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

Fig1Fig2.1.6-1: Difference of concept between traditional physics-based weather forecasting models and Artificial Intelligence models.  Physics-based models process observational data using physics equations as rules to provide forecasts of variables as output.  Machine Learning (or training) uses data from ERAS5 as input, checks the forecast results against later ERA5 data, and provides forecast algorithms. Once defined these algorithms are used by subsequent AI forecasts.  The AI model processes observational data using the algorithms to provide forecasts of variables as output.

Fig2Fig2.1.6-2: Comparing IFS and AI forecasting suites and flow and approximate size of data. 

Table1Table2.1.6-1: Observed and forecast variables and constants at pressure levels used by the machine learning process and forecast process within ECMWF AIFS.  AIFS receives as input a representation of the state of the atmosphere - from ERA5 for the ML process; from the ECMWF operational analysis for the AI forecast process.  Both ML during the training process and AI during the forecast process predict the atmospheric state six hours into the future (with repetitions of this procedure in the forecast process enabling much longer lead time forecasts to be created)Currently AIFS only uses data at the surface and at standard pressure levels (diagram on the right).

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