{"data":{"id":"10.6084/m9.figshare.1378931","type":"dois","attributes":{"doi":"10.6084/m9.figshare.1378931","prefix":"10.6084","suffix":"m9.figshare.1378931","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Castruccio, Stefano","nameType":"Personal","givenName":"Stefano","familyName":"Castruccio","affiliation":[],"nameIdentifiers":[]},{"name":"Genton, Marc G.","nameType":"Personal","givenName":"Marc G.","familyName":"Genton","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Compressing an Ensemble With Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature"}],"publisher":"Taylor \u0026 Francis","container":{},"publicationYear":2016,"subjects":[{"subject":"Genetics"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Biological sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Ecology"},{"subject":"69999 Biological Sciences not elsewhere classified","subjectScheme":"FOR"},{"subject":"80699 Information Systems not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"19999 Mathematical Sciences not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Mathematics","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Science Policy"},{"subject":"Plant Biology"}],"contributors":[],"dates":[{"date":"2015-04-14","dateType":"Created"},{"date":"2016-07-08","dateType":"Updated"},{"date":"2016","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1080/00401706.2015.1027068","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["61458600 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"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\u003csup\u003e18\u003c/sup\u003e entries. Supplementary materials for this article are available online.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://tandf.figshare.com/articles/dataset/Compressing_an_Ensemble_with_Statistical_Models_An_Algorithm_for_Global_3D_Spatio_Temporal_Temperature/1378931","contentUrl":null,"metadataVersion":3,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"sta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