Parameter sensitivity of the WRF–LETKF system for assimilation of radar observations: Imperfect-model observing system simulation experiments

Autores
Maldonado, Paula Soledad; Ruiz, Juan Jose; Saulo, Andrea Celeste
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Specification of suitable initial conditions to accurately forecast high-impact weather events associated with intense thunderstorms still poses a significant challenge for convective-scale forecasting. Radar data assimilation has been showing encouraging results to produce an accurate estimate of the state of the atmosphere at the mesoscale, as it combines high-spatiotemporal-resolution observations with convection-permitting numerical weather prediction models. However, many open questions remain regarding the configuration of state-of-the-art data assimilation systems at the mesoscale and their potential impact upon short-range weather forecasts. In this work, several observing system simulation experiments of a mesoscale convective system were performed to assess the sensitivity of the local ensemble transform Kalman filter to both relaxation-to-prior spread (RTPS) inflation and horizontal localization of the error covariance matrix. Realistic large-scale forcing and model errors have been taken into account in the simulation of reflectivity and Doppler velocity observations. Overall, the most accurate analyses in terms of RMSE were produced with a relatively small horizontal localization cutoff radius (;3.6–7.3 km) and large RTPS inflation parameter (;0.9–0.95). Additionally, the impact of horizontal localization on short-range ensemble forecast was larger compared to inflation, almost doubling the lead times up to which the effect of using a more accurate state to initialize the forecast persisted.
Fil: Maldonado, Paula Soledad. Instituto Franco-argentino sobre Estudios del Clima y sus Impactos; Argentina. 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
Fil: Ruiz, Juan Jose. Instituto Franco-argentino sobre Estudios del Clima y sus Impactos; Argentina. 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
Fil: Saulo, Andrea Celeste. Ministerio de Defensa. Secretaria de Planeamiento. Servicio Meteorológico Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
data assimilation
radar
kalman filter
localization
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/143672
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Parameter sensitivity of the WRF–LETKF system for assimilation of radar observations: Imperfect-model observing system simulation experimentsMaldonado, Paula SoledadRuiz, Juan JoseSaulo, Andrea Celestedata assimilationradarkalman filterlocalizationhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Specification of suitable initial conditions to accurately forecast high-impact weather events associated with intense thunderstorms still poses a significant challenge for convective-scale forecasting. Radar data assimilation has been showing encouraging results to produce an accurate estimate of the state of the atmosphere at the mesoscale, as it combines high-spatiotemporal-resolution observations with convection-permitting numerical weather prediction models. However, many open questions remain regarding the configuration of state-of-the-art data assimilation systems at the mesoscale and their potential impact upon short-range weather forecasts. In this work, several observing system simulation experiments of a mesoscale convective system were performed to assess the sensitivity of the local ensemble transform Kalman filter to both relaxation-to-prior spread (RTPS) inflation and horizontal localization of the error covariance matrix. Realistic large-scale forcing and model errors have been taken into account in the simulation of reflectivity and Doppler velocity observations. Overall, the most accurate analyses in terms of RMSE were produced with a relatively small horizontal localization cutoff radius (;3.6–7.3 km) and large RTPS inflation parameter (;0.9–0.95). Additionally, the impact of horizontal localization on short-range ensemble forecast was larger compared to inflation, almost doubling the lead times up to which the effect of using a more accurate state to initialize the forecast persisted.Fil: Maldonado, Paula Soledad. Instituto Franco-argentino sobre Estudios del Clima y sus Impactos; Argentina. 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; ArgentinaFil: Ruiz, Juan Jose. Instituto Franco-argentino sobre Estudios del Clima y sus Impactos; Argentina. 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; ArgentinaFil: Saulo, Andrea Celeste. Ministerio de Defensa. Secretaria de Planeamiento. Servicio Meteorológico Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaAmerican Meteorological Society2020-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/143672Maldonado, Paula Soledad; Ruiz, Juan Jose; Saulo, Andrea Celeste; Parameter sensitivity of the WRF–LETKF system for assimilation of radar observations: Imperfect-model observing system simulation experiments; American Meteorological Society; Weather and Forecasting; 35; 4; 5-2020; 1345-13620882-8156CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://journals.ametsoc.org/doi/10.1175/WAF-D-19-0161.1info:eu-repo/semantics/altIdentifier/doi/10.1175/WAF-D-19-0161.1info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:49:58Zoai:ri.conicet.gov.ar:11336/143672instacron: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 09:49:58.969CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Parameter sensitivity of the WRF–LETKF system for assimilation of radar observations: Imperfect-model observing system simulation experiments
title Parameter sensitivity of the WRF–LETKF system for assimilation of radar observations: Imperfect-model observing system simulation experiments
spellingShingle Parameter sensitivity of the WRF–LETKF system for assimilation of radar observations: Imperfect-model observing system simulation experiments
