paper
2026
Post-processing & calibration
New model or method
Classical ML (trees, GP, linear, kernels)6 h–3 daysColorado State University (incl. CIRA)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Tomorrow.ioUniversity of Oklahoma (AI2ES) Journal
2026-01-07
047
Jan 2026
Michael D. Pletcher, Peter G. Veals, Randy J. Chase et al.
Weather and Forecasting
Colorado State University (incl. CIRA), NOAA (incl. GFDL, PSL, GSL, NCEP/EMC), Tomorrow.io, University of Oklahoma (AI2ES)
Random forest snow-to-liquid ratio model applied to HRRR over CONUS cuts MAE to 2.92 versus 3.51–4.76 for operational methods.
Snow-to-Liquid Ratio Prediction over the Contiguous United States Using Machine Learning
In one sentence. Random forest snow-to-liquid ratio model applied to HRRR over CONUS cuts MAE to 2.92 versus 3.51–4.76 for operational methods.
Labels
- Topic
- Post-processing & calibration
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- Station / in-situ, ERA5 / reanalysis, NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Pre-operational / experimental
- Identifiers
- DOI:10.1175/waf-d-25-0124.1 OpenAlex:W7119005367
- Added
- 2026-10-22
Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.