Análise Comparativa Da Precipitação No Estado Da Paraíba Utilizando Modelos De Regressão Polinomial

Gomes, Oseas Machado; Santos, Carlos Antônio Costa dos; Souza, Francisco de; Paiva, Willian de; Olinda, Ricardo Alves de

doi:10.1590/0102-778620120454

Cited by 7 publications

(8 citation statements)

References 3 publications

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“…The anti-correlation between the solar activity and cover of clouds has been explained by using parameters of the cosmic rays. This anti-correlation has been also observed in the present work [45]. The cosmic rays decrease with increasing solar activity.…”

Section: Resultssupporting

confidence: 91%

Long-term Observation of Nighttime Clouds over São João do Cariri (7.4º S, 36.5º W)

Moraes¹,

Paulino²,

Martins³

et al. 2017

JESE-A

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Using images collected by an all sky imager deployed in São João do Cariri (7.4º S, 36.5º W) from September 2000 to November 2010, the nighttime incidence of clouds has been observed and studied. Most of the nighttime cloudy occurred from February to July, which is related to the rainy season in the area of the observatory. The best period to run optical observations over São João do Cariri starts in the early night up to 22:00 LT (Local Time) throughout the period between August and December. An anti-correlation between the F10.7 cm solar flux index and the number of cloudy nights was observed, which is an evidence of the cosmic rays activity in the lower earth's atmosphere. The location of the all-sky imager provides reliable optical observations all year through.

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Section: Resultssupporting

confidence: 91%

Long-term Observation of Nighttime Clouds over São João do Cariri (7.4º S, 36.5º W)

Moraes¹,

Paulino²,

Martins³

et al. 2017

JESE-A

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“…Com base no fato de que a altitude, a latitude e a longitude influenciam nas condições climáticas de uma região, é possível, aplicando-se técnicas estatísticas, ajustar equações de regressão polinomial múltipla para estimar os totais médios precipitados em função destas variáveis (GOMES et al, 2015;SILVA, 2009).…”

Section: Introductionunclassified

Estimativa Da Precipitação No Espírito Santo Por Intermédio De Regressão Polinomial

Abreu

Cecílio

Zanetti

et al. 2019

NAT

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A precipitação é um dos principais elementos da hidrologia, sendo uma variável de grande importância para a compreensão da dinâmica do ciclo hidrológico. Apesar da sua importância, a disponibilidade de dados hidroclimáticos é baixa. Dentre as alternativas para suprir a necessidade de informações da precipitação, a modelagem matemática é uma importante ferramenta que visa e sua estimativa. Assim, este trabalho avaliou as precipitações mensais e anuais de 110 estações pluviométricas do estado do Espírito Santo e avaliou o ajuste de modelos polinomiais de ordem 1 a 4 utilizando a longitude, latitude e altitude como variáveis explicativas para a previsão dessas precipitações. A precipitação no Espírito Santo mostrou variabilidade considerável, indicando grande influência do relevo, sendo observado também que localidades com maiores altitudes apresentaram maiores totais precipitados. A regressão polinomial de quarto grau se mostrou a mais adequada em representar as precipitações médias mensais e anual. Os ajustes foram considerados suficientes para representar as precipitações do Espírito Santo, com coeficientes de determinação superiores a 0,7 e erros percentuais absolutos médios entre 5,9% e 16,6%. Foi observada uma leve tendência dos modelos em subestimar os valores observados. De maneira geral, os meses do período seco, especialmente de maio a julho, obtiveram melhor desempenho dos modelos.Palavras-chave: modelagem matemática, climatologia, hidrologia, chuva. ESTIMATION OF PRECIPITATION IN THE ESPÍRITO SANTO STATE BY POLYNOMIAL REGRESSION ABSTRACT: Precipitation is one of the main elements of hydrology, being a variable of great importance for understanding the dynamics of the hydrological cycle. Despite their importance, the availability of hydroclimatic data is low. Among the alternatives to meet the need for precipitation information, mathematical modeling is an important tool that aims its estimate. This work evaluated the monthly and annual rainfall of 110 rainfall gauges in the state of Espírito Santo and evaluated the adjustment of polynomial models of order 1 to 4 using longitude, latitude and altitude as explanatory variables to predict these precipitations. Precipitation in Espírito Santo showed considerable variability, indicating great influence of the relief, being observed that location with higher altitudes presented higher precipitated totals. The fourth-degree polynomial regression proved to be the most adequate to represent the mean monthly and annual precipitations. The adjustments were considered sufficient to represent the Espírito Santo precipitation, with coefficients of determination higher than 0.7 and mean absolute percentage errors between 5.9% and 16.6%. A slight trend of the models was observed in underestimating the observed values. In general, the months of the dry period, especially from May to July, obtained better performance of the models.Keywords: mathematical modeling, climatology, hydrology, rainfall.

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“…Thus, the need for space-time modeling of these residues arises. Previous studies did not take into account this spatial and/or temporal residue dependence [7,33]. Consequently, we modeled the spatiotemporal dependence structure of this stochastic component, using a covariance function.…”

Section: Discussionmentioning

confidence: 99%

Space–Time Kriging of Precipitation: Modeling the Large-Scale Variation with Model GAMLSS

Medeiros

Lima

Olinda

et al. 2019

Water

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Knowing the dynamics of spatial–temporal precipitation distribution is of vital significance for the management of water resources, in highlight, in the northeast region of Brazil (NEB). Several models of large-scale precipitation variability are based on the normal distribution, not taking into consideration the excess of null observations that are prevalent in the daily or even monthly precipitation information of the region under study. This research proposes a novel way of modeling the trend component by using an inflated gamma distribution of zeros. The residuals of this regression are generally space–time dependent and have been modeled by a space–time covariance function. The findings show that the new techniques have provided reliable and precise precipitation estimates, exceeding the techniques used previously. The modeling provided estimates of precipitation in nonsampled locations and unobserved periods, thus serving as a tool to assist the government in improving water management, anticipating society’s needs and preventing water crises.

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Análise Comparativa Da Precipitação No Estado Da Paraíba Utilizando Modelos De Regressão Polinomial

Cited by 7 publications

References 3 publications

Long-term Observation of Nighttime Clouds over São João do Cariri (7.4º S, 36.5º W)

Long-term Observation of Nighttime Clouds over São João do Cariri (7.4º S, 36.5º W)

Estimativa Da Precipitação No Espírito Santo Por Intermédio De Regressão Polinomial

Space–Time Kriging of Precipitation: Modeling the Large-Scale Variation with Model GAMLSS

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