Ensemble estimates of the wave state related parameters in a sea spray parameterization scheme
Abstract Uncertainties of the wave state parameters in a sea spray parameterization scheme can be a major source of errors for air-sea turbulent (momentum, sensible and latent) fluxes parameterizations resulting in biases in numerical ocean simulations and forecasts. In this study, we explore applic...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Lianxin [verfasserIn] Wu, Xinrong [verfasserIn] Perrie, William [verfasserIn] Zhang, Xuefeng [verfasserIn] Guan, Changlong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Ocean dynamics - Berlin : Springer, 1948, 69(2019), 6 vom: 08. Mai, Seite 719-735 |
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| Übergeordnetes Werk: |
volume:69 ; year:2019 ; number:6 ; day:08 ; month:05 ; pages:719-735 |
| Links: |
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| DOI / URN: |
10.1007/s10236-019-01270-6 |
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| Katalog-ID: |
SPR00921285X |
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| 520 | |a Abstract Uncertainties of the wave state parameters in a sea spray parameterization scheme can be a major source of errors for air-sea turbulent (momentum, sensible and latent) fluxes parameterizations resulting in biases in numerical ocean simulations and forecasts. In this study, we explore applications of the ensemble adjustment Kalman filter (EAKF) data assimilation method to optimize the wave states parameters the 1-D POM ocean model for a full range of wind speed conditions. Thus, we assimilate sea surface temperature (SST) synthesized observations to improve our estimates of the SST analysis and prediction skill from low to high winds. Two types of experiments are conducted. In the first type, in a “twin” experiment framework, the SST “observations” generated by a “truth” model are assimilated into an imperfect, biased model to investigate the extent to which the parameters are able to be optimized, with respect to the “truth” values based on data from Station Papa and the Kuroshio Extension Observatory (KEO). In the second type, real SST observations from KEO are assimilated to obtain optimized parameters. With these optimized parameters, the SST analysis and prediction errors are significantly reduced, especially for high wind conditions. | ||
| 650 | 4 | |a Parameters optimization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Ensemble data assimilation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Wave state parameters |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Ocean mixed layer |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Sea spray parameterization scheme |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Wu, Xinrong |e verfasserin |4 aut | |
| 700 | 1 | |a Perrie, William |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xuefeng |e verfasserin |4 aut | |
| 700 | 1 | |a Guan, Changlong |e verfasserin |4 aut | |
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10.1007/s10236-019-01270-6 doi (DE-627)SPR00921285X (SPR)s10236-019-01270-6-e DE-627 ger DE-627 rakwb eng 550 ASE 38.90 bkl Zhang, Lianxin verfasserin aut Ensemble estimates of the wave state related parameters in a sea spray parameterization scheme 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Uncertainties of the wave state parameters in a sea spray parameterization scheme can be a major source of errors for air-sea turbulent (momentum, sensible and latent) fluxes parameterizations resulting in biases in numerical ocean simulations and forecasts. In this study, we explore applications of the ensemble adjustment Kalman filter (EAKF) data assimilation method to optimize the wave states parameters the 1-D POM ocean model for a full range of wind speed conditions. Thus, we assimilate sea surface temperature (SST) synthesized observations to improve our estimates of the SST analysis and prediction skill from low to high winds. Two types of experiments are conducted. In the first type, in a “twin” experiment framework, the SST “observations” generated by a “truth” model are assimilated into an imperfect, biased model to investigate the extent to which the parameters are able to be optimized, with respect to the “truth” values based on data from Station Papa and the Kuroshio Extension Observatory (KEO). In the second type, real SST observations from KEO are assimilated to obtain optimized parameters. With these optimized parameters, the SST analysis and prediction errors are significantly reduced, especially for high wind conditions. Parameters optimization (dpeaa)DE-He213 Ensemble data assimilation (dpeaa)DE-He213 Wave state parameters (dpeaa)DE-He213 Ocean mixed layer (dpeaa)DE-He213 Sea spray parameterization scheme (dpeaa)DE-He213 Wu, Xinrong verfasserin aut Perrie, William verfasserin aut Zhang, Xuefeng verfasserin aut Guan, Changlong verfasserin aut Enthalten in Ocean dynamics Berlin : Springer, 1948 69(2019), 6 vom: 08. Mai, Seite 719-735 (DE-627)337809313 (DE-600)2063267-8 1616-7228 nnns volume:69 year:2019 number:6 day:08 month:05 pages:719-735 https://dx.doi.org/10.1007/s10236-019-01270-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.90 ASE AR 69 2019 6 08 05 719-735 |
| spelling |
