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Hail in Northeast Italy: A Neural Network Ensemble Forecast Using Sounding-Derived Indices.
- Source :
- Weather & Forecasting; Feb2013, Vol. 28 Issue 1, p3-28, 26p, 1 Diagram, 5 Charts, 8 Graphs, 1 Map
- Publication Year :
- 2013
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08828156
- Volume :
- 28
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Weather & Forecasting
- Publication Type :
- Academic Journal
- Accession number :
- 85789885
- Full Text :
- https://doi.org/10.1175/WAF-D-12-00034.1