Compressing an Ensemble with Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature

Handle URI:
http://hdl.handle.net/10754/348634
Title:
Compressing an Ensemble with Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature
Authors:
Castruccio, Stefano; Genton, Marc G. ( 0000-0001-6467-2998 )
Abstract:
One of the main challenges when working with modern climate model ensembles is the increasingly larger size of the data produced, and the consequent difficulty in storing large amounts of spatio-temporally resolved information. Many compression algorithms can be used to mitigate this problem, but since they are designed to compress generic scientific data sets, they do not account for the nature of climate model output and they compress only individual simulations. In this work, we propose a different, statistics-based approach that explicitly accounts for the space-time dependence of the data for annual global three-dimensional temperature fields in an initial condition ensemble. The set of estimated parameters is small (compared to the data size) and can be regarded as a summary of the essential structure of the ensemble output; therefore, it can be used to instantaneously reproduce the temperature fields in an ensemble with a substantial saving in storage and time. The statistical model exploits the gridded geometry of the data and parallelization across processors. It is therefore computationally convenient and allows to fit a non-trivial model to a data set of one billion data points with a covariance matrix comprising of 10^18 entries.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Compressing an Ensemble with Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature 2015:0 Technometrics
Publisher:
Informa UK Limited
Journal:
Technometrics
Issue Date:
2-Apr-2015
DOI:
10.1080/00401706.2015.1027068
Type:
Article
ISSN:
0040-1706; 1537-2723
Is Supplemented By:
Castruccio, S., & Genton, M. G. (2016). Compressing an Ensemble With Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature. Figshare. https://doi.org/10.6084/m9.figshare.1378931; DOI:10.6084/M9.FIGSHARE.1378931; HANDLE:http://hdl.handle.net/10754/624122
Additional Links:
http://www.tandfonline.com/doi/full/10.1080/00401706.2015.1027068
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorCastruccio, Stefanoen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2015-04-08T12:27:41Zen
dc.date.available2015-04-08T12:27:41Zen
dc.date.issued2015-04-02en
dc.identifier.citationCompressing an Ensemble with Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature 2015:0 Technometricsen
dc.identifier.issn0040-1706en
dc.identifier.issn1537-2723en
dc.identifier.doi10.1080/00401706.2015.1027068en
dc.identifier.urihttp://hdl.handle.net/10754/348634en
dc.description.abstractOne of the main challenges when working with modern climate model ensembles is the increasingly larger size of the data produced, and the consequent difficulty in storing large amounts of spatio-temporally resolved information. Many compression algorithms can be used to mitigate this problem, but since they are designed to compress generic scientific data sets, they do not account for the nature of climate model output and they compress only individual simulations. In this work, we propose a different, statistics-based approach that explicitly accounts for the space-time dependence of the data for annual global three-dimensional temperature fields in an initial condition ensemble. The set of estimated parameters is small (compared to the data size) and can be regarded as a summary of the essential structure of the ensemble output; therefore, it can be used to instantaneously reproduce the temperature fields in an ensemble with a substantial saving in storage and time. The statistical model exploits the gridded geometry of the data and parallelization across processors. It is therefore computationally convenient and allows to fit a non-trivial model to a data set of one billion data points with a covariance matrix comprising of 10^18 entries.en
dc.publisherInforma UK Limiteden
dc.relation.urlhttp://www.tandfonline.com/doi/full/10.1080/00401706.2015.1027068en
dc.rightsThis is an Accepted Manuscript of an article published by Taylor & Francis in Technometrics on April 2, 2015, available online: http://wwww.tandfonline.com/10.1080/00401706.2015.1027068.en
dc.titleCompressing an Ensemble with Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperatureen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalTechnometricsen
dc.eprint.versionPost-printen
dc.contributor.institutionSchool of Mathematics & Statistics, Newcastle University, Newcastle Upon Tyne, NE1 7RU United Kingdomen
kaust.authorGenton, Marc G.en
dc.relation.isSupplementedByCastruccio, S., & Genton, M. G. (2016). Compressing an Ensemble With Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature. Figshare. https://doi.org/10.6084/m9.figshare.1378931en
dc.relation.isSupplementedByDOI:10.6084/M9.FIGSHARE.1378931en
dc.relation.isSupplementedByHANDLE:http://hdl.handle.net/10754/624122en
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