paper
2018
Post-processing & calibration
Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)Climate projections / centennial Journal
2018-07-27
022
Jul 2018
Bin Wang, Lihong Zheng, De Li Liu et al.
International Journal of Climatology
Random forest and SVM multi-model ensembles outperform simple averaging and Bayesian averaging in reproducing observed Australian monthly rainfall and temperature from CMIP5 GCMs.
Using multi‐model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia
In one sentence. Random forest and SVM multi-model ensembles outperform simple averaging and Bayesian averaging in reproducing observed Australian monthly rainfall and temperature from CMIP5 GCMs.
Labels
- Topic
- Post-processing & calibration
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Climate projections / centennial
- Domain
- Regional
- Data
- CMIP / climate model output, Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1002/joc.5705 OpenAlex:W2884721255
- Added
- 2026-10-22
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