Open AccessHail in Northeast Italy: A Neural Network Ensemble Forecast Using Sounding-Derived Indices
Author(s) -
Agostino Manzato
Publication year - 2013
Publication title -
weather and forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.393
H-Index - 106
eISSN - 1520-0434
pISSN - 0882-8156
DOI - 10.1175/waf-d-12-00034.1
Subject(s) - bivariate analysis , artificial neural network , computer science , multivariate statistics , ensemble forecasting , bayesian multivariate linear regression , meteorology , linear regression , artificial intelligence , machine learning , geography
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.