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dc.contributor.authorJeong, Jaehong
dc.contributor.authorCastruccio, Stefano
dc.contributor.authorCrippa, Paola
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2017-12-28T07:32:16Z
dc.date.available2017-12-28T07:32:16Z
dc.date.issued2017-02-07
dc.identifier.urihttp://hdl.handle.net/10754/626552
dc.description.abstractWind has the potential to make a significant contribution to future energy resources. Locating the sources of this renewable energy on a global scale is however extremely challenging, given the difficulty to store very large data sets generated by modern computer models. We propose a statistical model that aims at reproducing the data-generating mechanism of an ensemble of runs via a Stochastic Generator (SG) of global annual wind data. We introduce an evolutionary spectrum approach with spatially varying parameters based on large-scale geographical descriptors such as altitude to better account for different regimes across the Earth's orography. We consider a multi-step conditional likelihood approach to estimate the parameters that explicitly accounts for nonstationary features while also balancing memory storage and distributed computation. We apply the proposed model to more than 18 million points of yearly global wind speed. The proposed SG requires orders of magnitude less storage for generating surrogate ensemble members from wind than does creating additional wind fields from the climate model, even if an effective lossy data compression algorithm is applied to the simulation output.
dc.publisherarXiv
dc.relation.urlhttp://arxiv.org/abs/1702.01995v3
dc.relation.urlhttp://arxiv.org/pdf/1702.01995v3
dc.rightsArchived with thanks to arXiv
dc.titleStatistics-Based Compression of Global Wind Fields
dc.typePreprint
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.eprint.versionPre-print
dc.contributor.institutionUniversity of Notre Dame
dc.identifier.arxivid1702.01995
kaust.personJeong, Jaehong
kaust.personGenton, Marc G.
kaust.grant.numberOSR-2015-CRG4-2640
refterms.dateFOA2018-06-14T09:29:55Z


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