Downscaling statistical model techniques for climate change analysis applied to the Amazon Region
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRN |
Texto Completo: | https://repositorio.ufrn.br/jspui/handle/123456789/29241 |
Resumo: | The Amazon is an area covered predominantly by dense tropical rainforest with relatively small inclusions of several other types of vegetation. In the last decades, scientific research has suggested a strong link between the health of the Amazon and the integrity of the global climate: tropical forests and woodlands (e.g., savannas) exchange vast amounts of water and energy with the atmosphere and are thought to be important in controlling local and regional climates. Consider the importance of the Amazon biome to the global climate changes impacts and the role of the protected area in the conservation of biodiversity and state-of-art of downscaling model techniques based on ANN Calibrate and run a downscaling model technique based on the Artificial Neural Network (ANN) that is applied to the Amazon region in order to obtain regional and local climate predicted data (e.g., precipitation). Considering the importance of the Amazon biome to the global climate changes impacts and the state-of-art of downscaling techniques for climate models, the shower of this work is presented as follows: the use of ANNs good similarity with the observation in the cities of Belém and Manaus, with correlations of approximately 88.9% and 91.3%, respectively, and spatial distribution, especially in the correction process, representing a good fit |
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Mendes, DavidMarengo, José AntonioRodrigues, SidneyOliveira, Magaly2020-06-11T13:03:03Z2020-06-11T13:03:03Z2014-05-29MENDES, David; MARENGO, Jose; SIDNEY, Rodrigues; Oliveira, Magaly . Downscaling statistical model techniques for climate change analysis applied to the Amazon Region. Advances in Artificial Neural Systems, v. 2014, p. 1-10, 2014. Disponível em: http://downloads.hindawi.com/archive/2014/595462.pdf. Acesso em: 01 Junho 2020. https://doi.org/10.1155/2014/595462https://repositorio.ufrn.br/jspui/handle/123456789/2924110.1155/2014/595462Advances in Artificial Neural SystemsAttribution 3.0 Brazilhttp://creativecommons.org/licenses/by/3.0/br/info:eu-repo/semantics/openAccessClimate changeAmazon rainforestDownscaling statistical model techniques for climate change analysis applied to the Amazon Regioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleThe Amazon is an area covered predominantly by dense tropical rainforest with relatively small inclusions of several other types of vegetation. In the last decades, scientific research has suggested a strong link between the health of the Amazon and the integrity of the global climate: tropical forests and woodlands (e.g., savannas) exchange vast amounts of water and energy with the atmosphere and are thought to be important in controlling local and regional climates. Consider the importance of the Amazon biome to the global climate changes impacts and the role of the protected area in the conservation of biodiversity and state-of-art of downscaling model techniques based on ANN Calibrate and run a downscaling model technique based on the Artificial Neural Network (ANN) that is applied to the Amazon region in order to obtain regional and local climate predicted data (e.g., precipitation). Considering the importance of the Amazon biome to the global climate changes impacts and the state-of-art of downscaling techniques for climate models, the shower of this work is presented as follows: the use of ANNs good similarity with the observation in the cities of Belém and Manaus, with correlations of approximately 88.9% and 91.3%, respectively, and spatial distribution, especially in the correction process, representing a good fitengreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNORIGINALDownscalingStatisticalModel_Mendes_2014.pdfDownscalingStatisticalModel_Mendes_2014.pdfapplication/pdf2789082https://repositorio.ufrn.br/bitstream/123456789/29241/4/DownscalingStatisticalModel_Mendes_2014.pdf0a8237e81d54bf0b2b3e19cb4f305654MD54CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8914https://repositorio.ufrn.br/bitstream/123456789/29241/5/license_rdf4d2950bda3d176f570a9f8b328dfbbefMD55LICENSElicense.txtlicense.txttext/plain; charset=utf-81484https://repositorio.ufrn.br/bitstream/123456789/29241/6/license.txte9597aa2854d128fd968be5edc8a28d9MD56TEXTDownscalingStatisticalModel_Mendes_2014.pdf.txtDownscalingStatisticalModel_Mendes_2014.pdf.txtExtracted texttext/plain38847https://repositorio.ufrn.br/bitstream/123456789/29241/7/DownscalingStatisticalModel_Mendes_2014.pdf.txt663a4bde44b4ea568c4ce8357415ee60MD57THUMBNAILDownscalingStatisticalModel_Mendes_2014.pdf.jpgDownscalingStatisticalModel_Mendes_2014.pdf.jpgGenerated Thumbnailimage/jpeg1640https://repositorio.ufrn.br/bitstream/123456789/29241/8/DownscalingStatisticalModel_Mendes_2014.pdf.jpged430f0a9743b5669314a3fc0925fc58MD58123456789/292412020-06-14 04:38:03.039oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2020-06-14T07:38:03Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false |
