paper
2022
Extremes & hazards
Post-processing & calibration New model or method
Classical ML (trees, GP, linear, kernels)6 h–3 days Journal
2022-12-17
008
Dec 2022
Sushant Kumar, Anumeha Dube, Raghavendra Ashrit et al.
Pure and Applied Geophysics
Applies machine learning to improve tropical cyclone intensity forecasts from the NCMRWF ensemble prediction system; abstract not provided, so results are unknown.
A Machine Learning (ML)-Based Approach to Improve Tropical Cyclone Intensity Prediction of NCMRWF Ensemble Prediction System
In one sentence. Applies machine learning to improve tropical cyclone intensity forecasts from the NCMRWF ensemble prediction system; abstract not provided, so results are unknown.
Labels
- Topic
- Extremes & hazards, Post-processing & calibration
- Type
- New model or method
- 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:10.1007/s00024-022-03206-6 OpenAlex:W4313352115
- Added
- 2026-10-22
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