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

Handle URI:
http://hdl.handle.net/10754/624122
Title:
Supplementary Material for: 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:
<p>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 datasets, 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 nontrivial model to a dataset of 1 billion data points with a covariance matrix comprising of 10<sup>18</sup> entries. Supplementary materials for this article are available online.</p>
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
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
Publisher:
Figshare
Issue Date:
2016
DOI:
10.6084/M9.FIGSHARE.1378931
Type:
Dataset
Is Supplement To:
Compressing an Ensemble with Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature 2015:0 Technometrics; DOI:10.1080/00401706.2015.1027068; HANDLE:http://hdl.handle.net/10754/348634
Appears in Collections:
Datasets; 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.accessioned2017-06-06T07:44:31Z-
dc.date.available2017-06-06T07:44:31Z-
dc.date.created2015-04-14en
dc.date.issued2016en
dc.identifier.citationCastruccio, 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.identifier.doi10.6084/M9.FIGSHARE.1378931en
dc.identifier.urihttp://hdl.handle.net/10754/624122-
dc.description.abstract<p>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 datasets, 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 nontrivial model to a dataset of 1 billion data points with a covariance matrix comprising of 10<sup>18</sup> entries. Supplementary materials for this article are available online.</p>en
dc.format.extent61458600 Bytesen
dc.publisherFigshareen
dc.rightsCC-BYen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/en
dc.subjectGeneticsen
dc.subjectEcologyen
dc.subjectScience Policyen
dc.subjectPlant Biologyen
dc.titleSupplementary Material for: Compressing an Ensemble With Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperatureen
dc.typeDataseten
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
kaust.authorGenton, Marc G.en
dc.type.resourceFileseten
dc.relation.isSupplementToCompressing an Ensemble with Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature 2015:0 Technometricsen
dc.relation.isSupplementToDOI:10.1080/00401706.2015.1027068en
dc.relation.isSupplementToHANDLE:http://hdl.handle.net/10754/348634en
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