dc.contributor.author | López Novoa, Unai | |
dc.contributor.author | Sáenz Aguirre, Jon ![ORCID](/themes/Mirage2//images/orcid_16x16.png) | |
dc.contributor.author | Mendiburu Alberro, Alexander | |
dc.contributor.author | Miguel Alonso, José | |
dc.contributor.author | Errasti Arrieta, Iñigo | |
dc.contributor.author | Esnaola Aldanondo, Ganix | |
dc.contributor.author | Ezcurra Talegón, Agustín | |
dc.contributor.author | Ibarra Berastegi, Gabriel | |
dc.date.accessioned | 2024-02-08T11:42:24Z | |
dc.date.available | 2024-02-08T11:42:24Z | |
dc.date.issued | 2015-01-01 | |
dc.identifier.citation | Environmental Modelling & Software 63 : 123-136 (2015) | |
dc.identifier.issn | 1364-8152 | |
dc.identifier.issn | 1873-6726 | |
dc.identifier.uri | http://hdl.handle.net/10810/65706 | |
dc.description.abstract | 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. | es_ES |
dc.description.sponsorship | 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 Excellence | |
dc.description.sponsorship | info:eu-repo/grantAgreement/MINECO/CGL2013-45198-C2-1-R | |
dc.language.iso | eng | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | multivariate kernel density estimation | es_ES |
dc.subject | multidimensional kernel density estimation | es_ES |
dc.subject | multi-core implementation | es_ES |
dc.subject | environmental model evaluation | es_ES |
dc.title | Multi-objective environmental model evaluation by means of multidimensional kernel density estimators: Efficient and multi-core implementations | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2014 Elsevier under CC BY-NC-ND license | |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S1364815214002837 | |
dc.identifier.doi | 10.1016/j.envsoft.2014.09.019 | |
dc.departamentoes | Arquitectura y Tecnología de Computadores | es_ES |
dc.departamentoeu | Konputagailuen Arkitektura eta Teknologia | es_ES |