A neural network ensemble downscaling system ( SIBILLA ) for seasonal forecasts over Italy: winter case studies

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.