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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/186674
Ver los metadatos del registro completo
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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 |
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info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
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https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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Association for Computing Machinery |
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Association for Computing Machinery |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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