Analyses of Shallow Convection over the Amazon Coastal Region Using Satellite Images, Data Observations and Modeling

Detalhes bibliográficos
Autor(a) principal: Pires,Luciana Bassi Marinho
Data de Publicação: 2018
Outros Autores: Suselj,Kay, Rossato,Luciana, Teixeira,João
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista Brasileira de Meteorologia (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862018000200366
Resumo: Abstract The Belem region of the state of Para, which is located in northern of Brazil and part of the Amazon biome is characterized by high temperatures, strong convection, unstable air conditions and high humidity favoring the formation of convective clouds. Shallow convection and deep convection are among the main components of the local energy balance. Typically a deep convection over the continents is preceded by a shallow convection. An analysis of the performance of the Jet Propulsion Laboratory / National Aeronautics and Space Administration (JPL/NASA) model of shallow convection parameterization in a framework of the single column model (SCM), in relation to the cluster of cumulus clouds formed in the coastal region of the Amazon forest due to squall lines, is provided. To achieve this purpose enhanced satellite images and infrared images from channels 2 and 4 from the GOES-12 satellite, and data obtained by the “Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GPM (GlobAl Precipitation Measurement)” - CHUVA - campaign, during the month of June of 2011, were used. During that period, clusters of cumulus clouds penetrated the interior of the Amazon, causing heavy rains. Results demonstrated that the parameterizations performed well in the case where only a core of clouds was observed, such as at 18:00h on 14 June. This period of the day also presents the smallest bias and root mean square error (rmse) values for the relative humidity. For the potential temperature the smallest value of bias is at 12:00h on June 7th (0.18 K), the largest one is on June 11th (-2.32 K) and the rmse ranges from 0.59 to 2.99 K.
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spelling Analyses of Shallow Convection over the Amazon Coastal Region Using Satellite Images, Data Observations and Modelingconvective parameterizationsSingle Column Model (SCM)CHUVA experimentsinfrared imagesAbstract The Belem region of the state of Para, which is located in northern of Brazil and part of the Amazon biome is characterized by high temperatures, strong convection, unstable air conditions and high humidity favoring the formation of convective clouds. Shallow convection and deep convection are among the main components of the local energy balance. Typically a deep convection over the continents is preceded by a shallow convection. An analysis of the performance of the Jet Propulsion Laboratory / National Aeronautics and Space Administration (JPL/NASA) model of shallow convection parameterization in a framework of the single column model (SCM), in relation to the cluster of cumulus clouds formed in the coastal region of the Amazon forest due to squall lines, is provided. To achieve this purpose enhanced satellite images and infrared images from channels 2 and 4 from the GOES-12 satellite, and data obtained by the “Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GPM (GlobAl Precipitation Measurement)” - CHUVA - campaign, during the month of June of 2011, were used. During that period, clusters of cumulus clouds penetrated the interior of the Amazon, causing heavy rains. Results demonstrated that the parameterizations performed well in the case where only a core of clouds was observed, such as at 18:00h on 14 June. This period of the day also presents the smallest bias and root mean square error (rmse) values for the relative humidity. For the potential temperature the smallest value of bias is at 12:00h on June 7th (0.18 K), the largest one is on June 11th (-2.32 K) and the rmse ranges from 0.59 to 2.99 K.Sociedade Brasileira de Meteorologia2018-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862018000200366Revista Brasileira de Meteorologia v.33 n.2 2018reponame:Revista Brasileira de Meteorologia (Online)instname:Sociedade Brasileira de Meteorologia (SBMET)instacron:SBMET10.1590/0102-7786332009info:eu-repo/semantics/openAccessPires,Luciana Bassi MarinhoSuselj,KayRossato,LucianaTeixeira,Joãoeng2019-05-27T00:00:00Zoai:scielo:S0102-77862018000200366Revistahttp://www.rbmet.org.br/port/index.phpONGhttps://old.scielo.br/oai/scielo-oai.php||rbmet@rbmet.org.br1982-43510102-7786opendoar:2019-05-27T00:00Revista Brasileira de Meteorologia (Online) - Sociedade Brasileira de Meteorologia (SBMET)false
