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dc.contributor.authorCobos Budia, Manuel 
dc.contributor.authorOtiñar Morillas, Pedro 
dc.contributor.authorMagaña Redondo, Pedro Javier 
dc.contributor.authorBaquerizo Azofra, Asunción 
dc.date.accessioned2022-05-16T06:23:03Z
dc.date.available2022-05-16T06:23:03Z
dc.date.issued2022-02-24
dc.identifier.citationM. Cobos... [et al.]. MarineTools.temporal: A Python package to simulate Earth and environmental time series, Environmental Modelling & Software, Volume 150, 2022, 105359, ISSN 1364-8152, [https://doi.org/10.1016/j.envsoft.2022.105359]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/74837
dc.descriptionThis work was performed within the framework of the following projects: (1) AQUACLEW, which is part of ERA4CS, an ERA-NET initiative by JPI Climate, and funded by FORMAS (SE) , DLR (DE) , BMWFW (AT) , IFD (DK) , MINECO (ES) , ANR (FR) with co-funding by the European Commission [Grant 690462] and (2) Flooding and erosion works in coastal areas of Andalusia under a climate change scenario, funded by the Ministry of Agriculture, Livestock, Fisheries and Sus-tainable Development of the Junta de Andalucia [Contrat No. CONTR 2018 66984] . Part of this study has been conducted using E.U. Copernicus Marine Service Information. Funding for open access charge: Universidad de Granada/CBUA.es_ES
dc.description.abstractThe assessment of the uncertainty about the evolution of complex processes usually requires different realizations consisting of multivariate temporal signals of environmental data. However, it is common to have only one observational set. MarineTools.temporal is an open-source Python package for the non-stationary parametric statistical analysis of vector random processes suitable for environmental and Earth modelling. It takes a single timeseries of observations and allows the simulation of many time series with the same probabilistic behavior. The software generalizes the use of piecewise and compound distributions with any number of arbitrary continuous distributions. The code contains, among others, multi-model negative log-likely functions, wrappednormal distributions, and generalized Fourier timeseries expansion. Its programming philosophy significantly improves the computing time and makes it compatible with future extensions of scipy.stats. We apply it to the analysis of freshwater river discharge, water currents, and the simulation of ensemble projections of sea waves, to show its capabilities.es_ES
dc.description.sponsorshipSwedish Research Council Formases_ES
dc.description.sponsorshipHelmholtz Associationes_ES
dc.description.sponsorshipGerman Aerospace Centre (DLR)es_ES
dc.description.sponsorshipBMWFWes_ES
dc.description.sponsorshipSpanish Governmentes_ES
dc.description.sponsorshipFrench National Research Agency (ANR)es_ES
dc.description.sponsorshipEuropean Commission European Commission Joint Research Centrees_ES
dc.description.sponsorshipMinistry of Agriculture, Livestock, Fisheries and Sus-tainable Development of the Junta de Andalucia 690462es_ES
dc.description.sponsorshipCONTR 2018 66984es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectTime expansion of parameterses_ES
dc.subjectNon-stationary probability modelses_ES
dc.subjectStochastic characterizationes_ES
dc.subjectEnvironmental modellinges_ES
dc.titleMarineTools.temporal: A Python package to simulate Earth and environmental time serieses_ES
dc.typejournal articlees_ES
dc.rights.accessRightsopen accesses_ES
dc.identifier.doi10.1016/j.envsoft.2022.105359
dc.type.hasVersionVoRes_ES


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