THE INFLUENCE OF THE REMOTELY SENSED RAINFALL PRODUCTS’ SPATIAL RESOLUTION TO UNMASK EXTREME EVENTS IN NORTHEAST BRAZIL
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Boletim de Ciências Geodésicas |
Texto Completo: | https://revistas.ufpr.br/bcg/article/view/84201 |
Resumo: | This work presents the influence of the spatial resolution on precipitation samples to understand extreme events in the Agreste region of Pernambuco, northeast of Brazil. Among the materials used, the following sources of precipitation data (1998 to 2019) can be cited: The Tropical Rainfall Measuring Mission (TRMM), the Climatic Research Unit (CRU), and weather stations. In the process of validating the precipitation time series with the weather stations, the TRMM data showed a strong Pearson correlation (0.86 - 0.90) and the CRU data a moderate one (0.71 - 0.76). The relative bias (RB) and the standard deviation of observation ratio (RSR) were also calculated to identify the data’s trend, which showed an overestimation for both sources. The extreme events were identified through the calculation of the Standardized Precipitation Index (SPI), where the TRMM with strong correlation (0.80 - 0.91) obtained a better performance than the CRU data. The TRMM data were selected to understand the extreme drought events in the study area, where the cities with altitudes above 500m obtained maximum values of probability of occurrence with 19%. Conversely, for extreme humidity events, the maximum was 14% for those with altitudes below 200m. |
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Boletim de Ciências Geodésicas |
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THE INFLUENCE OF THE REMOTELY SENSED RAINFALL PRODUCTS’ SPATIAL RESOLUTION TO UNMASK EXTREME EVENTS IN NORTHEAST BRAZILGeociências, Ciências da TerraPrecipitation; Climate extremes; TRMM; CRU; SPI.This work presents the influence of the spatial resolution on precipitation samples to understand extreme events in the Agreste region of Pernambuco, northeast of Brazil. Among the materials used, the following sources of precipitation data (1998 to 2019) can be cited: The Tropical Rainfall Measuring Mission (TRMM), the Climatic Research Unit (CRU), and weather stations. In the process of validating the precipitation time series with the weather stations, the TRMM data showed a strong Pearson correlation (0.86 - 0.90) and the CRU data a moderate one (0.71 - 0.76). The relative bias (RB) and the standard deviation of observation ratio (RSR) were also calculated to identify the data’s trend, which showed an overestimation for both sources. The extreme events were identified through the calculation of the Standardized Precipitation Index (SPI), where the TRMM with strong correlation (0.80 - 0.91) obtained a better performance than the CRU data. The TRMM data were selected to understand the extreme drought events in the study area, where the cities with altitudes above 500m obtained maximum values of probability of occurrence with 19%. Conversely, for extreme humidity events, the maximum was 14% for those with altitudes below 200m.Boletim de Ciências GeodésicasBulletin of Geodetic SciencesFADE / UFPE / City Hall of Caruaru, CNPq, CAPESNova, Raquel Arcoverde VilaGonçalves, Rodrigo MikoszFerreira, Lígia Albuquerque de AlcântaraLima, Fábio Vinícius Marley Santos2022-01-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.ufpr.br/bcg/article/view/84201Boletim de Ciências Geodésicas; Vol 27, No 3 (2021)Bulletin of Geodetic Sciences; Vol 27, No 3 (2021)1982-21701413-4853reponame:Boletim de Ciências Geodésicasinstname:Universidade Federal do Paraná (UFPR)instacron:UFPRenghttps://revistas.ufpr.br/bcg/article/view/84201/45593Copyright (c) 2022 Raquel Arcoverde Vila Nova, Rodrigo Mikosz Gonçalves, Lígia Albuquerque de Alcântara Ferreira, Fábio Vinícius Marley Santos Limahttp://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccess2022-01-03T16:07:23Zoai:revistas.ufpr.br:article/84201Revistahttps://revistas.ufpr.br/bcgPUBhttps://revistas.ufpr.br/bcg/oaiqdalmolin@ufpr.br|| danielsantos@ufpr.br||qdalmolin@ufpr.br|| danielsantos@ufpr.br1982-21701413-4853opendoar:2022-01-03T16:07:23Boletim de Ciências Geodésicas - Universidade Federal do Paraná (UFPR)false |
dc.title.none.fl_str_mv |
THE INFLUENCE OF THE REMOTELY SENSED RAINFALL PRODUCTS’ SPATIAL RESOLUTION TO UNMASK EXTREME EVENTS IN NORTHEAST BRAZIL |
title |
