Recalibration of a wind atlas using local wind measurements

Abstract

Abstract Wind atlases are very useful for getting a first insight of the wind resource at a given site before local measurement is available. The more accurate a wind atlas, the lower the uncertainty in the wind resource and the lower the risk of investing into a new wind project. A new method for improving the accuracy of wind atlases is presented in this article. Using kriging, a statistical technique applied to georeferenced data, existing atlases are combined with past wind measurement campaigns in order to reduce the error in wind speed prediction on a given territory. This methodology being only based on existing datasets, it is quite inexpensive compared to the costs required for building a new atlas from scratch. It is validated considering two different wind atlases, the Global Wind Atlas (GWA) and the AROME-based atlas, which are recalibrated thanks to 55 local wind measurement campaigns available in the north-eastern region of France. The results show a reduction in the root-mean-square error (RMSE) in the wind speed prediction for both atlases. This reduction is particularly significant for the GWA whose initial bias is very strong: its RMSE is decreased by 68%. For the AROME-based atlas, the reduction of RMSE is lower (∼ 28%) but still noticeable. Even with a low number of wind measurement campaigns used for the recalibration, there are still benefits to applying the kriging process. For now, this methodology has only been tested and validated in simple terrain on the wind speed variable, and further research would be needed in order to investigate how it could also be extended to complex terrain.