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A0233
Title: Global wind modeling with transformed Gaussian processes Authors:  Jaehong Jeong - Hanyang University (Korea, South) [presenting]
Abstract: 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. In the end, we discuss a general framework for modeling non-Gaussian multivariate stochastic processes by transforming underlying multivariate Gaussian processes.