Comparing hybrid data assimilation methods on the Lorenz 1963 model with increasing non-linearity

We systematically compare the performance of ETKF-4DVAR, 4DVAR-BEN and 4DENVAR with respect to two traditional methods (4DVAR and ETKF) and an ensemble transform Kalman smoother (ETKS) on the Lorenz 1963 model. We specifically investigated this performance with increasing non-linearity and using a q...
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Autor*in:	Michael Goodliff [verfasserIn] Javier Amezcua [verfasserIn] Peter Jan Van Leeuwen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	data assimilation hybrid methods flow dependence

Übergeordnetes Werk:	In: Tellus: Series A, Dynamic Meteorology and Oceanography - Stockholm University Press, 2012, 67(2015), 0, Seite 13
Übergeordnetes Werk:	volume:67 ; year:2015 ; number:0 ; pages:13

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3402/tellusa.v67.26928

Katalog-ID:	DOAJ024791555

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allfields	10.3402/tellusa.v67.26928 doi (DE-627)DOAJ024791555 (DE-599)DOAJ5e4af77f98d1493c8f540e184043fce7 DE-627 ger DE-627 rakwb eng GC1-1581 QC851-999 Michael Goodliff verfasserin aut Comparing hybrid data assimilation methods on the Lorenz 1963 model with increasing non-linearity 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We systematically compare the performance of ETKF-4DVAR, 4DVAR-BEN and 4DENVAR with respect to two traditional methods (4DVAR and ETKF) and an ensemble transform Kalman smoother (ETKS) on the Lorenz 1963 model. We specifically investigated this performance with increasing non-linearity and using a quasi-static variational assimilation algorithm as a comparison. Using the analysis root mean square error (RMSE) as a metric, these methods have been compared considering (1) assimilation window length and observation interval size and (2) ensemble size to investigate the influence of hybrid background error covariance matrices and non-linearity on the performance of the methods. For short assimilation windows with close to linear dynamics, it has been shown that all hybrid methods show an improvement in RMSE compared to the traditional methods. For long assimilation window lengths in which non-linear dynamics are substantial, the variational framework can have difficulties finding the global minimum of the cost function, so we explore a quasi-static variational assimilation (QSVA) framework. Of the hybrid methods, it is seen that under certain parameters, hybrid methods which do not use a climatological background error covariance do not need QSVA to perform accurately. Generally, results show that the ETKS and hybrid methods that do not use a climatological background error covariance matrix with QSVA outperform all other methods due to the full flow dependency of the background error covariance matrix which also allows for the most non-linearity. data assimilation hybrid methods flow dependence Oceanography Meteorology. Climatology Javier Amezcua verfasserin aut Peter Jan Van Leeuwen verfasserin aut In Tellus: Series A, Dynamic Meteorology and Oceanography Stockholm University Press, 2012 67(2015), 0, Seite 13 (DE-627)324455895 (DE-600)2026987-0 16000870 nnns volume:67 year:2015 number:0 pages:13 https://doi.org/10.3402/tellusa.v67.26928 kostenfrei https://doaj.org/article/5e4af77f98d1493c8f540e184043fce7 kostenfrei http://www.tellusa.net/index.php/tellusa/article/view/26928/pdf_31 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 13
spelling	10.3402/tellusa.v67.26928 doi (DE-627)DOAJ024791555 (DE-599)DOAJ5e4af77f98d1493c8f540e184043fce7 DE-627 ger DE-627 rakwb eng GC1-1581 QC851-999 Michael Goodliff verfasserin aut Comparing hybrid data assimilation methods on the Lorenz 1963 model with increasing non-linearity 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We systematically compare the performance of ETKF-4DVAR, 4DVAR-BEN and 4DENVAR with respect to two traditional methods (4DVAR and ETKF) and an ensemble transform Kalman smoother (ETKS) on the Lorenz 1963 model. We specifically investigated this performance with increasing non-linearity and using a quasi-static variational assimilation algorithm as a comparison. Using the analysis root mean square error (RMSE) as a metric, these methods have been compared considering (1) assimilation window length and observation interval size and (2) ensemble size to investigate the influence of hybrid background error covariance matrices and