Validation of rainfall data estimated by GPM satellite on Southern Amazon region
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista Ambiente & Água |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1980-993X2019000100300 |
Resumo: | Abstract Rainfall is a meteorological variable of great importance for hydric balance and for weather studies. Rainfall estimation, carried out by satellites, has increased the climatological dataset related to precipitation. However, the accuracy of these data is questionable. This paper aimed to validate the estimates done by the Global Precipitation Measurement (GPM) satellite for the mesoregion of Southern Amazonas State, Brazil. The surface data were collected by the National Water Agency - ANA and National Institute of Meteorology - INMET, and is available at both institutions’ websites. The satellite precipitation data were accessed directly from the NASA webpage. Statistical analysis of Pearson correlation was used, as well as the Willmott’s “d” index and errors from the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error). The GPM satellite satisfactorily estimated the precipitation, once it had correlations above 73% and high Willmott coefficients (between 0.86 and 0.97). The MAE and RMSE showed values that varied from 36.50 mm to 72.49 mm and 13.81 mm to 71.76 mm, respectively. Seasonal rain variations are represented accordingly. In some cases, either an underestimation or an overestimation of the rain data was observed. In the yearly totals, a high rate of similarity between the estimated and measured values was observed. We concluded that the GPM-based multi-satellite precipitation estimates can be used, even though they are not 100% reliable. However, adjustments in calibration for the region are necessary and recommended. |
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Validation of rainfall data estimated by GPM satellite on Southern Amazon regionremote sensingstatistical analysisweather monitoringAbstract Rainfall is a meteorological variable of great importance for hydric balance and for weather studies. Rainfall estimation, carried out by satellites, has increased the climatological dataset related to precipitation. However, the accuracy of these data is questionable. This paper aimed to validate the estimates done by the Global Precipitation Measurement (GPM) satellite for the mesoregion of Southern Amazonas State, Brazil. The surface data were collected by the National Water Agency - ANA and National Institute of Meteorology - INMET, and is available at both institutions’ websites. The satellite precipitation data were accessed directly from the NASA webpage. Statistical analysis of Pearson correlation was used, as well as the Willmott’s “d” index and errors from the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error). The GPM satellite satisfactorily estimated the precipitation, once it had correlations above 73% and high Willmott coefficients (between 0.86 and 0.97). The MAE and RMSE showed values that varied from 36.50 mm to 72.49 mm and 13.81 mm to 71.76 mm, respectively. Seasonal rain variations are represented accordingly. In some cases, either an underestimation or an overestimation of the rain data was observed. In the yearly totals, a high rate of similarity between the estimated and measured values was observed. We concluded that the GPM-based multi-satellite precipitation estimates can be used, even though they are not 100% reliable. However, adjustments in calibration for the region are necessary and recommended.Instituto de Pesquisas Ambientais em Bacias Hidrográficas2019-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1980-993X2019000100300Revista Ambiente & Água v.14 n.1 2019reponame:Revista Ambiente & Águainstname:Instituto de Pesquisas Ambientais em Bacias Hidrográficas (IPABHI)instacron:IPABHI10.4136/ambi-agua.2249info:eu-repo/semantics/openAccessSantos,Luiz Octavio Fabricio dosQuerino,Carlos Alexandre SantosQuerino,Juliane Kayse Albuquerque da SilvaPedreira Junior,Altemar LopesMoura,Aryanne Resende de MeloMachado,Nadja GomesBiudes,Marcelo Sacardieng2018-12-18T00:00:00Zoai:scielo:S1980-993X2019000100300Revistahttp://www.ambi-agua.net/PUBhttps://old.scielo.br/oai/scielo-oai.php||ambi.agua@gmail.com1980-993X1980-993Xopendoar:2018-12-18T00:00Revista Ambiente & Água - Instituto de Pesquisas Ambientais em Bacias Hidrográficas (IPABHI)false |
