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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
19/12/2013 |
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Data da última atualização: |
08/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
NUNES, S. A.; ROMANI, L. A. S.; AVILA, A. M. H.; COLTRI, P. P.; TRAINA JÚNIOR, C.; CORDEIRO, R. L. F.; SOUSA, E. P. M.; TRAINA, A. J. M. |
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Afiliação: |
SANTIAGO A. NUNES, ICMC/USP; LUCIANA ALVIM SANTOS ROMANI, CNPTIA; ANA M. H. AVILA, Cepagri/Unicamp; PRISCILA P. COLTRI, Cepagri/Unicamp; CAETANO TRAINA JÚNIOR, ICMC/USP; ROBSON L. F. CORDEIRO, ICMC/USP; ELAINE P. M. SOUSA, ICMC/USP; AGMA J. M. TRAINA, ICMC/USP. |
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Título: |
Analysis of large scale climate data: how well climate change models and data from real sensor networks agree? |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
In: INTERNATIONAL WORLD WIDE WEB CONFERENCE, 22., 2013, Rio de Janeiro. Proceedings... New York: ACM, 2013. |
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Páginas: |
p. 517-526. |
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ISBN: |
978-1-4503-2038-2 |
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Idioma: |
Inglês |
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Notas: |
WWW 2013. |
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Conteúdo: |
Research 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. MenosResearch 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 thei... Mostrar Tudo |
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Palavras-Chave: |
Análise fractal; Climate data; Dados climáticos; Data streams; Fractal analysis; Modelos de mudanças climáticas; Sensor networks. |
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Thesaurus Nal: |
Climate change; Models. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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| 1. |  | BOURASSET, A.-X.; COSTE, C.; GARCIA, G.; GIGAULT, J.; LAUNOIS, M.; MONARD-JAHIEL, A. Mission conjointe NMA/PRIFAS d´étude et de prospective dans Le Sudeste du Brésil, du 27 octobre au 10 novembre 1990. Montpellier: CIRAD-PRIFAS; Campinas: Embrapa-NMA, 1991. 27 p. apostila, A4 (D., 405)| Tipo: Comunicado Técnico/Recomendações Técnicas |
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