On the Systematic Reduction of Data Complexity in Multi-model Atmospheric Dispersion Ensemble Modeling

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.

RICCIO Angelo;  CIARAMELLA Angelo;  GIUNTA Giulio;  GALMARINI Stefano;  POTEMPSKI Slawomir;  SOLAZZO Efisio; 

2012-12-05

AMER GEOPHYSICAL UNION

JRC65675

0148-0227,   

http://www.agu.org/pubs/crossref/2012/2011JD016503.shtml,    https://publications.jrc.ec.europa.eu/repository/handle/JRC65675,   

10.1029/2011JD016503,