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Hail in Northeast Italy: A Neural Network Ensemble Forecast Using Sounding-Derived Indices

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  • 1 Osservatorio Meteorologico Regionale dell’ARPA FVG (OSMER), Visco, Italy
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Abstract

In a previous work, the hailpad data collected over the plain of the Friuli Venezia Giulia region in northeast Italy during the April–September 1992–2009 period were studied through a bivariate analysis with 52 sounding-derived indices from the Udine–Campoformido station (WMO code 16044). The results showed statistically significant relations but, nevertheless, were not completely satisfactory from a practical point of view. In the current work, a prognostic multivariate analysis is performed, using linear and nonlinear approaches, finding the best results with an ensemble of neural networks. For the hail occurrence–classification problem, a novel method for combining binary classifiers (a variant of the Mojirsheibani major voting algorithm) is introduced. For the hail extension–regression problem the ensemble is built by choosing the members with a bagging algorithm, but combining them with a linear multiregression, in order to increase the forecast variability.

Corresponding author address: Agostino Manzato, Osservatorio Meteorologico Regionale dell’ARPA FVG (OSMER), Via Oberdan 18/a, I-33040 Visco (UD), Italy. E-mail: agostino.manzato@osmer.fvg.it

Abstract

In a previous work, the hailpad data collected over the plain of the Friuli Venezia Giulia region in northeast Italy during the April–September 1992–2009 period were studied through a bivariate analysis with 52 sounding-derived indices from the Udine–Campoformido station (WMO code 16044). The results showed statistically significant relations but, nevertheless, were not completely satisfactory from a practical point of view. In the current work, a prognostic multivariate analysis is performed, using linear and nonlinear approaches, finding the best results with an ensemble of neural networks. For the hail occurrence–classification problem, a novel method for combining binary classifiers (a variant of the Mojirsheibani major voting algorithm) is introduced. For the hail extension–regression problem the ensemble is built by choosing the members with a bagging algorithm, but combining them with a linear multiregression, in order to increase the forecast variability.

Corresponding author address: Agostino Manzato, Osservatorio Meteorologico Regionale dell’ARPA FVG (OSMER), Via Oberdan 18/a, I-33040 Visco (UD), Italy. E-mail: agostino.manzato@osmer.fvg.it
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