Journal Article

Wind-Climate Estimation Based on Mesoscale and Microscale Modeling

Badger, Jake, Frank, Helmut, Hahmann, Andrea N., Giebel, Gregor

2014

Overview

This paper demonstrates that a statistical–dynamical method can be used to accurately estimate the wind climate at a wind farm site. In particular, postprocessing of mesoscale model output allows an efficient calculation of the local wind climate required for wind resource estimation at a wind turbine site. The method is divided into two parts: 1) preprocessing, in which the configurations for the mesoscale model simulations are determined, and 2) postprocessing, in which the data from the mesoscale simulations are prepared for wind energy application. Results from idealized mesoscale modeling experiments for a challenging wind farm site in northern Spain are presented to support the preprocessing method. Comparisons of modeling results with measurements from the same wind farm site are presented to support the postprocessing method. The crucial element in postprocessing is the bridging of mesoscale modeling data to microscale modeling input data, via a so-called generalization method. With this method, very high-resolution wind resource mapping can be achieved.

• Publisher: American Meteorological Society
• Subject: Climate / Climate models / Cluster analysis / Datasets / Froude number / Geostrophic wind / Local winds / Mathematical models / Mesoscale phenomena / Meteorology / Methods / Modeling / Modelling / Preprocessing / Sea level / Simulation / Studies / Topography / Turbines / Wind / Wind direction / Wind farms / Wind power / Wind turbines / Wind velocity