Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation Systems

Autores
Casaretto, Gimena; Schwartz, Craig S.; Dillon, María Eugenia; Garcia Skabar, Yanina; Ruiz, Juan Jose
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This study applies the ensemble forecast sensitivity to observation impact (EFSOI) technique to two 80-member ensemble Kalman filter (EnKF) data assimilation (DA) systems over the United States, differing only in cycling strategy: continuous cycling (CC) and partial cycling (PC). EFSOI calculations were performed using 1-, 6-, and 12-h evaluation forecast times, verified against the Rapid Refresh (RAP) model analysis. Beneficial impact rates indicated that most observations were beneficial for both DA systems and forecast times, with no significant difference between PC and CC. Differences in cumulative observation impacts were statistically significant only for sources with few observations and small impacts, like mesonet observations. For numerous and impactful observations, such as rawinsondes and aircraft, differences were not statistically significant, suggesting similar use of important observations by PC and CC. PC forecasts were better than CC forecasts, but this improvement is not clearly due to better use of observations. Variable-wise analysis showed similar behavior between PC and CC for impact rates and cumulative impacts of U, V, T, relative humidity (RH), and surface zonal wind. Overall, there was no evidence that either PC or CC systematically used observations better, with mixed results across observation types and sources. Differences between PC and CC were typically small and not statistically significant for the most impactful observations and variables. Fundamental methodological differences between PC and CC did not significantly impact their ability to assimilate observations, with the process of ingesting global fields likely responsible for improved PC forecasts relative to CC.
Fil: Casaretto, Gimena. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Ministerio de Defensa. Secretaría de Planeamiento. Servicio Meteorológico Nacional. Servicio Meteorológico Nacional (sede Dorrego); Argentina
Fil: Schwartz, Craig S.. National Center for Atmospheric Research; Estados Unidos
Fil: Dillon, María Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Ministerio de Defensa. Secretaría de Planeamiento. Servicio Meteorológico Nacional. Servicio Meteorológico Nacional (sede Dorrego); Argentina
Fil: Garcia Skabar, Yanina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Ministerio de Defensa. Secretaria de Planeamiento. Servicio Meteorológico Nacional. Servicio Metereológico Nacional (sede Dorrego).; Argentina
Fil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina
Materia
OBSERVATION IMPACT
ENSEMBLE FORECAST
CONTINUOUS AND PARTIAL CYCLING
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:11336/267468
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation SystemsCasaretto, GimenaSchwartz, Craig S.Dillon, María EugeniaGarcia Skabar, YaninaRuiz, Juan JoseOBSERVATION IMPACTENSEMBLE FORECASTCONTINUOUS AND PARTIAL CYCLINGhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1This study applies the ensemble forecast sensitivity to observation impact (EFSOI) technique to two 80-member ensemble Kalman filter (EnKF) data assimilation (DA) systems over the United States, differing only in cycling strategy: continuous cycling (CC) and partial cycling (PC). EFSOI calculations were performed using 1-, 6-, and 12-h evaluation forecast times, verified against the Rapid Refresh (RAP) model analysis. Beneficial impact rates indicated that most observations were beneficial for both DA systems and forecast times, with no significant difference between PC and CC. Differences in cumulative observation impacts were statistically significant only for sources with few observations and small impacts, like mesonet observations. For numerous and impactful observations, such as rawinsondes and aircraft, differences were not statistically significant, suggesting similar use of important observations by PC and CC. PC forecasts were better than CC forecasts, but this improvement is not clearly due to better use of observations. Variable-wise analysis showed similar behavior between PC and CC for impact rates and cumulative impacts of U, V, T, relative humidity (RH), and surface zonal wind. Overall, there was no evidence that either PC or CC systematically used observations better, with mixed results across observation types and sources. Differences between PC and CC were typically small and not statistically significant for the most impactful observations and variables. Fundamental methodological differences between PC and CC did not significantly impact their ability to assimilate observations, with the process of ingesting global fields likely responsible for improved PC forecasts relative to CC.Fil: Casaretto, Gimena. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Ministerio de Defensa. Secretaría de Planeamiento. Servicio Meteorológico Nacional. Servicio Meteorológico Nacional (sede Dorrego); ArgentinaFil: Schwartz, Craig S.. National Center for Atmospheric Research; Estados UnidosFil: Dillon, María Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Ministerio de Defensa. Secretaría de Planeamiento. Servicio Meteorológico Nacional. Servicio Meteorológico Nacional (sede Dorrego); ArgentinaFil: Garcia Skabar, Yanina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Ministerio de Defensa. Secretaria de Planeamiento. Servicio Meteorológico Nacional. Servicio Metereológico Nacional (sede Dorrego).; ArgentinaFil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; ArgentinaAmerican Meteorological Society2025-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/267468Casaretto, Gimena; Schwartz, Craig S.; Dillon, María Eugenia; Garcia Skabar, Yanina; Ruiz, Juan Jose; Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation Systems; American Meteorological Society; Weather and Forecasting; 40; 6; 6-2025; 781-7940882-8156CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://journals.ametsoc.org/view/journals/wefo/aop/WAF-D-24-0127.1/WAF-D-24-0127.1.xmlinfo:eu-repo/semantics/altIdentifier/doi/10.1175/WAF-D-24-0127.1info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:41:37Zoai:ri.conicet.gov.ar:11336/267468instacron: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:34982025-09-29 10:41:37.734CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation Systems
title Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation Systems
spellingShingle Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation Systems
Casaretto, Gimena
OBSERVATION IMPACT
ENSEMBLE FORECAST
CONTINUOUS AND PARTIAL CYCLING
title_short Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation Systems
title_full Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation Systems
title_fullStr Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation Systems
title_full_unstemmed Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation Systems
title_sort Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation Systems
dc.creator.none.fl_str_mv Casaretto, Gimena
Schwartz, Craig S.
