American Journal of Engineering and Applied Sciences

Observational Study of Wind Shear in Northeastern Brazil

Fernando R. Martins, André R. Gonçalves and Enio B. Pereira

DOI : 10.3844/ajeassp.2016.484.504

American Journal of Engineering and Applied Sciences

Volume 9, Issue 3

Pages 484-504


The wind energy share is growing fast in Brazil and a better understanding of wind speed vertical profiles is essential for accurate power density estimates. The wind shear is highly variable in space and time, being influenced by surface layer stability. Studies concerning wind vertical profiles may have a significant contribution to the Brazilian energy sector. However, wind observational data are very scarce and most of them were acquired in automated weather stations. This work investigates the wind vertical profile data acquired at a wind mast in a semi-arid region of Brazilian Northeastern region, correlating it to stability conditions of the surface boundary layer, surface roughness and friction velocity. The results indicated that strong winds (>7 m sec-1) overcome the stability effects, allowing a better estimate of roughness length, friction velocity and exponential coefficient. In a second step, the wind speed at 50 m agl. Were estimated from wind measurements at 25 m agl. The wind estimates were compared to observations, showing that exponential and logarithmic approaches were able to simulate wind profile. The exponential approach presented the lowest BIAS for rainy season, but overestimated high wind velocities. Finally, a sensitivity analysis demonstrated how uncertainties on roughness length and exponential coefficient impacts on BIAS deviation and reduce the confidence on wind power density estimates. The results demonstrated the importance in understanding how wind vertical profile is related to atmospheric stability condition in order to get reliable values for the shear parameters from mast observations. It is still more critical if wind database is short-term and when dealing with lower wind speeds.


© 2016 Fernando R. Martins, André R. Gonçalves and Enio B. Pereira. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.