Statistics-Based Compression of Global Wind Fields

Handle URI:
http://hdl.handle.net/10754/626552
Title:
Statistics-Based Compression of Global Wind Fields
Authors:
Jeong, Jaehong; Castruccio, Stefano; Crippa, Paola; Genton, Marc G. ( 0000-0001-6467-2998 )
Abstract:
Wind 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program
Publisher:
arXiv
KAUST Grant Number:
OSR-2015-CRG4-2640
Issue Date:
7-Feb-2017
ARXIV:
arXiv:1702.01995
Type:
Preprint
Additional Links:
http://arxiv.org/abs/1702.01995v3; http://arxiv.org/pdf/1702.01995v3
Appears in Collections:
Other/General Submission; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorJeong, Jaehongen
dc.contributor.authorCastruccio, Stefanoen
dc.contributor.authorCrippa, Paolaen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2017-12-28T07:32:16Z-
dc.date.available2017-12-28T07:32:16Z-
dc.date.issued2017-02-07en
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.en
dc.publisherarXiven
dc.relation.urlhttp://arxiv.org/abs/1702.01995v3en
dc.relation.urlhttp://arxiv.org/pdf/1702.01995v3en
dc.rightsArchived with thanks to arXiven
dc.titleStatistics-Based Compression of Global Wind Fieldsen
dc.typePreprinten
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.eprint.versionPre-printen
dc.contributor.institutionUniversity of Notre Dameen
dc.identifier.arxividarXiv:1702.01995en
kaust.authorJeong, Jaehongen
kaust.authorGenton, Marc G.en
kaust.grant.numberOSR-2015-CRG4-2640en
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