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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. |
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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: |
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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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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| Registros recuperados : 210 | |
| 201. |  | BERGIER, I.; SILVA, R.; PAPA, M.; SANTOS, P. M.; OLIVEIRA, S. R. de M.; VACARI, I.; ABREU, U. G. P. de; ROMANI, L. A. S.; PIEROZZI JUNIOR, I.; INAMASU, R. Y.; MASSRUHA, S. M. F. S. BovTrace: interoperabilidade e acessibilidade para a rastreabilidade animal SISBOV e do AgriTrace CNA/Senar. Campinas: Embrapa Agricultura Digital, 2023. 19 p. il. color. (Embrapa Agricultura Digital. Comunicado técnico, 136).| Tipo: Comunicado Técnico/Recomendações Técnicas |
| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Instrumentação; Embrapa Pantanal; Embrapa Pecuária Sudeste. |
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| 202. | | 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. Analysis of large scale climate data: how well climate change models and data from real sensor networks agree? In: INTERNATIONAL WORLD WIDE WEB CONFERENCE, 22., 2013, Rio de Janeiro. Proceedings... New York: ACM, 2013. p. 517-526. WWW 2013.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital. |
|  |
| 203. | | ROMANI, L. A. S.; GONÇALVES, R. R. V.; AMARAL, B. F.; CHINO, D. Y. T.; ZULLO JUNIOR, J.; TRAINA JUNIOR, C.; SOUSA, E. P. M.; TRAINA, A. J. M. TRAINA. Clustering analysis applied to NOAA/AVHRR multitemporal images to improve the monitoring process of sugarcane crops. In: INTERNATIONAL WORKSHOP ON THE ANALYSIS OF MULTI-TEMPORAL REMOTE SENSING IMAGES, 6., 2011, Trento. Proceedings... Piscataway: IEEE; Italy: University of Trento, 2011. p. 33-36.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 205. | | CORDEIRO, R. L. F.; GUO, F.; HAVERKAMP, D. S.; HORNE, J. H.; HUGHES, E. K.; KIM, G.; ROMANI, L. A. S.; COLTRI, P. P.; SOUZA, T. T.; TRAINA, A. J. M.; TRAINA JÚNIOR, C.; FALOUTSOS, C. QuMinS: fast and scalable querying, mining and summarizing multi-modal databases. Information Sciences, New York, v. 164, p. 211-229, Apr. 2014.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 206. | | GONÇALVES, R. R. do V.; ZULLO JUNIOR, J.; COLTRI, P. P.; AVILA, A. M. H. de; AMARAL, B. F. do; SOUSA, E. B. M. de; ROMANI, L. A. S. Relação entre o índice EVI e dados de precipitação nas áreas de plantio de cana-de-açúcar na região central do Brasil. In: SIMPÓSIO DE GEOTECNOLOGIAS NO PANTANAL, 4., 2012, Bonito, MS. Anais... Brasília, DF: Embrapa, 2012. p. 1054-1063. 1 CD-ROM. Geopantanal 2012.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 207. | | ROMANI, L. A. S.; BARIANI, J. M.; DRUCKER, D. P.; VAZ, G. J.; MONDO, V. H. V.; MOURA, M. F.; BOLFE, E. L.; SOUSA, P. H. P. de; OLIVEIRA, S. R. de M.; LUCHIARI JUNIOR, A. Role of research and development institutions and AgTechs in the digital transformation of agriculture in Brazil. Revista Ciência Agronômica, v. 51, n. 5, 20207800, 2020.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 209. | | ROMANI, L. A. S.; BAMBINI, M. D.; BARIANI, J. M.; DRUCKER, D. P.; MINITTI, A. F.; GONZALEZ, A. F.; KUROMOTO, V. M.; TELLES, G. A. de S.; ARAÚJO, R. F.; DIAS, C. N.; ASSUNÇÃO, B. S. B. de; SOUZA, S. S. de; LUCHIARI JÚNIOR, A.; MEIRA, C. A. A. Ecossistema de inovação em agricultura: evolução e contribuições da Embrapa. In: MASSRUHÁ, S. M. F. S.; LEITE, M. A. de A.; OLIVEIRA, S. R. de M.; MEIRA, C. A. A.; LUCHIARI JUNIOR, A.; BOLFE, E. L. (Ed.). Agricultura digital: pesquisa, desenvolvimento e inovação nas cadeias produtivas. Brasília, DF: Embrapa, 2020. cap. 12, p. 278-304. Na publicação: Debora Pignatai Drucker, Adriana Farah, Ricardo Araújo, Shalon Silva Souza.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Unidades Centrais. |
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| 210. | | ROMANI, L. A. S.; BAMBINI, M. D.; BARIANI, J. M.; DRUCKER, D. P.; MINITTI, A. F.; GONZALEZ, A. F.; KUROMOTO, V. M.; TELLES, G. A. de S.; ARAÚJO, R. F.; DIAS, C. N.; ASSUNÇÃO, B. S. B. de; SOUZA, S. S. de; LUCHIARI JÚNIOR, A.; MEIRA, C. A. A. Innovation ecosystem in agriculture: Embrapa's evolution and contributions. In: MASSRUHÁ, S. M. F. S.; LEITE, M. A. de A.; OLIVEIRA, S. R. de M.; MEIRA, C. A. A.; LUCHIARI JUNIOR, A.; BOLFE, E. L. (ed.). Digital agriculture: research, development and innovation in production chains. Brasília, DF: Embrapa, 2023. cap. 12, p. 209-227. Na publicação: Debora Pignatai Drucker, Adriana Farah, Ricardo Araújo, Shalon Silva Souza.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| Registros recuperados : 210 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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