Title: On the Systematic Reduction of Data Complexity in Multi-model Atmospheric Dispersion Ensemble Modeling
Authors: RICCIO AngeloCIARAMELLA AngeloGiunta GiulioGALMARINI StefanoPOTEMPSKI SlawomirSOLAZZO EFISIO
Citation: JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES vol. 117 p. D05314
Publisher: AMER GEOPHYSICAL UNION
Publication Year: 2012
JRC N°: JRC65675
ISSN: 0148-0227
URI: http://www.agu.org/pubs/crossref/2012/2011JD016503.shtml
http://publications.jrc.ec.europa.eu/repository/handle/JRC65675
DOI: 10.1029/2011JD016503
Type: Articles in Journals
Abstract: Aim of this work is to explore the effectiveness of theoretical infor7 mation approaches for the reduction of data complexity in multi-model ensemble systems. We first exploit a weak form of independence, i.e. uncorrelation, as a mechanism for detecting linear relationships. Then, stronger and more general forms of independence measure, such as Mutual Information, are used to investigate dependence structures for models data selection. A distance matrix, measuring the inter-dependence between data, is derived for the investigated measures, with the scope of clustering cor related/dependent models together. Redundant information is discarded by selecting a few representative models from each cluster.
JRC Institute:Institute for Environment and Sustainability

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