Correlações de longo alcance em séries temporais da velocidade e da direção do vento

Abstract

The study of climate has great economic end environmental importance, given that a single large and unexpected variation of a climatic element may devastate plantations or cities, and thus affect the economy of a region and life of the inhabitants. Climate can be influenced by diverse factors, such as latitude, altitude, air mass, proximity to sea, sea currents, terrain topology, vegetation, etc. The most important climate elements are temperature, humidity, atmospheric pressure, solar radiation, precipitation and wind. The wind is generated by atmospheric air mass movement, and may influence various phenomena such as soil erosion, pollutant dispersal, transport of pollen and seeds, propagation of diseases, as well as generation of eolic energy. Surface wind velocity is natural example of the phenomenon of turbulence, which represents a stochastic process characterized by temporal and spatial scale invariance. In this work we study long range correlations in temporal series of wind speed and direction registered at four meteorological stations in the cities of Arcoverde, Cabrobro, Garanhuns and Petrolina, in the state of Pernambuco, Brazil. To this end we apply Detrended Fluctuation Analysis (DFA) which was developed for quantification of long range correlations in non-stationary temporal series. We analyze the original wind speed series together with volatility (absolute value of increments) of the wind direction. All the analyzed series exhibit persistent long range correlations with the scale exponent above 0.5. In all cases the exponent values were found to be lower for wind direction then those for wind speed, indicating weaker persistence. No correlation was detected between the exponent values and the geographic parameters: longitutde, latitude and altitude of the station. The results of these analyses contribute to a better understanding of the nature of stochastic processes governing wind dynamics, necessary for development of more realistic theoretical and computational models as a base for modeling diverse phenomena influenced by climatic conditions.