paper
2021
Downscaling & super-resolution
Post-processing & calibration New model or method
Classical ML (trees, GP, linear, kernels)Climate projections / centennial Journal
2021-02-19
008
Feb 2021
P. Loganathan, Amit Baburao Mahindrakar
Climate Research
Proposes XGBoost-based statistical downscaling of ACCESS1.0 GCM output for Indian river basin, with bias near zero and NSE 0.75-0.85 against 35 stations.
Downscaling & super-resolution
Intercomparison of statistical downscaling models: a case study of a large-scale river basin
In one sentence. Proposes XGBoost-based statistical downscaling of ACCESS1.0 GCM output for Indian river basin, with bias near zero and NSE 0.75-0.85 against 35 stations.
Labels
- Topic
- Downscaling & super-resolution, Post-processing & calibration
- Type
- New model or method
- 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:10.3354/cr01642 OpenAlex:W3131989680
- Added
- 2026-10-22
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