On the systematic reduction of data complexity in multimodel atmospheric dispersion ensemble modeling

Abstract

The aim of this work is to explore the effectiveness of theoretical information approaches for the reduction of data complexity in multimodel 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 model selection. A distance matrix, measuring the interdependence between data, is derived for the investigated measures, with the scope of clustering correlated/dependent models together. Redundant information is discarded by selecting a few representative models from each cluster. We apply the clustering analysis in the context of atmospheric dispersion modeling, by using the ETEX‐1 data set. We show how the selection of a small subset of models, according to uncorrelation or mutual information distance criteria, usually suffices to achieve a statistical performance comparable to, or even better than, that achieved from the whole ensemble data set, thus providing a simpler description of ensemble results without sacrificing accuracy.