paper
2019
Extremes & hazards
Nowcasting New model or method
Classical ML (trees, GP, linear, kernels)0–6 hNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Oklahoma (AI2ES) Journal
2019-07-25
051
Jul 2019
Amy McGovern, Christopher D. Karstens, Travis M. Smith et al.
Weather and Forecasting
NOAA (incl. GFDL, PSL, GSL, NCEP/EMC), University of Oklahoma (AI2ES)
Random forests, boosted trees and elastic nets predict storm longevity in NWS quasi-operational testing, with lower error than human forecasters.
Quasi-Operational Testing of Real-Time Storm-Longevity Prediction via Machine Learning
In one sentence. Random forests, boosted trees and elastic nets predict storm longevity in NWS quasi-operational testing, with lower error than human forecasters.
Labels
- Topic
- Extremes & hazards, Nowcasting
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 0–6 h
- Domain
- Regional
- Data
- Radar, Satellite
- Status
- Pre-operational / experimental
- Identifiers
- DOI:10.1175/waf-d-18-0141.1 OpenAlex:W2963292962
- Added
- 2026-10-22
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