doctoralThesis
Modelagem estocástica da distribuição de probabilidade da precipitação pluvial via métodos computacionalmente intensivos
Fecha
2017-11-24Registro en:
SANTOS, Marconio Silva dos. Modelagem estocástica da distribuição de probabilidade da precipitação pluvial via métodos computacionalmente intensivos. 2017. 96f. Tese (Doutorado em Ciências Climáticas) - Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Natal, 2017.
Autor
Santos, Marconio Silva dos
Resumen
In this work, it was made a statistical modeling of precipitation. This is a methodological
work that uses stochastic simulations to estimate the probability distributions
related to this atmospheric variable. In order to estimate the parameters of these distributions,
Markov chain Monte Carlo methods were used to generate large size synthetic
samples from observed data. The used methods were the Metropolis-Hastings algorithm
and the Gibbs sampler. The simulations were performed under the hypothesis that the
days of of the same period of the year (month or rainy season) can be considered to be
identically distributed concernig the probability of precipitation. This research allowed
the production of four papers. The first paper used the Metropolis-Hastings algorithm
to model the probability of occurrence of precipitation on any day of the month. The
simulations of this paper were perfomed with observed data of some Brazilian cities. The
other papers used the Gibbs sampler and the proposed methods were applied to data from
cities in the Northeast Brazil. In the second paper, Beta and Binomial distributions were
used to model the number of days of the month with occurrence of precipitation. In the
third paper, the Poisson distribution was used to model the number of days with precipitation
extreme values in the rainy season. An alternative method for estimating these
extreme values and their distribution is presented in the fourth paper, using the Gamma
distribution. According to the results obtained by these researches, the Gibbs sampler
was considered to be adequate to estimate distributions in the modeling of precipitation
on cities for which there are few historical data.