paper
2023
Post-processing & calibration
Application study
CNN / U-NetRNN / LSTM / ConvLSTMClimate projections / centennial Journal
2023-10-13
008
Oct 2023
A. Sabarinath, A. Naga Rajesh, Sachin S. Gunthe et al.
International Journal of Climatology
CNN-1D and CNN-LSTM bias correction reduces CMIP6 surface air temperature warm bias over India's monsoon core to 0.35°C RMSE in the historical period.
Application of deep learning algorithms to correct bias in CMIP6 simulations of surface air temperature over the Indian monsoon core region
In one sentence. CNN-1D and CNN-LSTM bias correction reduces CMIP6 surface air temperature warm bias over India's monsoon core to 0.35°C RMSE in the historical period.
Labels
- Topic
- Post-processing & calibration
- Type
- Application study
- Method
- CNN / U-Net, RNN / LSTM / ConvLSTM
- Time scale
- Climate projections / centennial
- Domain
- Regional
- Data
- CMIP / climate model output, ERA5 / reanalysis
- Status
- Research
- Identifiers
- DOI:10.1002/joc.8276 OpenAlex:W4387617059
- Added
- 2026-10-22
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