Data assimilation using a climatologically augmented local ensemble transform Kalman filter

Ensemble data assimilation methods are potentially attractive because they provide a computationally affordable (and computationally parallel) means of obtaining flow-dependent background-error statistics. However, a limitation of these methods is that the rank of their flow-dependent background-err...
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 climatological covariance data assimilation

Übergeordnetes Werk:	In: Tellus: Series A, Dynamic Meteorology and Oceanography - Stockholm University Press, 2012, 67(2015), 0, Seite 9
Übergeordnetes Werk:	volume:67 ; year:2015 ; number:0 ; pages:9

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3402/tellusa.v67.26617

Katalog-ID:	DOAJ028960491

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ028960491
003	DE-627
005	20230502205950.0
007	cr uuu---uuuuu
008	230226s2015 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.3402/tellusa.v67.26617 \|2 doi
035			\|a (DE-627)DOAJ028960491
035			\|a (DE-599)DOAJefd40924d1b14970aff23fc2c622943d
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a GC1-1581
050		0	\|a QC851-999
100	0		\|a Matthew Kretschmer \|e verfasserin \|4 aut
245	1	0	\|a Data assimilation using a climatologically augmented local ensemble transform Kalman filter
264		1	\|c 2015
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Ensemble data assimilation methods are potentially attractive because they provide a computationally affordable (and computationally parallel) means of obtaining flow-dependent background-error statistics. However, a limitation of these methods is that the rank of their flow-dependent background-error covariance estimate, and hence the space of possible analysis increments, is limited by the number of forecast ensemble members. To overcome this deficiency ensemble methods typically use empirical localisation, which allows more degrees of freedom for the analysis increment by suppressing spatially distant background correlations. The method presented here improves the performance of an Ensemble Kalman filter by increasing the size of the ensemble at analysis time in order to boost the rank of its background-error covariance estimate. The additional ensemble members added to the forecast ensemble at analysis time are created by adding a collection of ‘climatological’ perturbations to the forecast ensemble mean. These perturbations are constant in time and provide state space directions, possibly missed by the dynamically forecasted background ensemble, in which the analysis increment can correct the forecast mean based on observations. As the climatological perturbations are calculated once, there is negligible computational cost in obtaining the additional ensemble members at each analysis cycle. Included here are a formulation of the method, results of numerical experiments conducted with a spatiotemporally chaotic model in one spatial dimension and discussion of possible future extensions and applications. The numerical tests indicate that the method presented here has significant potential for improving analyses and forecasts.
650		4	\|a ensemble Kalman filter
650		4	\|a climatological covariance
650		4	\|a data assimilation
653		0	\|a Oceanography
653		0	\|a Meteorology. Climatology
700	0		\|a Brian R. Hunt \|e verfasserin \|4 aut
700	0		\|a Edward Ott \|e verfasserin \|4 aut
773	0	8	\|i In \|t Tellus: Series A, Dynamic Meteorology and Oceanography \|d Stockholm University Press, 2012 \|g 67(2015), 0, Seite 9 \|w (DE-627)324455895 \|w (DE-600)2026987-0 \|x 16000870 \|7 nnns
773	1	8	\|g volume:67 \|g year:2015 \|g number:0 \|g pages:9
856	4	0	\|u https://doi.org/10.3402/tellusa.v67.26617 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/efd40924d1b14970aff23fc2c622943d \|z kostenfrei
856	4	0	\|u http://www.tellusa.net/index.php/tellusa/article/view/26617/pdf_34 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/1600-0870 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a SSG-OLC-PHA
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2119
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 67 \|j 2015 \|e 0 \|h 9

Indexfelder

author_variant	m k mk b r h brh e o eo
matchkey_str	article:16000870:2015----::aasiiainsnalmtlgclyumnelclnebe
hierarchy_sort_str	2015
callnumber-subject-code	GC
publishDate	2015
