Multi-objective environmental model evaluation by means of multidimensional kernel density estimators: Efficient and multi-core implementations. (January 2015)

• Record Type: Journal Article
• Authors: Lopez-Novoa, Unai; Sáenz, Jon; Mendiburu, Alexander; Miguel-Alonso, Jose; Errasti, Iñigo; Esnaola, Ganix; Ezcurra, Agustín; Ibarra-Berastegi, Gabriel
• Is Part Of: Environmental modelling & software. Volume 63(2015:Jan.)
• Page Start: 123
• Page End: 136
• Publication Date: 2015-01
• Subjects: Multivariate kernel density estimation -- Multidimensional kernel density estimation -- Multi-core implementation -- Environmental model evaluation; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2014.09.019
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: The performance index based on the area under two PDFs is extended to several dimensions. The evaluation of the performance of models can be done for several variables, resulting in a single skill score. A fast and parallel implementation that allows to apply the method with highly dimensional problems is presented. The method is illustrated with two case-studies. The sensitivity of the results to the bias between models and observations or the bandwidth is presented.