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ML when completed returns weights for all variables that give the best forecast at T+6.   These weights are different according to the variable being forecast (e.g. the weight given to surface pressure used in calculating a forecast surface temperature is different from weight given to surface pressure used in calculating a forecast surface dew point).   These relationships are in the form of a set of algorithms that can be used by subsequent AI forecasts.

Error loss metrics

AIFS Single:

The error metric (loss function) is RMS.

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Fig2.1.6.1-1: Machine learning training process in deriving an algorithm for use in AIFS single.  A range of observed data is processed using random weighting functions for each parameter.  The forecast results are then compared with verifying data and the difference between them (the loss or error) is fed back to the processor (back propagation).  This induces modification of the weighting functions for each parameter and the resulting forecast is compared with the verifying data giving new loss/error value.  Iteration continues until the loss/error is minimised and the set of weights for each parameter becomes the algorithm for the forecasting process.

AIFS ENS:

The error metric (loss function) is the almost fair CRPS.   For the ensemble, it is necessary also to include some form of uncertainty during evaluation of the algorithms.  To do this, white noise is injected into the neural network during the training phase.  The model learns to shape this noise to capture the uncertainty in future weather conditions, so that in the forecast phase when new white noise is injected, the model can create a well-calibrated ensemble.  

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