Seasonal Analisys of Rio Grande do Sul Regions Affected by Severe Events Generated by MCS from 2004 to 2008
Autor(a) principal: | |
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Data de Publicação: | 2013 |
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Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Anuário do Instituto de Geociências (Online) |
Texto Completo: | https://revistas.ufrj.br/index.php/aigeo/article/view/6963 |
Resumo: | Disaster episodes caused by severe meteorological phenomena, also known as Severe Events (SE), such as windstorms, hail and flood have been studied extensively due to the hazard posed to society. One of the meteorological systems that is fairly common in Rio Grande do Sul state (RS), Brazil, and that is often associated with the SE are Mesoscale Convective Systems (MCS). Since the economy of RS is mostly based on agriculture, which is an activity very susceptible to changes in weather, economic losses caused by SE in RS are rather frequent. Given this point, the objective of this work was to analyze the seasonal distribution of regions affected by SE generated by MCSs that reached RS (SE MCSRS) in the period from 2004 to 2008. For this study, different data sources were used: SE reports and municipalities affected by SE (MA SE) obtained from RS Civil Defense data base; trajectories of the MCS that reached RS (MCS RS) based on Forecasting and Tracking of Active Cloud Clusters (ForTrACC) analysis tool; and raw infra-red (channel 4) satellite imagery from GOES 10 and 12 satellites. The results showed that: i) about 45% of the reported SE in RS were associated to MCS RS; ii) 58% of the MASE were affected by MCS RS; iii) the north portion of RS was the most affected by SE MCSRS; iv) windstorm and hail were the types of SEMCSRS that affected the largest number of municipalities and v) JAS (Jul-Aug-Sep) was the quarter that presented the largest number of municipalities affected by SE SCMRS (MA SE-MCSRS). |
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Seasonal Analisys of Rio Grande do Sul Regions Affected by Severe Events Generated by MCS from 2004 to 2008Análise Sazonal das Regiões do Rio Grande do Sul Atingidas por Eventos Severos Gerados por SCM no Período de 2004 a 2008Disaster episodes caused by severe meteorological phenomena, also known as Severe Events (SE), such as windstorms, hail and flood have been studied extensively due to the hazard posed to society. One of the meteorological systems that is fairly common in Rio Grande do Sul state (RS), Brazil, and that is often associated with the SE are Mesoscale Convective Systems (MCS). Since the economy of RS is mostly based on agriculture, which is an activity very susceptible to changes in weather, economic losses caused by SE in RS are rather frequent. Given this point, the objective of this work was to analyze the seasonal distribution of regions affected by SE generated by MCSs that reached RS (SE MCSRS) in the period from 2004 to 2008. For this study, different data sources were used: SE reports and municipalities affected by SE (MA SE) obtained from RS Civil Defense data base; trajectories of the MCS that reached RS (MCS RS) based on Forecasting and Tracking of Active Cloud Clusters (ForTrACC) analysis tool; and raw infra-red (channel 4) satellite imagery from GOES 10 and 12 satellites. The results showed that: i) about 45% of the reported SE in RS were associated to MCS RS; ii) 58% of the MASE were affected by MCS RS; iii) the north portion of RS was the most affected by SE MCSRS; iv) windstorm and hail were the types of SEMCSRS that affected the largest number of municipalities and v) JAS (Jul-Aug-Sep) was the quarter that presented the largest number of municipalities affected by SE SCMRS (MA SE-MCSRS).Os Episódios de desastre desencadeados por fenômenos meteorológicos severos, também conhecidos como Eventos Severos (ES), como por exemplo, vendaval, granizo e enchente, têm sido estudados com frequência devido à gravidade dos danos que estes causam à sociedade. Um dos sistemas meteorológicos que é bastante comum no Rio Grande do Sul (RS), e que frequentemente está associado aos ES são os Sistemas Convectivos de Mesoescala (SCM). Como a economia do RS é voltada majoritariamente para a agricultura, que é bastante suscetível às mudanças do tempo, é frequente no Estado a ocorrência de prejuízos econômicos causados por ES. Diante disso, o objetivo deste trabalho foi analisar a distribuição sazonal das regiões atingidas por ES gerados por SCM que afetaram o RS (ES SCMRS) no período de 2004 a 2008. Foram utilizados, para o período de estudo, dados de ocorrências de ES e municípios atingidos por ES (MA ES) obtidos no banco de dados da Coordenadoria Estadual de Defesa Civil do RS; trajetórias dos SCM que afetaram o RS (SCM RS) geradas a partir de informações fornecidas pela ferramenta ForTrACC (Forecasting and Tracking of Active Cloud Clusters) e imagens brutas do satélite GOES 10 e 12 do canal 4. Os resultados obtidos mostraram que: i) ~45% dos ES observados foram gerados por SCM RS; ii) ~58% dos MA ES foram atingidos por SCM RS; iii) a porção norte do RS foi a mais atingida por ES SCMRS; iv) vendaval e granizo foram os tipos de ES SCMRS que atingiram o maior número de municípios e v) JAS (jul-ago-set) foi o trimestre que apresentou o maior número de municípios atingidos por ES SCMRS (MA ES-SCMRS).Universidade Federal do Rio de