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

Meteorological applications

article

Stefano Amendola¹  Filippo Maimone²  Antonello Pasini³  Fabrizio Ciciulla²  Vinicio Pelino² 

[1] Department of Physics, University of Tor Vergata;COMet, Aeroporto De Bernardi;Institute of Atmospheric Pollution Research

关键词: empirical statistical downscaling;    seasonal forecast;    neural network;    Bayesian regularization;   

DOI  :  10.1002/met.1615

学科分类：社会科学、人文和艺术（综合）

来源: Wiley

【 摘 要 】

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 system for Large to Local area 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.

【 授权许可】

CC BY|CC BY-NC|CC BY-NC-ND