dc.title.none.fl_str_mv |
Validation of rainfall data estimated by GPM satellite on Southern Amazon region |
title |
Validation of rainfall data estimated by GPM satellite on Southern Amazon region |
spellingShingle |
Validation of rainfall data estimated by GPM satellite on Southern Amazon region Santos,Luiz Octavio Fabricio dos remote sensing statistical analysis weather monitoring |
title_short |
Validation of rainfall data estimated by GPM satellite on Southern Amazon region |
title_full |
Validation of rainfall data estimated by GPM satellite on Southern Amazon region |
title_fullStr |
Validation of rainfall data estimated by GPM satellite on Southern Amazon region |
title_full_unstemmed |
Validation of rainfall data estimated by GPM satellite on Southern Amazon region |
title_sort |
Validation of rainfall data estimated by GPM satellite on Southern Amazon region |
author |
Santos,Luiz Octavio Fabricio dos |
author_facet |
Santos,Luiz Octavio Fabricio dos Querino,Carlos Alexandre Santos Querino,Juliane Kayse Albuquerque da Silva Pedreira Junior,Altemar Lopes Moura,Aryanne Resende de Melo Machado,Nadja Gomes Biudes,Marcelo Sacardi |
author_role |
author |
author2 |
Querino,Carlos Alexandre Santos Querino,Juliane Kayse Albuquerque da Silva Pedreira Junior,Altemar Lopes Moura,Aryanne Resende de Melo Machado,Nadja Gomes Biudes,Marcelo Sacardi |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Santos,Luiz Octavio Fabricio dos Querino,Carlos Alexandre Santos Querino,Juliane Kayse Albuquerque da Silva Pedreira Junior,Altemar Lopes Moura,Aryanne Resende de Melo Machado,Nadja Gomes Biudes,Marcelo Sacardi |
dc.subject.por.fl_str_mv |
remote sensing statistical analysis weather monitoring |
topic |
remote sensing statistical analysis weather monitoring |
description |
Abstract Rainfall is a meteorological variable of great importance for hydric balance and for weather studies. Rainfall estimation, carried out by satellites, has increased the climatological dataset related to precipitation. However, the accuracy of these data is questionable. This paper aimed to validate the estimates done by the Global Precipitation Measurement (GPM) satellite for the mesoregion of Southern Amazonas State, Brazil. The surface data were collected by the National Water Agency - ANA and National Institute of Meteorology - INMET, and is available at both institutions’ websites. The satellite precipitation data were accessed directly from the NASA webpage. Statistical analysis of Pearson correlation was used, as well as the Willmott’s “d” index and errors from the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error). The GPM satellite satisfactorily estimated the precipitation, once it had correlations above 73% and high Willmott coefficients (between 0.86 and 0.97). The MAE and RMSE showed values that varied from 36.50 mm to 72.49 mm and 13.81 mm to 71.76 mm, respectively. Seasonal rain variations are represented accordingly. In some cases, either an underestimation or an overestimation of the rain data was observed. In the yearly totals, a high rate of similarity between the estimated and measured values was observed. We concluded that the GPM-based multi-satellite precipitation estimates can be used, even though they are not 100% reliable. However, adjustments in calibration for the region are necessary and recommended. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1980-993X2019000100300 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1980-993X2019000100300 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.4136/ambi-agua.2249 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Instituto de Pesquisas Ambientais em Bacias Hidrográficas |
publisher.none.fl_str_mv |
Instituto de Pesquisas Ambientais em Bacias Hidrográficas |
dc.source.none.fl_str_mv |
Revista Ambiente & Água v.14 n.1 2019 reponame:Revista Ambiente & Água instname:Instituto de Pesquisas Ambientais em Bacias Hidrográficas (IPABHI) instacron:IPABHI |
instname_str |
Instituto de Pesquisas Ambientais em Bacias Hidrográficas (IPABHI) |
instacron_str |
IPABHI |
institution |
IPABHI |
reponame_str |
Revista Ambiente & Água |
collection |
Revista Ambiente & Água |
repository.name.fl_str_mv |
Revista Ambiente & Água - Instituto de Pesquisas Ambientais em Bacias Hidrográficas (IPABHI) |
repository.mail.fl_str_mv |
||ambi.agua@gmail.com |
_version_ |
1752129750733684736 |