To be or not to be real: fractal analysis of data streams from a regional climate change model

Authors:
Santiago A. Nunes;Ana M. H. Avila;Luciana A. S. Romani;Agma J. M. Traina;Priscila P. Coltri;Elaine P. M. Sousa
Affiliations:
University of São Paulo, Brazil;University of Campinas, Brazil;Embrapa Agriculture, Informatics, Brazil;University of São Paulo, Brazil;University of Campinas, Brazil;University of São Paulo, Brazil
Venue:
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Year:
2012

Citing 2
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Abstract

This paper proposes a new analysis process aimed at discriminating the temporal behavior of the data generated by climate models from the real climate observations gathered from ground-based meteorological stations. Our approach combines fractal data analysis and the monitoring of the real and the model-generated data streams to detect deviations considering the intrinsic correlation among climate time series. Experimental studies showed that our approach can discriminate the data either as real or as generated by a model. Those results suggest that there are yet space to improve the climate change models, and that the fractal-based concepts may contribute in this improvement.