allfields	10.3402/tellusa.v67.26617 doi (DE-627)DOAJ028960491 (DE-599)DOAJefd40924d1b14970aff23fc2c622943d DE-627 ger DE-627 rakwb eng GC1-1581 QC851-999 Matthew Kretschmer verfasserin aut Data assimilation using a climatologically augmented local ensemble transform Kalman filter 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Ensemble data assimilation methods are potentially attractive because they provide a computationally affordable (and computationally parallel) means of obtaining flow-dependent background-error statistics. However, a limitation of these methods is that the rank of their flow-dependent background-error covariance estimate, and hence the space of possible analysis increments, is limited by the number of forecast ensemble members. To overcome this deficiency ensemble methods typically use empirical localisation, which allows more degrees of freedom for the analysis increment by suppressing spatially distant background correlations. The method presented here improves the performance of an Ensemble Kalman filter by increasing the size of the ensemble at analysis time in order to boost the rank of its background-error covariance estimate. The additional ensemble members added to the forecast ensemble at analysis time are created by adding a collection of ‘climatological’ perturbations to the forecast ensemble mean. These perturbations are constant in time and provide state space directions, possibly missed by the dynamically forecasted background ensemble, in which the analysis increment can correct the forecast mean based on observations. As the climatological perturbations are calculated once, there is negligible computational cost in obtaining the additional ensemble members at each analysis cycle. Included here are a formulation of the method, results of numerical experiments conducted with a spatiotemporally chaotic model in one spatial dimension and discussion of possible future extensions and applications. The numerical tests indicate that the method presented here has significant potential for improving analyses and forecasts. ensemble Kalman filter climatological covariance data assimilation 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 9 (DE-627)324455895 (DE-600)2026987-0 16000870 nnns volume:67 year:2015 number:0 pages:9 https://doi.org/10.3402/tellusa.v67.26617 kostenfrei https://doaj.org/article/efd40924d1b14970aff23fc2c622943d kostenfrei http://www.tellusa.net/index.php/tellusa/article/view/26617/pdf_34 kostenfrei https://doaj.org/toc/1600-0870 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 9
spelling	10.3402/tellusa.v67.26617 doi (DE-627)DOAJ028960491 (DE-599)DOAJefd40924d1b14970aff23fc2c622943d DE-627 ger DE-627 rakwb eng GC1-1581 QC851-999 Matthew Kretschmer verfasserin aut Data assimilation using a climatologically augmented local ensemble transform Kalman filter 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Ensemble data assimilation methods are potentially attractive because they provide a computationally affordable (and computationally parallel) means of obtaining flow-dependent background-error statistics. However, a limitation of these methods is that the rank of their flow-dependent background-error covariance estimate, and hence the space of possible analysis increments, is limited by the number of forecast ensemble members. To overcome this deficiency ensemble methods typically use empirical localisation, which allows more degrees of freedom for the analysis increment by suppressing spatially distant background correlations. The method presented here improves the performance of an Ensemble Kalman filter by increasing the size of the ensemble at analysis time in order to boost the rank of its background-error covariance estimate. The additional ensemble members added to the forecast ensemble at analysis time are created by adding a collection of ‘climatological’ perturbations to the forecast ensemble mean. These perturbations are constant in time and provide state space directions, possibly missed by the dynamically forecasted background ensemble, in which the analysis increment can correct the forecast mean based on observations. As the climatological perturbations are calculated once, there is negligible computational cost in obtaining the additional ensemble members at each analysis cycle. Included here are a formulation of the method, results of numerical experiments conducted with a spatiotemporally chaotic model in one spatial dimension and discussion of possible future extensions and applications. The numerical tests indicate that the method presented here has significant potential for improving analyses and forecasts. ensemble Kalman filter climatological covariance data assimilation 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 9 (DE-627)324455895 (DE-600)2026987-0 16000870 nnns volume:67 year:2015 number:0 pages:9 https://doi.org/10.3402/tellusa.v67.26617 kostenfrei https://doaj.org/article/efd40924d1b14970aff23fc2c622943d kostenfrei http://www.tellusa.net/index.php/tellusa/article/view/26617/pdf_34 kostenfrei https://doaj.org/toc/1600-0870 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 9
