EGU23-10719, updated on 09 Jan 2024
https://doi.org/10.5194/egusphere-egu23-10719
EGU General Assembly 2023
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Seasonal prediction and predictability of wind power potential over North America

Xiaosong Yang1, Thomas Delworth1, Liwei Jia1,2, Nathaniel Johnson1, Feiyu Lu3, and Colleen McHugh4
Xiaosong Yang et al.
  • 1GFDL, Princeton, United States of America (xiaosong.yang@noaa.gov)
  • 2University Corporation for Atmospheric Research, Boulder, CO, United States
  • 3Department of Geosciences, Princeton University, Princeton, NJ, United States
  • 4Science Applications International Corporation, Reston, VA, United States

The capacity factor (CF) is a critical indicator for quantifying wind turbine efficiency, and therefore has been widely used to measure the impact of interannual wind variability on wind energy production. Using the seasonal prediction products from GFDL’s Seamless System for Predicton and Earth System (SPEAR), we assess the seasonal prediction skill of CF over North America. SPEAR shows high skill in predicting winter CF over the western United States. The seasonal wind speed and CF variations associated with large-scale circulation anomalies are examined to understand the predictability mechanism of CF. The source of the skillful seasonal CF prediction can be attributed to year-to-year variations of ENSO and North Pacific Oscillation, which produce large-scale anomalous wind patterns over North America. The skillful seasonal prediction of CF is potentially beneficial to various stakeholders in the energy sector, including wind energy production, trading, and transmission.  

How to cite: Yang, X., Delworth, T., Jia, L., Johnson, N., Lu, F., and McHugh, C.: Seasonal prediction and predictability of wind power potential over North America, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10719, https://doi.org/10.5194/egusphere-egu23-10719, 2023.