THE INFLUENCE OF THE REMOTELY SENSED RAINFALL PRODUCTS’ SPATIAL RESOLUTION TO UNMASK EXTREME EVENTS IN NORTHEAST BRAZIL |
spellingShingle |
THE INFLUENCE OF THE REMOTELY SENSED RAINFALL PRODUCTS’ SPATIAL RESOLUTION TO UNMASK EXTREME EVENTS IN NORTHEAST BRAZIL Nova, Raquel Arcoverde Vila Geociências, Ciências da Terra Precipitation; Climate extremes; TRMM; CRU; SPI. |
title_short |
THE INFLUENCE OF THE REMOTELY SENSED RAINFALL PRODUCTS’ SPATIAL RESOLUTION TO UNMASK EXTREME EVENTS IN NORTHEAST BRAZIL |
title_full |
THE INFLUENCE OF THE REMOTELY SENSED RAINFALL PRODUCTS’ SPATIAL RESOLUTION TO UNMASK EXTREME EVENTS IN NORTHEAST BRAZIL |
title_fullStr |
THE INFLUENCE OF THE REMOTELY SENSED RAINFALL PRODUCTS’ SPATIAL RESOLUTION TO UNMASK EXTREME EVENTS IN NORTHEAST BRAZIL |
title_full_unstemmed |
THE INFLUENCE OF THE REMOTELY SENSED RAINFALL PRODUCTS’ SPATIAL RESOLUTION TO UNMASK EXTREME EVENTS IN NORTHEAST BRAZIL |
title_sort |
THE INFLUENCE OF THE REMOTELY SENSED RAINFALL PRODUCTS’ SPATIAL RESOLUTION TO UNMASK EXTREME EVENTS IN NORTHEAST BRAZIL |
author |
Nova, Raquel Arcoverde Vila |
author_facet |
Nova, Raquel Arcoverde Vila Gonçalves, Rodrigo Mikosz Ferreira, Lígia Albuquerque de Alcântara Lima, Fábio Vinícius Marley Santos |
author_role |
author |
author2 |
Gonçalves, Rodrigo Mikosz Ferreira, Lígia Albuquerque de Alcântara Lima, Fábio Vinícius Marley Santos |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
FADE / UFPE / City Hall of Caruaru, CNPq, CAPES |
dc.contributor.author.fl_str_mv |
Nova, Raquel Arcoverde Vila Gonçalves, Rodrigo Mikosz Ferreira, Lígia Albuquerque de Alcântara Lima, Fábio Vinícius Marley Santos |
dc.subject.por.fl_str_mv |
Geociências, Ciências da Terra Precipitation; Climate extremes; TRMM; CRU; SPI. |
topic |
Geociências, Ciências da Terra Precipitation; Climate extremes; TRMM; CRU; SPI. |
description |
This work presents the influence of the spatial resolution on precipitation samples to understand extreme events in the Agreste region of Pernambuco, northeast of Brazil. Among the materials used, the following sources of precipitation data (1998 to 2019) can be cited: The Tropical Rainfall Measuring Mission (TRMM), the Climatic Research Unit (CRU), and weather stations. In the process of validating the precipitation time series with the weather stations, the TRMM data showed a strong Pearson correlation (0.86 - 0.90) and the CRU data a moderate one (0.71 - 0.76). The relative bias (RB) and the standard deviation of observation ratio (RSR) were also calculated to identify the data’s trend, which showed an overestimation for both sources. The extreme events were identified through the calculation of the Standardized Precipitation Index (SPI), where the TRMM with strong correlation (0.80 - 0.91) obtained a better performance than the CRU data. The TRMM data were selected to understand the extreme drought events in the study area, where the cities with altitudes above 500m obtained maximum values of probability of occurrence with 19%. Conversely, for extreme humidity events, the maximum was 14% for those with altitudes below 200m. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-03 |
dc.type.none.fl_str_mv |
|
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://revistas.ufpr.br/bcg/article/view/84201 |
url |
https://revistas.ufpr.br/bcg/article/view/84201 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://revistas.ufpr.br/bcg/article/view/84201/45593 |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by-nc/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Boletim de Ciências Geodésicas Bulletin of Geodetic Sciences |
publisher.none.fl_str_mv |
Boletim de Ciências Geodésicas Bulletin of Geodetic Sciences |
dc.source.none.fl_str_mv |
Boletim de Ciências Geodésicas; Vol 27, No 3 (2021) Bulletin of Geodetic Sciences; Vol 27, No 3 (2021) 1982-2170 1413-4853 reponame:Boletim de Ciências Geodésicas instname:Universidade Federal do Paraná (UFPR) instacron:UFPR |
instname_str |
Universidade Federal do Paraná (UFPR) |
instacron_str |
UFPR |
institution |
UFPR |
reponame_str |
Boletim de Ciências Geodésicas |
collection |
Boletim de Ciências Geodésicas |
repository.name.fl_str_mv |
Boletim de Ciências Geodésicas - Universidade Federal do Paraná (UFPR) |
repository.mail.fl_str_mv |
qdalmolin@ufpr.br|| danielsantos@ufpr.br||qdalmolin@ufpr.br|| danielsantos@ufpr.br |
_version_ |
1799771720068890624 |