allfields_unstemmed	10.3402/tellusa.v67.26617 doi (DE-627)DOAJ028960491 (DE-599)DOAJefd40924d1b14970aff23fc2c622943d DE-627 ger DE-627 rakwb eng GC1-1581 QC851-999 Matthew Kretschmer verfasserin aut Data assimilation using a climatologically augmented local ensemble transform Kalman filter 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Ensemble data assimilation methods are potentially attractive because they provide a computationally affordable (and computationally parallel) means of obtaining flow-dependent background-error statistics. However, a limitation of these methods is that the rank of their flow-dependent background-error covariance estimate, and hence the space of possible analysis increments, is limited by the number of forecast ensemble members. To overcome this deficiency ensemble methods typically use empirical localisation, which allows more degrees of freedom for the analysis increment by suppressing spatially distant background correlations. The method presented here improves the performance of an Ensemble Kalman filter by increasing the size of the ensemble at analysis time in order to boost the rank of its background-error covariance estimate. The additional ensemble members added to the forecast ensemble at analysis time are created by adding a collection of ‘climatological’ perturbations to the forecast ensemble mean. These perturbations are constant in time and provide state space directions, possibly missed by the dynamically forecasted background ensemble, in which the analysis increment can correct the forecast mean based on observations. As the climatological perturbations are calculated once, there is negligible computational cost in obtaining the additional ensemble members at each analysis cycle. Included here are a formulation of the method, results of numerical experiments conducted with a spatiotemporally chaotic model in one spatial dimension and discussion of possible future extensions and applications. The numerical tests indicate that the method presented here has significant potential for improving analyses and forecasts. ensemble Kalman filter climatological covariance data assimilation 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 9 (DE-627)324455895 (DE-600)2026987-0 16000870 nnns volume:67 year:2015 number:0 pages:9 https://doi.org/10.3402/tellusa.v67.26617 kostenfrei https://doaj.org/article/efd40924d1b14970aff23fc2c622943d kostenfrei http://www.tellusa.net/index.php/tellusa/article/view/26617/pdf_34 kostenfrei https://doaj.org/toc/1600-0870 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 9
allfieldsGer	10.3402/tellusa.v67.26617 doi (DE-627)DOAJ028960491 (DE-599)DOAJefd40924d1b14970aff23fc2c622943d DE-627 ger DE-627 rakwb eng GC1-1581 QC851-999 Matthew Kretschmer verfasserin aut Data assimilation using a climatologically augmented local ensemble transform Kalman filter 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Ensemble data assimilation methods are potentially attractive because they provide a computationally affordable (and computationally parallel) means of obtaining flow-dependent background-error statistics. However, a limitation of these methods is that the rank of their flow-dependent background-error covariance estimate, and hence the space of possible analysis increments, is limited by the number of forecast ensemble members. To overcome this deficiency ensemble methods typically use empirical localisation, which allows more degrees of freedom for the analysis increment by suppressing spatially distant background correlations. The method presented here improves the performance of an Ensemble Kalman filter by increasing the size of the ensemble at analysis time in order to boost the rank of its background-error covariance estimate. The additional ensemble members added to the forecast ensemble at analysis time are created by adding a collection of ‘climatological’ perturbations to the forecast ensemble mean. These perturbations are constant in time and provide state space directions, possibly missed by the dynamically forecasted background ensemble, in which the analysis increment can correct the forecast mean based on observations. As the climatological perturbations are calculated once, there is negligible computational cost in obtaining the additional ensemble members at each analysis cycle. Included here are a formulation of the method, results of numerical experiments conducted with a spatiotemporally chaotic model in one spatial dimension and discussion of possible future extensions and applications. The numerical tests indicate that the method presented here has significant potential for improving analyses and forecasts. ensemble Kalman filter climatological covariance data assimilation 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 9 (DE-627)324455895 (DE-600)2026987-0 16000870 nnns volume:67 year:2015 number:0 pages:9 https://doi.org/10.3402/tellusa.v67.26617 kostenfrei https://doaj.org/article/efd40924d1b14970aff23fc2c622943d kostenfrei http://www.tellusa.net/index.php/tellusa/article/view/26617/pdf_34 kostenfrei https://doaj.org/toc/1600-0870 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 9
