paper
2019
Post-processing & calibration
Sub-seasonal to seasonal New model or method
Classical ML (trees, GP, linear, kernels)Seasonal to annualChina Meteorological Administration (CMA) Journal
2019-01-03
022
Jan 2019
Lei Xu, Nengcheng Chen, Xiang Zhang et al.
Climate Dynamics
China Meteorological Administration (CMA)
Combines wavelet decomposition and machine learning to improve local precipitation forecasts from the NMME multi-model seasonal ensemble.
Improving the North American multi-model ensemble (NMME) precipitation forecasts at local areas using wavelet and machine learning
In one sentence. Combines wavelet decomposition and machine learning to improve local precipitation forecasts from the NMME multi-model seasonal ensemble.
Labels
- Topic
- Post-processing & calibration, Sub-seasonal to seasonal
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Seasonal to annual
- Domain
- Local / station
- Data
- CMIP / climate model output
- Status
- Research
- Identifiers
- DOI:10.1007/s00382-018-04605-z OpenAlex:W2908061964
- Added
- 2026-10-22
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