02626nam a2200337 a 450000100080000000500110000800800410001902000220006010000170008224501380009926001110023730000160034850000140036452015790037865000190195765000110197665300210198765300170200865300220202565300170204765300210206465300370208565300200212270000210214270000200216370000180218370000230220170000230222470000200224770000210226719744382020-01-08       2013    bl uuuu     u00u1 u    #d  a978-1-4503-2038-21 aNUNES, S. A.  aAnalysis of large scale climate databhow well climate change models and data from real sensor networks agree?h[electronic resource]  aIn: INTERNATIONAL WORLD WIDE WEB CONFERENCE, 22., 2013, Rio de Janeiro. Proceedings... New York: ACMc2013  ap. 517-526.  aWWW 2013.  aResearch on global warming and climate changes has attracted a huge attention of the scientific community and of the media in general, mainly due to the social and economic impacts they pose over the entire planet. Climate change simulation models have been developed and improved to provide reliable data, which are employed to forecast effects of increasing emissions of greenhouse gases on a future global climate. The data generated by each model simulation amount to Terabytes of data, and demand fast and scalable methods to process them. In this context, we propose a new process of analysis aimed at discriminating between the temporal behavior of the data generated by climate models and the real climate observations gathered from ground-based meteorological station networks. Our approach combines fractal data analysis and the monitoring of real and model-generated data streams to detect deviations on the intrinsic correlation among the time series defined by different climate variables. Our measurements were made using series from a regional climate model and the corresponding real data from a network of sensors from meteorological stations existing in the analyzed region. The results show that our approach can correctly discriminate the data either as real or as simulated, even when statistical tests fail. Those results suggest that there is still room for improvement of the state-of-the-art climate change models, and that the fractal-based concepts may contribute for their improvement, besides being a fast, parallelizable, and scalable approach.  aClimate change  aModels  aAnálise fractal  aClimate data  aDados climáticos  aData streams  aFractal analysis  aModelos de mudanças climáticas  aSensor networks1 aROMANI, L. A. S.1 aAVILA, A. M. H.1 aCOLTRI, P. P.1 aTRAINA JÚNIOR, C.1 aCORDEIRO, R. L. F.1 aSOUSA, E. P. M.1 aTRAINA, A. J. M.