Long-range correlations of the wind speed in a northeast region of Brazil

Abstract

The objective of this work is to analyze the scaling behavior of wind speed in the region of the state of Bahia, northeastern Brazil, in search of long-range correlations and other information about the crossover phenomena. Thus, data from 41 automatic surface stations were used for a period of five years—between 2015 and 2020—for onshore reading. For offshore readings, data from a surface station located in the Abrolhos Archipelago were used. To achieve this goal, the DFA (detrended fluctuation analysis) technique was used in the analysis of measured data at the stations, along with numerical simulations using the WRF (weather research and forecasting) mesoscale model. The results of the analysis of hourly average wind speed from the measured and simulated data show the existence of scale behavior with the appearance, in most cases, of a double crossover—onshore and offshore. This suggests the phenomenon's dependence on the time period of the analyzed data, and also on the geographic location, showing a strong correlation with the Atlantic and Pacific oscillations (La Niña and El Niño), indicating the influence of local, mesoscale, and macroscale effects in the region of study. For the offshore case, the measured data and simulations presented a subdiffusive behavior (α≥1) before the first crossover, and persistence (0.5<α<1) for the other two scales. For the onshore case, the results showed different behaviors, with some stations and simulations showing subdiffusive behavior and others showing persistence before and after the first and second crossovers, but most showed persistence between the first and second crossovers. The results of the analysis of daily averages of station data and simulations confirm the existence of only one crossover as a reflection of global effects (macroscale), since local effects have a daily cycle (mesoscale). The DFA method does not detect fluctuations in the measured and simulated data at all locations—especially in regions with extreme slopes, or in lowland plains—as double crossover, and in a few cases even simple crossovers are not recorded. From a practical point of view, unlike other methods to detect wind persistence, the methodology considered in this study reveal the multifractality in the wind speed data, showing that fluctuations in wind speed can be dominated by atmospheric phenomena governed by a local or regional meteorological system, while fluctuations on long-range time scales in some locations can be influenced by global weather patterns, providing new perspectives about the best locations for wind energy.