allfieldsSound	10.3402/tellusa.v67.26617 doi (DE-627)DOAJ028960491 (DE-599)DOAJefd40924d1b14970aff23fc2c622943d DE-627 ger DE-627 rakwb eng GC1-1581 QC851-999 Matthew Kretschmer verfasserin aut Data assimilation using a climatologically augmented local ensemble transform Kalman filter 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Ensemble data assimilation methods are potentially attractive because they provide a computationally affordable (and computationally parallel) means of obtaining flow-dependent background-error statistics. However, a limitation of these methods is that the rank of their flow-dependent background-error covariance estimate, and hence the space of possible analysis increments, is limited by the number of forecast ensemble members. To overcome this deficiency ensemble methods typically use empirical localisation, which allows more degrees of freedom for the analysis increment by suppressing spatially distant background correlations. The method presented here improves the performance of an Ensemble Kalman filter by increasing the size of the ensemble at analysis time in order to boost the rank of its background-error covariance estimate. The additional ensemble members added to the forecast ensemble at analysis time are created by adding a collection of ‘climatological’ perturbations to the forecast ensemble mean. These perturbations are constant in time and provide state space directions, possibly missed by the dynamically forecasted background ensemble, in which the analysis increment can correct the forecast mean based on observations. As the climatological perturbations are calculated once, there is negligible computational cost in obtaining the additional ensemble members at each analysis cycle. Included here are a formulation of the method, results of numerical experiments conducted with a spatiotemporally chaotic model in one spatial dimension and discussion of possible future extensions and applications. The numerical tests indicate that the method presented here has significant potential for improving analyses and forecasts. ensemble Kalman filter climatological covariance data assimilation 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 9 (DE-627)324455895 (DE-600)2026987-0 16000870 nnns volume:67 year:2015 number:0 pages:9 https://doi.org/10.3402/tellusa.v67.26617 kostenfrei https://doaj.org/article/efd40924d1b14970aff23fc2c622943d kostenfrei http://www.tellusa.net/index.php/tellusa/article/view/26617/pdf_34 kostenfrei https://doaj.org/toc/1600-0870 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 9
language	English
source	In Tellus: Series A, Dynamic Meteorology and Oceanography 67(2015), 0, Seite 9 volume:67 year:2015 number:0 pages:9
sourceStr	In Tellus: Series A, Dynamic Meteorology and Oceanography 67(2015), 0, Seite 9 volume:67 year:2015 number:0 pages:9
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	ensemble Kalman filter climatological covariance data assimilation Oceanography Meteorology. Climatology
isfreeaccess_bool	true
container_title	Tellus: Series A, Dynamic Meteorology and Oceanography
authorswithroles_txt_mv	Matthew Kretschmer @@aut@@ Brian R. Hunt @@aut@@ Edward Ott @@aut@@
publishDateDaySort_date	2015-01-01T00:00:00Z
hierarchy_top_id	324455895
id	DOAJ028960491
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ028960491</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502205950.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2015 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3402/tellusa.v67.26617</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ028960491</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJefd40924d1b14970aff23fc2c622943d</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">GC1-1581</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QC851-999</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Matthew Kretschmer</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Data assimilation using a climatologically augmented local ensemble transform Kalman filter</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Ensemble data assimilation methods are potentially attractive because