A Neural Network Approach for blocking recognition

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Abstract
We propose to use an Artificial Neural Network (ANN) for meteorological blocking recognition. The network output is presented as a number which ranges between 0 (absence of blocking) and 1 (blocked situation). This output is then compared with the step function obtained with a blocking index used in meteorological analysis and in the recognition of synoptic maps. We show that the ANN can pick events which are disregarded by the TM index and that ANN performances are equivalent and in some cases better than those indicated by an analytically computed blocking index.