U.S. Department Of Energy Allocates Funding To Improve Wind Forecasting

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The U.S. Department of Energy (DOE) will fund a new research project conducted by Vaisala to analyze the atmospheric processes that generate wind in mountain-valley regions.

According to the DOE, the results from the $2.5 million initiative will be used to improve the wind industry's weather models for short-term wind forecasts, especially for those issued less than 15 hours in advance. With access to better forecasts, wind energy plant operators and industry professionals can ensure wind turbines operate closer to maximum capacity, leading to lower energy costs for consumers, notes the DOE.

Due to the complexity of terrain in mountain-valley regions as well as 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 turbines in these areas.

This funding will allow Vaisala and its partners to use advanced meteorological equipment to analyze specific environmental characteristics that affect wind flow patterns in the Columbia River Gorge region of Washington and Oregon.

Data collected during the project will be shared in near real-time with the National Oceanic and Atmospheric Administration (NOAA) and the Energy Department's national laboratories. This data 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.

The DOE notes that this latest effort builds on the Energy Department's Wind Forecast Improvement Project, which previously explored wind energy resources in the northern Great Plains and western Texas.

The DOE and NOAA used radar and LIDAR to assimilate wind data from tall turbines and nacelle anemometers into meteorological observations. Integrating the new data into existing models produced forecasts that were up to 15% more accurate at predicting future wind conditions in nearly flat terrain.

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