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    A Stochastic Generator of Global Monthly Wind Energy with Tukey g-and-h Autoregressive Processes

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    A29n32.pdf
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    Type
    Article
    Authors
    Jeong, Jaehong
    Yan, Yuan cc
    Castruccio, Stefano
    Genton, Marc G. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Spatio-Temporal Statistics and Data Analysis Group
    Statistics Program
    KAUST Grant Number
    OSR-2015-CRG4-2640
    Date
    2018-10-09
    Print Publication Date
    2019
    Embargo End Date
    2019-10-09
    Permanent link to this record
    http://hdl.handle.net/10754/661059
    
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    Abstract
    Quantifying the uncertainty of wind energy potential from climate models is a time-consuming task and requires considerable computational resources. A statistical model trained on a small set of runs can act as a stochastic approximation of the original climate model, and can assess the uncertainty considerably faster than by resorting to the original climate model for additional runs. While Gaussian models have been widely employed as means to approximate climate simulations, the Gaussianity assumption is not suitable for winds at policy-relevant (i.e., subannual) time scales. We propose a trans-Gaussian model for monthly wind speed that relies on an autoregressive structure with a Tukey g-and-h transformation, a flexible new class that can separately model skewness and tail behavior. This temporal structure is integrated into a multi-step spectral framework that can account for global nonstationarities across land/ocean boundaries, as well as across mountain ranges. Inferences are achieved by balancing memory storage and distributed computation for a big data set of 220 million points. Once the statistical model was fitted using as few as five runs, it can generate surrogates rapidly and efficiently on a simple laptop. Furthermore, it provides uncertainty assessments very close to those obtained from all available climate simulations (40) on a monthly scale.
    Citation
    Jeong, J., Yan, Y., Castruccio, S., & Genton, M. G. (2019). A Stochastic Generator of Global Monthly Wind Energy with Tukey g-and-h Autoregressive Processes. Statistica Sinica. doi:10.5705/ss.202017.0474
    Sponsors
    This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2640.
    Publisher
    Statistica Sinica (Institute of Statistical Science)
    Journal
    Statistica Sinica
    DOI
    10.5705/ss.202017.0474
    arXiv
    1711.03930
    Additional Links
    http://www3.stat.sinica.edu.tw/statistica/J29N3/J29N32/J29N32.html
    ae974a485f413a2113503eed53cd6c53
    10.5705/ss.202017.0474
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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