Validation of uncertainty reduction by using multiple transfer locations for WRF-CFD coupling in numerical wind energy assessments

Abstract

Abstract. This paper describes a method for reducing the uncertainty associated with utilizing fully numerical models for wind resource assessment in the early stages of project development. The presented method is based on a combination of numerical weather predictions (NWP) and microscale downscaling using computational fluid dynamics (CFD) to predict the local wind resource. Numerical modelling is (at least) two orders of magnitude less expensive and time consuming compared to conventional measurements. As a consequence, using numerical methods could enable a wind project developer to evaluate a larger number of potential sites before making an investment. This would likely increase the chances of finding the best available projects. A technique is described, multiple transfer location analysis (MTLA), where several different locations for performing the data transfer between the NWP and the CFD model are evaluated. Independent CFD analyses are conducted for each evaluated data transfer location. As a result, MTLA will generate multiple independent observations of the data transfer between the NWP and the CFD model. This results in a reduced uncertainty in the data transfer, but more importantly MTLA will identify locations where the result of the data transfer deviates from the neighboring locations. This will enable further investigation of the outliers, and give the analyst a possibility to corrected erroneous predictions. The second part is found to reduce the number and magnitude of large deviations in the numerical predictions relative to the reference measurements. The Modern Energy Wind Assessment Model (ME-WAM) with and without MTLA is validated against field measurements. The validation sample for ME-WAM without MTLA consist of 35 observations, and gives a mean bias of −0.10 m/s and a standard deviation of 0.44 m/s. ME-WAM with MTLA is validated against a sample of 45 observations, and the mean bias is found to be +0.05 m/s with a standard deviation of 0.26 m/s. After adjusting for the composition of the two samples with regards to number of sites with complex terrain, the reduction in variability achieved by MTLA is quantified to 11 % of the standard deviation for non-complex sites and 35 % for complex sites.