non-linearity on the performance of the methods. For short assimilation windows with close to linear dynamics, it has been shown that all hybrid methods show an improvement in RMSE compared to the traditional methods. For long assimilation window lengths in which non-linear dynamics are substantial, the variational framework can have difficulties finding the global minimum of the cost function, so we explore a quasi-static variational assimilation (QSVA) framework. Of the hybrid methods, it is seen that under certain parameters, hybrid methods which do not use a climatological background error covariance do not need QSVA to perform accurately. Generally, results show that the ETKS and hybrid methods that do not use a climatological background error covariance matrix with QSVA outperform all other methods due to the full flow dependency of the background error covariance matrix which also allows for the most non-linearity. data assimilation hybrid methods flow dependence Oceanography Meteorology. Climatology Javier Amezcua verfasserin aut Peter Jan Van Leeuwen verfasserin aut In Tellus: Series A, Dynamic Meteorology and Oceanography Stockholm University Press, 2012 67(2015), 0, Seite 13 (DE-627)324455895 (DE-600)2026987-0 16000870 nnns volume:67 year:2015 number:0 pages:13 https://doi.org/10.3402/tellusa.v67.26928 kostenfrei https://doaj.org/article/5e4af77f98d1493c8f540e184043fce7 kostenfrei http://www.tellusa.net/index.php/tellusa/article/view/26928/pdf_31 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 13
allfields_unstemmed	10.3402/tellusa.v67.26928 doi (DE-627)DOAJ024791555 (DE-599)DOAJ5e4af77f98d1493c8f540e184043fce7 DE-627 ger DE-627 rakwb eng GC1-1581 QC851-999 Michael Goodliff verfasserin aut Comparing hybrid data assimilation methods on the Lorenz 1963 model with increasing non-linearity 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We systematically compare the performance of ETKF-4DVAR, 4DVAR-BEN and 4DENVAR with respect to two traditional methods (4DVAR and ETKF) and an ensemble transform Kalman smoother (ETKS) on the Lorenz 1963 model. We specifically investigated this performance with increasing non-linearity and using a quasi-static variational assimilation algorithm as a comparison. Using the analysis root mean square error (RMSE) as a metric, these methods have been compared considering (1) assimilation window length and observation interval size and (2) ensemble size to investigate the influence of hybrid background error covariance matrices and non-linearity on the performance of the methods. For short assimilation windows with close to linear dynamics, it has been shown that all hybrid methods show an improvement in RMSE compared to the traditional methods. For long assimilation window lengths in which non-linear dynamics are substantial, the variational framework can have difficulties finding the global minimum of the cost function, so we explore a quasi-static variational assimilation (QSVA) framework. Of the hybrid methods, it is seen that under certain parameters, hybrid methods which do not use a climatological background error covariance do not need QSVA to perform accurately. Generally, results show that the ETKS and hybrid methods that do not use a climatological background error covariance matrix with QSVA outperform all other methods due to the full flow dependency of the background error covariance matrix which also allows for the most non-linearity. data assimilation hybrid methods flow dependence Oceanography Meteorology. Climatology Javier Amezcua verfasserin aut Peter Jan Van Leeuwen verfasserin aut In Tellus: Series A, Dynamic Meteorology and Oceanography Stockholm University Press, 2012 67(2015), 0, Seite 13 (DE-627)324455895 (DE-600)2026987-0 16000870 nnns volume:67 year:2015 number:0 pages:13 https://doi.org/10.3402/tellusa.v67.26928 kostenfrei https://doaj.org/article/5e4af77f98d1493c8f540e184043fce7 kostenfrei http://www.tellusa.net/index.php/tellusa/article/view/26928/pdf_31 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 13
