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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.

Artificial Artificial Intelligence forecasting uses the algorithms derived by Machine Learning to produce forecasts.  These can be as a single forecast (AIFS Single) or as an ensemble of forecasts (AIFS-ENS).

Nevertheless, physics-based numerical weather prediction models remain key for these fully ML approaches. The IFS is used to create both training and validation data (using ERA5 and a few years of operational analysis).  AIFS and other machine learning models have been trained to minimise some measure of the error of forecast parameters.  In this way they have ordinarily been trained for use as a deterministic model.  In turn, AIFS relies on the IFS to provide initial conditions for each forecast.

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