An investigation of ensemble-based assimilation of satellite altimetry and tide gauge data in storm surge prediction

Autores
Etala, Paula; Saraceno, Martin; Echevarria, Pablo
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Cyclogenesis and long-fetched winds along the southeastern coast of South America may lead to floods in populated areas, as the Buenos Aires Province, with important economic and social impacts. A numerical model (SMARA) has already been implemented in the region to forecast storm surges. The propagation time of the surge in such extensive and shallow area allows the detection of anomalies based on observations from several hours up to the order of a day prior to the event. Here, we investigate the impact and potential benefit of storm surge level data assimilation into the SMARA model, with the objective of improving the forecast. In the experiments, the surface wind stress from an ensemble prediction sys- tem drives a storm surge model ensemble, based on the operational 2-D depth-averaged SMARA model. A 4-D Local Ensemble Transform Kalman Filter (4D-LETKF) initializes the ensemble in a 6-h cycle, assimilating the very few tide gauge observations available along the north- ern coast and satellite altimeter data. The sparse coverage of the altimeters is a challenge to data assimilation; how- ever, the 4D-LETKF evolving covariance of the ensemble perturbations provides realistic cross-track analysis incre- ments. Improvements on the forecast ensemble mean show the potential of an effective use of the sparse satellite altime- ter and tidal gauges observations in the data assimilation prototype. Furthermore, the effects of the localization scale and of the observational errors of coastal altimetry and tidal gauges in the data assimilation approach are assessed.
Fil: Etala, Paula. Ministerio de Defensa. Armada Argentina. Servicio de Hidrografia Naval; Argentina
Fil: Saraceno, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinacion Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera; Argentina
Fil: Echevarria, Pablo. Ministerio de Defensa. Secretaria de Planeamiento. Servicio Meteorológico Nacional; Argentina
Materia
Data Assimilation
Strom Surge
Satellite Altimetry
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:11336/4672
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling An investigation of ensemble-based assimilation of satellite altimetry and tide gauge data in storm surge predictionEtala, PaulaSaraceno, MartinEchevarria, PabloData AssimilationStrom SurgeSatellite Altimetryhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Cyclogenesis and long-fetched winds along the southeastern coast of South America may lead to floods in populated areas, as the Buenos Aires Province, with important economic and social impacts. A numerical model (SMARA) has already been implemented in the region to forecast storm surges. The propagation time of the surge in such extensive and shallow area allows the detection of anomalies based on observations from several hours up to the order of a day prior to the event. Here, we investigate the impact and potential benefit of storm surge level data assimilation into the SMARA model, with the objective of improving the forecast. In the experiments, the surface wind stress from an ensemble prediction sys- tem drives a storm surge model ensemble, based on the operational 2-D depth-averaged SMARA model. A 4-D Local Ensemble Transform Kalman Filter (4D-LETKF) initializes the ensemble in a 6-h cycle, assimilating the very few tide gauge observations available along the north- ern coast and satellite altimeter data. The sparse coverage of the altimeters is a challenge to data assimilation; how- ever, the 4D-LETKF evolving covariance of the ensemble perturbations provides realistic cross-track analysis incre- ments. Improvements on the forecast ensemble mean show the potential of an effective use of the sparse satellite altime- ter and tidal gauges observations in the data assimilation prototype. Furthermore, the effects of the localization scale and of the observational errors of coastal altimetry and tidal gauges in the data assimilation approach are assessed.Fil: Etala, Paula. Ministerio de Defensa. Armada Argentina. Servicio de Hidrografia Naval; ArgentinaFil: Saraceno, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinacion Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Echevarria, Pablo. Ministerio de Defensa. Secretaria de Planeamiento. Servicio Meteorológico Nacional; ArgentinaSpringer2015-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/4672Etala, Paula; Saraceno, Martin; Echevarria, Pablo; An investigation of ensemble-based assimilation of satellite altimetry and tide gauge data in storm surge prediction; Springer; Ocean Dynamics; 65; 3; 1-2015; 435-4471616-7341enginfo:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007/s10236-015-0808-zinfo:eu-repo/semantics/altIdentifier/doi/10.1007/s10236-015-0808-zinfo:eu-repo/semantics/altIdentifier/issn/1616-7341info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:03:14Zoai:ri.conicet.gov.ar:11336/4672instacron: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:34982025-09-10 13:03:14.966CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An investigation of ensemble-based assimilation of satellite altimetry and tide gauge data in storm surge prediction
title An investigation of ensemble-based assimilation of satellite altimetry and tide gauge data in storm surge prediction
spellingShingle An investigation of ensemble-based assimilation of satellite altimetry and tide gauge data in storm surge prediction
Etala, Paula
Data Assimilation
Strom Surge
Satellite Altimetry
