Stochastic modeling of time series with intermittency, persistence and extreme variability, with application to spatio-temporal averages of rainfall fields
Abstract
Motivated by rainfall research, this thesis contributes new insights on mechanisms of precipitation. This is accomplished through a stochastic modelling approach of time series representing intermittency and variability of precipitation cumulatively at large spatial scales. Our objective is to obtain a parsimonious but flexible stochastic model that can capture adequately the spectral power distribution and the marginal probability distribution of time series of spatio-temporal averages of rain rate (STARR) at such large spatial scales, presumably under stationarity conditions. The model conceived and presented in this thesis treats intermittency and variability as two stochastically independent multiplicative components, each contributing partially to the overall persistence of memory or dependence of the model. Specifically, we model intermittency by a stationary renewal process in discrete time, where instants of renewals are marked with the value {1} and otherwise the process attai ...
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