Modeling Spatiotemporal Rainfall Variability in Paraíba, Brazil

Hidden Markov models for modeling daily rainfall occurrence over Brazil

A hidden Markov model (HMM) is used to describe daily rainfall occurrence at ten gauge stations in the state of Ceará in northeast Brazil during the February–April wet season 1975–2002. The model assumes that rainfall occurrence is governed by a few discrete states, with Markovian daily transitions between them. Four “hidden” rainfall states are identified. One pair of the states represents wet...

[Modeling of dengue incidence in Paraíba State, Brazil, using distributed lag models].

The literature provides several statistical models to explain dengue incidence. However, there is disagreement as to the real contribution of models based on climate factors or models based on relative variables to combat the vector, because their variability fails to provide a satisfactory statistical explanation for the behavior of dengue incidence. Distributed lag models (DLM) posit that a v...

H variability in UK rainfall

Spatiotemporal monthly rainfall reconstruction via artificial neural network – case study: south of Brazil

Climatological records users, frequently, request time series for geographical locations where there is no observed meteorological attributes. Climatological conditions of the areas or points of interest have to be calculated interpolating observations in the time of neighboring stations and climate proxy. The aim of the present work is the application of reliable and robust procedures for mont...

[Productivity of Aedes albopictus' breeding containers in Paraíba Valley, Brazil].

OBJECTIVE To estimate the potential productivity of artificial, permanent, and natural breeding containers of Aedes albopictus, potential vector species of dengue. METHODS Three locations were selected for this study: a) warehouse of disposable material goods in the urban area of Pindamonhangaba, state of São Paulo, Brazil; b) a small leisure farmland in Tremembé, state of São Paulo; and c) r...