Wind profiling to support renewable energy development

Yelena Pichugina (1,2), Robert Banta (2), Alan Brewer(2), Aditya Choukulkar (1,2), Mike Hardesty (1,2), Ann Weickmann (1,2), Scott Sandberg (2), Raul Alvarez (2), Brandy McCarty(1,2), and Richard Marchbanks (1,2)

Abstract
Electricity generation is the largest source of air pollution in the United States producing about 6 billion metric tons of carbon dioxide (CO2) annually. Renewable Energy provides electricity without emissions, displacing CO2 and other greenhouse gases; thus increase of this clean electricity source into the U.S. energy portfolio will help mitigate climate change. The rapidly expanding wind-energy industry requires better characterization of wind flow at turbine heights, understanding of meteorological processes controlling boundary layer, and estimations of wind resources and potential power production. A potentially important tool in providing characteristics and behavior of the Boundary Layer (BL) in response to various atmospheric conditions, stability, seasonality and diurnal cycle is the numerical weather prediction (NWP) model, but without measurements in the turbine-rotor layer for verification, the accuracy and fidelity of model output is unknown. With a rapidly expanding wind-energy industry, it is crucial to have reliable data to understand meteorological processes controlling boundary-layer winds, and to improve forecasts of wind resources. To address the need for wind flow measurements at turbine-rotor heights, Doppler- lidar technologies are an attractive option. These technologies are aimed at providing cost effective data through the layer swept by modern turbine rotor blades at needed temporal and vertical resolutions. This presentation will provide an overview of some collaborative projects between NOAA, CU and DOE involving lidar measurements to support Wind Energy development inland and offshore, including a study of turbine wake dynamics and an evaluation of the uncertainty in prediction of wind flow offshore by the Numerical Weather Prediction models