paper
2022
Post-processing & calibration
Climate analysis & attribution New model or method
OtherClimate projections / centennialOak Ridge National Laboratory Journal
2022-01-01
022
Jan 2022
Ming Fan, Dan Lu, Deeksha Rastogi et al.
Journal of Machine Learning for Modeling and Computing
Oak Ridge National Laboratory
A Bayesian neural network ensemble weights CMIP6 models by location and season, calibrating projections to observations with interpretable weights and uncertainty growing away from constrained periods.
A SPATIOTEMPORAL-AWARE WEIGHTING SCHEME FOR IMPROVING CLIMATE MODEL ENSEMBLE PREDICTIONS
In one sentence. A Bayesian neural network ensemble weights CMIP6 models by location and season, calibrating projections to observations with interpretable weights and uncertainty growing away from constrained periods.
Labels
- Topic
- Post-processing & calibration, Climate analysis & attribution
- Type
- New model or method
- Method
- Other
- Time scale
- Climate projections / centennial
- Domain
- Global
- Data
- CMIP / climate model output
- Status
- Research
- Identifiers
- DOI:10.1615/jmachlearnmodelcomput.2022046715 OpenAlex:W4312539167
- Added
- 2026-10-22
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