摘要：Wind forecasts are widely spread because of the growth in wind power, but also because there are other applications to consider, such as the long-term scenario forecasts regarding the effects of global warming. Overall, there have been big developments in global circulation models (GCM) that inform future scenarios at the large scale, but wind forecast at a local scale is a problem that has not totally been solved. It should be possible to estimate the winds in the near field with a certain accuracy, which is interesting for aspects such as the blowing of incident wind at wind farms, the wind on a dune in movement, or the wind blowing in a harbour. Therefore, a data-driven wind transference equation at a local scale is needed. Among the conclusions, it is worthy to state that the statistical downscaling techniques are suitable for application as a statistical inference at small scales. The aim of this paper is therefore to review the current methods and techniques used and show the different methodologies and their applications in the field. Additional targets will be to identify the advantages, disadvantages, or limitations of the current models at the local scale and propose research to find possible improvements.