allfieldsGer	10.3402/tellusa.v67.26928 doi (DE-627)DOAJ024791555 (DE-599)DOAJ5e4af77f98d1493c8f540e184043fce7 DE-627 ger DE-627 rakwb eng GC1-1581 QC851-999 Michael Goodliff verfasserin aut Comparing hybrid data assimilation methods on the Lorenz 1963 model with increasing non-linearity 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We systematically compare the performance of ETKF-4DVAR, 4DVAR-BEN and 4DENVAR with respect to two traditional methods (4DVAR and ETKF) and an ensemble transform Kalman smoother (ETKS) on the Lorenz 1963 model. We specifically investigated this performance with increasing non-linearity and using a quasi-static variational assimilation algorithm as a comparison. Using the analysis root mean square error (RMSE) as a metric, these methods have been compared considering (1) assimilation window length and observation interval size and (2) ensemble size to investigate the influence of hybrid background error covariance matrices and non-linearity on the performance of the methods. For short assimilation windows with close to linear dynamics, it has been shown that all hybrid methods show an improvement in RMSE compared to the traditional methods. For long assimilation window lengths in which non-linear dynamics are substantial, the variational framework can have difficulties finding the global minimum of the cost function, so we explore a quasi-static variational assimilation (QSVA) framework. Of the hybrid methods, it is seen that under certain parameters, hybrid methods which do not use a climatological background error covariance do not need QSVA to perform accurately. Generally, results show that the ETKS and hybrid methods that do not use a climatological background error covariance matrix with QSVA outperform all other methods due to the full flow dependency of the background error covariance matrix which also allows for the most non-linearity. data assimilation hybrid methods flow dependence Oceanography Meteorology. Climatology Javier Amezcua verfasserin aut Peter Jan Van Leeuwen verfasserin aut In Tellus: Series A, Dynamic Meteorology and Oceanography Stockholm University Press, 2012 67(2015), 0, Seite 13 (DE-627)324455895 (DE-600)2026987-0 16000870 nnns volume:67 year:2015 number:0 pages:13 https://doi.org/10.3402/tellusa.v67.26928 kostenfrei https://doaj.org/article/5e4af77f98d1493c8f540e184043fce7 kostenfrei http://www.tellusa.net/index.php/tellusa/article/view/26928/pdf_31 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 13
allfieldsSound	10.3402/tellusa.v67.26928 doi (DE-627)DOAJ024791555 (DE-599)DOAJ5e4af77f98d1493c8f540e184043fce7 DE-627 ger DE-627 rakwb eng GC1-1581 QC851-999 Michael Goodliff verfasserin aut Comparing hybrid data assimilation methods on the Lorenz 1963 model with increasing non-linearity 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We systematically compare the performance of ETKF-4DVAR, 4DVAR-BEN and 4DENVAR with respect to two traditional methods (4DVAR and ETKF) and an ensemble transform Kalman smoother (ETKS) on the Lorenz 1963 model. We specifically investigated this performance with increasing non-linearity and using a quasi-static variational assimilation algorithm as a comparison. Using the analysis root mean square error (RMSE) as a metric, these methods have been compared considering (1) assimilation window length and observation interval size and (2) ensemble size to investigate the influence of hybrid background error covariance matrices and non-linearity on the performance of the methods. For short assimilation windows with close to linear dynamics, it has been shown that all hybrid methods show an improvement in RMSE compared to the traditional methods. For long assimilation window lengths in which non-linear dynamics are substantial, the variational framework can have difficulties finding the global minimum of the cost function, so we explore a quasi-static variational assimilation (QSVA) framework. Of the hybrid methods, it is seen that under certain parameters, hybrid methods which do not use a climatological background error covariance do not need QSVA to perform accurately. Generally, results show that the ETKS and hybrid methods that do not use a climatological background error covariance matrix with QSVA outperform all other methods due to the full flow dependency of the background error covariance matrix which also allows for the most non-linearity. data assimilation hybrid methods flow dependence Oceanography Meteorology. Climatology Javier Amezcua verfasserin aut Peter Jan Van Leeuwen verfasserin aut In Tellus: Series A, Dynamic Meteorology and Oceanography Stockholm University Press, 2012 67(2015), 0, Seite 13 (DE-627)324455895 (DE-600)2026987-0 16000870 nnns volume:67 year:2015 number:0 pages:13 https://doi.org/10.3402/tellusa.v67.26928 kostenfrei https://doaj.org/article/5e4af77f98d1493c8f540e184043fce7 kostenfrei http://www.tellusa.net/index.php/tellusa/article/view/26928/pdf_31 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 13
