2023년 10월 27일 진행

개요:

Quantifying the uncertainty of global wind energy potential using climate models can be limited by computational and time requirements. We propose a statistical model that aims to reproduce 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. Additionally, we discuss a general framework for modeling non-Gaussian stochastic processes by transforming underlying Gaussian processes.