Algorithm 900: A discrete time kalman filter package for large scale problems

Autores
Torres, German Ariel
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Data assimilation is the process of feeding a partially unknown prediction model with available information from observations, with the objective of correcting and improving the modeled results. One of the most important mathematical tools to perform data assimilation is the Kalman filter. This is essentially a predictor-corrector algorithm that is optimal in the sense of minimizing the trace of the covariance matrix of the errors. Unfortunately, the computational cost of applying the filter to large scale problems is enormous, and the programming of the filter is highly dependent on the model and the format of the data involved. The first objective of this article is to present a set of Fortran 90 modules that implement the reduced rank square root versions of the Kalman filter, adapted for the assimilation of a very large number of variables. The second objective is to present a Kalman filter implementation whose code is independent of both the model and observations and is easy to use. A detailed description of the algorithms, structure, parallelization is given along with examples of using the package to solve practical problems. © 2010 ACM.
Fil: Torres, German Ariel. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; Argentina
Materia
DATA ASSIMILATION
KALMAN FILTER
LARGE SCALE PROBLEMS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/186674

id CONICETDig_8e0a6ca4fcf8e8053aa5d356a88de3ec
oai_identifier_str oai:ri.conicet.gov.ar:11336/186674
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Algorithm 900: A discrete time kalman filter package for large scale problemsTorres, German ArielDATA ASSIMILATIONKALMAN FILTERLARGE SCALE PROBLEMShttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1Data assimilation is the process of feeding a partially unknown prediction model with available information from observations, with the objective of correcting and improving the modeled results. One of the most important mathematical tools to perform data assimilation is the Kalman filter. This is essentially a predictor-corrector algorithm that is optimal in the sense of minimizing the trace of the covariance matrix of the errors. Unfortunately, the computational cost of applying the filter to large scale problems is enormous, and the programming of the filter is highly dependent on the model and the format of the data involved. The first objective of this article is to present a set of Fortran 90 modules that implement the reduced rank square root versions of the Kalman filter, adapted for the assimilation of a very large number of variables. The second objective is to present a Kalman filter implementation whose code is independent of both the model and observations and is easy to use. A detailed description of the algorithms, structure, parallelization is given along with examples of using the package to solve practical problems. © 2010 ACM.Fil: Torres, German Ariel. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; ArgentinaAssociation for Computing Machinery2010-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/186674Torres, German Ariel; Algorithm 900: A discrete time kalman filter package for large scale problems; Association for Computing Machinery; Acm Transactions On Mathematical Software; 37; 1; 1-2010; 1-160098-35001557-7295CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1145/1644001.1644012info:eu-repo/semantics/altIdentifier/url/https://dl.acm.org/doi/10.1145/1644001.1644012info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:54:52Zoai:ri.conicet.gov.ar:11336/186674instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:54:52.399CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Algorithm 900: A discrete time kalman filter package for large scale problems
title Algorithm 900: A discrete time kalman filter package for large scale problems
spellingShingle Algorithm 900: A discrete time kalman filter package for large scale problems
Torres, German Ariel
DATA ASSIMILATION
KALMAN FILTER
LARGE SCALE PROBLEMS
title_short Algorithm 900: A discrete time kalman filter package for large scale problems
title_full Algorithm 900: A discrete time kalman filter package for large scale problems
title_fullStr Algorithm 900: A discrete time kalman filter package for large scale problems
title_full_unstemmed Algorithm 900: A discrete time kalman filter package for large scale problems
title_sort Algorithm 900: A discrete time kalman filter package for large scale problems
dc.creator.none.fl_str_mv Torres, German Ariel
author Torres, German Ariel
author_facet Torres, German Ariel
author_role author
dc.subject.none.fl_str_mv DATA ASSIMILATION
KALMAN FILTER
LARGE SCALE PROBLEMS
topic DATA ASSIMILATION
KALMAN FILTER
LARGE SCALE PROBLEMS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Data assimilation is the process of feeding a partially unknown prediction model with available information from observations, with the objective of correcting and improving the modeled results. One of the most important mathematical tools to perform data assimilation is the Kalman filter. This is essentially a predictor-corrector algorithm that is optimal in the sense of minimizing the trace of the covariance matrix of the errors. Unfortunately, the computational cost of applying the filter to large scale problems is enormous, and the programming of the filter is highly dependent on the model and the format of the data involved. The first objective of this article is to present a set of Fortran 90 modules that implement the reduced rank square root versions of the Kalman filter, adapted for the assimilation of a very large number of variables. The second objective is to present a Kalman filter implementation whose code is independent of both the model and observations and is easy to use. A detailed description of the algorithms, structure, parallelization is given along with examples of using the package to solve practical problems. © 2010 ACM.
Fil: Torres, German Ariel. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; Argentina
description Data assimilation is the process of feeding a partially unknown prediction model with available information from observations, with the objective of correcting and improving the modeled results. One of the most important mathematical tools to perform data assimilation is the Kalman filter. This is essentially a predictor-corrector algorithm that is optimal in the sense of minimizing the trace of the covariance matrix of the errors. Unfortunately, the computational cost of applying the filter to large scale problems is enormous, and the programming of the filter is highly dependent on the model and the format of the data involved. The first objective of this article is to present a set of Fortran 90 modules that implement the reduced rank square root versions of the Kalman filter, adapted for the assimilation of a very large number of variables. The second objective is to present a Kalman filter implementation whose code is independent of both the model and observations and is easy to use. A detailed description of the algorithms, structure, parallelization is given along with examples of using the package to solve practical problems. © 2010 ACM.
publishDate 2010
dc.date.none.fl_str_mv 2010-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/186674
Torres, German Ariel; Algorithm 900: A discrete time kalman filter package for large scale problems; Association for Computing Machinery; Acm Transactions On Mathematical Software; 37; 1; 1-2010; 1-16
0098-3500
1557-7295
CONICET Digital
CONICET
url http://hdl.handle.net/11336/186674
identifier_str_mv Torres, German Ariel; Algorithm 900: A discrete time kalman filter package for large scale problems; Association for Computing Machinery; Acm Transactions On Mathematical Software; 37; 1; 1-2010; 1-16
0098-3500
1557-7295
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1145/1644001.1644012
info:eu-repo/semantics/altIdentifier/url/https://dl.acm.org/doi/10.1145/1644001.1644012
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Association for Computing Machinery
publisher.none.fl_str_mv Association for Computing Machinery
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842269311902679040
score 13.13397