they provide a computationally affordable (and computationally parallel) means of obtaining flow-dependent background-error statistics. However, a limitation of these methods is that the rank of their flow-dependent background-error covariance estimate, and hence the space of possible analysis increments, is limited by the number of forecast ensemble members. To overcome this deficiency ensemble methods typically use empirical localisation, which allows more degrees of freedom for the analysis increment by suppressing spatially distant background correlations. The method presented here improves the performance of an Ensemble Kalman filter by increasing the size of the ensemble at analysis time in order to boost the rank of its background-error covariance estimate. The additional ensemble members added to the forecast ensemble at analysis time are created by adding a collection of ‘climatological’ perturbations to the forecast ensemble mean. These perturbations are constant in time and provide state space directions, possibly missed by the dynamically forecasted background ensemble, in which the analysis increment can correct the forecast mean based on observations. As the climatological perturbations are calculated once, there is negligible computational cost in obtaining the additional ensemble members at each analysis cycle. Included here are a formulation of the method, results of numerical experiments conducted with a spatiotemporally chaotic model in one spatial dimension and discussion of possible future extensions and applications. The numerical tests indicate that the method presented here has significant potential for improving analyses and forecasts.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ensemble Kalman filter</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">climatological covariance</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">data assimilation</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Oceanography</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Meteorology. Climatology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Brian R. Hunt</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Edward Ott</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Tellus: Series A, Dynamic Meteorology and Oceanography</subfield><subfield code="d">Stockholm University Press, 2012</subfield><subfield code="g">67(2015), 0, Seite 9</subfield><subfield code="w">(DE-627)324455895</subfield><subfield code="w">(DE-600)2026987-0</subfield><subfield code="x">16000870</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:67</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:0</subfield><subfield code="g">pages:9</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3402/tellusa.v67.26617</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/efd40924d1b14970aff23fc2c622943d</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.tellusa.net/index.php/tellusa/article/view/26617/pdf_34</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1600-0870</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2031</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2119</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">67</subfield><subfield code="j">2015</subfield><subfield code="e">0</subfield><subfield code="h">9</subfield></datafield></record></collection>
callnumber-first	G - Geography, Anthropology, Recreation
author	Matthew Kretschmer
spellingShingle	Matthew Kretschmer misc GC1-1581 misc QC851-999 misc ensemble Kalman filter misc climatological covariance misc data assimilation misc Oceanography misc Meteorology. Climatology Data assimilation using a climatologically augmented local ensemble transform Kalman filter
authorStr	Matthew Kretschmer
ppnlink_with_tag_str_mv	@@773@@(DE-627)324455895
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	GC1-1581
illustrated	Not Illustrated
issn	16000870
topic_title	GC1-1581 QC851-999 Data assimilation using a climatologically augmented local ensemble transform Kalman filter ensemble Kalman filter climatological covariance data assimilation
topic	misc GC1-1581 misc QC851-999 misc ensemble Kalman filter misc climatological covariance misc data assimilation misc Oceanography misc Meteorology. Climatology
topic_unstemmed	misc GC1-1581 misc QC851-999 misc ensemble Kalman filter misc climatological covariance misc data assimilation misc Oceanography misc Meteorology. Climatology
topic_browse	misc GC1-1581 misc QC851-999 misc ensemble Kalman filter misc climatological covariance misc data assimilation misc Oceanography misc Meteorology. Climatology
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Tellus: Series A, Dynamic Meteorology and Oceanography
