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|Title:||On the Systematic Reduction of Data Complexity in Multi-model Atmospheric Dispersion Ensemble Modeling|
|Authors:||RICCIO Angelo; CIARAMELLA Angelo; Giunta Giulio; GALMARINI Stefano; POTEMPSKI Slawomir; SOLAZZO EFISIO|
|Citation:||JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES vol. 117 p. D05314|
|Publisher:||AMER GEOPHYSICAL UNION|
|Type:||Articles in periodicals and books|
|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:||Sustainable Resources|
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