paper
2022
Climate analysis & attribution
Benchmarks, datasets & evaluation Application study
Classical ML (trees, GP, linear, kernels)Columbia University (LEAP, Lamont-Doherty)NASA Journal
2022-10-23
047
Oct 2022
Kate Marvel, Benjamin I. Cook
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
Columbia University (LEAP, Lamont-Doherty), NASA
Unsupervised ML on tree-ring PDSI drought atlases finds outlier drought patterns in 80% of 21st-century years, showing unusual hydroclimate states.
Climate analysis & attribution
Using machine learning to identify novel hydroclimate states
In one sentence. Unsupervised ML on tree-ring PDSI drought atlases finds outlier drought patterns in 80% of 21st-century years, showing unusual hydroclimate states.
Labels
- Topic
- Climate analysis & attribution, Benchmarks, datasets & evaluation
- Type
- Application study
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Global
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1098/rsta.2021.0287 OpenAlex:W4307329875
- Added
- 2026-10-22
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