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A 2013 study conducted for the U.S. Department of Energy (DOE) by the National Oceanic and Atmospheric Administration (NOAA), AWS Truepower, and WindLogics in the Great Plains and Western Texas, demonstrated that wind power forecasts can be improved substantially using data collected from tall towers, remote sensors, and other devices, and incorporated into improved forecasting models in near real time. The study also showed that the improvement increases with the number of observations as well as the area over which they are spread.
To expand on this study and further improve the forecasting models, DOE recently awarded $2.5 million to Vaisala of Louisville, Colorado, to investigate the atmospheric processes that generate wind in mountain-valley regions. Because of the complexity of terrain in these regions and varying degrees of soil moisture and surface temperatures, predicting specific wind conditions presents a major challenge to utility operators looking to optimize the performance of wind farms in these areas. This funding will allow Vaisala and its partners to use advanced meteorological equipment such as in situ and remote sensors to analyze specific physical environmental characteristics that affect wind flow patterns in the study area: the complex terrain of the Columbia River Gorge region of Washington and Oregon. By examining the physics of atmospheric phenomena that impact wind farms in mountain-valley regions, the study will improve the precision and reliability of short-term wind forecasting models, and consequently, increase wind farms’ power production.
Data collected during the project will be shared in near real time with NOAA and DOE’s national laboratories, and will be used to develop improved atmospheric simulations for the Weather Research and Forecasting model, a widely used weather prediction system. These new wind measurements and simulations will also be incorporated into NOAA’s numerical weather prediction models to improve short-term wind forecasts in complex terrain.
Learn more about DOE’s Wind Forecast Improvement Project.