Janeiro2013-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.ufrj.br/index.php/aigeo/article/view/696310.11137/2013_2_61_69Anuário do Instituto de Geociências; Vol 36, No 2 (2013); 61-69Anuário do Instituto de Geociências; Vol 36, No 2 (2013); 61-691982-39080101-9759reponame:Anuário do Instituto de Geociências (Online)instname:Universidade Federal do Rio de Janeiro (UFRJ)instacron:UFRJporhttps://revistas.ufrj.br/index.php/aigeo/article/view/6963/5530Copyright (c) 2013 Anuário do Instituto de Geociênciashttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessRasera, GustavoCampos, Cláudia Rejane Jacondino de2017-01-23T22:39:33Zoai:www.revistas.ufrj.br:article/6963Revistahttps://revistas.ufrj.br/index.php/aigeo/indexPUBhttps://revistas.ufrj.br/index.php/aigeo/oaianuario@igeo.ufrj.br||1982-39080101-9759opendoar:2017-01-23T22:39:33Anuário do Instituto de Geociências (Online) - Universidade Federal do Rio de Janeiro (UFRJ)false |
dc.title.none.fl_str_mv |
Seasonal Analisys of Rio Grande do Sul Regions Affected by Severe Events Generated by MCS from 2004 to 2008 Análise Sazonal das Regiões do Rio Grande do Sul Atingidas por Eventos Severos Gerados por SCM no Período de 2004 a 2008 |
title |
Seasonal Analisys of Rio Grande do Sul Regions Affected by Severe Events Generated by MCS from 2004 to 2008 |
spellingShingle |
Seasonal Analisys of Rio Grande do Sul Regions Affected by Severe Events Generated by MCS from 2004 to 2008 Rasera, Gustavo |
title_short |
Seasonal Analisys of Rio Grande do Sul Regions Affected by Severe Events Generated by MCS from 2004 to 2008 |
title_full |
Seasonal Analisys of Rio Grande do Sul Regions Affected by Severe Events Generated by MCS from 2004 to 2008 |
title_fullStr |
Seasonal Analisys of Rio Grande do Sul Regions Affected by Severe Events Generated by MCS from 2004 to 2008 |
title_full_unstemmed |
Seasonal Analisys of Rio Grande do Sul Regions Affected by Severe Events Generated by MCS from 2004 to 2008 |
title_sort |
Seasonal Analisys of Rio Grande do Sul Regions Affected by Severe Events Generated by MCS from 2004 to 2008 |
author |
Rasera, Gustavo |
author_facet |
Rasera, Gustavo Campos, Cláudia Rejane Jacondino de |
author_role |
author |
author2 |
Campos, Cláudia Rejane Jacondino de |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Rasera, Gustavo Campos, Cláudia Rejane Jacondino de |
description |
Disaster episodes caused by severe meteorological phenomena, also known as Severe Events (SE), such as windstorms, hail and flood have been studied extensively due to the hazard posed to society. One of the meteorological systems that is fairly common in Rio Grande do Sul state (RS), Brazil, and that is often associated with the SE are Mesoscale Convective Systems (MCS). Since the economy of RS is mostly based on agriculture, which is an activity very susceptible to changes in weather, economic losses caused by SE in RS are rather frequent. Given this point, the objective of this work was to analyze the seasonal distribution of regions affected by SE generated by MCSs that reached RS (SE MCSRS) in the period from 2004 to 2008. For this study, different data sources were used: SE reports and municipalities affected by SE (MA SE) obtained from RS Civil Defense data base; trajectories of the MCS that reached RS (MCS RS) based on Forecasting and Tracking of Active Cloud Clusters (ForTrACC) analysis tool; and raw infra-red (channel 4) satellite imagery from GOES 10 and 12 satellites. The results showed that: i) about 45% of the reported SE in RS were associated to MCS RS; ii) 58% of the MASE were affected by MCS RS; iii) the north portion of RS was the most affected by SE MCSRS; iv) windstorm and hail were the types of SEMCSRS that affected the largest number of municipalities and v) JAS (Jul-Aug-Sep) was the quarter that presented the largest number of municipalities affected by SE SCMRS (MA SE-MCSRS). |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-01-01 |
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.ufrj.br/index.php/aigeo/article/view/6963 10.11137/2013_2_61_69 |
url |
https://revistas.ufrj.br/index.php/aigeo/article/view/6963 |
identifier_str_mv |
10.11137/2013_2_61_69 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://revistas.ufrj.br/index.php/aigeo/article/view/6963/5530 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2013 Anuário do Instituto de Geociências http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2013 Anuário do Instituto de Geociências http://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal do Rio de Janeiro |
publisher.none.fl_str_mv |
Universidade Federal do Rio de Janeiro |
dc.source.none.fl_str_mv |
Anuário do Instituto de Geociências; Vol 36, No 2 (2013); 61-69 Anuário do Instituto de Geociências; Vol 36, No 2 (2013); 61-69 1982-3908 0101-9759 reponame:Anuário do Instituto de Geociências (Online) instname:Universidade Federal do Rio de Janeiro (UFRJ) instacron:UFRJ |
instname_str |
Universidade Federal do Rio de Janeiro (UFRJ) |
instacron_str |
UFRJ |
institution |
UFRJ |
reponame_str |
Anuário do Instituto de Geociências (Online) |
collection |
Anuário do Instituto de Geociências (Online) |
repository.name.fl_str_mv |
Anuário do Instituto de Geociências (Online) - Universidade Federal do Rio de Janeiro (UFRJ) |
repository.mail.fl_str_mv |
anuario@igeo.ufrj.br|| |
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1797053539627827200 |