A_CNN
CNN based all-season ENSO forecast model
Processes of the Nino3.4 prediction
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Main training with CMIP5 data (csh/1.main_training.sh)
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Fine Tuning with SODA data (reanalysis) (csh/2.fine_tuning.sh)
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Heatmap analysis (csh/3.heatmap.sh)
Data set (netCDF4)
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you can download data set here: https://drive.google.com/file/d/1JxSbxu1bNHqw-FFcLnY-jXWeGxo1t45z/view?usp=sharing
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The data set consists of the following:
(1) Training set for main training (CMIP5): Input: [CMIP5_tr.input.1861_2001.nc] Label: [CMIP5_tr.label.1863_2003.nc] (2) validation set for main training (CMIP5): Input: [CMIP5_val.input.1861_2001.nc] Label: [CMIP5_val.label.1861_2001.nc] (3) Training set for fine tuning (SODA): Input: [soda.input.1871_1970.nc] Label: [soda.label.1871_1970.nc] (4) Test set (GODAS): Input: [godas.input.1980_2017.nc] Label: [godas.label.1980_2017.nc]
References
- Ham, Y. G., Kim, J. H., Kim, E. S., & On, K. W. Unified deep learning model for El Niño/Southern Oscillation forecasts by incorporating seasonality in climate data. Science Bulletin 66, https://doi.org/10.1016/j.scib.2021.03.009 (2021).
- Ham, Y. G., Kim, J. H. & Luo, J.-J. Deep learning for multi-year ENSO forecasts. Nature 573, https://doi.org/10.1029/2010JC006695 (2019).
Requirement (python packages)
- Tensowflow (> version 2.0, https://www.tensorflow.org/install/)
- netCDF4
- numpy