Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment

A newly introduced stochastic data assimilation method, the Ensemble Kalman Filter Semi-Qualitative (EnKF-SQ) is applied to a realistic coupled ice-ocean model of the Arctic, the TOPAZ4 configuration, in a twin experiment framework. The method is shown to add value to range-limited thin ice thicknes...
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Autor*in:	Abhishek Shah [verfasserIn] Laurent Bertino [verfasserIn] François Counillon [verfasserIn] Mohamad El Gharamti [verfasserIn] Jiping Xie [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	semi-qualitative observations range limitation smos ice thickness topaz4 enkf-sq

Übergeordnetes Werk:	In: Tellus: Series A, Dynamic Meteorology and Oceanography - Stockholm University Press, 2012, 72(2020), 1, Seite 15
Übergeordnetes Werk:	volume:72 ; year:2020 ; number:1 ; pages:15

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1080/16000870.2019.1697166

Katalog-ID:	DOAJ029321328

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allfields	10.1080/16000870.2019.1697166 doi (DE-627)DOAJ029321328 (DE-599)DOAJ677cfefb01c740ff81ec5550263a2507 DE-627 ger DE-627 rakwb eng GC1-1581 QC851-999 Abhishek Shah verfasserin aut Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A newly introduced stochastic data assimilation method, the Ensemble Kalman Filter Semi-Qualitative (EnKF-SQ) is applied to a realistic coupled ice-ocean model of the Arctic, the TOPAZ4 configuration, in a twin experiment framework. The method is shown to add value to range-limited thin ice thickness measurements, as obtained from passive microwave remote sensing, with respect to more trivial solutions like neglecting the out-of-range values or assimilating climatology instead. Some known properties inherent to the EnKF-SQ are evaluated: the tendency to draw the solution closer to the thickness threshold, the skewness of the resulting analysis ensemble and the potential appearance of outliers. The experiments show that none of these properties prove deleterious in light of the other sub-optimal characters of the sea ice data assimilation system used here (non-linearities, non-Gaussian variables, lack of strong coupling). The EnKF-SQ has a single tuning parameter that is adjusted for best performance of the system at hand. The sensitivity tests reveal that the tuning parameter does not critically influence the results. The EnKF-SQ makes overall a valid approach for assimilating semi-qualitative observations into high-dimensional nonlinear systems. semi-qualitative observations range limitation smos ice thickness topaz4 enkf-sq Oceanography Meteorology. Climatology Laurent Bertino verfasserin aut François Counillon verfasserin aut Mohamad El Gharamti verfasserin aut Jiping Xie verfasserin aut In Tellus: Series A, Dynamic Meteorology and Oceanography Stockholm University Press, 2012 72(2020), 1, Seite 15 (DE-627)324455895 (DE-600)2026987-0 16000870 nnns volume:72 year:2020 number:1 pages:15 https://doi.org/10.1080/16000870.2019.1697166 kostenfrei https://doaj.org/article/677cfefb01c740ff81ec5550263a2507 kostenfrei http://dx.doi.org/10.1080/16000870.2019.1697166 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_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 72 2020 1 15
spelling	10.1080/16000870.2019.1697166 doi (DE-627)DOAJ029321328 (DE-599)DOAJ677cfefb01c740ff81ec5550263a2507 DE-627 ger DE-627 rakwb eng GC1-1581 QC851-999 Abhishek Shah verfasserin aut Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A newly introduced stochastic data assimilation method, the Ensemble Kalman Filter Semi-Qualitative (EnKF-SQ) is applied to a realistic coupled ice-ocean model of the Arctic, the TOPAZ4 configuration, in a twin experiment framework. The method is shown to add value to range-limited thin ice thickness measurements, as obtained from passive microwave remote sensing, with respect to more trivial solutions like neglecting the out-of-range values or assimilating climatology instead. Some known properties inherent to the EnKF-SQ are evaluated: the tendency to draw the solution closer to the thickness threshold, the skewness of the resulting analysis ensemble and the potential appearance of outliers. The experiments show that none of these properties prove deleterious in light of the other sub-optimal characters of the sea ice data assimilation system used here (non-linearities, non-Gaussian variables, lack of strong coupling). The EnKF-SQ has a single tuning parameter that is adjusted for best performance of the system at hand. The sensitivity tests reveal that the tuning parameter does not critically influence the results. The EnKF-SQ makes overall a valid approach