Wind Energy Prediction
NCAR is developing and applying several weather model and statistical processing technologies to improve the prediction of wind for the wind energy industry. Wind is among the most difficult weather variables to forecast. A number of factors can affect it, such as topography, ground cover, temperature inversions, and even the state of the vegetation at ans around the wind farms. NCAR's task is especially challenging because researchers will try to pinpoint wind direction and strength in the vicinity of individual wind turbines, which are generally about 200 to 400 feet above the ground and arrayed in wind farms.
To generate the wind energy forecasts, NCAR will incorporate observations of current atmospheric conditions from a variety of sources, including satellites, aircraft, weather radars, ground-based weather stations, and even sensors on the wind turbines. The information will be fed into three powerful NCAR-based tools:
- The Weather Research and Forecasting (WRF) computer model, which generates finely detailed simulations of future atmospheric conditions
- The Real–Time Four–Dimensional Data Assimilation System (RTFDDA), which continuously updates the simulations with the most recent observations
- The Dynamic Integrated Forecast System (DICast®), which statistically optimizes the output based on recent performance.
The wind prediction system will be based on the WRF–RTFDDA, an NCAR technology based on a computer model of the weather which has been developed and refined over the last decade, specifically for predicting these kinds of fine–scale local impacts of weather. Combining this modeling technology with studies that help us to better understand atmospheric processes at wind farms will lead to more accurate forecasts.
The wind predictions will be translated into wind energy predictions by integrating individual wind turbine data (energy generated, wind speed, wind direction, propeller revolutions per minute, etc.) with the predicted wind speeds. Statistical relationships will be developed for various weather scenarios and applied to the system in real-time.
A real–time verification system will also be developed to provide performance information to end users and to provide feedback to researchers who will use the information to refine the system.