Maldonado, Paula Soledad
data assimilation
radar
kalman filter
localization
title_short Parameter sensitivity of the WRF–LETKF system for assimilation of radar observations: Imperfect-model observing system simulation experiments
title_full Parameter sensitivity of the WRF–LETKF system for assimilation of radar observations: Imperfect-model observing system simulation experiments
title_fullStr Parameter sensitivity of the WRF–LETKF system for assimilation of radar observations: Imperfect-model observing system simulation experiments
title_full_unstemmed Parameter sensitivity of the WRF–LETKF system for assimilation of radar observations: Imperfect-model observing system simulation experiments
title_sort Parameter sensitivity of the WRF–LETKF system for assimilation of radar observations: Imperfect-model observing system simulation experiments
dc.creator.none.fl_str_mv Maldonado, Paula Soledad
Ruiz, Juan Jose
Saulo, Andrea Celeste
author Maldonado, Paula Soledad
author_facet Maldonado, Paula Soledad
Ruiz, Juan Jose
Saulo, Andrea Celeste
author_role author
author2 Ruiz, Juan Jose
Saulo, Andrea Celeste
author2_role author
author
dc.subject.none.fl_str_mv data assimilation
radar
kalman filter
localization
topic data assimilation
radar
kalman filter
localization
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Specification of suitable initial conditions to accurately forecast high-impact weather events associated with intense thunderstorms still poses a significant challenge for convective-scale forecasting. Radar data assimilation has been showing encouraging results to produce an accurate estimate of the state of the atmosphere at the mesoscale, as it combines high-spatiotemporal-resolution observations with convection-permitting numerical weather prediction models. However, many open questions remain regarding the configuration of state-of-the-art data assimilation systems at the mesoscale and their potential impact upon short-range weather forecasts. In this work, several observing system simulation experiments of a mesoscale convective system were performed to assess the sensitivity of the local ensemble transform Kalman filter to both relaxation-to-prior spread (RTPS) inflation and horizontal localization of the error covariance matrix. Realistic large-scale forcing and model errors have been taken into account in the simulation of reflectivity and Doppler velocity observations. Overall, the most accurate analyses in terms of RMSE were produced with a relatively small horizontal localization cutoff radius (;3.6–7.3 km) and large RTPS inflation parameter (;0.9–0.95). Additionally, the impact of horizontal localization on short-range ensemble forecast was larger compared to inflation, almost doubling the lead times up to which the effect of using a more accurate state to initialize the forecast persisted.
Fil: Maldonado, Paula Soledad. Instituto Franco-argentino sobre Estudios del Clima y sus Impactos; Argentina. 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
Fil: Ruiz, Juan Jose. Instituto Franco-argentino sobre Estudios del Clima y sus Impactos; Argentina. 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
Fil: Saulo, Andrea Celeste. Ministerio de Defensa. Secretaria de Planeamiento. Servicio Meteorológico Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Specification of suitable initial conditions to accurately forecast high-impact weather events associated with intense thunderstorms still poses a significant challenge for convective-scale forecasting. Radar data assimilation has been showing encouraging results to produce an accurate estimate of the state of the atmosphere at the mesoscale, as it combines high-spatiotemporal-resolution observations with convection-permitting numerical weather prediction models. However, many open questions remain regarding the configuration of state-of-the-art data assimilation systems at the mesoscale and their potential impact upon short-range weather forecasts. In this work, several observing system simulation experiments of a mesoscale convective system were performed to assess the sensitivity of the local ensemble transform Kalman filter to both relaxation-to-prior spread (RTPS) inflation and horizontal localization of the error covariance matrix. Realistic large-scale forcing and model errors have been taken into account in the simulation of reflectivity and Doppler velocity observations. Overall, the most accurate analyses in terms of RMSE were produced with a relatively small horizontal localization cutoff radius (;3.6–7.3 km) and large RTPS inflation parameter (;0.9–0.95). Additionally, the impact of horizontal localization on short-range ensemble forecast was larger compared to inflation, almost doubling the lead times up to which the effect of using a more accurate state to initialize the forecast persisted.
publishDate 2020
dc.date.none.fl_str_mv 2020-05
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/11336/143672
Maldonado, Paula Soledad; Ruiz, Juan Jose; Saulo, Andrea Celeste; Parameter sensitivity of the WRF–LETKF system for assimilation of radar observations: Imperfect-model observing system simulation experiments; American Meteorological Society; Weather and Forecasting; 35; 4; 5-2020; 1345-1362
0882-8156
CONICET Digital
CONICET
url http://hdl.handle.net/11336/143672
identifier_str_mv Maldonado, Paula Soledad; Ruiz, Juan Jose; Saulo, Andrea Celeste; Parameter sensitivity of the WRF–LETKF system for assimilation of radar observations: Imperfect-model observing system simulation experiments; American Meteorological Society; Weather and Forecasting; 35; 4; 5-2020; 1345-1362
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/http://journals.ametsoc.org/doi/10.1175/WAF-D-19-0161.1
info:eu-repo/semantics/altIdentifier/doi/10.1175/WAF-D-19-0161.1
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv 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 13.13397

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