Reducing storage of global wind ensembles with stochastic generators

Handle URI:
http://hdl.handle.net/10754/627535
Title:
Reducing storage of global wind ensembles with stochastic generators
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; Statistics Program; Cemse Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, , Saudi Arabia
Citation:
Jeong J, Castruccio S, Crippa P, Genton MG (2018) Reducing storage of global wind ensembles with stochastic generators. The Annals of Applied Statistics 12: 490–509. Available: http://dx.doi.org/10.1214/17-AOAS1105.
Publisher:
Institute of Mathematical Statistics
Journal:
The Annals of Applied Statistics
KAUST Grant Number:
OSR-2015-CRG4-2640
Issue Date:
9-Mar-2018
DOI:
10.1214/17-AOAS1105
Type:
Article
ISSN:
1932-6157
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.
Additional Links:
https://projecteuclid.org/euclid.aoas/1520564481
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Statistics Program

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.accessioned2018-04-16T11:27:44Z-
dc.date.available2018-04-16T11:27:44Z-
dc.date.issued2018-03-09en
dc.identifier.citationJeong J, Castruccio S, Crippa P, Genton MG (2018) Reducing storage of global wind ensembles with stochastic generators. The Annals of Applied Statistics 12: 490–509. Available: http://dx.doi.org/10.1214/17-AOAS1105.en
dc.identifier.issn1932-6157en
dc.identifier.doi10.1214/17-AOAS1105en
dc.identifier.urihttp://hdl.handle.net/10754/627535-
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.description.sponsorshipThis 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.en
dc.publisherInstitute of Mathematical Statisticsen
dc.relation.urlhttps://projecteuclid.org/euclid.aoas/1520564481en
dc.rightsArchived with thanks to The Annals of Applied Statisticsen
dc.subjectAxial symmetryen
dc.subjectNonstationarityen
dc.subjectSpatio-temporal covariance modelen
dc.subjectSphereen
dc.subjectStochastic generatoren
dc.subjectSurface wind speeden
dc.titleReducing storage of global wind ensembles with stochastic generatorsen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentStatistics Programen
dc.contributor.departmentCemse Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, , Saudi Arabiaen
dc.identifier.journalThe Annals of Applied Statisticsen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDepartment of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, 46556, , United Statesen
dc.contributor.institutionDepartment of Civil & Environmental Engineering & Earth Science, University of Notre Dame, Notre Dame, IN, 46556, , United Statesen
kaust.authorJeong, Jaehongen
kaust.authorGenton, Marc G.en
kaust.grant.numberOSR-2015-CRG4-2640en
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