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Abstract:
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Uncertainty quantification of wind energy potential from climate models can be limited because it requires considerable computational resources and is time-consuming. We propose a stochastic generator that aims at reproducing the data-generating mechanism of climate ensembles for global annual, monthly, and daily wind data. Inferences based on a multi-step conditional likelihood approach are achieved by balancing memory storage and distributed computation for a large data set.
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