dc.title.pt_BR.fl_str_mv |
Downscaling statistical model techniques for climate change analysis applied to the Amazon Region |
title |
Downscaling statistical model techniques for climate change analysis applied to the Amazon Region |
spellingShingle |
Downscaling statistical model techniques for climate change analysis applied to the Amazon Region Mendes, David Climate change Amazon rainforest |
title_short |
Downscaling statistical model techniques for climate change analysis applied to the Amazon Region |
title_full |
Downscaling statistical model techniques for climate change analysis applied to the Amazon Region |
title_fullStr |
Downscaling statistical model techniques for climate change analysis applied to the Amazon Region |
title_full_unstemmed |
Downscaling statistical model techniques for climate change analysis applied to the Amazon Region |
title_sort |
Downscaling statistical model techniques for climate change analysis applied to the Amazon Region |
author |
Mendes, David |
author_facet |
Mendes, David Marengo, José Antonio Rodrigues, Sidney Oliveira, Magaly |
author_role |
author |
author2 |
Marengo, José Antonio Rodrigues, Sidney Oliveira, Magaly |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Mendes, David Marengo, José Antonio Rodrigues, Sidney Oliveira, Magaly |
dc.subject.por.fl_str_mv |
Climate change Amazon rainforest |
topic |
Climate change Amazon rainforest |
description |
The Amazon is an area covered predominantly by dense tropical rainforest with relatively small inclusions of several other types of vegetation. In the last decades, scientific research has suggested a strong link between the health of the Amazon and the integrity of the global climate: tropical forests and woodlands (e.g., savannas) exchange vast amounts of water and energy with the atmosphere and are thought to be important in controlling local and regional climates. Consider the importance of the Amazon biome to the global climate changes impacts and the role of the protected area in the conservation of biodiversity and state-of-art of downscaling model techniques based on ANN Calibrate and run a downscaling model technique based on the Artificial Neural Network (ANN) that is applied to the Amazon region in order to obtain regional and local climate predicted data (e.g., precipitation). Considering the importance of the Amazon biome to the global climate changes impacts and the state-of-art of downscaling techniques for climate models, the shower of this work is presented as follows: the use of ANNs good similarity with the observation in the cities of Belém and Manaus, with correlations of approximately 88.9% and 91.3%, respectively, and spatial distribution, especially in the correction process, representing a good fit |
publishDate |
2014 |
dc.date.issued.fl_str_mv |
2014-05-29 |
dc.date.accessioned.fl_str_mv |
2020-06-11T13:03:03Z |
dc.date.available.fl_str_mv |
2020-06-11T13:03:03Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
MENDES, David; MARENGO, Jose; SIDNEY, Rodrigues; Oliveira, Magaly . Downscaling statistical model techniques for climate change analysis applied to the Amazon Region. Advances in Artificial Neural Systems, v. 2014, p. 1-10, 2014. Disponível em: http://downloads.hindawi.com/archive/2014/595462.pdf. Acesso em: 01 Junho 2020. https://doi.org/10.1155/2014/595462 |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufrn.br/jspui/handle/123456789/29241 |
dc.identifier.doi.none.fl_str_mv |
10.1155/2014/595462 |
identifier_str_mv |
MENDES, David; MARENGO, Jose; SIDNEY, Rodrigues; Oliveira, Magaly . Downscaling statistical model techniques for climate change analysis applied to the Amazon Region. Advances in Artificial Neural Systems, v. 2014, p. 1-10, 2014. Disponível em: http://downloads.hindawi.com/archive/2014/595462.pdf. Acesso em: 01 Junho 2020. https://doi.org/10.1155/2014/595462 10.1155/2014/595462 |
url |
https://repositorio.ufrn.br/jspui/handle/123456789/29241 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
Attribution 3.0 Brazil http://creativecommons.org/licenses/by/3.0/br/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution 3.0 Brazil http://creativecommons.org/licenses/by/3.0/br/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Advances in Artificial Neural Systems |
publisher.none.fl_str_mv |
Advances in Artificial Neural Systems |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFRN instname:Universidade Federal do Rio Grande do Norte (UFRN) instacron:UFRN |
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Universidade Federal do Rio Grande do Norte (UFRN) |
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UFRN |
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UFRN |
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Repositório Institucional da UFRN |
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