17:15 〜 18:45
[ACG31-P03] Skillful Seasonal Prediction of Wind Energy Resources in the contiguous United States
キーワード：Seasonal prediction, Wind energy, ENSO
In the United States, with the steady increase of wind energy production over the last 20 years, the wind-powered electricity has reached comparable amount of energy supply in comparison with both coal-fired and nuclear electricity generations. One challenge that remains, however, is that wind power availability is highly variable with atmospheric variability associated with seasonal-interannual climate oscillations. Therefore, there is a growing need of skillful seasonal wind energy prediction for energy system planning and operation.
Here we use the seasonal prediction products from GFDL’s Seamless System for Prediction and Earth System (SPEAR) for assessing the seasonal prediction skill of wind power over the contiguous United States. SPEAR shows high skill in predicting winter energy over the U.S. Great Plains during the energy peak seasons multiple months in advance. An advanced predictability analysis is applied to show that the dominant source of the skillful seasonal wind energy prediction can be attributed to year-to-year variations of ENSO, which alters large-scale anomalous wind patterns over the United States. As a showcase during the energy peak season in the southern Great Plains, in which more than half of total U.S. wind capacity locates, the seasonal wind energy outlook produced by the model can capture the strong year-to-year changes of wind resources with high correlation skill multiple months in advance. Therefore, SPEAR’s seasonal wind energy prediction capability offers potential benefits for optimizing wind energy utilization during the energy peak seasons.
Here we use the seasonal prediction products from GFDL’s Seamless System for Prediction and Earth System (SPEAR) for assessing the seasonal prediction skill of wind power over the contiguous United States. SPEAR shows high skill in predicting winter energy over the U.S. Great Plains during the energy peak seasons multiple months in advance. An advanced predictability analysis is applied to show that the dominant source of the skillful seasonal wind energy prediction can be attributed to year-to-year variations of ENSO, which alters large-scale anomalous wind patterns over the United States. As a showcase during the energy peak season in the southern Great Plains, in which more than half of total U.S. wind capacity locates, the seasonal wind energy outlook produced by the model can capture the strong year-to-year changes of wind resources with high correlation skill multiple months in advance. Therefore, SPEAR’s seasonal wind energy prediction capability offers potential benefits for optimizing wind energy utilization during the energy peak seasons.