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Before an AI forecasting system can be implemented it has to have been trained on a large amount of observed data.  At ECMWF the AIFS is trained mainly to produce six hour forecasts.  

ML Machine Learning uses statistical methods and numerical optimisation to define and incrementally improve the relationship between a set of different observations and forecast values at a later time.   AIFS uses Graph Neural Networks which allows flexibility with grids and parameter efficiency.

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Multi-date verification suggests ML broadscale forecasts score better than classical NWP.   However, shorter wave length features and fine detail tend to not be well predicted in deterministic mode, particularly as forecast lead time although increases. However, this is much less of an issues issue for AIFS-ENS.

Users should try to stay up to date with the latest AIFS developments.

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