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Seasonal predictability of energy-relevant weather/climate variables
Hye-Mi Kim, Georgia Institute of Technology, Atlanta, GA; and P. J. Webster and J. A. Curry
Probabilistic seasonal forecasts can provide important information on future energy demand and also on the supply of energy from renewables. The forecast skill of the ECMWF System 3.0 seasonal forecast over the continental U.S. is assessed using retrospective hindcast simulations initialized every month with 7 month lead time since 1980. The spatial distribution of the anomaly correlation for surface air temperature shows substantial variability in predictability, with forecasts initialized in July showing the greatest predictability. Useful predictability is found 3 months in advance except for forecasts initialized in Novermber and December. Skill is higher in the western sector of U.S. than the eastern part, especially in the summer season. Forecasts for summer 2009 predicted the high temperature over the North-western part of U.S. in the late July for 3 months in advance. Predictability for wind speed at 10 meter is generally low with a rapid decrease of skill with lead time over the entire U.S. Predictability of precipitation is investigated as well.
Joint Poster Session , New Energy Economy Poster Session
Wednesday, 20 January 2010, 2:30 PM-4:00 PM, Exhibit Hall B2
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