for Global Warming
Five tree-based ML models correct NMME seasonal precipitation over the Himalayas, cutting the 20 mm/day multi-model wet bias with RMSE near 2 mm/day.
IMPROVING THE NORTH AMERICAN MULTI-MODEL ENSEMBLE (NMME) PRECIPITATION FORECASTS AT SEASONAL SCALE OVER THE HIMALAYAN REGION USING MACHINE LEARNING
In one sentence. Five tree-based ML models correct NMME seasonal precipitation over the Himalayas, cutting the 20 mm/day multi-model wet bias with RMSE near 2 mm/day.
Labels
- Topic
- Post-processing & calibration
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Seasonal to annual
- Domain
- Regional
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1142/s263053482150008x OpenAlex:W3206716911
- Added
- 2026-10-22
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