Non-Gaussian data assimilation via ensembles: A DA application on tourism demand

Data Assimilation, DA, is the process by which an imperfect numerical forecast is corrected according to real observations. The aim of Data Assimilation is to improve the accuracy of forecast methods estimates, by incorporating observations optimally. The main goal of this research is to develop met...

Full description

Autores:
Beltrán Arrieta, Rolando
Tipo de recurso:
Doctoral thesis
Fecha de publicación:
2019
Institución:
Universidad del Norte
Repositorio:
Repositorio Uninorte
Idioma:
eng
OAI Identifier:
oai:manglar.uninorte.edu.co:10584/13328
Acceso en línea:
http://hdl.handle.net/10584/13328
Palabra clave:
Datos masivos
Turismo -- Modelos matemáticos
Rights
openAccess
License
https://creativecommons.org/licenses/by/4.0/
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dc.title.en_US.fl_str_mv Non-Gaussian data assimilation via ensembles: A DA application on tourism demand
title Non-Gaussian data assimilation via ensembles: A DA application on tourism demand
spellingShingle Non-Gaussian data assimilation via ensembles: A DA application on tourism demand
Datos masivos
Turismo -- Modelos matemáticos
title_short Non-Gaussian data assimilation via ensembles: A DA application on tourism demand
title_full Non-Gaussian data assimilation via ensembles: A DA application on tourism demand
title_fullStr Non-Gaussian data assimilation via ensembles: A DA application on tourism demand
title_full_unstemmed Non-Gaussian data assimilation via ensembles: A DA application on tourism demand
title_sort Non-Gaussian data assimilation via ensembles: A DA application on tourism demand
dc.creator.fl_str_mv Beltrán Arrieta, Rolando
dc.contributor.advisor.none.fl_str_mv Niño Ruiz, Elías David
dc.contributor.author.none.fl_str_mv Beltrán Arrieta, Rolando
dc.subject.lemb.none.fl_str_mv Datos masivos
Turismo -- Modelos matemáticos
topic Datos masivos
Turismo -- Modelos matemáticos
description Data Assimilation, DA, is the process by which an imperfect numerical forecast is corrected according to real observations. The aim of Data Assimilation is to improve the accuracy of forecast methods estimates, by incorporating observations optimally. The main goal of this research is to develop methods to overcome the limitations of some traditional DA techniques. In particular, the performance of traditional DA methods is greatly a ected in the following circumstances: 1. The prior probability distribution is non-Gaussian. 2. The operator of the observations is non-linear and therefore the probability distribution likelihood is non-Gaussian. The main goals of this research are described below: 1. To develop a Data Assimilation framework wherein prior errors are non-Gaussian. 2. To develop a Data Assimilation framework wherein observational errors are non-Gaussian. 3. To adapt and validate a Data Assimilation scheme for AR-models with data of tourism demand.
publishDate 2019
dc.date.issued.none.fl_str_mv 2019
dc.date.accessioned.none.fl_str_mv 2025-05-27T19:39:29Z
dc.date.available.none.fl_str_mv 2025-05-27T19:39:29Z
dc.type.es_ES.fl_str_mv Trabajo de grado - Doctorado
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dc.type.driver.es_ES.fl_str_mv info:eu-repo/semantics/doctoralThesis
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dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10584/13328
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dc.language.iso.es_ES.fl_str_mv eng
language eng
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dc.format.es_ES.fl_str_mv application/pdf
dc.format.extent.es_ES.fl_str_mv 124 páginas
dc.publisher.es_ES.fl_str_mv Universidad del Norte
dc.publisher.program.es_ES.fl_str_mv Doctorado en Ingeniería de Sistemas y Computación
dc.publisher.department.es_ES.fl_str_mv Departamento de ingeniería de sistemas
dc.publisher.place.es_ES.fl_str_mv Barranquilla, Colombia
institution Universidad del Norte
bitstream.url.fl_str_mv https://manglar.uninorte.edu.co/bitstream/10584/13328/1/Resumen%20Tesis%20Doctorado.pdf
https://manglar.uninorte.edu.co/bitstream/10584/13328/2/license.txt
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spelling Niño Ruiz, Elías DavidBeltrán Arrieta, Rolando2025-05-27T19:39:29Z2025-05-27T19:39:29Z2019http://hdl.handle.net/10584/13328Data Assimilation, DA, is the process by which an imperfect numerical forecast is corrected according to real observations. The aim of Data Assimilation is to improve the accuracy of forecast methods estimates, by incorporating observations optimally. The main goal of this research is to develop methods to overcome the limitations of some traditional DA techniques. In particular, the performance of traditional DA methods is greatly a ected in the following circumstances: 1. The prior probability distribution is non-Gaussian. 2. The operator of the observations is non-linear and therefore the probability distribution likelihood is non-Gaussian. The main goals of this research are described below: 1. To develop a Data Assimilation framework wherein prior errors are non-Gaussian. 2. To develop a Data Assimilation framework wherein observational errors are non-Gaussian. 3. To adapt and validate a Data Assimilation scheme for AR-models with data of tourism demand.DoctoradoDoctor en Ingeniería de Sistemas y Computaciónapplication/pdf124 páginasengUniversidad del NorteDoctorado en Ingeniería de Sistemas y ComputaciónDepartamento de ingeniería de sistemasBarranquilla, ColombiaNon-Gaussian data assimilation via ensembles: A DA application on tourism demandTrabajo de grado - Doctoradohttp://purl.org/coar/resource_type/c_db06info:eu-repo/semantics/doctoralThesisTexthttp://purl.org/coar/version/c_71e4c1898caa6e32https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Datos masivosTurismo -- Modelos matemáticosEstudiantesDoctoradoORIGINALResumen Tesis Doctorado.pdfResumen Tesis Doctorado.pdfapplication/pdf327129https://manglar.uninorte.edu.co/bitstream/10584/13328/1/Resumen%20Tesis%20Doctorado.pdf49bac43e3026548f8741caa7b5f18ccbMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://manglar.uninorte.edu.co/bitstream/10584/13328/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD5210584/13328oai:manglar.uninorte.edu.co:10584/133282025-05-27 16:03:37.06Repositorio Digital de la Universidad del Nortemauribe@uninorte.edu.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