Banca de QUALIFICAÇÃO: JOSIEL OLIVEIRA DA LUZ

Uma banca de QUALIFICAÇÃO de MESTRADO foi cadastrada pelo programa.
STUDENT : JOSIEL OLIVEIRA DA LUZ
DATE: 25/06/2024
TIME: 14:00
LOCAL: Ambiente virtual
TITLE:

Statistical Analyses of Extreme Rainfall on the East Coast of the Northeast Using Spatio-
Temporal Models

KEY WORDS:

Geostatistics; Spatio-temporal models; R packages; Climate extremes; Natural
disasters.

PAGES: 100
BIG AREA: Ciências Exatas e da Terra
AREA: Probabilidade e Estatística
SUMMARY:

The study of climatic extremes is essential for understanding the occurrence of natural
disasters. It becomes even more important when the tools available for this analysis do not
perform their functions accurately. This is the case with the extreme precipitation events on the
east coast of north-eastern Brazil, where satellite estimates tend to underestimate extreme
precipitation. With this in mind, the study explored the behavior of rainfall extremes in this
region to build up the knowledge needed to support the development of natural disaster
mitigation measures. To do this, the study extreme was the number of times that the daily
rainfall accumulated exceeded the 30-millimeter threshold over one year. The time series is 32
years old, with observations at 37 stations. As well as rainfall, it contains the latitude, longitude,
and altitude of the stations. The data was taken from the National Meteorological Institute
(INMET) and the National Water and Sanitation Agency (ANA). The study selected spatial and
spatiotemporal models available in the literature and implemented in the R language from a
Bayesian inference perspective. In this context, the study has not yet been finalized and not all
the results are available. However, the results so far have shown extremes occurring more
frequently in regions close to the coast, with spatial dependence and unclear temporal
dependence. Furthermore, in a purely observational analysis, there is an indication of patterns
of extremes repeating every 10 years from 1991 onwards. The next steps in the study are to
adjust the 5 spatial and spatiotemporal models; taken from the geoR, geoRglm, geoCount, and
INLA packages; to the data set. The forecasts from these models will then be compared with the
satellite forecasts. Case the results of the models are positive compared to the satellite
estimates. In that case, a database will be set up with the best-fit model, thus contributing to
future research that wishes to work with data that is closer to real data when compared to
satellite data and with a greater sample density when compared to real data, since the number
of meteorological stations available is small about the size of the area.

COMMITTEE MEMBERS:
Interno - 3010614 - ELIARDO GUIMARAES DA COSTA
Presidente - 1781198 - FIDEL ERNESTO CASTRO MORALES
Externo à Instituição - JORIO BEZERRA CABRAL JUNIOR - UFAL
Externa à Instituição - JOSIMARA TATIANE DA SILVA