paper
2021
Extremes & hazards
Post-processing & calibration Operational implementation report
Classical ML (trees, GP, linear, kernels)6 h–3 days3–15 daysColorado State University (incl. CIRA)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC) Journal
2021-05-24
065
May 2021
Russ S. Schumacher, Aaron J. Hill, Mark Klein et al.
Bulletin of the American Meteorological Society
Colorado State University (incl. CIRA), NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
CSU-MLP random-forest excessive rainfall forecasts, built from ensemble reforecasts, were transitioned into operational use at NOAA's Weather Prediction Center.
From Random Forests to Flood Forecasts: A Research to Operations Success Story
In one sentence. CSU-MLP random-forest excessive rainfall forecasts, built from ensemble reforecasts, were transitioned into operational use at NOAA's Weather Prediction Center.
Labels
- Topic
- Extremes & hazards, Post-processing & calibration
- Type
- Operational implementation report
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days, 3–15 days
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Other
- Status
- Operational
- Identifiers
- DOI:10.1175/bams-d-20-0186.1 OpenAlex:W3164895358
- Added
- 2026-10-22
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