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dc.contributor.authorTagle, Felipe
dc.contributor.authorCastruccio, Stefano
dc.contributor.authorCrippa, Paola
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2018-12-19T13:19:13Z
dc.date.available2018-12-19T13:19:13Z
dc.date.issued2018-12-03
dc.identifier.citationTagle F, Castruccio S, Crippa P, Genton MG (2018) A Non-Gaussian Spatio-Temporal Model for Daily Wind Speeds Based on a Multi-Variate Skew-\n t\n Distribution. Journal of Time Series Analysis. Available: http://dx.doi.org/10.1111/jtsa.12437.
dc.identifier.issn0143-9782
dc.identifier.issn1467-9892
dc.identifier.doi10.1111/jtsa.12437
dc.identifier.urihttp://hdl.handle.net/10754/630312
dc.description.abstractFacing increasing domestic energy consumption from population growth and industrialization, Saudi Arabia is aiming to reduce its reliance on fossil fuels and to broaden its energy mix by expanding investment in renewable energy sources, including wind energy. A preliminary task in the development of wind energy infrastructure is the assessment of wind energy potential, a key aspect of which is the characterization of its spatio-temporal behavior. In this study we examine the impact of internal climate variability on seasonal wind power density fluctuations over Saudi Arabia using 30 simulations from the Large Ensemble Project (LENS) developed at the National Center for Atmospheric Research. Furthermore, a spatio-temporal model for daily wind speed is proposed with neighbor-based cross-temporal dependence, and a multi-variate skew-t distribution to capture the spatial patterns of higher-order moments. The model can be used to generate synthetic time series over the entire spatial domain that adequately reproduce the internal variability of the LENS dataset.
dc.description.sponsorshipThis publication is based on work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2640.
dc.publisherWiley
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/full/10.1111/jtsa.12437
dc.rightsArchived with thanks to Journal of Time Series Analysis
dc.subjectDaily wind speed
dc.subjectinternal climate variability
dc.subjectskew-t distribution
dc.subjectwind power density
dc.titleA Non-Gaussian Spatio-Temporal Model for Daily Wind Speeds Based on a Multi-Variate Skew-\n t\n Distribution
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalJournal of Time Series Analysis
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, , United States
dc.contributor.institutionDepartment of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, IN, , United States
kaust.personGenton, Marc G.
kaust.grant.numberOSR-2015-CRG4-2640
dc.date.accepted2019
refterms.dateFOA2018-12-19T13:22:26Z


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