/DL_ENSO

CNN for climate forecast

Primary LanguagePythonMIT LicenseMIT

Directory

  • CNN: source code for ENSO forecast
  • figure: source code for drawing main figures

Processes of the Nino3.4 prediction

  • Training with CMIP5 (csh/nino34.cmip.csh)

    (1) training (import from sample/nino34.train_cmip.sample)

    (2) validation (import from sample/nino34.valid.sample)

    (3) ensemble mean (import from sample/nino34.ensmean.sample)

  • Transfer learning with SODA (csh/nino34.transfer.csh)

    (1) training (import from sample/nino34.train_transfer.sample)

    (2) validation (import from sample/nino34.valid.sample)

    (3) ensemble mean (import from sample/nino34.ensmean.sample)

  • Heatmap analysis (csh/nino34.heatmap.csh) (import from sample/nino34.heatmap.sample)

Processes of the El Nino type prediction

  • Training with CMIP5 (csh/nino_type.cmip.csh)

    (1) training (import from sample/nino_type.train_cmip.sample)

    (2) validation (import from sample/nino_type.valid.sample)

    (3) ensemble mean (import from sample/nino_type.ensmean.sample)

  • Heatmap analysis (csh/nino_type.heatmap.csh) (import from nino_type.heatmap.sample)

Data set (netCDF4)

  • you can download data set here (1.13GB): https://168.131.122.201/OCL/Data/DL_ENSO/H19_dataset.zip.

  • The data set consists of the following:

    • Dataset for Nino3.4 forecast

      (1) Training set (CMIP5/):

       Input: [CMIP5.input.36mn.1861_2001.nc]
 Label for 2-23month lead: [CMIP5.label.nino34.12mn_3mv.1863_2003.nc]
 Label for 1month lead: [CMIP5.label.nino34.12mn_2mv.1863_2003.nc]

      (2) Training set for transfer learning (SODA/):

       Input: [SODA.input.36mn.1871_1970.nc]
 Label for 2-23month lead: [SODA.label.nino34.12mn_3mv.1873_1972.nc]
 Label for 1month lead: [SODA.label.nino34.12mn_2mv.1873_1972.nc]

      (3) validation set (GODAS/):

       Input: [GODAS.input.36mn.1980_2015.nc]
 Label for 2-23month lead: [GODAS.label.12mn_3mv.1982_2017.nc]
 Label for 1month lead: [GODAS.label.12mn_2mv.1982_2017.nc]

    • Dataset for El Nino type forecast

      (1) Training set (CMIP5/):

      Input: [CMIP5.input.type.NDJ.1861_2001.nc]
Label: [CMIP5.label.type.DJF.1863_2003.nc]

      (2) validation set (GODAS/):

      Input: [GODAS.input.36mn.1980_2015.nc]
Label: [GODAS.label.type.DJF.1982_2017.nc]

Reference

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)

Basic tutorial: https://www.tensorflow.org/tutorials/