Análise do vento sobre a bacia do Oceano Atlântico Tropical: observações e modelagem dinâmica de mesosescala

Abstract

The economic and social structure of a country is linked mainly to the energy sector. This sector is vulnerable to climate change being responsible for most of the emission of greenhouse gases. From this premise, investments are needed in new alternative energy generation, such as wind power generation in offshore regions in view of its great potential for power generation. However, estimating the wind potential of a region, especially offshore, is difficult because of the low availability of observed wind speed data at these sites. Therefore, the objectives of this study are to evaluate the performance of the RegCM4.2 regional climate model in simulating the annual and seasonal wind speed climatology in the Tropical Atlantic Ocean Basin and to identify the wind potential for energy generation. Firstly, a comparison was performed between the data from the PIRATA (Pilot Research Moored Array in the Tropical Atlantic) program with satellite data from the Blended Sea Winds (BSW) product, in order to verify the BSW quality. Then the model simulations were compared with the BSW data, using root mean square error, BIAS and Pearson (general) and Spearman (monthly) correlation coefficient. Finally, after the validation of the data, the wind potential was calculated by calculating the power density. In relation to the BSW wind speed data validation, it was possible to identify that the BSW was able to represent wind speed data well, presenting a high correlation coefficient and a relatively low error. Similarly, the model presented better simulation of the wind velocity in the regions near the coast than in the more distant regions of the coast, when analyzed seasonally, it was verified that the model has better performance during the winter and spring periods. Regarding the PD (Power Density) results, during the summer and fall, the highest values of PD were concentrated in the coastal regions located to the north of the equator, while during winter and spring the highest values of PD were observed in the northern Brazilian coast. In conclusion, the validation of the BSW data showed low errors, proving to be an efficient database. RegCM4.2 showed simulations consistent with the wind speed distribution obtained by the satellites, and for PD, NEB (Brasilian Northwest) presented the greatest potential for wind power generation.