Processos convectivos identificados por uma microrrede de estações automáticas de superfície
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Manancial - Repositório Digital da UFSM |
dARK ID: | ark:/26339/001300000kt3d |
Texto Completo: | http://repositorio.ufsm.br/handle/1/22380 |
Resumo: | This study explored surface automated weather systems that sample and record data in high frequency to demonstrate the importance of this strategy of data collection in the characterization and monitoring of atmospheric disturbances generated by convective storms. To that end, data from a micrometeorological tower, an adaptive micronet of surface stations, and a portable meteorological station designed for monitoring convective phenomena (Mesomóvel station) were analyzed for a number of situations in which deep convection was observed over these stations. Time series of atmospheric pressure, air temperature, and wind gusts produced at high sampling frequency were investigated to assess the value added by these data in the characterization of surface circulations generated by storms, in comparison with hourly observations from the operational network maintained by INMET. One hypothesis tested was whether the 1st and 99th percentiles of the rates of change per minute of pressure and temperature displayed any skill in discriminating between the fast mode of variation associated with convective circulations and the slow mode of variation of the background synoptic scale. The results highlighted the important value added by the high frequency measurements performed with the special stations in the characterization of disturbances in temperature, pressure and winds generated by deep convection, such as gust fronts, cold pools, mesohighs and mesolows, including non-hydrostatic mechanismos affecting the pressure disturbances. None of the convectively-generated disturbances was adequately characterized by the hourly reports from INMET automated stations situated in the same observing sites or in the vicinities of the special stations. The time series analysis of the rates of change of pressure and temperature contributed to better detect the passage of the convectively-induced disturbances. However, it was shown that the 1st and 99th percentiles of these rates of change can also be attained in the absence of convective storms and other sub-synoptic disturbances. |
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Processos convectivos identificados por uma microrrede de estações automáticas de superfícieConvective processes identified by a micro-network of automatic surface stationsMeteorologia de mesoescalaMicrorrede de observação meteorológica de superfícieTempestades convectivas locaisMesoscale meteorologySurface meteorological observation networkLocal convective stormsCNPQ::CIENCIAS EXATAS E DA TERRA::GEOCIENCIAS::METEOROLOGIAThis study explored surface automated weather systems that sample and record data in high frequency to demonstrate the importance of this strategy of data collection in the characterization and monitoring of atmospheric disturbances generated by convective storms. To that end, data from a micrometeorological tower, an adaptive micronet of surface stations, and a portable meteorological station designed for monitoring convective phenomena (Mesomóvel station) were analyzed for a number of situations in which deep convection was observed over these stations. Time series of atmospheric pressure, air temperature, and wind gusts produced at high sampling frequency were investigated to assess the value added by these data in the characterization of surface circulations generated by storms, in comparison with hourly observations from the operational network maintained by INMET. One hypothesis tested was whether the 1st and 99th percentiles of the rates of change per minute of pressure and temperature displayed any skill in discriminating between the fast mode of variation associated with convective circulations and the slow mode of variation of the background synoptic scale. The results highlighted the important value added by the high frequency measurements performed with the special stations in the characterization of disturbances in temperature, pressure and winds generated by deep convection, such as gust fronts, cold pools, mesohighs and mesolows, including non-hydrostatic mechanismos affecting the pressure disturbances. None of the convectively-generated disturbances was adequately characterized by the hourly reports from INMET automated stations situated in the same observing sites or in the vicinities of the special stations. The time series analysis of the rates of change of pressure and temperature contributed to better detect the passage of the convectively-induced disturbances. However, it was shown that the 1st and 99th percentiles of these rates of change can also be attained in the absence of convective storms and other sub-synoptic disturbances.O presente trabalho explorou sistemas de observação automática de superfície com amostragem e registro de dados meteorológicos em alta frequência para demonstrar a importância desta estratégia de coleta de dados na caracterização e monitoramento de distúrbios atmosféricos produzidos por tempestades convectivas locais. Para isto, dados provenientes de uma torre micrometeorológica, de uma micro-rede adaptativa de estações de superfície, e de uma estação meteorológica portátil concebida para o monitoramento de fenômenos convectivos (estação Mesomóvel) foram analisados para diversas situações em que convecção profunda foi observada sobre estas estações. Séries temporais de pressão atmosférica, temperatura do ar, e rajada de vento produzidas com alta frequência de amostragem foram investigadas para avaliar o valor adicionado por estes dados na caracterização de circulações de superfície geradas por tempestades, em comparação com as observações horárias provenientes da rede operacional de observação mantida pelo INMET. Uma hipótese testada foi se os percentis extremos (1% e 99%) das taxas de variação por minuto da pressão e da temperatura apresentavam poder discriminatório entre o modo de variação rápida associada às circulações convectivas e o modo de variação lenta da escala sinótica. Os resultados destacaram o importante valor agregado pelas medições em alta frequência realizadas com as estações especiais na caracterização de distúrbios de temperatura, pressão e vento gerados pela convecção profunda, como frentes de rajada, piscinas frias, mesoaltas e mesobaixas, incluindo-se contribuições não-hidrostáticas para as perturbações de pressão. Nenhum destes distúrbios foi adequadamente caracterizado pelos registros horários das estações automáticas do INMET locaizadas