Complex terrain and synoptic scale meteorological processes are contributors to variability in the wind energy resource. The effect of terrain on the local wind flow is investigated using data from nacelle-mounted anemometers and Doppler lidar acquired during the second W
roject (WFIP-2). Two west to east transects approximately 10 km in distance are identified in a region with variable terrain, heavily utilized by turbines for wind power production. Wind speed and direction measurements are available from 55 nacelle-mounted anemometers and a Leosphere WindCube 200S Doppler lidar located near the Wasco State Airport, OR. Under westerly flow conditions, nightly mean wind speeds are calculated for the time period January 2016 – October 2016. Groups of nacelle-mounted anemometers are classified by similar hub-heights and longitudinal position within each of the two transects. Using orthogonal regression analysis, only anemometer measurements of wind speed with statistically similar wind flow (nearly linear relationship and r2
> 0.95 between instruments) are included in the group. A terrain influence on the pattern of westerly, night-time wind speed is persistently observed over each of the two transects independent of the magnitude of the mean wind speed, power production, or season.
Lidar observations of hub height winds were used to evaluate the reliability of the nacelle-mounted anemometer measurements. Data from four turbines, within the field of view of the lidar, were compared to the lidar measurements through orthogonal regression analysis. These statistical results show good agreement and indicate the nacelle-measured wind speeds are representative for analysis. Analysis of the nacelle-mounted anemometer measurements of westerly winds for the each night of the year shows clear indication of a terrain effect on mean wind speed regardless of the months or season. In addition to, the terrain effect seems to dominate any wind farm induced effects. Once the terrain influence is properly quantified, it can be removed from the wind measurements to investigate the effects of wind farms. This analysis will allow developing improved wind farm parameterizations for use in the numerical weather prediction models.
*The Wind Forecast Improvement Project is a public-private partnership. This work is supported by U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy and the National Oceanic and Atmospheric Administration. Data were accessed using the U.S. Department of Energy Wind Energy Technologies Offices Atmosphere to Electrons (A2E) Data Archive and Portal (DAP)