10.1007/s10236-019-01270-6 doi (DE-627)SPR00921285X (SPR)s10236-019-01270-6-e DE-627 ger DE-627 rakwb eng 550 ASE 38.90 bkl Zhang, Lianxin verfasserin aut Ensemble estimates of the wave state related parameters in a sea spray parameterization scheme 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Uncertainties of the wave state parameters in a sea spray parameterization scheme can be a major source of errors for air-sea turbulent (momentum, sensible and latent) fluxes parameterizations resulting in biases in numerical ocean simulations and forecasts. In this study, we explore applications of the ensemble adjustment Kalman filter (EAKF) data assimilation method to optimize the wave states parameters the 1-D POM ocean model for a full range of wind speed conditions. Thus, we assimilate sea surface temperature (SST) synthesized observations to improve our estimates of the SST analysis and prediction skill from low to high winds. Two types of experiments are conducted. In the first type, in a “twin” experiment framework, the SST “observations” generated by a “truth” model are assimilated into an imperfect, biased model to investigate the extent to which the parameters are able to be optimized, with respect to the “truth” values based on data from Station Papa and the Kuroshio Extension Observatory (KEO). In the second type, real SST observations from KEO are assimilated to obtain optimized parameters. With these optimized parameters, the SST analysis and prediction errors are significantly reduced, especially for high wind conditions. Parameters optimization (dpeaa)DE-He213 Ensemble data assimilation (dpeaa)DE-He213 Wave state parameters (dpeaa)DE-He213 Ocean mixed layer (dpeaa)DE-He213 Sea spray parameterization scheme (dpeaa)DE-He213 Wu, Xinrong verfasserin aut Perrie, William verfasserin aut Zhang, Xuefeng verfasserin aut Guan, Changlong verfasserin aut Enthalten in Ocean dynamics Berlin : Springer, 1948 69(2019), 6 vom: 08. Mai, Seite 719-735 (DE-627)337809313 (DE-600)2063267-8 1616-7228 nnns volume:69 year:2019 number:6 day:08 month:05 pages:719-735 https://dx.doi.org/10.1007/s10236-019-01270-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.90 ASE AR 69 2019 6 08 05 719-735 |
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10.1007/s10236-019-01270-6 doi (DE-627)SPR00921285X (SPR)s10236-019-01270-6-e DE-627 ger DE-627 rakwb eng 550 ASE 38.90 bkl Zhang, Lianxin verfasserin aut Ensemble estimates of the wave state related parameters in a sea spray parameterization scheme 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Uncertainties of the wave state parameters in a sea spray parameterization scheme can be a major source of errors for air-sea turbulent (momentum, sensible and latent) fluxes parameterizations resulting in biases in numerical ocean simulations and forecasts. In this study, we explore applications of the ensemble adjustment Kalman filter (EAKF) data assimilation method to optimize the wave states parameters the 1-D POM ocean model for a full range of wind speed conditions. Thus, we assimilate sea surface temperature (SST) synthesized observations to improve our estimates of the SST analysis and prediction skill from low to high winds. Two types of experiments are conducted. In the first type, in a “twin” experiment framework, the SST “observations” generated by a “truth” model are assimilated into an imperfect, biased model to investigate the extent to which the parameters are able to be optimized, with respect to the “truth” values based on data from Station Papa and the Kuroshio Extension Observatory (KEO). In the second type, real SST observations from KEO are assimilated to obtain optimized parameters. With these optimized parameters, the SST analysis and prediction errors are significantly reduced, especially for high wind conditions. Parameters optimization (dpeaa)DE-He213 Ensemble data assimilation (dpeaa)DE-He213 Wave state parameters (dpeaa)DE-He213 Ocean mixed layer (dpeaa)DE-He213 Sea spray parameterization scheme (dpeaa)DE-He213 Wu, Xinrong verfasserin aut Perrie, William verfasserin aut Zhang, Xuefeng verfasserin aut Guan, Changlong verfasserin aut Enthalten in Ocean dynamics Berlin : Springer, 1948 69(2019), 6 vom: 08. Mai, Seite 719-735 (DE-627)337809313 (DE-600)2063267-8 1616-7228 nnns volume:69 year:2019 number:6 day:08 month:05 pages:719-735 https://dx.doi.org/10.1007/s10236-019-01270-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.90 ASE AR 69 2019 6 08 05 719-735 |
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10.1007/s10236-019-01270-6 doi (DE-627)SPR00921285X (SPR)s10236-019-01270-6-e DE-627 ger DE-627 rakwb eng 550 ASE 38.90 bkl Zhang, Lianxin verfasserin aut Ensemble estimates of the wave state related parameters in a sea spray parameterization scheme 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Uncertainties of the wave state parameters in a sea spray parameterization scheme can be a major source of errors for air-sea turbulent (momentum, sensible and latent) fluxes parameterizations resulting in biases in numerical ocean simulations and forecasts. In this study, we explore applications of the ensemble adjustment Kalman filter (EAKF) data assimilation method to optimize the wave states parameters the 1-D POM ocean model for a full range of wind speed conditions. Thus, we assimilate sea surface temperature (SST) synthesized observations to improve our estimates of the SST analysis and prediction skill from low to high winds. Two types of experiments are conducted. In the first type, in a “twin” experiment framework, the SST “observations” generated by a “truth” model are assimilated into an imperfect, biased model to investigate the extent to which the parameters are able to be optimized, with respect to the “truth” values based on data from Station Papa and the Kuroshio Extension Observatory (KEO). In the second type, real SST observations from KEO are assimilated to obtain optimized parameters. With these optimized parameters, the SST analysis and prediction errors are significantly reduced, especially for high wind conditions. Parameters optimization (dpeaa)DE-He213 Ensemble data assimilation (dpeaa)DE-He213 Wave state parameters (dpeaa)DE-He213 Ocean mixed layer (dpeaa)DE-He213 Sea spray parameterization scheme (dpeaa)DE-He213 Wu, Xinrong verfasserin aut Perrie, William verfasserin aut Zhang, Xuefeng verfasserin aut Guan, Changlong verfasserin aut Enthalten in Ocean dynamics Berlin : Springer, 1948 69(2019), 6 vom: 08. Mai, Seite 719-735 (DE-627)337809313 (DE-600)2063267-8 1616-7228 nnns volume:69 year:2019 number:6 day:08 month:05 pages:719-735 https://dx.doi.org/10.1007/s10236-019-01270-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.90 ASE AR 69 2019 6 08 05 719-735 |
