Posters

Abstract

Real-world wind measurements are subjected to some degree of system, environmental, and campaign unpredictability that often manifests as gaps in what would ideally be continuous datasets. Although remote sensors have gained prominence in their utility for wind resource assessment campaigns through supplementation or replacement of traditional meteorological masts, they are not without limitations. One of the main sources of missing data from remote sensors are related to challenging environmental conditions, such as those featuring limited aerosol availability, fog, or periods of intense precipitation – all of which have the potential to impact data availability from Sodar and Lidar-derived observations. In collaboration with Vortex, NRG has explored a unique solution to augment remotely sensed data from direct-detect Lidar with that from a large eddy simulation (LES). The goal of our approach is to identify time periods of measurement uncertainty resulting from challenging environmental conditions and reduce said uncertainty through synthesis of Lidar and LES data. Additionally, this investigation has the ability to build a broader foundation for observational network-model interaction to deepen our capacity for more informative insights for wind resource assessment.