paper
2022
Applications & impacts
Sub-seasonal to seasonal New model or method
Classical ML (trees, GP, linear, kernels)Seasonal to annualKNMI (Royal Netherlands Meteorological Institute) Journal
2022-11-24
047
Nov 2022
Sem Vijverberg, Raed Hamed, Dim Coumou
Artificial Intelligence for the Earth Systems
KNMI (Royal Netherlands Meteorological Institute)
Statistical ML seasonal forecasts of poor US soybean harvests from Pacific SST and soil moisture precursors reach 67% correct at three months before sowing.
Skillful U.S. Soy Yield Forecasts at Presowing Lead Times
In one sentence. Statistical ML seasonal forecasts of poor US soybean harvests from Pacific SST and soil moisture precursors reach 67% correct at three months before sowing.
Labels
- Topic
- Applications & impacts, Sub-seasonal to seasonal
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Seasonal to annual
- Domain
- Regional
- Data
- ERA5 / reanalysis
- Status
- Research
- Identifiers
- DOI:10.1175/aies-d-21-0009.1 OpenAlex:W4309878702
- Added
- 2026-10-22
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