Avaliação da previsão numérica sazonal de precipitação para o Rio Grande do Sul

Detalhes bibliográficos
Ano de defesa: 2015
Autor(a) principal: Gonçalves, Jéssica Stobienia
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de Santa Maria
BR
Meteorologia
UFSM
Programa de Pós-Graduação em Meteorologia
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://repositorio.ufsm.br/handle/1/10281
Resumo: In order to obtain an increase of quality seasonal climate forecast of precipitation, for the state of Rio Grande do Sul, were implemented and evaluated nine types of simulations that uses different cumulus parameterization schemes available in the Regional Climate Model version 4 RegCM4.The tested parameterizations were Grell with closure Arakawa and Schubert - AS and Fritsch and Chappell - FC, MIT-Emanuel and mixed convection that is the use of different convection schemes on the land and the sea. The evaluation method consisted of analysis qualitative and quantitative statistics of seasonal precipitation climate forecasts of five regions of Rio Grande do Sul, from August 2013 to August 2014.The statistics applied were Taylor diagram, random and systematic error analysis, concordance index and contingency table. The forecasts were evaluated using observed data from meteorological stations of the Instituto Nacional de Meteorogia (INMET). The analysis showed the RegCM4 had higher correlations and lower errors compared to the Global Model. The best results were observed in the northern and western part of the state with the parameterizations Grell FC, Grell AS and the combination of Emanuel simulated ocean and Grell AS on land. Although some regions were not adequately represented by the Regional Climate Model RegCM4, yet it performed well reducing the overestimation of precipitation observed in the simulation of the Global Model and improved temporal distribution of the same.