Machine Learning / Artificial Intelligence for S2S prediction 

There is currently a lot excitement in the weather and climate communities to explore the potential of data driven approaches based on Artificial Intelligence/Machine Learning/Deep learning for S2S prediction through, for instance, improved parameterization, improved calibration and multi-model calibration, extreme event attribution, verification... The publicly available S2S database which contains a considerable amount of data (re-forecasts and real-time forecasts from 11 operational centres) represents an ideal testbed for these data-driven methods. The SubX database provides another such opportunity (http://cola.gmu.edu/subx/index.html).

Potential applications :
 1. Improved data assimilation (e.g. better quality control of observations)
 2. Improved parameterization (e.g. radiative schemes)
 3. Improved post-processing (model calibration, bias-correction, multi-ensemble combination...)
 4. Predictability diagnostics (e.g. teleconnections)
 5. S2S event attribution (e.g. origins of extreme events)
 6. Empirical forecasts

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