2011
DOI: 10.1590/s0102-77862011000400009
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Metodologia para análise de desempenho de simulações de sistemas convectivos na região metropolitana de São Paulo com o modelo ARPS: sensibilidade a variações com os esquemas de advecção e assimilação de dados

Abstract: RESUMOTempestades seguidas de enchentes e alagamentos em pontos da Região Metropolitana de São Paulo são eventos recorrentes nas estações chuvosas. O desempenho do modelo ARPS nas simulações numéricas de alta resolução espacial, para o evento de 04 de fevereiro de 2004, é avaliado por meio de erros estatísticos e índices de acurácia, com base no confronto entre a distribuição espacial da precipitação acumulada simulada e a estimada com o radar meteorológico de São Paulo. Os resultados quantificam a influência … Show more

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Cited by 52 publications
(35 citation statements)
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“…Table 3 lists all surface stations used to evaluate the model. Temperature and specific humidity taken at 2 m agl were used for sake of validation, using root mean square error (RMSE), bias [45] and Pielke index (D Pielke ) [46], which considers the Pielke model skill definition [47] as presented in Equation (4):…”
Section: Simulations Descriptionmentioning
confidence: 99%
“…Table 3 lists all surface stations used to evaluate the model. Temperature and specific humidity taken at 2 m agl were used for sake of validation, using root mean square error (RMSE), bias [45] and Pielke index (D Pielke ) [46], which considers the Pielke model skill definition [47] as presented in Equation (4):…”
Section: Simulations Descriptionmentioning
confidence: 99%
“…1). The analysis of meteorological variables has been conducted based on Pielke's skill score (S pielke ) (Pielke, 2002;Hallak and Perreira Filho, 2011), Pearson's correlation coefficient (r), and mean bias (MB). For the air pollutants, in addition to r and MB, two statistical indexes were used, the mean normalized bias error (MNBE) and the mean normalized gross error (MNGE).…”
Section: Numerical Scenariosmentioning
confidence: 99%
“…The root-mean-square error (RMSE) informs the actual value of the error produced by the model (Souza et al 2011). It usually expresses the accuracy of numerical results with the advantage that RMSE has errors in the same dimensions of the analyzed variable (Hallak and Pereira Filho 2011). The lower this measure, the better the model performance in making estimates (Souza and Escobedo 2013).…”
Section: Discussionmentioning
confidence: 99%