language	English
source	In Tellus: Series A, Dynamic Meteorology and Oceanography 67(2015), 0, Seite 13 volume:67 year:2015 number:0 pages:13
sourceStr	In Tellus: Series A, Dynamic Meteorology and Oceanography 67(2015), 0, Seite 13 volume:67 year:2015 number:0 pages:13
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	data assimilation hybrid methods flow dependence Oceanography Meteorology. Climatology
isfreeaccess_bool	true
container_title	Tellus: Series A, Dynamic Meteorology and Oceanography
authorswithroles_txt_mv	Michael Goodliff @@aut@@ Javier Amezcua @@aut@@ Peter Jan Van Leeuwen @@aut@@
publishDateDaySort_date	2015-01-01T00:00:00Z
hierarchy_top_id	324455895
id	DOAJ024791555
language_de	englisch
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callnumber-first	G - Geography, Anthropology, Recreation
author	Michael Goodliff
spellingShingle	Michael Goodliff misc GC1-1581 misc QC851-999 misc data assimilation misc hybrid methods misc flow dependence misc Oceanography misc Meteorology. Climatology Comparing hybrid data assimilation methods on the Lorenz 1963 model with increasing non-linearity
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topic_title	GC1-1581 QC851-999 Comparing hybrid data assimilation methods on the Lorenz 1963 model with increasing non-linearity data assimilation hybrid methods flow dependence
topic	misc GC1-1581 misc QC851-999 misc data assimilation misc hybrid methods misc flow dependence misc Oceanography misc Meteorology. Climatology
topic_unstemmed	misc GC1-1581 misc QC851-999 misc data assimilation misc hybrid methods misc flow dependence misc Oceanography misc Meteorology. Climatology
topic_browse	misc GC1-1581 misc QC851-999 misc data assimilation misc hybrid methods misc flow dependence misc Oceanography misc Meteorology. Climatology
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title	Comparing hybrid data assimilation methods on the Lorenz 1963 model with increasing non-linearity
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title_full	Comparing hybrid data assimilation methods on the Lorenz 1963 model with increasing non-linearity
author_sort	Michael Goodliff
journal	Tellus: Series A, Dynamic Meteorology and Oceanography
journalStr	Tellus: Series A, Dynamic Meteorology and Oceanography
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author_browse	Michael Goodliff Javier Amezcua Peter Jan Van Leeuwen
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author-letter	Michael Goodliff
doi_str_mv	10.3402/tellusa.v67.26928
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title_sort	comparing hybrid data assimilation methods on the lorenz 1963 model with increasing non-linearity
callnumber	GC1-1581
title_auth	Comparing hybrid data assimilation methods on the Lorenz 1963 model with increasing non-linearity
abstract	We systematically compare the performance of ETKF-4DVAR, 4DVAR-BEN and 4DENVAR with respect to two traditional methods (4DVAR and ETKF) and an ensemble transform Kalman smoother (ETKS) on the Lorenz 1963 model. We specifically investigated this performance with increasing non-linearity and using a quasi-static variational assimilation algorithm as a comparison. Using the analysis root mean square error (RMSE) as a metric, these methods have been compared considering (1) assimilation window length and observation interval size and (2) ensemble size to investigate the influence of hybrid background error covariance matrices and non-linearity on the performance of the methods. For short assimilation windows with close to linear dynamics, it has been shown that all hybrid methods show an improvement in RMSE compared to the traditional methods. For long assimilation window lengths in which non-linear dynamics are substantial, the variational framework can have difficulties finding the global minimum of the cost function, so we explore a quasi-static variational assimilation (QSVA) framework. Of the hybrid methods, it is seen that under certain parameters, hybrid methods which do not use a climatological background error covariance do not need QSVA to perform accurately. Generally, results show that the ETKS and hybrid methods that do not use a climatological background error covariance matrix with QSVA outperform all other methods due to the full flow dependency of the background error covariance matrix which also allows for the most non-linearity.