hierarchy_parent_id	324455895
hierarchy_top_title	Tellus: Series A, Dynamic Meteorology and Oceanography
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)324455895 (DE-600)2026987-0
title	Data assimilation using a climatologically augmented local ensemble transform Kalman filter
ctrlnum	(DE-627)DOAJ028960491 (DE-599)DOAJefd40924d1b14970aff23fc2c622943d
title_full	Data assimilation using a climatologically augmented local ensemble transform Kalman filter
author_sort	Matthew Kretschmer
journal	Tellus: Series A, Dynamic Meteorology and Oceanography
journalStr	Tellus: Series A, Dynamic Meteorology and Oceanography
callnumber-first-code	G
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2015
contenttype_str_mv	txt
container_start_page	9
author_browse	Matthew Kretschmer Brian R. Hunt Edward Ott
container_volume	67
class	GC1-1581 QC851-999
format_se	Elektronische Aufsätze
author-letter	Matthew Kretschmer
doi_str_mv	10.3402/tellusa.v67.26617
author2-role	verfasserin
title_sort	data assimilation using a climatologically augmented local ensemble transform kalman filter
callnumber	GC1-1581
title_auth	Data assimilation using a climatologically augmented local ensemble transform Kalman filter
abstract	Ensemble data assimilation methods are potentially attractive because they provide a computationally affordable (and computationally parallel) means of obtaining flow-dependent background-error statistics. However, a limitation of these methods is that the rank of their flow-dependent background-error covariance estimate, and hence the space of possible analysis increments, is limited by the number of forecast ensemble members. To overcome this deficiency ensemble methods typically use empirical localisation, which allows more degrees of freedom for the analysis increment by suppressing spatially distant background correlations. The method presented here improves the performance of an Ensemble Kalman filter by increasing the size of the ensemble at analysis time in order to boost the rank of its background-error covariance estimate. The additional ensemble members added to the forecast ensemble at analysis time are created by adding a collection of ‘climatological’ perturbations to the forecast ensemble mean. These perturbations are constant in time and provide state space directions, possibly missed by the dynamically forecasted background ensemble, in which the analysis increment can correct the forecast mean based on observations. As the climatological perturbations are calculated once, there is negligible computational cost in obtaining the additional ensemble members at each analysis cycle. Included here are a formulation of the method, results of numerical experiments conducted with a spatiotemporally chaotic model in one spatial dimension and discussion of possible future extensions and applications. The numerical tests indicate that the method presented here has significant potential for improving analyses and forecasts.
abstractGer	Ensemble data assimilation methods are potentially attractive because they provide a computationally affordable (and computationally parallel) means of obtaining flow-dependent background-error statistics. However, a limitation of these methods is that the rank of their flow-dependent background-error covariance estimate, and hence the space of possible analysis increments, is limited by the number of forecast ensemble members. To overcome this deficiency ensemble methods typically use empirical localisation, which allows more degrees of freedom for the analysis increment by suppressing spatially distant background correlations. The method presented here improves the performance of an Ensemble Kalman filter by increasing the size of the ensemble at analysis time in order to boost the rank of its background-error covariance estimate. The additional ensemble members added to the forecast ensemble at analysis time are created by adding a collection of ‘climatological’ perturbations to the forecast ensemble mean. These perturbations are constant in time and provide state space directions, possibly missed by the dynamically forecasted background ensemble, in which the analysis increment can correct the forecast mean based on observations. As the climatological perturbations are calculated once, there is negligible computational cost in obtaining the additional ensemble members at each analysis cycle. Included here are a formulation of the method, results of numerical experiments conducted with a spatiotemporally chaotic model in one spatial dimension and discussion of possible future extensions and applications. The numerical tests indicate that the method presented here has significant potential for improving analyses and forecasts.