for assimilating semi-qualitative observations into high-dimensional nonlinear systems. semi-qualitative observations range limitation smos ice thickness topaz4 enkf-sq Oceanography Meteorology. Climatology Laurent Bertino verfasserin aut François Counillon verfasserin aut Mohamad El Gharamti verfasserin aut Jiping Xie verfasserin aut In Tellus: Series A, Dynamic Meteorology and Oceanography Stockholm University Press, 2012 72(2020), 1, Seite 15 (DE-627)324455895 (DE-600)2026987-0 16000870 nnns volume:72 year:2020 number:1 pages:15 https://doi.org/10.1080/16000870.2019.1697166 kostenfrei https://doaj.org/article/677cfefb01c740ff81ec5550263a2507 kostenfrei http://dx.doi.org/10.1080/16000870.2019.1697166 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_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 72 2020 1 15
allfields_unstemmed	10.1080/16000870.2019.1697166 doi (DE-627)DOAJ029321328 (DE-599)DOAJ677cfefb01c740ff81ec5550263a2507 DE-627 ger DE-627 rakwb eng GC1-1581 QC851-999 Abhishek Shah verfasserin aut Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A newly introduced stochastic data assimilation method, the Ensemble Kalman Filter Semi-Qualitative (EnKF-SQ) is applied to a realistic coupled ice-ocean model of the Arctic, the TOPAZ4 configuration, in a twin experiment framework. The method is shown to add value to range-limited thin ice thickness measurements, as obtained from passive microwave remote sensing, with respect to more trivial solutions like neglecting the out-of-range values or assimilating climatology instead. Some known properties inherent to the EnKF-SQ are evaluated: the tendency to draw the solution closer to the thickness threshold, the skewness of the resulting analysis ensemble and the potential appearance of outliers. The experiments show that none of these properties prove deleterious in light of the other sub-optimal characters of the sea ice data assimilation system used here (non-linearities, non-Gaussian variables, lack of strong coupling). The EnKF-SQ has a single tuning parameter that is adjusted for best performance of the system at hand. The sensitivity tests reveal that the tuning parameter does not critically influence the results. The EnKF-SQ makes overall a valid approach for assimilating semi-qualitative observations into high-dimensional nonlinear systems. semi-qualitative observations range limitation smos ice thickness topaz4 enkf-sq Oceanography Meteorology. Climatology Laurent Bertino verfasserin aut François Counillon verfasserin aut Mohamad El Gharamti verfasserin aut Jiping Xie verfasserin aut In Tellus: Series A, Dynamic Meteorology and Oceanography Stockholm University Press, 2012 72(2020), 1, Seite 15 (DE-627)324455895 (DE-600)2026987-0 16000870 nnns volume:72 year:2020 number:1 pages:15 https://doi.org/10.1080/16000870.2019.1697166 kostenfrei https://doaj.org/article/677cfefb01c740ff81ec5550263a2507 kostenfrei http://dx.doi.org/10.1080/16000870.2019.1697166 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_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 72 2020 1 15
allfieldsGer	10.1080/16000870.2019.1697166 doi (DE-627)DOAJ029321328 (DE-599)DOAJ677cfefb01c740ff81ec5550263a2507 DE-627 ger DE-627 rakwb eng GC1-1581 QC851-999 Abhishek Shah verfasserin aut Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A newly introduced stochastic data assimilation method, the Ensemble Kalman Filter Semi-Qualitative (EnKF-SQ) is applied to a realistic coupled ice-ocean model of the Arctic, the TOPAZ4 configuration, in a twin experiment framework. The method is shown to add value to range-limited thin ice thickness measurements, as obtained from passive microwave remote sensing, with respect to more trivial solutions like neglecting the out-of-range values or assimilating climatology instead. Some known properties inherent to the EnKF-SQ are evaluated: the tendency to draw the solution closer to the thickness threshold, the skewness of the resulting analysis ensemble and the potential appearance of outliers. The experiments show that none of these properties prove deleterious in light of the other sub-optimal characters of the sea ice data assimilation system used here (non-linearities, non-Gaussian variables, lack of strong coupling). The EnKF-SQ has a single tuning parameter that is adjusted for best performance of the system at hand. The sensitivity tests reveal that the tuning parameter does not critically influence the results. The EnKF-SQ makes overall a valid approach for assimilating semi-qualitative observations into high-dimensional nonlinear systems. semi-qualitative observations range limitation smos ice thickness topaz4 enkf-sq Oceanography Meteorology. Climatology Laurent Bertino verfasserin aut François Counillon verfasserin aut Mohamad El Gharamti verfasserin aut Jiping Xie verfasserin aut In Tellus: Series A, Dynamic Meteorology and Oceanography Stockholm University Press, 2012 72(2020), 1, Seite 15 (DE-627)324455895 (DE-600)2026987-0 16000870 nnns volume:72 year:2020 number:1 pages:15 https://doi.org/10.1080/16000870.2019.1697166 kostenfrei https://doaj.org/article/677cfefb01c740ff81ec5550263a2507 kostenfrei http://dx.doi.org/10.1080/16000870.2019.1697166 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_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 72 2020 1 15
