Multi-objective environmental model evaluation by means of multidimensional kernel density estimators: Efficient and multi-core implementations

We propose an extension to multiple dimensions of the univariate index of agreement between Probability Density Functions (PDFs) used in climate studies. We also provide a set of high-performance programs targeted both to single and multi-core processors. They compute multivariate PDFs by means of k...

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Autores: López Novoa, Unai, Sáenz Aguirre, Jon, Mendiburu Alberro, Alexander, Miguel Alonso, José, Errasti Arrieta, Iñigo, Esnaola Aldanondo, Ganix, Ezcurra Talegón, Agustín, Ibarra Berastegi, Gabriel
Tipo de recurso: artículo
Fecha de publicación:2015
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/65706
Acceso en línea:http://hdl.handle.net/10810/65706
Access Level:acceso abierto
Palabra clave:multivariate kernel density estimation
multidimensional kernel density estimation
multi-core implementation
environmental model evaluation
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spelling Multi-objective environmental model evaluation by means of multidimensional kernel density estimators: Efficient and multi-core implementationsLópez Novoa, UnaiSáenz Aguirre, JonMendiburu Alberro, AlexanderMiguel Alonso, JoséErrasti Arrieta, IñigoEsnaola Aldanondo, GanixEzcurra Talegón, AgustínIbarra Berastegi, Gabrielmultivariate kernel density estimationmultidimensional kernel density estimationmulti-core implementationenvironmental model evaluationWe propose an extension to multiple dimensions of the univariate index of agreement between Probability Density Functions (PDFs) used in climate studies. We also provide a set of high-performance programs targeted both to single and multi-core processors. They compute multivariate PDFs by means of kernels, the optimal bandwidth using smoothed bootstrap and the index of agreement between multidimensional PDFs. Their use is illustrated with two case-studies. The first one assesses the ability of seven global climate models to reproduce the seasonal cycle of zonally averaged temperature. The second case study analyzes the ability of an oceanic reanalysis to reproduce global Sea Surface Temperature and Sea Surface Height. Results show that the proposed methodology is robust to variations in the optimal bandwidth used. The technique is able to process multivariate datasets corresponding to different physical dimensions. The methodology is very sensitive to the existence of a bias in the model with respect to observations.Authors thank financial funding by project CGL2013-45198-C2-1-R (MINECO, National R þ D þ i plan), the SAIOTEK program from the Basque Government (project S-P11UN137). Additional funding from different calls from the University of the Basque Country (UFI 11/55, PPM12/01 and GIU 11/01) has allowed this paper to be finished. This work has also been partially supported by the Saiotek and Research Groups 2013e2018 (IT-609-13) programs (Basque Government),TIN2013- 41272P (Ministry of Science and Technology), COMBIOMED-RD07/ 0067/0003 network in computational bio-medicine (Carlos III Health Institute). U. Lopez-Novoa holds a grant from the Basque Government. J. Miguel-Alonso and A. Mendiburu are members of the HiPEAC European Network of Excellenceinfo:eu-repo/grantAgreement/MINECO/CGL2013-45198-C2-1-R202420242015info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/65706reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoIngléshttps://www.sciencedirect.com/science/article/pii/S1364815214002837info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/© 2014 Elsevier under CC BY-NC-ND licenseoai:addi.ehu.eus:10810/657062026-06-18T09:23:17Z
dc.title.none.fl_str_mv Multi-objective environmental model evaluation by means of multidimensional kernel density estimators: Efficient and multi-core implementations
title Multi-objective environmental model evaluation by means of multidimensional kernel density estimators: Efficient and multi-core implementations
spellingShingle Multi-objective environmental model evaluation by means of multidimensional kernel density estimators: Efficient and multi-core implementations
López Novoa, Unai
multivariate kernel density estimation
multidimensional kernel density estimation
multi-core implementation
environmental model evaluation
title_short Multi-objective environmental model evaluation by means of multidimensional kernel density estimators: Efficient and multi-core implementations
title_full Multi-objective environmental model evaluation by means of multidimensional kernel density estimators: Efficient and multi-core implementations
title_fullStr Multi-objective environmental model evaluation by means of multidimensional kernel density estimators: Efficient and multi-core implementations
title_full_unstemmed Multi-objective environmental model evaluation by means of multidimensional kernel density estimators: Efficient and multi-core implementations
title_sort Multi-objective environmental model evaluation by means of multidimensional kernel density estimators: Efficient and multi-core implementations
dc.creator.none.fl_str_mv López Novoa, Unai
Sáenz Aguirre, Jon
Mendiburu Alberro, Alexander
Miguel Alonso, José
Errasti Arrieta, Iñigo
Esnaola Aldanondo, Ganix
Ezcurra Talegón, Agustín
Ibarra Berastegi, Gabriel
author López Novoa, Unai
author_facet López Novoa, Unai
Sáenz Aguirre, Jon
Mendiburu Alberro, Alexander
Miguel Alonso, José
Errasti Arrieta, Iñigo
Esnaola Aldanondo, Ganix
Ezcurra Talegón, Agustín
Ibarra Berastegi, Gabriel
author_role author
author2 Sáenz Aguirre, Jon
Mendiburu Alberro, Alexander
Miguel Alonso, José
Errasti Arrieta, Iñigo
Esnaola Aldanondo, Ganix
Ezcurra Talegón, Agustín
Ibarra Berastegi, Gabriel
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv multivariate kernel density estimation
multidimensional kernel density estimation
multi-core implementation
environmental model evaluation
topic multivariate kernel density estimation
multidimensional kernel density estimation
multi-core implementation
environmental model evaluation
description We propose an extension to multiple dimensions of the univariate index of agreement between Probability Density Functions (PDFs) used in climate studies. We also provide a set of high-performance programs targeted both to single and multi-core processors. They compute multivariate PDFs by means of kernels, the optimal bandwidth using smoothed bootstrap and the index of agreement between multidimensional PDFs. Their use is illustrated with two case-studies. The first one assesses the ability of seven global climate models to reproduce the seasonal cycle of zonally averaged temperature. The second case study analyzes the ability of an oceanic reanalysis to reproduce global Sea Surface Temperature and Sea Surface Height. Results show that the proposed methodology is robust to variations in the optimal bandwidth used. The technique is able to process multivariate datasets corresponding to different physical dimensions. The methodology is very sensitive to the existence of a bias in the model with respect to observations.
publishDate 2015
dc.date.none.fl_str_mv 2015
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/65706
url http://hdl.handle.net/10810/65706
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.sciencedirect.com/science/article/pii/S1364815214002837
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
© 2014 Elsevier under CC BY-NC-ND license
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
© 2014 Elsevier under CC BY-NC-ND license
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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