nos mesmos sítios observacionais ou nas proximidades das estações especiais. A análise das séries temporais das taxas de variação de pressão e temperatura ajudaram a detectar melhor a passagem das perturbações induzidas pela convecção, porém ficou mostrado que os percentis extremos destas taxas de variação também podem ser atingidos em situações de ausência de tempestades e de outros distúrbios sub-sinóticos.Universidade Federal de Santa MariaBrasilMeteorologiaUFSMPrograma de Pós-Graduação em MeteorologiaCentro de Ciências Naturais e ExatasNascimento, Ernani de Limahttp://lattes.cnpq.br/1074092256181192Roberti, Débora ReginaQuadro, Mário Francisco Leal deFeldhaus, Patricia Fernanda2021-10-13T11:23:18Z2021-10-13T11:23:18Z2020-09-18info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://repositorio.ufsm.br/handle/1/22380ark:/26339/001300000kt3dporAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessreponame:Manancial - Repositório Digital da UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM2022-08-10T14:16:45Zoai:repositorio.ufsm.br:1/22380Biblioteca Digital de Teses e Dissertaçõeshttps://repositorio.ufsm.br/ONGhttps://repositorio.ufsm.br/oai/requestatendimento.sib@ufsm.br||tedebc@gmail.comopendoar:2022-08-10T14:16:45Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)false |
dc.title.none.fl_str_mv |
Processos convectivos identificados por uma microrrede de estações automáticas de superfície Convective processes identified by a micro-network of automatic surface stations |
title |
Processos convectivos identificados por uma microrrede de estações automáticas de superfície |
spellingShingle |
Processos convectivos identificados por uma microrrede de estações automáticas de superfície Feldhaus, Patricia Fernanda Meteorologia de mesoescala Microrrede de observação meteorológica de superfície Tempestades convectivas locais Mesoscale meteorology Surface meteorological observation network Local convective storms CNPQ::CIENCIAS EXATAS E DA TERRA::GEOCIENCIAS::METEOROLOGIA |
title_short |
Processos convectivos identificados por uma microrrede de estações automáticas de superfície |
title_full |
Processos convectivos identificados por uma microrrede de estações automáticas de superfície |
title_fullStr |
Processos convectivos identificados por uma microrrede de estações automáticas de superfície |
title_full_unstemmed |
Processos convectivos identificados por uma microrrede de estações automáticas de superfície |
title_sort |
Processos convectivos identificados por uma microrrede de estações automáticas de superfície |
author |
Feldhaus, Patricia Fernanda |
author_facet |
Feldhaus, Patricia Fernanda |
author_role |
author |
dc.contributor.none.fl_str_mv |
Nascimento, Ernani de Lima http://lattes.cnpq.br/1074092256181192 Roberti, Débora Regina Quadro, Mário Francisco Leal de |
dc.contributor.author.fl_str_mv |
Feldhaus, Patricia Fernanda |
dc.subject.por.fl_str_mv |
Meteorologia de mesoescala Microrrede de observação meteorológica de superfície Tempestades convectivas locais Mesoscale meteorology Surface meteorological observation network Local convective storms CNPQ::CIENCIAS EXATAS E DA TERRA::GEOCIENCIAS::METEOROLOGIA |
topic |
Meteorologia de mesoescala Microrrede de observação meteorológica de superfície Tempestades convectivas locais Mesoscale meteorology Surface meteorological observation network Local convective storms CNPQ::CIENCIAS EXATAS E DA TERRA::GEOCIENCIAS::METEOROLOGIA |
description |
This study explored surface automated weather systems that sample and record data in high frequency to demonstrate the importance of this strategy of data collection in the characterization and monitoring of atmospheric disturbances generated by convective storms. To that end, data from a micrometeorological tower, an adaptive micronet of surface stations, and a portable meteorological station designed for monitoring convective phenomena (Mesomóvel station) were analyzed for a number of situations in which deep convection was observed over these stations. Time series of atmospheric pressure, air temperature, and wind gusts produced at high sampling frequency were investigated to assess the value added by these data in the characterization of surface circulations generated by storms, in comparison with hourly observations from the operational network maintained by INMET. One hypothesis tested was whether the 1st and 99th percentiles of the rates of change per minute of pressure and temperature displayed any skill in discriminating between the fast mode of variation associated with convective circulations and the slow mode of variation of the background synoptic scale. The results highlighted the important value added by the high frequency measurements performed with the special stations in the characterization of disturbances in temperature, pressure and winds generated by deep convection, such as gust fronts, cold pools, mesohighs and mesolows, including non-hydrostatic mechanismos affecting the pressure disturbances. None of the convectively-generated disturbances was adequately characterized by the hourly reports from INMET automated stations situated in the same observing sites or in the vicinities of the special stations. The time series analysis of the rates of change of pressure and temperature contributed to better detect the passage of the convectively-induced disturbances. However, it was shown that the 1st and 99th percentiles of these rates of change can also be attained in the absence of convective storms and other sub-synoptic disturbances. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-09-18 2021-10-13T11:23:18Z 2021-10-13T11:23:18Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufsm.br/handle/1/22380 |
dc.identifier.dark.fl_str_mv |
ark:/26339/001300000kt3d |
url |
http://repositorio.ufsm.br/handle/1/22380 |
identifier_str_mv |
ark:/26339/001300000kt3d |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Brasil Meteorologia UFSM Programa de Pós-Graduação em Meteorologia Centro de Ciências Naturais e Exatas |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Brasil Meteorologia UFSM Programa de Pós-Graduação em Meteorologia Centro de Ciências Naturais e Exatas |
dc.source.none.fl_str_mv |
reponame:Manancial - Repositório Digital da UFSM instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
instname_str |
Universidade Federal de Santa Maria (UFSM) |
instacron_str |
UFSM |
institution |
UFSM |
reponame_str |
Manancial - Repositório Digital da UFSM |
collection |
Manancial - Repositório Digital da UFSM |
repository.name.fl_str_mv |
Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM) |
repository.mail.fl_str_mv |
atendimento.sib@ufsm.br||tedebc@gmail.com |
_version_ |
1815172358086852608 |