allfieldsSound	10.1080/16000870.2019.1697166 doi (DE-627)DOAJ029321328 (DE-599)DOAJ677cfefb01c740ff81ec5550263a2507 DE-627 ger DE-627 rakwb eng GC1-1581 QC851-999 Abhishek Shah verfasserin aut Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A newly introduced stochastic data assimilation method, the Ensemble Kalman Filter Semi-Qualitative (EnKF-SQ) is applied to a realistic coupled ice-ocean model of the Arctic, the TOPAZ4 configuration, in a twin experiment framework. The method is shown to add value to range-limited thin ice thickness measurements, as obtained from passive microwave remote sensing, with respect to more trivial solutions like neglecting the out-of-range values or assimilating climatology instead. Some known properties inherent to the EnKF-SQ are evaluated: the tendency to draw the solution closer to the thickness threshold, the skewness of the resulting analysis ensemble and the potential appearance of outliers. The experiments show that none of these properties prove deleterious in light of the other sub-optimal characters of the sea ice data assimilation system used here (non-linearities, non-Gaussian variables, lack of strong coupling). The EnKF-SQ has a single tuning parameter that is adjusted for best performance of the system at hand. The sensitivity tests reveal that the tuning parameter does not critically influence the results. The EnKF-SQ makes overall a valid approach for assimilating semi-qualitative observations into high-dimensional nonlinear systems. semi-qualitative observations range limitation smos ice thickness topaz4 enkf-sq Oceanography Meteorology. Climatology Laurent Bertino verfasserin aut François Counillon verfasserin aut Mohamad El Gharamti verfasserin aut Jiping Xie verfasserin aut In Tellus: Series A, Dynamic Meteorology and Oceanography Stockholm University Press, 2012 72(2020), 1, Seite 15 (DE-627)324455895 (DE-600)2026987-0 16000870 nnns volume:72 year:2020 number:1 pages:15 https://doi.org/10.1080/16000870.2019.1697166 kostenfrei https://doaj.org/article/677cfefb01c740ff81ec5550263a2507 kostenfrei http://dx.doi.org/10.1080/16000870.2019.1697166 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_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 72 2020 1 15
language	English
source	In Tellus: Series A, Dynamic Meteorology and Oceanography 72(2020), 1, Seite 15 volume:72 year:2020 number:1 pages:15
sourceStr	In Tellus: Series A, Dynamic Meteorology and Oceanography 72(2020), 1, Seite 15 volume:72 year:2020 number:1 pages:15
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	semi-qualitative observations range limitation smos ice thickness topaz4 enkf-sq Oceanography Meteorology. Climatology
isfreeaccess_bool	true
container_title	Tellus: Series A, Dynamic Meteorology and Oceanography
authorswithroles_txt_mv	Abhishek Shah @@aut@@ Laurent Bertino @@aut@@ François Counillon @@aut@@ Mohamad El Gharamti @@aut@@ Jiping Xie @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	324455895
id	DOAJ029321328
language_de	englisch
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callnumber-first	G - Geography, Anthropology, Recreation
author	Abhishek Shah
spellingShingle	Abhishek Shah misc GC1-1581 misc QC851-999 misc semi-qualitative observations misc range limitation misc smos misc ice thickness misc topaz4 misc enkf-sq misc Oceanography misc Meteorology. Climatology Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment
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topic_title	GC1-1581 QC851-999 Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment semi-qualitative observations range limitation smos ice thickness topaz4 enkf-sq
topic	misc GC1-1581 misc QC851-999 misc semi-qualitative observations misc range limitation misc smos misc ice thickness misc topaz4 misc enkf-sq misc Oceanography misc Meteorology. Climatology
topic_unstemmed	misc GC1-1581 misc QC851-999 misc semi-qualitative observations misc range limitation misc smos misc ice thickness misc topaz4 misc enkf-sq misc Oceanography misc Meteorology. Climatology
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title	Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment
