Distributions and convergence of forecast variables in a 1,000-member convection-permitting ensemble

Autores
Craig, George C.; Puh, Matjaž; Keil, Christian; Tempest, Kirsten; Necker, Tobias; Ruiz, Juan Jose; Weissmann, Martin; Miyoshi, Takemasa
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The errors in numerical weather forecasts resulting from limited ensemble size are explored using 1,000-member forecasts of convective weather over Germany at 3-km resolution. A large number of forecast variables at different lead times were examined, and their distributions could be classified into three categories: quasi-normal (e.g., tropospheric temperature), highly skewed (e.g. precipitation), and mixtures (e.g., humidity). Dependence on ensemble size was examined in comparison to the asymptotic convergence law that the sampling error decreases proportional to N−1/2 for large enough ensemble size N, independent of the underlying distribution shape. The asymptotic convergence behavior was observed for the ensemble mean of all forecast variables, even for ensemble sizes less than 10. For the ensemble standard deviation, sizes of up to 100 were required for the convergence law to apply. In contrast, there was no clear sign of convergence for the 95th percentile even with 1,000 members. Methods such as neighborhood statistics or prediction of area-averaged quantities were found to improve accuracy, but only for variables with random small-scale variability, such as convective precipitation.
Fil: Craig, George C.. Ludwig Maximilians Universitat; Alemania
Fil: Puh, Matjaž. Ludwig Maximilians Universitat; Alemania
Fil: Keil, Christian. Ludwig Maximilians Universitat; Alemania
Fil: Tempest, Kirsten. Ludwig Maximilians Universitat; Alemania
Fil: Necker, Tobias. Universidad de Viena; Austria
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. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina
Fil: Weissmann, Martin. Universidad de Viena; Austria
Fil: Miyoshi, Takemasa. Riken Center For Computational Science; Japón
Materia
ENSEMBLE
FORECAST UNCERTAINTY
PROBABILITY DISTRIBUTION
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:11336/213084
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/213084
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Distributions and convergence of forecast variables in a 1,000-member convection-permitting ensembleCraig, George C.Puh, MatjažKeil, ChristianTempest, KirstenNecker, TobiasRuiz, Juan JoseWeissmann, MartinMiyoshi, TakemasaENSEMBLEFORECAST UNCERTAINTYPROBABILITY DISTRIBUTIONhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1The errors in numerical weather forecasts resulting from limited ensemble size are explored using 1,000-member forecasts of convective weather over Germany at 3-km resolution. A large number of forecast variables at different lead times were examined, and their distributions could be classified into three categories: quasi-normal (e.g., tropospheric temperature), highly skewed (e.g. precipitation), and mixtures (e.g., humidity). Dependence on ensemble size was examined in comparison to the asymptotic convergence law that the sampling error decreases proportional to N−1/2 for large enough ensemble size N, independent of the underlying distribution shape. The asymptotic convergence behavior was observed for the ensemble mean of all forecast variables, even for ensemble sizes less than 10. For the ensemble standard deviation, sizes of up to 100 were required for the convergence law to apply. In contrast, there was no clear sign of convergence for the 95th percentile even with 1,000 members. Methods such as neighborhood statistics or prediction of area-averaged quantities were found to improve accuracy, but only for variables with random small-scale variability, such as convective precipitation.Fil: Craig, George C.. Ludwig Maximilians Universitat; AlemaniaFil: Puh, Matjaž. Ludwig Maximilians Universitat; AlemaniaFil: Keil, Christian. Ludwig Maximilians Universitat; AlemaniaFil: Tempest, Kirsten. Ludwig Maximilians Universitat; AlemaniaFil: Necker, Tobias. Universidad de Viena; AustriaFil: 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. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; ArgentinaFil: Weissmann, Martin. Universidad de Viena; AustriaFil: Miyoshi, Takemasa. Riken Center For Computational Science; JapónJohn Wiley & Sons Ltd2022-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/213084Craig, George C.; Puh, Matjaž; Keil, Christian; Tempest, Kirsten; Necker, Tobias; et al.; Distributions and convergence of forecast variables in a 1,000-member convection-permitting ensemble; John Wiley & Sons Ltd; Quarterly Journal of the Royal Meteorological Society; 148; 746; 5-2022; 2325-23430035-9009CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.4305info:eu-repo/semantics/altIdentifier/doi/10.1002/qj.4305info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:14:17Zoai:ri.conicet.gov.ar:11336/213084instacron: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 10:14:17.531CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Distributions and convergence of forecast variables in a 1,000-member convection-permitting ensemble
title Distributions and convergence of forecast variables in a 1,000-member convection-permitting ensemble
spellingShingle Distributions and convergence of forecast variables in a 1,000-member convection-permitting ensemble
Craig, George C.
