Autor(es): Rodrigues, Ângela Afonso
Data: 2017
Identificador Persistente: http://hdl.handle.net/10362/34215
Origem: Repositório Institucional da UNL
Assunto(s): Tornados; County-level tornado modelling; R-INLA; Arcpy; Python; Point-processes; Areal Modelling; Spatio-temporal analysis; Spatio-temporal modelling; Bayesian Statistics
Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
The United States of America is the county in the world that is more prone to tornado occurrence. This fact led many researchers, for the past years, to study and formulate theories about tornado occurrence, and which factors promote tornadogenesis. The theories around tornados are always coupled with an attempt to predict their occurrence, for better disaster alertness, and response, in case they happen. At the country level, the tornado occurrence is highly studied and understood. But the same does not happen for the state level, or county level. In this thesis, it is proposed a statistical model to characterize the occurrence of tornados in a state, given physical (terrain roughness and land-cover types)and demographic properties of its counties. This model also takes into consideration the spatial and temporal dimensions, as well as a space time interaction component. This model was applied for Oklahoma and Texas. The model with the covariates fits Texas‟ tornado occurrence, but for Oklahoma, only the spatio-temporal formulation can be applied. For Texas, the model explains the covariates as being congruent with the low-level inflow hypothesis, with tornados decreasing in zones where natural barriers for the flow can be constituted. Under the Bayesian framework, maps of spatial risk and probability of tornado occurrence for Texas and Oklahoma were computed, that can be used to make predictions in the future.
