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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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• IFS has high temporal, horizontal and vertical resolution and is unparalleled for the breadth of predicted variables.  ECMWF remains firmly committed to further improvement of the IFS.  
• AIFS has lower temporal, horizontal and vertical resolution and has relatively limited number of predicted variables (Table1).  ML models are evolving quickly and the number of variables accounted for will increase as AIFS develops.

Ensemble forecasting (AIFS ENS) brings an ability to quantify uncertainty and to identify possible extremes in the evolution.  An ensemble of the AI forecast procedure can be executed because AI forecasting is rapid and cheap to run.  At present AIFS ENS has 50 members plus a nominal "control run" and is run at six hour intervals.   However there is potential for much larger ensembles and/or more frequent forecasts.

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