paper
2021
Extremes & hazards
Climate analysis & attribution Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels) Journal
2021-03-20
012
Mar 2021
Laiyin Zhu, Pascual Aguilera
Journal of Geophysical Research Atmospheres
Random forest over a 99-year climatology identifies drivers of tropical cyclone precipitation in eastern Mexico, with distance to track the strongest predictor for events.
Evaluating Variations in Tropical Cyclone Precipitation in Eastern Mexico Using Machine Learning Techniques
In one sentence. Random forest over a 99-year climatology identifies drivers of tropical cyclone precipitation in eastern Mexico, with distance to track the strongest predictor for events.
Labels
- Topic
- Extremes & hazards, Climate analysis & attribution
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Regional
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1029/2021jd034604 OpenAlex:W3121620620
- Added
- 2026-10-22
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