A neural network approach to local downscaling of GCM output for assessing wind power implications of climate change |
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| Authors: | D J Sailor T Hu X Li J N Rosen |
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| Affiliation: | 1. Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan;2. Research Center of Environmental Changes, Academia Sinica, Taipei, Taiwan;3. Key Lab of Aerosol Chemistry & Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi''an, China;4. Department of Atmospheric and Oceanic Sciences and Laboratory for Climate and Ocean-Atmosphere Studies, Peking University, Beijing, China;5. Taiwan Typhoon and Flood Research Institute, National Applies Research Laboratories, Taipei, Taiwan;1. Institute for a Secure and Sustainable Environment, University of Tennessee, Knoxville, 37996, USA;2. Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, 37830, USA;3. Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, 80523, USA;4. Atmospheric and Oceanic Sciences Program, Princeton University, Princeton, 08544, USA;5. Computational Sciences and Engineering Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, 37830, USA |
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| Abstract: | A methodology is presented for downscaling General Circulation Model (GCM) output to predict surface wind speeds at scales of interest in the wind power industry under expected future climatic conditions. The approach involves a combination of Neural Network tools and traditional weather forecasting techniques. A Neural Network transfer function is developed to relate local wind speed observations to large scale GCM predictions of atmospheric properties under current climatic conditions. By assuming the invariability of this transfer function under conditions of doubled atmospheric carbon dioxide, the resulting transfer function is then applied to GCM output for a transient run of the National Center for Atmospheric Research coupled ocean-atmosphere GCM. This methodology is applied to three test sites in regions relevant to the wind power industry—one in Texas and two in California. Changes in daily mean wind speeds at each location are presented and discussed with respect to potential implications for wind power generation. |
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