title_short An investigation of ensemble-based assimilation of satellite altimetry and tide gauge data in storm surge prediction
title_full An investigation of ensemble-based assimilation of satellite altimetry and tide gauge data in storm surge prediction
title_fullStr An investigation of ensemble-based assimilation of satellite altimetry and tide gauge data in storm surge prediction
title_full_unstemmed An investigation of ensemble-based assimilation of satellite altimetry and tide gauge data in storm surge prediction
title_sort An investigation of ensemble-based assimilation of satellite altimetry and tide gauge data in storm surge prediction
dc.creator.none.fl_str_mv Etala, Paula
Saraceno, Martin
Echevarria, Pablo
author Etala, Paula
author_facet Etala, Paula
Saraceno, Martin
Echevarria, Pablo
author_role author
author2 Saraceno, Martin
Echevarria, Pablo
author2_role author
author
dc.subject.none.fl_str_mv Data Assimilation
Strom Surge
Satellite Altimetry
topic Data Assimilation
Strom Surge
Satellite Altimetry
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Cyclogenesis and long-fetched winds along the southeastern coast of South America may lead to floods in populated areas, as the Buenos Aires Province, with important economic and social impacts. A numerical model (SMARA) has already been implemented in the region to forecast storm surges. The propagation time of the surge in such extensive and shallow area allows the detection of anomalies based on observations from several hours up to the order of a day prior to the event. Here, we investigate the impact and potential benefit of storm surge level data assimilation into the SMARA model, with the objective of improving the forecast. In the experiments, the surface wind stress from an ensemble prediction sys- tem drives a storm surge model ensemble, based on the operational 2-D depth-averaged SMARA model. A 4-D Local Ensemble Transform Kalman Filter (4D-LETKF) initializes the ensemble in a 6-h cycle, assimilating the very few tide gauge observations available along the north- ern coast and satellite altimeter data. The sparse coverage of the altimeters is a challenge to data assimilation; how- ever, the 4D-LETKF evolving covariance of the ensemble perturbations provides realistic cross-track analysis incre- ments. Improvements on the forecast ensemble mean show the potential of an effective use of the sparse satellite altime- ter and tidal gauges observations in the data assimilation prototype. Furthermore, the effects of the localization scale and of the observational errors of coastal altimetry and tidal gauges in the data assimilation approach are assessed.
Fil: Etala, Paula. Ministerio de Defensa. Armada Argentina. Servicio de Hidrografia Naval; Argentina
Fil: Saraceno, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinacion Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera; Argentina
Fil: Echevarria, Pablo. Ministerio de Defensa. Secretaria de Planeamiento. Servicio Meteorológico Nacional; Argentina
description Cyclogenesis and long-fetched winds along the southeastern coast of South America may lead to floods in populated areas, as the Buenos Aires Province, with important economic and social impacts. A numerical model (SMARA) has already been implemented in the region to forecast storm surges. The propagation time of the surge in such extensive and shallow area allows the detection of anomalies based on observations from several hours up to the order of a day prior to the event. Here, we investigate the impact and potential benefit of storm surge level data assimilation into the SMARA model, with the objective of improving the forecast. In the experiments, the surface wind stress from an ensemble prediction sys- tem drives a storm surge model ensemble, based on the operational 2-D depth-averaged SMARA model. A 4-D Local Ensemble Transform Kalman Filter (4D-LETKF) initializes the ensemble in a 6-h cycle, assimilating the very few tide gauge observations available along the north- ern coast and satellite altimeter data. The sparse coverage of the altimeters is a challenge to data assimilation; how- ever, the 4D-LETKF evolving covariance of the ensemble perturbations provides realistic cross-track analysis incre- ments. Improvements on the forecast ensemble mean show the potential of an effective use of the sparse satellite altime- ter and tidal gauges observations in the data assimilation prototype. Furthermore, the effects of the localization scale and of the observational errors of coastal altimetry and tidal gauges in the data assimilation approach are assessed.
publishDate 2015
dc.date.none.fl_str_mv 2015-01
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/11336/4672
Etala, Paula; Saraceno, Martin; Echevarria, Pablo; An investigation of ensemble-based assimilation of satellite altimetry and tide gauge data in storm surge prediction; Springer; Ocean Dynamics; 65; 3; 1-2015; 435-447
1616-7341
url http://hdl.handle.net/11336/4672
identifier_str_mv Etala, Paula; Saraceno, Martin; Echevarria, Pablo; An investigation of ensemble-based assimilation of satellite altimetry and tide gauge data in storm surge prediction; Springer; Ocean Dynamics; 65; 3; 1-2015; 435-447
1616-7341
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007/s10236-015-0808-z
info:eu-repo/semantics/altIdentifier/doi/10.1007/s10236-015-0808-z
info:eu-repo/semantics/altIdentifier/issn/1616-7341
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 Springer
publisher.none.fl_str_mv Springer
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
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score 12.993085

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