Open AccessA neural network ensemble downscaling system ( SIBILLA ) for seasonal forecasts over Italy: winter case studies
Author(s) -
Amendola Stefano,
Maimone Filippo,
Pasini Antonello,
Ciciulla Fabrizio,
Pelino Vinicio
Publication year - 2017
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.1615
Subject(s) - downscaling , bayesian probability , computer science , artificial neural network , empirical orthogonal functions , canonical correlation , meteorology , environmental science , gaussian , autoregressive integrated moving average , range (aeronautics) , time series , artificial intelligence , precipitation , machine learning , geography , physics , materials science , quantum mechanics , composite material
A novel statistical downscaling system for seasonal predictions is presented, based on an ensemble of neural networks with Bayesian regularization. The system SIBILLA (Statistical Integrated Bayesian Information s ystem for Large to Local a rea Analysis) is able to take multiple predictor fields and/or time series as inputs. Gridded fields are compressed using empirical orthogonal functions, and a canonical correlation analysis is performed between predictors and each predictand. The first canonical variates are used as effective predictors in a neural network ensemble system. Final outputs for each parameter are expressed as a probability distribution for each station/grid point in the space of observations, as a result of the convolution of Gaussian mixtures. A first example of application in the Italian area is presented. An overall increase in skill score performances with respect to European Centre for Medium‐Range Weather Forecasts ( ECMWF ) System 4 direct model output for the period 1981–2010 was found, even if probably not as high as desirable in a fully operational system.