Estimating forecast model bias in coupled global and limited-area models

Idealised perfect model experiments suggest that performing data assimilation on a ‘composite’ state vector, constructed from global and limited-area model states, can be beneficial to both model states. Here, an illustrative scheme is implemented to account for systematic forecast errors attributed...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Matthew Kretschmer [verfasserIn] Brian R. Hunt [verfasserIn] Edward Ott [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	ensemble Kalman Filter limited-area models composite state bias correction

Übergeordnetes Werk:	In: Tellus: Series A, Dynamic Meteorology and Oceanography - Stockholm University Press, 2012, 67(2015), 0, Seite 7
Übergeordnetes Werk:	volume:67 ; year:2015 ; number:0 ; pages:7

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3402/tellusa.v67.28040

Katalog-ID:	DOAJ038237768

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allfieldsSound	10.3402/tellusa.v67.28040 doi (DE-627)DOAJ038237768 (DE-599)DOAJ5497fbb3c5914f089b8a99b7160e1189 DE-627 ger DE-627 rakwb eng GC1-1581 QC851-999 Matthew Kretschmer verfasserin aut Estimating forecast model bias in coupled global and limited-area models 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Idealised perfect model experiments suggest that performing data assimilation on a ‘composite’ state vector, constructed from global and limited-area model states, can be beneficial to both model states. Here, an illustrative scheme is implemented to account for systematic forecast errors attributed to the imperfect model dynamics. Results from numerical experiments suggest that even simple bias correction schemes can correct forecast errors in composite states. ensemble Kalman Filter limited-area models composite state bias correction Oceanography Meteorology. Climatology Brian R. Hunt verfasserin aut Edward Ott verfasserin aut In Tellus: Series A, Dynamic Meteorology and Oceanography Stockholm University Press, 2012 67(2015), 0, Seite 7 (DE-627)324455895 (DE-600)2026987-0 16000870 nnns volume:67 year:2015 number:0 pages:7 https://doi.org/10.3402/tellusa.v67.28040 kostenfrei https://doaj.org/article/5497fbb3c5914f089b8a99b7160e1189 kostenfrei http://www.tellusa.net/index.php/tellusa/article/view/28040/pdf_65 kostenfrei https://doaj.org/toc/1600-0870 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 67 2015 0 7
language	English
source	In Tellus: Series A, Dynamic Meteorology and Oceanography 67(2015), 0, Seite 7 volume:67 year:2015 number:0 pages:7
sourceStr	In Tellus: Series A, Dynamic Meteorology and Oceanography 67(2015), 0, Seite 7 volume:67 year:2015 number:0 pages:7
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topic_facet	ensemble Kalman Filter limited-area models composite state bias correction Oceanography Meteorology. Climatology
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authorswithroles_txt_mv	Matthew Kretschmer @@aut@@ Brian R. Hunt @@aut@@ Edward Ott @@aut@@
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callnumber-first	G - Geography, Anthropology, Recreation
author	Matthew Kretschmer
spellingShingle	Matthew Kretschmer misc GC1-1581 misc QC851-999 misc ensemble Kalman Filter misc limited-area models misc composite state misc bias correction misc Oceanography misc Meteorology. Climatology Estimating forecast model bias in coupled global and limited-area models
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topic_title	GC1-1581 QC851-999 Estimating forecast model bias in coupled global and limited-area models ensemble Kalman Filter limited-area models composite state bias correction
topic	misc GC1-1581 misc QC851-999 misc ensemble Kalman Filter misc limited-area models misc composite state misc bias correction misc Oceanography misc Meteorology. Climatology
topic_unstemmed	misc GC1-1581 misc QC851-999 misc ensemble Kalman Filter misc limited-area models misc composite state misc bias correction misc Oceanography misc Meteorology. Climatology
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title	Estimating forecast model bias in coupled global and limited-area models
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title_full	Estimating forecast model bias in coupled global and limited-area models
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author_browse	Matthew Kretschmer Brian R. Hunt Edward Ott
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title_sort	estimating forecast model bias in coupled global and limited-area models
callnumber	GC1-1581
title_auth	Estimating forecast model bias in coupled global and limited-area models
abstract	Idealised perfect model experiments suggest that performing data assimilation on a ‘composite’ state vector, constructed from global and limited-area model states, can be beneficial to both model states. Here, an illustrative scheme is implemented to account for systematic forecast errors attributed to the imperfect model dynamics. Results from numerical experiments suggest that even simple bias correction schemes can correct forecast errors in composite states.
abstractGer	Idealised perfect model experiments suggest that performing data assimilation on a ‘composite’ state vector, constructed from global and limited-area model states, can be beneficial to both model states. Here, an illustrative scheme is implemented to account for systematic forecast errors attributed to the imperfect model dynamics. Results from numerical experiments suggest that even simple bias correction schemes can correct forecast errors in composite states.
abstract_unstemmed	Idealised perfect model experiments suggest that performing data assimilation on a ‘composite’ state vector, constructed from global and limited-area model states, can be beneficial to both model states. Here, an illustrative scheme is implemented to account for systematic forecast errors attributed to the imperfect model dynamics. Results from numerical experiments suggest that even simple bias correction schemes can correct forecast errors in composite states.
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title_short	Estimating forecast model bias in coupled global and limited-area models
url	https://doi.org/10.3402/tellusa.v67.28040 https://doaj.org/article/5497fbb3c5914f089b8a99b7160e1189 http://www.tellusa.net/index.php/tellusa/article/view/28040/pdf_65 https://doaj.org/toc/1600-0870
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up_date	2024-07-03T16:50:29.100Z
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Estimating forecast model bias in coupled global and limited-area models

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