ENSEMBLE
FORECAST UNCERTAINTY
PROBABILITY DISTRIBUTION
title_short Distributions and convergence of forecast variables in a 1,000-member convection-permitting ensemble
title_full Distributions and convergence of forecast variables in a 1,000-member convection-permitting ensemble
title_fullStr Distributions and convergence of forecast variables in a 1,000-member convection-permitting ensemble
title_full_unstemmed Distributions and convergence of forecast variables in a 1,000-member convection-permitting ensemble
title_sort Distributions and convergence of forecast variables in a 1,000-member convection-permitting ensemble
dc.creator.none.fl_str_mv Craig, George C.
Puh, Matjaž
Keil, Christian
Tempest, Kirsten
Necker, Tobias
Ruiz, Juan Jose
Weissmann, Martin
Miyoshi, Takemasa
author Craig, George C.
author_facet Craig, George C.
Puh, Matjaž
Keil, Christian
Tempest, Kirsten
Necker, Tobias
Ruiz, Juan Jose
Weissmann, Martin
Miyoshi, Takemasa
author_role author
author2 Puh, Matjaž
Keil, Christian
Tempest, Kirsten
Necker, Tobias
Ruiz, Juan Jose
Weissmann, Martin
Miyoshi, Takemasa
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv ENSEMBLE
FORECAST UNCERTAINTY
PROBABILITY DISTRIBUTION
topic ENSEMBLE
FORECAST UNCERTAINTY
PROBABILITY DISTRIBUTION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The errors in numerical weather forecasts resulting from limited ensemble size are explored using 1,000-member forecasts of convective weather over Germany at 3-km resolution. A large number of forecast variables at different lead times were examined, and their distributions could be classified into three categories: quasi-normal (e.g., tropospheric temperature), highly skewed (e.g. precipitation), and mixtures (e.g., humidity). Dependence on ensemble size was examined in comparison to the asymptotic convergence law that the sampling error decreases proportional to N−1/2 for large enough ensemble size N, independent of the underlying distribution shape. The asymptotic convergence behavior was observed for the ensemble mean of all forecast variables, even for ensemble sizes less than 10. For the ensemble standard deviation, sizes of up to 100 were required for the convergence law to apply. In contrast, there was no clear sign of convergence for the 95th percentile even with 1,000 members. Methods such as neighborhood statistics or prediction of area-averaged quantities were found to improve accuracy, but only for variables with random small-scale variability, such as convective precipitation.
Fil: Craig, George C.. Ludwig Maximilians Universitat; Alemania
Fil: Puh, Matjaž. Ludwig Maximilians Universitat; Alemania
Fil: Keil, Christian. Ludwig Maximilians Universitat; Alemania
Fil: Tempest, Kirsten. Ludwig Maximilians Universitat; Alemania
Fil: Necker, Tobias. Universidad de Viena; Austria
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. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina
Fil: Weissmann, Martin. Universidad de Viena; Austria
Fil: Miyoshi, Takemasa. Riken Center For Computational Science; Japón
description The errors in numerical weather forecasts resulting from limited ensemble size are explored using 1,000-member forecasts of convective weather over Germany at 3-km resolution. A large number of forecast variables at different lead times were examined, and their distributions could be classified into three categories: quasi-normal (e.g., tropospheric temperature), highly skewed (e.g. precipitation), and mixtures (e.g., humidity). Dependence on ensemble size was examined in comparison to the asymptotic convergence law that the sampling error decreases proportional to N−1/2 for large enough ensemble size N, independent of the underlying distribution shape. The asymptotic convergence behavior was observed for the ensemble mean of all forecast variables, even for ensemble sizes less than 10. For the ensemble standard deviation, sizes of up to 100 were required for the convergence law to apply. In contrast, there was no clear sign of convergence for the 95th percentile even with 1,000 members. Methods such as neighborhood statistics or prediction of area-averaged quantities were found to improve accuracy, but only for variables with random small-scale variability, such as convective precipitation.
publishDate 2022
dc.date.none.fl_str_mv 2022-05
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/11336/213084
Craig, George C.; Puh, Matjaž; Keil, Christian; Tempest, Kirsten; Necker, Tobias; et al.; Distributions and convergence of forecast variables in a 1,000-member convection-permitting ensemble; John Wiley & Sons Ltd; Quarterly Journal of the Royal Meteorological Society; 148; 746; 5-2022; 2325-2343
0035-9009
CONICET Digital
CONICET
url http://hdl.handle.net/11336/213084
identifier_str_mv Craig, George C.; Puh, Matjaž; Keil, Christian; Tempest, Kirsten; Necker, Tobias; et al.; Distributions and convergence of forecast variables in a 1,000-member convection-permitting ensemble; John Wiley & Sons Ltd; Quarterly Journal of the Royal Meteorological Society; 148; 746; 5-2022; 2325-2343
0035-9009
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://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.4305
info:eu-repo/semantics/altIdentifier/doi/10.1002/qj.4305
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
dc.publisher.none.fl_str_mv John Wiley & Sons Ltd
publisher.none.fl_str_mv John Wiley & Sons Ltd
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_ 1844614069011611648
score 13.070432

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