Improving Regional Dynamic Downscaling with Multiple Linear Regression Model Using Components Principal Analysis: Precipitation over Amazon and Northeast Brazil

In the current context of climate change discussions, predictions of future scenarios of weather and climate are crucial for the generation of information of interest to the global community. Due to the atmosphere being a chaotic system, errors in predictions of future scenarios are systematically o...

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Main Authors: Aline Gomes da Silva, Claudio Moises Santos e Silva
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:Advances in Meteorology
Online Access:http://dx.doi.org/10.1155/2014/928729
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spelling doaj-a5265010435c4945bfc23ff1680a36ff2020-11-24T22:12:30ZengHindawi LimitedAdvances in Meteorology1687-93091687-93172014-01-01201410.1155/2014/928729928729Improving Regional Dynamic Downscaling with Multiple Linear Regression Model Using Components Principal Analysis: Precipitation over Amazon and Northeast BrazilAline Gomes da Silva0Claudio Moises Santos e Silva1Instituto Federal do Rio Grande do Norte, BrazilPrograma de Pós-Graduação em Ciências Climáticas, Rio Grande do Norte, BrazilIn the current context of climate change discussions, predictions of future scenarios of weather and climate are crucial for the generation of information of interest to the global community. Due to the atmosphere being a chaotic system, errors in predictions of future scenarios are systematically observed. Therefore, numerous techniques have been tested in order to generate more reliable predictions, and two techniques have excelled in science: dynamic downscaling, through regional models, and ensemble prediction, combining different outputs of climate models through the arithmetic average, in other words, a postprocessing of the output data species. Thus, this paper proposes a method of postprocessing outputs of regional climate models. This method consists in using the statistical tool multiple linear regression by principal components for combining different simulations obtained by dynamic downscaling with the regional climate model (RegCM4). Tests for the Amazon and Northeast region of Brazil (South America) showed that the method provided a more realistic prediction in terms of average daily rainfall for the analyzed period prescribed, after comparing with the prediction made by set through the arithmetic averages of the simulations. This method photographed the extreme events (outlier) that the prediction by averaging failed. Data from the Tropical Rainfall Measuring Mission (TRMM) were used to evaluate the method.http://dx.doi.org/10.1155/2014/928729
collection DOAJ
language English
format Article
sources DOAJ
author Aline Gomes da Silva
Claudio Moises Santos e Silva
spellingShingle Aline Gomes da Silva
Claudio Moises Santos e Silva
Improving Regional Dynamic Downscaling with Multiple Linear Regression Model Using Components Principal Analysis: Precipitation over Amazon and Northeast Brazil
Advances in Meteorology
author_facet Aline Gomes da Silva
Claudio Moises Santos e Silva
author_sort Aline Gomes da Silva
title Improving Regional Dynamic Downscaling with Multiple Linear Regression Model Using Components Principal Analysis: Precipitation over Amazon and Northeast Brazil
title_short Improving Regional Dynamic Downscaling with Multiple Linear Regression Model Using Components Principal Analysis: Precipitation over Amazon and Northeast Brazil
title_full Improving Regional Dynamic Downscaling with Multiple Linear Regression Model Using Components Principal Analysis: Precipitation over Amazon and Northeast Brazil
title_fullStr Improving Regional Dynamic Downscaling with Multiple Linear Regression Model Using Components Principal Analysis: Precipitation over Amazon and Northeast Brazil
title_full_unstemmed Improving Regional Dynamic Downscaling with Multiple Linear Regression Model Using Components Principal Analysis: Precipitation over Amazon and Northeast Brazil
title_sort improving regional dynamic downscaling with multiple linear regression model using components principal analysis: precipitation over amazon and northeast brazil
publisher Hindawi Limited
series Advances in Meteorology
issn 1687-9309
1687-9317
publishDate 2014-01-01
description In the current context of climate change discussions, predictions of future scenarios of weather and climate are crucial for the generation of information of interest to the global community. Due to the atmosphere being a chaotic system, errors in predictions of future scenarios are systematically observed. Therefore, numerous techniques have been tested in order to generate more reliable predictions, and two techniques have excelled in science: dynamic downscaling, through regional models, and ensemble prediction, combining different outputs of climate models through the arithmetic average, in other words, a postprocessing of the output data species. Thus, this paper proposes a method of postprocessing outputs of regional climate models. This method consists in using the statistical tool multiple linear regression by principal components for combining different simulations obtained by dynamic downscaling with the regional climate model (RegCM4). Tests for the Amazon and Northeast region of Brazil (South America) showed that the method provided a more realistic prediction in terms of average daily rainfall for the analyzed period prescribed, after comparing with the prediction made by set through the arithmetic averages of the simulations. This method photographed the extreme events (outlier) that the prediction by averaging failed. Data from the Tropical Rainfall Measuring Mission (TRMM) were used to evaluate the method.
url http://dx.doi.org/10.1155/2014/928729
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