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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

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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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Fig2.1.6.1-2: Machine learning training process in deriving an algorithm for use in AIFS - ENS.  A range of observed data is processed using four different sets of random weighting functions for each parameter.   White noise is introduced to each iteration to emulate model uncertainty.  The four forecast results are then compared with verifying data.  CRPS, which measures how good forecasts are, is evaluated for the results of the four forecasts.  The CRPS influences what is fed back to the processor (back propagation). This induces modification of the weighting functions for each parameter and the resulting forecasts are compared with the verifying data giving new CRPS value.  Iteration continues until the CRPS is minimised and the set of weights for each parameter becomes the algorithm for the forecasting process.

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