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| 1. |  | URREA, R.; CEBALLOS, H.; PANDEY, S.; BAHIA, A. F. C.; LEON, L. A.; FENALCE, A. A. Greenhouse screening technique for soil acidity tolerance in maize (ZEA MAYS L.). In: INTERNATIONAL SYMPOSIUM ON PLANT-SOIL INTERACTIONS AT LOW pH, 4., 1996, Belo Horizonte. Abstracts/Programs. Sete Lagoas: EMBRAPA-CNPMS, 1996. p.81.| Biblioteca(s): Embrapa Cerrados. |
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| 2. | | URREA, R.; CEBALLOS, H.; PANDEY, S.; BAHIA, A. F. C.; LEON, L. A.; FENALCE, A. A. Greenhouse screening technique for soil acidity tolerance in maize (Zea mays L.) In: INTERNATIONAL SYMPOSIUM ON PLANT SOIL INTERACTIONS AT LOW pH, 4., 1996, Belo Horizonte. Abstracts... Sete Lagoas: EMBRAPA-CNPMS, 1996. p. 47.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| Registros recuperados : 2 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
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: |
LEADER 02626nam a2200337 a 4500
001 1974438
005 2020-01-08
008 2013 bl uuuu u00u1 u #d
020 $a978-1-4503-2038-2
100 1 $aNUNES, S. A.
245 $aAnalysis of large scale climate data$bhow well climate change models and data from real sensor networks agree?$h[electronic resource]
260 $aIn: INTERNATIONAL WORLD WIDE WEB CONFERENCE, 22., 2013, Rio de Janeiro. Proceedings... New York: ACM$c2013
300 $ap. 517-526.
500 $aWWW 2013.
520 $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.
650 $aClimate change
650 $aModels
653 $aAnálise fractal
653 $aClimate data
653 $aDados climáticos
653 $aData streams
653 $aFractal analysis
653 $aModelos de mudanças climáticas
653 $aSensor networks
700 1 $aROMANI, L. A. S.
700 1 $aAVILA, A. M. H.
700 1 $aCOLTRI, P. P.
700 1 $aTRAINA JÚNIOR, C.
700 1 $aCORDEIRO, R. L. F.
700 1 $aSOUSA, E. P. M.
700 1 $aTRAINA, A. J. M.
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