abstractGer	We systematically compare the performance of ETKF-4DVAR, 4DVAR-BEN and 4DENVAR with respect to two traditional methods (4DVAR and ETKF) and an ensemble transform Kalman smoother (ETKS) on the Lorenz 1963 model. We specifically investigated this performance with increasing non-linearity and using a quasi-static variational assimilation algorithm as a comparison. Using the analysis root mean square error (RMSE) as a metric, these methods have been compared considering (1) assimilation window length and observation interval size and (2) ensemble size to investigate the influence of hybrid background error covariance matrices and non-linearity on the performance of the methods. For short assimilation windows with close to linear dynamics, it has been shown that all hybrid methods show an improvement in RMSE compared to the traditional methods. For long assimilation window lengths in which non-linear dynamics are substantial, the variational framework can have difficulties finding the global minimum of the cost function, so we explore a quasi-static variational assimilation (QSVA) framework. Of the hybrid methods, it is seen that under certain parameters, hybrid methods which do not use a climatological background error covariance do not need QSVA to perform accurately. Generally, results show that the ETKS and hybrid methods that do not use a climatological background error covariance matrix with QSVA outperform all other methods due to the full flow dependency of the background error covariance matrix which also allows for the most non-linearity.
abstract_unstemmed	We systematically compare the performance of ETKF-4DVAR, 4DVAR-BEN and 4DENVAR with respect to two traditional methods (4DVAR and ETKF) and an ensemble transform Kalman smoother (ETKS) on the Lorenz 1963 model. We specifically investigated this performance with increasing non-linearity and using a quasi-static variational assimilation algorithm as a comparison. Using the analysis root mean square error (RMSE) as a metric, these methods have been compared considering (1) assimilation window length and observation interval size and (2) ensemble size to investigate the influence of hybrid background error covariance matrices and non-linearity on the performance of the methods. For short assimilation windows with close to linear dynamics, it has been shown that all hybrid methods show an improvement in RMSE compared to the traditional methods. For long assimilation window lengths in which non-linear dynamics are substantial, the variational framework can have difficulties finding the global minimum of the cost function, so we explore a quasi-static variational assimilation (QSVA) framework. Of the hybrid methods, it is seen that under certain parameters, hybrid methods which do not use a climatological background error covariance do not need QSVA to perform accurately. Generally, results show that the ETKS and hybrid methods that do not use a climatological background error covariance matrix with QSVA outperform all other methods due to the full flow dependency of the background error covariance matrix which also allows for the most non-linearity.
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title_short	Comparing hybrid data assimilation methods on the Lorenz 1963 model with increasing non-linearity
url	https://doi.org/10.3402/tellusa.v67.26928 https://doaj.org/article/5e4af77f98d1493c8f540e184043fce7 http://www.tellusa.net/index.php/tellusa/article/view/26928/pdf_31 https://doaj.org/toc/1600-0870
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author2	Javier Amezcua Peter Jan Van Leeuwen
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Comparing hybrid data assimilation methods on the Lorenz 1963 model with increasing non-linearity

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