Michael Groom (CSIRO) – Interpretable forecasts of ENSO phase at multi-year lead times using entropic learning
Abstract Machine learning, in particular deep learning, has shown great potential in outperforming conventional GCMs at predicting ENSO, providing useful forecast skill beyond the Boreal spring predictability barrier and enabling the possibility of issuing ENSO forecasts at multi-year lead times. However, despite these advancements in forecast skill, much less progress has been made on understanding […]