As scientists look to the future of energy production, renewable resources remain one of the most important areas of study. By their very nature, resources like coal and oil will eventually run out. There are also extreme environmental impacts involved with every step of production when using fossil energy sources. Researchers from Cornell University recently published two companion papers that offer valuable insights into estimating future energy production from existing wind farms in North America.
Authors of the paper, Sara C. Pryor, a professor of earth and atmospheric sciences, and Jacob Coburn, a postdoctoral associate, both from Cornell University, utilized Indiana University’s ACCESS resource, Jetstream2, for their project.
Before launching into their methodology and research, the authors recognize the critical role of renewable energy, particularly wind power, in addressing climate change and promoting sustainability. “Wind energy is playing an increasingly important role in low-carbon-emission electricity generation.”1 The authors go on to explain how climate change may affect wind energy production. “[Wind energy] is a ‘weather dependent’ renewable energy source, and thus changes in the global atmosphere may cause changes in regional wind power production (PP) potential.”
This kind of research is beneficial to a cross-section of interested parties. Understanding how wind energy production may evolve in the future is essential for those planning energy production infrastructure for governments and industries deciding how best to invest. For example, if you’re an environmental engineer planning wind turbine placement in a field, you’d want to know if wind currents would change drastically over the next 10 years before deciding where to build something so permanent.
To study wind energy production changes, the research team employed two different methods: dynamical downscaling and statistical downscaling. Dynamical downscaling involves taking large-scale climate model data and refining it to provide detailed and localized predictions, while statistical downscaling uses historical data and statistical techniques to estimate future energy production.
Results of these studies indicate that future wind energy production in North America is indeed influenced by climate patterns, such as changes in wind speed and direction. By analyzing historical data, combining it with climate model simulations, and employing statistical methods, the researchers were able to project potential shifts in wind energy production at specific locations.
The work studying these changes isn’t finished, however. Both articles emphasized the importance of ongoing monitoring, refinement and updating of these projections as climate models improve and more data becomes available.
If you have a project that could benefit from access to supercomputing resources like the ones used in this story, you can visit the allocations page to get started with ACCESS. Click one of the links below to read the full study. Both papers were published in The Journal of Applied Meteorology and Climatology.
Footnotes
- Pryor, S. C., Coburn, J. J., Barthelmie, R. J., & Shepherd, T. J. (2023). Projecting Future Energy Production from Operating Wind Farms in North America. Part I: Dynamical Downscaling, Journal of Applied Meteorology and Climatology, 62(1), 63-80. doi: https://doi.org/10.1175/JAMC-D-22-0044.1
Project Details
Institution: Pervasive Technology Institute at Indiana University
University: Cornell University
Funding Agency: This work is supported by the U.S. Department of Energy (DE-SC0016605) and used computing resources from the National Science Foundation Extreme Science and Engineering Discovery Environment (XSEDE)
Allocation Number: TG-ATM170024
The science story featured here, allocated through August 31, 2022, was enabled through Extreme Science and Engineering Discovery Environment (XSEDE) and supported by National Science Foundation grant number #1548562. Projects allocated September 1, 2022 and beyond are enabled by the ACCESS program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.