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title_full	Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment
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title_sort	assimilation of semi-qualitative sea ice thickness data with the enkf-sq: a twin experiment
callnumber	GC1-1581
title_auth	Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment
abstract	A newly introduced stochastic data assimilation method, the Ensemble Kalman Filter Semi-Qualitative (EnKF-SQ) is applied to a realistic coupled ice-ocean model of the Arctic, the TOPAZ4 configuration, in a twin experiment framework. The method is shown to add value to range-limited thin ice thickness measurements, as obtained from passive microwave remote sensing, with respect to more trivial solutions like neglecting the out-of-range values or assimilating climatology instead. Some known properties inherent to the EnKF-SQ are evaluated: the tendency to draw the solution closer to the thickness threshold, the skewness of the resulting analysis ensemble and the potential appearance of outliers. The experiments show that none of these properties prove deleterious in light of the other sub-optimal characters of the sea ice data assimilation system used here (non-linearities, non-Gaussian variables, lack of strong coupling). The EnKF-SQ has a single tuning parameter that is adjusted for best performance of the system at hand. The sensitivity tests reveal that the tuning parameter does not critically influence the results. The EnKF-SQ makes overall a valid approach for assimilating semi-qualitative observations into high-dimensional nonlinear systems.
abstractGer	A newly introduced stochastic data assimilation method, the Ensemble Kalman Filter Semi-Qualitative (EnKF-SQ) is applied to a realistic coupled ice-ocean model of the Arctic, the TOPAZ4 configuration, in a twin experiment framework. The method is shown to add value to range-limited thin ice thickness measurements, as obtained from passive microwave remote sensing, with respect to more trivial solutions like neglecting the out-of-range values or assimilating climatology instead. Some known properties inherent to the EnKF-SQ are evaluated: the tendency to draw the solution closer to the thickness threshold, the skewness of the resulting analysis ensemble and the potential appearance of outliers. The experiments show that none of these properties prove deleterious in light of the other sub-optimal characters of the sea ice data assimilation system used here (non-linearities, non-Gaussian variables, lack of strong coupling). The EnKF-SQ has a single tuning parameter that is adjusted for best performance of the system at hand. The sensitivity tests reveal that the tuning parameter does not critically influence the results. The EnKF-SQ makes overall a valid approach for assimilating semi-qualitative observations into high-dimensional nonlinear systems.
abstract_unstemmed	A newly introduced stochastic data assimilation method, the Ensemble Kalman Filter Semi-Qualitative (EnKF-SQ) is applied to a realistic coupled ice-ocean model of the Arctic, the TOPAZ4 configuration, in a twin experiment framework. The method is shown to add value to range-limited thin ice thickness measurements, as obtained from passive microwave remote sensing, with respect to more trivial solutions like neglecting the out-of-range values or assimilating climatology instead. Some known properties inherent to the EnKF-SQ are evaluated: the tendency to draw the solution closer to the thickness threshold, the skewness of the resulting analysis ensemble and the potential appearance of outliers. The experiments show that none of these properties prove deleterious in light of the other sub-optimal characters of the sea ice data assimilation system used here (non-linearities, non-Gaussian variables, lack of strong coupling). The EnKF-SQ has a single tuning parameter that is adjusted for best performance of the system at hand. The sensitivity tests reveal that the tuning parameter does not critically influence the results. The EnKF-SQ makes overall a valid approach for assimilating semi-qualitative observations into high-dimensional nonlinear systems.
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title_short	Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment
url	https://doi.org/10.1080/16000870.2019.1697166 https://doaj.org/article/677cfefb01c740ff81ec5550263a2507 http://dx.doi.org/10.1080/16000870.2019.1697166 https://doaj.org/toc/1600-0870
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Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment

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