Downscaling statistical model techniques for climate change analysis applied to the Amazon Region

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 fore...

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Principais autores: Mendes, David, Marengo, José Antonio, Rodrigues, Sidney, Oliveira, Magaly
Formato: article
Idioma:English
Publicado em: Advances in Artificial Neural Systems
Assuntos:
Climate change
Amazon rainforest
Endereço do item:https://repositorio.ufrn.br/jspui/handle/123456789/29241
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spelling ri-123456789-292412020-06-14T07:38:03Z Downscaling statistical model techniques for climate change analysis applied to the Amazon Region Mendes, David Marengo, José Antonio Rodrigues, Sidney Oliveira, Magaly Climate change Amazon rainforest 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 2020-06-11T13:03:03Z 2020-06-11T13:03:03Z 2014-05-29 article 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 https://repositorio.ufrn.br/jspui/handle/123456789/29241 10.1155/2014/595462 en Attribution 3.0 Brazil http://creativecommons.org/licenses/by/3.0/br/ application/pdf Advances in Artificial Neural Systems
institution Repositório Institucional
collection RI - UFRN
language English
topic Climate change
Amazon rainforest
spellingShingle Climate change
Amazon rainforest
Mendes, David
Marengo, José Antonio
Rodrigues, Sidney
Oliveira, Magaly
Downscaling statistical model techniques for climate change analysis applied to the Amazon Region
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
format article
author Mendes, David
Marengo, José Antonio
Rodrigues, Sidney
Oliveira, Magaly
author_facet Mendes, David
Marengo, José Antonio
Rodrigues, Sidney
Oliveira, Magaly
author_sort Mendes, David
title Downscaling statistical model techniques for climate change analysis applied to the Amazon Region
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
publisher Advances in Artificial Neural Systems
publishDate 2020
url https://repositorio.ufrn.br/jspui/handle/123456789/29241
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AT marengojoseantonio downscalingstatisticalmodeltechniquesforclimatechangeanalysisappliedtotheamazonregion
AT rodriguessidney downscalingstatisticalmodeltechniquesforclimatechangeanalysisappliedtotheamazonregion
AT oliveiramagaly downscalingstatisticalmodeltechniquesforclimatechangeanalysisappliedtotheamazonregion
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