paper
2022
Sub-seasonal to seasonal
Post-processing & calibrationBenchmarks, datasets & evaluation Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)2 weeks–2 monthsColumbia University (LEAP, Lamont-Doherty)Environment and Climate Change Canada (ECCC)ECMWFMax Planck Institute for MeteorologySwiss Data Science Center (SDSC) Journal
2022-12-01
055
Dec 2022
F. Vitart, A. W. Robertson, A. Spring et al.
Bulletin of the American Meteorological Society
Columbia University (LEAP, Lamont-Doherty), Environment and Climate Change Canada (ECCC), ECMWF, Max Planck Institute for Meteorology, Swiss Data Science Center (SDSC)
WMO prize challenge winners used AI to post-process ECMWF S2S forecasts, beating bias-corrected references for weeks 3-6 precipitation and temperature.
Outcomes of the WMO Prize Challenge to Improve Subseasonal to Seasonal Predictions Using Artificial Intelligence
In one sentence. WMO prize challenge winners used AI to post-process ECMWF S2S forecasts, beating bias-corrected references for weeks 3-6 precipitation and temperature.
Labels
- Topic
- Sub-seasonal to seasonal, Post-processing & calibration, Benchmarks, datasets & evaluation
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 2 weeks–2 months
- Domain
- Global
- Data
- NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.1175/bams-d-22-0046.1 OpenAlex:W4298138444
- Added
- 2026-10-22
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