文章基本信息

标题：Downscaling Statistical Model Techniques for Climate Change Analysis Applied to the Amazon Region
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作者：David Mendes ; José Antonio Marengo ; Sidney Rodrigues 等
期刊名称：Advances in Artificial Neural Systems
印刷版ISSN：1687-7594
电子版ISSN：1687-7608
出版年度：2014
卷号：2014
DOI：10.1155/2014/595462
出版社：Hindawi Publishing Corporation
摘要：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.