Better wind forecasting could spell cheaper energy for consumers deriving power from wind farms.
Researchers at Texas Tech University's National Wind Institute will take part in a $2.5 million government research project to improve short-term wind forecasting capabilities in mountainous terrain to help enhance wind-energy creation.
Brian Ancell, an assistant professor of atmospheric sciences, said the project is led by Vaisala, a company known for manufacturing environmental measuring devices. It is part of the Wind Forecast Improvement Project funded by the U.S. Department of Energy.
“Quite a few wind farms are in areas of mountainous terrain, and forecasts are really bad there for a variety of reasons,” Ancell said. “Our study area is the Columbia River Gorge, which cuts through the Cascade Mountains between Washington State and Oregon. It's one of the hardest places to forecast because of all the variables. At night, you have these things called drainage flows, where cold air sinks down the mountainsides. At other times, the Pacific Ocean cools the western side, and there's hotter air on the eastern side of the Cascades leading to a temperature gradient that ultimately creates strong winds through the gap. It's very tricky.”
When forecasting wind, Ancell said modern weather models have a “resolution” of about a few kilometers and are forced to generalize data about topography and land surface characteristics like soil moisture. While this works fairly well for about 75 percent of the U.S. landmass, mountainous regions have terrains with an added degree of complexity, making the prediction of winds there very difficult.
The funding will allow researchers to use advanced meteorological equipment to analyze specific atmospheric characteristics that affect wind flow patterns in the gorge. Data will be shared with the National Oceanic and Atmospheric Administration (NOAA) and the Department of Energy'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.
This is the second Wind Forecast Improvement Project Ancell and his colleagues have worked on with the Department of Energy. From 2011 to 2014, he and others, led by AWS True Power, explored wind energy resources in the northern Great Plains and western Texas.
For the first time ever, NOAA assimilated wind data from tall turbines and nacelle anemometers into meteorological models for use by the wind industry and other sectors. Integrating these new data into existing models produced forecasts up to 15 percent more accurate at predicting future wind conditions in nearly flat terrain.
“This project could not only enhance wind forecasts but could lead to improved weather prediction models more generally,” Ancell said. “This is because the atmospheric layer near the ground influences a number of important phenomena, such as severe thunderstorms and winter storm precipitation type such as snow or freezing rain.”
Other partners include the University of Notre Dame, the University of Colorado, the National Center for Atmospheric Research, Sharply Focused, Lockheed Martin, Iberdrola Renewables, Southern California Edison, Cowlitz County Public Utility District, Eurus Energy, Bonneville Power Administration and Portland General Electric.