This work aims at introducing an approach to analyze the independence between different models in a multi-model ensemble context. The models are operational long-range transport and dispersion models, also used for the real-time simulation of pollutant dispersion or the accidental release of radioactive nuclides in the atmosphere. In order to compare models, an approach based on the hierarchical agglomeration of distributions of predicted radionuclide concentrations is proposed. We use two different similarity measures: Negentropy information and Kullback-Leibler divergence. These approaches are used to analyze the data obtained during the ETEX-1 exercise.

Independent Model Selection for Ensemble Dispersion Forecasting

CIARAMELLA, Angelo;GIUNTA, Giulio;RICCIO, Angelo;
2008

Abstract

This work aims at introducing an approach to analyze the independence between different models in a multi-model ensemble context. The models are operational long-range transport and dispersion models, also used for the real-time simulation of pollutant dispersion or the accidental release of radioactive nuclides in the atmosphere. In order to compare models, an approach based on the hierarchical agglomeration of distributions of predicted radionuclide concentrations is proposed. We use two different similarity measures: Negentropy information and Kullback-Leibler divergence. These approaches are used to analyze the data obtained during the ETEX-1 exercise.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/25314
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