Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloA neural network ensemble downscaling system (SIBILLA) for seasonal forecasts over Italy: winter case studies
Anno di pubblicazione2017
Formato-
Autore/iAmendola, Stefano; Maimone, Filippo; Pasini, Antonello; Ciciulla, Fabrizio; Pelino, Vinicio
Affiliazioni autoriUniversita degli Studi di Roma Tor Vergata; Italian Air Force; CNR-Institute of Atmospheric Pollution Research, Rome; CNMCA; Sysman Progetti Servizi srl
Autori CNR e affiliazioni
  • ANTONELLO PASINI
Lingua/e
  • inglese
AbstractA 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.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da157
Pagine a166
Pagine totali-
RivistaMeteorological applications (Print)
Attiva dal 1994
Editore: Royal Meteorological Society, - Reading
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 1350-4827
Titolo chiave: Meteorological applications (Print)
Titolo proprio: Meteorological applications. (Print)
Titolo abbreviato: Meteorol. appl. (Print)
Numero volume della rivista24
Fascicolo della rivista1
DOI10.1002/met.1615
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-85008461970)
  • ISI Web of Science (WOS) (Codice:000393217100016)
Parole chiaveBayesian regularization, empirical statistical downscaling, neural network, seasonal forecast
Link (URL, URI)http://www.scopus.com/record/display.url?eid=2-s2.0-85008461970&origin=inward
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione06/07/2016
Note/Altre informazioni-
Strutture CNR
  • IIA — Istituto sull'inquinamento atmosferico
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati