If the U.S. plans to develop wind farms across the country they need a better way to predict the wind direction and the duration. NCAR (National Center for Atmospheric Research) is looking to do just that. In December, UCAR signed an agreement with Xcel Energy to develop a wind prediction system for the company’s wind energy farms in Colorado, Minnesota, and Texas. Experimental forecasts may start as early as May. RAL has been discussing the scientific and engineering challenges of wind forecasting with Xcel Energy since last March, according to Bill Mahoney (RAL).
RAL researchers have built a unique modeling system, called Real-Time Four Dimensional Data Assimilation (RTFDDA), that is based on the Weather Research and Forecasting model (WRF). RTFDDA collects diverse weather observations from various platforms (upper-air and surface reports, commercial aircraft reports, mesonet data, and readings from radars, wind profilers, satellites, and other instruments) to provide regional weather analyses, nowcasts, and short-term forecasts.
The research behind RTFDDA sprang from RALs work in modeling weather for the U.S. Army test ranges and national security programs. Researchers enhanced WRF for this purpose to provide small-scale, high-resolution weather data. We designed the model for the Army test ranges, but the technology is readily applicable to wind energy, says lead RTFDDA modeler Yubao Liu.
At present, most wind forecasts rely heavily on statistical forecasting methods, since the numerical weather forecast products available from operational centers are produced with coarse-grid, larger-scale models. The RTFDDA system, however, is designed to provide a birds-eye view of local weather for small areas of special interest, like wind farms, through a multiple level downscaling algorithm. Wind power requires highly accurate wind forecasts, which is very challenging. With this contract, the WRF-based RTFDDA system will be adapted and expanded for individual wind farms, says Tom Warner (RAL).
Perhaps with the new forecasting, the Cape Wind Project and other wind farms will more efficiently generate electricity based on what the wind will do in the future. Maybe while the researches are at it, they could plug their forecasting models into the new Sequioa supercomputer to take advantage of the 1.6 million processors – that should help forecast the wind a little better!
More information: NCAR