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Machine Learning
The aim of ML is to continually develop (train) an empirical model directly from observations or reanalyses. Observations implicitly contain the physics of the atmosphere but it is not necessary for ML models emulate the underpinning physics that dictates the evolution of variables through a forecast. During the ML training process, ML considers all the set of observed or initial data, and using statistical methods relates these to observed variable (e.g.temperature) six hours later at each point. The initial data and corresponding data at the end of the forecast period have been extracted from some 40 years of ERA5 data. At ECMWF, machine learning training is aimed towards producing six hour forecasts. Table1 gives the set of observed and forecast variables and the constants considered during the machine learning process at ECMWF.
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