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Comparing Artificial Intelligence and Classical (physics-based) Forecasting Models

Main contrasts between Artificial Intelligence (AI) and Classical forecasting models

There is a huge and growing amount of both observed data and forecast products.   Artificial Intelligence (AI) and Machine Learning (ML) are able to deal with massive amounts of data economically.  They bring major speed and cost benefits to the practice of weather forecasting.   AI is destined to play a substantial role in meteorological forecasting in the future. 

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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.

Main contrasts between the ECMWF models

Currently the main contrasts between the ECMWF models are:

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

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Fig2B-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

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Fig2B-2: Comparing IFS and AI forecasting suites and the flow and approximate size of data for each. 

Additional Sources of Information

(Note: In older material there may be references to issues that have subsequently been addressed)

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