Description of the Probabilistic Wind Atlas Methodology

A new ensemble method is explored for estimating the uncertainty of the wind resource within Weather Research and Forecasting (WRF) model simulations. The output of the ensemble simulations is processed to create a "map" showing the uncertainty in the wind resource estimate at each geographic location. This new method is demonstrated by performing a collection of 9 different WRF model simulations using combinations of 3 planetary boundary layer schemes, 2 simulation re-initialization strategies, and 2 methods for initializing the land surface state. The results of the simulations are validated against data from 10 meteorological masts in South Africa, part of the Wind Atlas of South Africa (WASA) project, where a long-term set of high-quality observations exist. The results of the ensemble simulations are encouraging, but further analysis is needed to quantify their utility. A key disadvantage of the ensemble simulation strategy employed herein, is that some members may tend to be highly similar to others, leading to overconfidence in the mean and spread of the simulations. Such overconfidence yields misleading estimates of the accuracy, value, and uncertainty of the wind resource. The results show that we need to develop a method to determine whether any given set of ensemble simulations are statistically distinct (i.e., each simulation provides unique information). Statistically similar ensemble members provide redundant information, falsely increase confidence, and thus should be removed from the set. The next step is also to identify potential statistical techniques (e.g., machine learning) to optimally combine the results from the various ensemble members into a single wind resource map. We further describe a set of WRF sensitivity simulations for five domains in Europe. These simulations were carried out to determine a few fundamental settings and strategies that are known to have the largest impact on the wind resource. The results of the simulations show consistent systematic differences among the simulations in the various domains. This report also introduces and explores the applicability of the Analog Ensemble (AnEn) approach, another method to generate uncertainty information of the wind resource. Test results show that the AnEn is well-suited for estimating the long-term wind resource at target sites based on short-term measurements and historical reanalysis model data. A further benefit is that the AnEn technique adds uncertainty information to the long-term wind resource. Preliminary tests with mesoscale model data instead of observations show that the AnEn method could be applied to extend the high-resolution mesoscale wind atlas data set and to provide uncertainty information for the wind atlas data