paper
2019
Applications & impacts
Extremes & hazards Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)6 h–3 daysColorado State University (incl. CIRA) Journal
2019-11-14
022
Nov 2019
Stephanie Pilkington, Hussam N Mahmoud
Natural Hazards Review
Colorado State University (incl. CIRA)
Evaluates a neural-network hurricane impact-level forecast over 2017 storms, finding best accuracy about 30 h before landfall and wind speed as the key driver.
Concluding the 2017 Hurricane Season: Evaluation of Impact Level Forecasts with Varied Meteorological Hazards
In one sentence. Evaluates a neural-network hurricane impact-level forecast over 2017 storms, finding best accuracy about 30 h before landfall and wind speed as the key driver.
Labels
- Topic
- Applications & impacts, Extremes & hazards
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Pre-operational / experimental
- Identifiers
- DOI:10.1061/(asce)nh.1527-6996.0000345 OpenAlex:W2989306093
- Added
- 2026-10-22
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