abstract_unstemmed	Ensemble data assimilation methods are potentially attractive because they provide a computationally affordable (and computationally parallel) means of obtaining flow-dependent background-error statistics. However, a limitation of these methods is that the rank of their flow-dependent background-error covariance estimate, and hence the space of possible analysis increments, is limited by the number of forecast ensemble members. To overcome this deficiency ensemble methods typically use empirical localisation, which allows more degrees of freedom for the analysis increment by suppressing spatially distant background correlations. The method presented here improves the performance of an Ensemble Kalman filter by increasing the size of the ensemble at analysis time in order to boost the rank of its background-error covariance estimate. The additional ensemble members added to the forecast ensemble at analysis time are created by adding a collection of ‘climatological’ perturbations to the forecast ensemble mean. These perturbations are constant in time and provide state space directions, possibly missed by the dynamically forecasted background ensemble, in which the analysis increment can correct the forecast mean based on observations. As the climatological perturbations are calculated once, there is negligible computational cost in obtaining the additional ensemble members at each analysis cycle. Included here are a formulation of the method, results of numerical experiments conducted with a spatiotemporally chaotic model in one spatial dimension and discussion of possible future extensions and applications. The numerical tests indicate that the method presented here has significant potential for improving analyses and forecasts.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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
container_issue	0
title_short	Data assimilation using a climatologically augmented local ensemble transform Kalman filter
url	https://doi.org/10.3402/tellusa.v67.26617 https://doaj.org/article/efd40924d1b14970aff23fc2c622943d http://www.tellusa.net/index.php/tellusa/article/view/26617/pdf_34 https://doaj.org/toc/1600-0870
remote_bool	true
author2	Brian R. Hunt Edward Ott
author2Str	Brian R. Hunt Edward Ott
ppnlink	324455895
callnumber-subject	GC - Oceanography
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.3402/tellusa.v67.26617
callnumber-a	GC1-1581
up_date	2024-07-03T20:22:14.643Z
_version_	1803590707076661248
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ028960491</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502205950.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2015 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3402/tellusa.v67.26617</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ028960491</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJefd40924d1b14970aff23fc2c622943d</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">GC1-1581</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QC851-999</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Matthew Kretschmer</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Data assimilation using a climatologically augmented local ensemble transform Kalman filter</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Ensemble data assimilation methods are potentially attractive because they provide a computationally affordable (and computationally parallel) means of obtaining flow-dependent background-error statistics. However, a limitation of these methods is that the rank of their flow-dependent background-error covariance estimate, and hence the space of possible analysis increments, is limited by the number of forecast ensemble members. To overcome this deficiency ensemble methods typically use empirical localisation, which allows more degrees of freedom for the analysis increment by suppressing spatially distant background correlations. The method presented here improves the performance of an Ensemble Kalman filter by increasing the size of the ensemble at analysis time in order to boost the rank of its background-error covariance estimate. The additional ensemble members added to the forecast ensemble at analysis time are created by adding a collection of ‘climatological’ perturbations to the forecast ensemble mean. These perturbations are constant in time and provide state space directions, possibly missed by the dynamically forecasted background ensemble, in which the analysis increment can correct the forecast mean based on observations. As the climatological perturbations are calculated once, there is negligible computational cost in obtaining the additional ensemble members at each analysis cycle. Included here are a formulation of the method, results of numerical experiments conducted with a spatiotemporally chaotic model in one spatial dimension and discussion of possible future extensions and applications. The numerical tests indicate that the method presented here has significant potential for improving analyses and forecasts.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ensemble Kalman filter</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">climatological covariance</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">data assimilation</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Oceanography</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Meteorology. Climatology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Brian R. Hunt</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Edward Ott</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Tellus: Series A, Dynamic Meteorology and Oceanography</subfield><subfield code="d">Stockholm University Press, 2012</subfield><subfield code="g">67(2015), 0, Seite 9</subfield><subfield code="w">(DE-627)324455895</subfield><subfield code="w">(DE-600)2026987-0</subfield><subfield code="x">16000870</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:67</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:0</subfield><subfield code="g">pages:9</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3402/tellusa.v67.26617</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/efd40924d1b14970aff23fc2c622943d</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.tellusa.net/index.php/tellusa/article/view/26617/pdf_34</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1600-0870</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2031</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2119</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">67</subfield><subfield code="j">2015</subfield><subfield code="e">0</subfield><subfield code="h">9</subfield></datafield></record></collection>
score	7.3983297

Nicht das Richtige dabei?

Schreiben Sie uns!

Data assimilation using a climatologically augmented local ensemble transform Kalman filter

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?