Dillon, María Eugenia
Garcia Skabar, Yanina
Ruiz, Juan Jose
author Casaretto, Gimena
author_facet Casaretto, Gimena
Schwartz, Craig S.
Dillon, María Eugenia
Garcia Skabar, Yanina
Ruiz, Juan Jose
author_role author
author2 Schwartz, Craig S.
Dillon, María Eugenia
Garcia Skabar, Yanina
Ruiz, Juan Jose
author2_role author
author
author
author
dc.subject.none.fl_str_mv OBSERVATION IMPACT
ENSEMBLE FORECAST
CONTINUOUS AND PARTIAL CYCLING
topic OBSERVATION IMPACT
ENSEMBLE FORECAST
CONTINUOUS AND PARTIAL CYCLING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv This study applies the ensemble forecast sensitivity to observation impact (EFSOI) technique to two 80-member ensemble Kalman filter (EnKF) data assimilation (DA) systems over the United States, differing only in cycling strategy: continuous cycling (CC) and partial cycling (PC). EFSOI calculations were performed using 1-, 6-, and 12-h evaluation forecast times, verified against the Rapid Refresh (RAP) model analysis. Beneficial impact rates indicated that most observations were beneficial for both DA systems and forecast times, with no significant difference between PC and CC. Differences in cumulative observation impacts were statistically significant only for sources with few observations and small impacts, like mesonet observations. For numerous and impactful observations, such as rawinsondes and aircraft, differences were not statistically significant, suggesting similar use of important observations by PC and CC. PC forecasts were better than CC forecasts, but this improvement is not clearly due to better use of observations. Variable-wise analysis showed similar behavior between PC and CC for impact rates and cumulative impacts of U, V, T, relative humidity (RH), and surface zonal wind. Overall, there was no evidence that either PC or CC systematically used observations better, with mixed results across observation types and sources. Differences between PC and CC were typically small and not statistically significant for the most impactful observations and variables. Fundamental methodological differences between PC and CC did not significantly impact their ability to assimilate observations, with the process of ingesting global fields likely responsible for improved PC forecasts relative to CC.
Fil: Casaretto, Gimena. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Ministerio de Defensa. Secretaría de Planeamiento. Servicio Meteorológico Nacional. Servicio Meteorológico Nacional (sede Dorrego); Argentina
Fil: Schwartz, Craig S.. National Center for Atmospheric Research; Estados Unidos
Fil: Dillon, María Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Ministerio de Defensa. Secretaría de Planeamiento. Servicio Meteorológico Nacional. Servicio Meteorológico Nacional (sede Dorrego); Argentina
Fil: Garcia Skabar, Yanina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Ministerio de Defensa. Secretaria de Planeamiento. Servicio Meteorológico Nacional. Servicio Metereológico Nacional (sede Dorrego).; Argentina
Fil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina
description This study applies the ensemble forecast sensitivity to observation impact (EFSOI) technique to two 80-member ensemble Kalman filter (EnKF) data assimilation (DA) systems over the United States, differing only in cycling strategy: continuous cycling (CC) and partial cycling (PC). EFSOI calculations were performed using 1-, 6-, and 12-h evaluation forecast times, verified against the Rapid Refresh (RAP) model analysis. Beneficial impact rates indicated that most observations were beneficial for both DA systems and forecast times, with no significant difference between PC and CC. Differences in cumulative observation impacts were statistically significant only for sources with few observations and small impacts, like mesonet observations. For numerous and impactful observations, such as rawinsondes and aircraft, differences were not statistically significant, suggesting similar use of important observations by PC and CC. PC forecasts were better than CC forecasts, but this improvement is not clearly due to better use of observations. Variable-wise analysis showed similar behavior between PC and CC for impact rates and cumulative impacts of U, V, T, relative humidity (RH), and surface zonal wind. Overall, there was no evidence that either PC or CC systematically used observations better, with mixed results across observation types and sources. Differences between PC and CC were typically small and not statistically significant for the most impactful observations and variables. Fundamental methodological differences between PC and CC did not significantly impact their ability to assimilate observations, with the process of ingesting global fields likely responsible for improved PC forecasts relative to CC.
publishDate 2025
dc.date.none.fl_str_mv 2025-06
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/11336/267468
Casaretto, Gimena; Schwartz, Craig S.; Dillon, María Eugenia; Garcia Skabar, Yanina; Ruiz, Juan Jose; Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation Systems; American Meteorological Society; Weather and Forecasting; 40; 6; 6-2025; 781-794
0882-8156
CONICET Digital
CONICET
url http://hdl.handle.net/11336/267468
identifier_str_mv Casaretto, Gimena; Schwartz, Craig S.; Dillon, María Eugenia; Garcia Skabar, Yanina; Ruiz, Juan Jose; Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation Systems; American Meteorological Society; Weather and Forecasting; 40; 6; 6-2025; 781-794
0882-8156
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://journals.ametsoc.org/view/journals/wefo/aop/WAF-D-24-0127.1/WAF-D-24-0127.1.xml
info:eu-repo/semantics/altIdentifier/doi/10.1175/WAF-D-24-0127.1
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
application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Meteorological Society
publisher.none.fl_str_mv American Meteorological Society
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 13.070432

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