dc.title.none.fl_str_mv Analyses of Shallow Convection over the Amazon Coastal Region Using Satellite Images, Data Observations and Modeling
title Analyses of Shallow Convection over the Amazon Coastal Region Using Satellite Images, Data Observations and Modeling
spellingShingle Analyses of Shallow Convection over the Amazon Coastal Region Using Satellite Images, Data Observations and Modeling
Pires,Luciana Bassi Marinho
convective parameterizations
Single Column Model (SCM)
CHUVA experiments
infrared images
title_short Analyses of Shallow Convection over the Amazon Coastal Region Using Satellite Images, Data Observations and Modeling
title_full Analyses of Shallow Convection over the Amazon Coastal Region Using Satellite Images, Data Observations and Modeling
title_fullStr Analyses of Shallow Convection over the Amazon Coastal Region Using Satellite Images, Data Observations and Modeling
title_full_unstemmed Analyses of Shallow Convection over the Amazon Coastal Region Using Satellite Images, Data Observations and Modeling
title_sort Analyses of Shallow Convection over the Amazon Coastal Region Using Satellite Images, Data Observations and Modeling
author Pires,Luciana Bassi Marinho
author_facet Pires,Luciana Bassi Marinho
Suselj,Kay
Rossato,Luciana
Teixeira,João
author_role author
author2 Suselj,Kay
Rossato,Luciana
Teixeira,João
author2_role author
author
author
dc.contributor.author.fl_str_mv Pires,Luciana Bassi Marinho
Suselj,Kay
Rossato,Luciana
Teixeira,João
dc.subject.por.fl_str_mv convective parameterizations
Single Column Model (SCM)
CHUVA experiments
infrared images
topic convective parameterizations
Single Column Model (SCM)
CHUVA experiments
infrared images
description Abstract The Belem region of the state of Para, which is located in northern of Brazil and part of the Amazon biome is characterized by high temperatures, strong convection, unstable air conditions and high humidity favoring the formation of convective clouds. Shallow convection and deep convection are among the main components of the local energy balance. Typically a deep convection over the continents is preceded by a shallow convection. An analysis of the performance of the Jet Propulsion Laboratory / National Aeronautics and Space Administration (JPL/NASA) model of shallow convection parameterization in a framework of the single column model (SCM), in relation to the cluster of cumulus clouds formed in the coastal region of the Amazon forest due to squall lines, is provided. To achieve this purpose enhanced satellite images and infrared images from channels 2 and 4 from the GOES-12 satellite, and data obtained by the “Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GPM (GlobAl Precipitation Measurement)” - CHUVA - campaign, during the month of June of 2011, were used. During that period, clusters of cumulus clouds penetrated the interior of the Amazon, causing heavy rains. Results demonstrated that the parameterizations performed well in the case where only a core of clouds was observed, such as at 18:00h on 14 June. This period of the day also presents the smallest bias and root mean square error (rmse) values for the relative humidity. For the potential temperature the smallest value of bias is at 12:00h on June 7th (0.18 K), the largest one is on June 11th (-2.32 K) and the rmse ranges from 0.59 to 2.99 K.
publishDate 2018
dc.date.none.fl_str_mv 2018-06-01
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=S0102-77862018000200366
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862018000200366
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0102-7786332009
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Meteorologia
publisher.none.fl_str_mv Sociedade Brasileira de Meteorologia
dc.source.none.fl_str_mv Revista Brasileira de Meteorologia v.33 n.2 2018
reponame:Revista Brasileira de Meteorologia (Online)
instname:Sociedade Brasileira de Meteorologia (SBMET)
instacron:SBMET
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institution SBMET
reponame_str Revista Brasileira de Meteorologia (Online)
collection Revista Brasileira de Meteorologia (Online)
repository.name.fl_str_mv Revista Brasileira de Meteorologia (Online) - Sociedade Brasileira de Meteorologia (SBMET)
repository.mail.fl_str_mv ||rbmet@rbmet.org.br
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