O uso de ferramentas fractais e redes complexas no estudo da variabilidade pluviom´etrica do Nordeste do Brasil

Abstract

Brazil Northeast s climate is usually described as semi-arid, characterized by hard dry seasons intermingled by hard rainfall seasons. Some areas of Northeast present annual pluviometric measure about 400 mm in mean, while in others the annual pluviometric measure is about 2000 mm. Rain events result from interplay of several physical phenomena, most of which can be individually described on the basic laws of mechanics and thermodynamics in a rather adequate way. Because of this, a huge progress has been achieved in recent years in relation to weather forecast with the use of very precise algorithms in large scale computing resources. They take into account the variables that are relevant for the atmospheric and ocean circulation and input of large amount of physical data obtained from a dense set of stations scattered around the world. In order to improve the interpretation of the accurate data resulting from the description of atmospheric phenomena and rain events, it is necessary to proceed with sophisticated analyses of recorded and simulated data, as spatial and temporal statistical correlations, scale properties, topological properties of spatial event distribution, ad so on. They indicate the extent of statistical relevance of the data, local and global effects, typical patterns, and other topological features related to the phenomena.In this work, we explore the usefulness of complex network framework for the analysis and nderstanding of rain events, based solely on recorded data from a set of stations in Northeast Brazil. The method is inspired on a proposal to characterize actual sequences of earthquake events where, like precipitation phenomena, the available data stems from complex systems with a very large number of physical variables. The potential network nodes are the meteorological stations where the rain events have been recorded, while the network edges are placed according to rules that take into account temporal and spatial correlation criteria between events occurring at different stations, for a time span as large as one month. We evaluate usual network properties based on diameter, node degrees, clustering coefficient, minimal inter-node distance along network edges. This allows for a characterization of networks based on seasonality and on spatial span of the region where the stations are distributed. The obtained results are discussed, taking into account the known precipitation patterns of the investigated region. rainfall variability, complex networks, fractals.