/OHW22-ENSO_Prediction

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OHW22 ENSO Prediction

Summary

We propose a project to develop a framework for ENSO prediction using a range of machine learning approaches and benchmark their skill against other methods. Identify observables that are most relevant for ENSO predictability at a particular timescale.

Data description

  • Coarsened CESM2 Large Ensemble members (SST fields) from 1° to 3° resolution
  • Used the first 10 of 100 CESM2 LE members

Deep learning components

  • 2D CNN (relu/selu, adam/nadam/adamx)
  • 3D CNN (relu/selu, adam/nadam/adamx)
  • EOF/LSTM
  • CNN/LSTM
  • Transformers?
  • Create a baseline method to compare these methods to (i.e. persistence method, AR1 model)
  • Interpretability– why do the results look this way?

Opportunities for growth

  • EOF-LSTM: Instead of EOFs, we can use a CNN to predict one number (or however number modes we want to keep). This is a more complicated dimension reduction than PCA and then use LSTM. So CNN then LSTM as the workflow.