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...
- 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 |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_71e4c1898caa6e32 |
dc.type.coar.es_ES.fl_str_mv |
http://purl.org/coar/resource_type/c_db06 |
dc.type.driver.es_ES.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
dc.type.content.es_ES.fl_str_mv |
Text |
format |
http://purl.org/coar/resource_type/c_db06 |
dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10584/13328 |
url |
http://hdl.handle.net/10584/13328 |
dc.language.iso.es_ES.fl_str_mv |
eng |
language |
eng |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.rights.creativecommons.es_ES.fl_str_mv |
https://creativecommons.org/licenses/by/4.0/ |
dc.rights.accessrights.es_ES.fl_str_mv |
info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/4.0/ http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
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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MD5 MD5 |
repository.name.fl_str_mv |
Repositorio Digital de la Universidad del Norte |
repository.mail.fl_str_mv |
mauribe@uninorte.edu.co |
_version_ |
1834108940846104576 |
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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 |