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10.1007/s10236-019-01270-6 doi (DE-627)SPR00921285X (SPR)s10236-019-01270-6-e DE-627 ger DE-627 rakwb eng 550 ASE 38.90 bkl Zhang, Lianxin verfasserin aut Ensemble estimates of the wave state related parameters in a sea spray parameterization scheme 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Uncertainties of the wave state parameters in a sea spray parameterization scheme can be a major source of errors for air-sea turbulent (momentum, sensible and latent) fluxes parameterizations resulting in biases in numerical ocean simulations and forecasts. In this study, we explore applications of the ensemble adjustment Kalman filter (EAKF) data assimilation method to optimize the wave states parameters the 1-D POM ocean model for a full range of wind speed conditions. Thus, we assimilate sea surface temperature (SST) synthesized observations to improve our estimates of the SST analysis and prediction skill from low to high winds. Two types of experiments are conducted. In the first type, in a “twin” experiment framework, the SST “observations” generated by a “truth” model are assimilated into an imperfect, biased model to investigate the extent to which the parameters are able to be optimized, with respect to the “truth” values based on data from Station Papa and the Kuroshio Extension Observatory (KEO). In the second type, real SST observations from KEO are assimilated to obtain optimized parameters. With these optimized parameters, the SST analysis and prediction errors are significantly reduced, especially for high wind conditions. Parameters optimization (dpeaa)DE-He213 Ensemble data assimilation (dpeaa)DE-He213 Wave state parameters (dpeaa)DE-He213 Ocean mixed layer (dpeaa)DE-He213 Sea spray parameterization scheme (dpeaa)DE-He213 Wu, Xinrong verfasserin aut Perrie, William verfasserin aut Zhang, Xuefeng verfasserin aut Guan, Changlong verfasserin aut Enthalten in Ocean dynamics Berlin : Springer, 1948 69(2019), 6 vom: 08. Mai, Seite 719-735 (DE-627)337809313 (DE-600)2063267-8 1616-7228 nnns volume:69 year:2019 number:6 day:08 month:05 pages:719-735 https://dx.doi.org/10.1007/s10236-019-01270-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.90 ASE AR 69 2019 6 08 05 719-735 |
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Enthalten in Ocean dynamics 69(2019), 6 vom: 08. Mai, Seite 719-735 volume:69 year:2019 number:6 day:08 month:05 pages:719-735 |
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Enthalten in Ocean dynamics 69(2019), 6 vom: 08. Mai, Seite 719-735 volume:69 year:2019 number:6 day:08 month:05 pages:719-735 |
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Parameters optimization Ensemble data assimilation Wave state parameters Ocean mixed layer Sea spray parameterization scheme |
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Ocean dynamics |
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Zhang, Lianxin @@aut@@ Wu, Xinrong @@aut@@ Perrie, William @@aut@@ Zhang, Xuefeng @@aut@@ Guan, Changlong @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR00921285X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220110210320.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10236-019-01270-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR00921285X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10236-019-01270-6-e</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="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">38.90</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhang, Lianxin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Ensemble estimates of the wave state related parameters in a sea spray parameterization scheme</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</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">Abstract Uncertainties of the wave state parameters in a sea spray parameterization scheme can be a major source of errors for air-sea turbulent (momentum, sensible and latent) fluxes parameterizations resulting in biases in numerical ocean simulations and forecasts. In this study, we explore applications of the ensemble adjustment Kalman filter (EAKF) data assimilation method to optimize the wave states parameters the 1-D POM ocean model for a full range of wind speed conditions. Thus, we assimilate sea surface temperature (SST) synthesized observations to improve our estimates of the SST analysis and prediction skill from low to high winds. Two types of experiments are conducted. In the first type, in a “twin” experiment framework, the SST “observations” generated by a “truth” model are assimilated into an imperfect, biased model to investigate the extent to which the parameters are able to be optimized, with respect to the “truth” values based on data from Station Papa and the Kuroshio Extension Observatory (KEO). In the second type, real SST observations from KEO are assimilated to obtain optimized parameters. 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|
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Zhang, Lianxin |
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Zhang, Lianxin ddc 550 bkl 38.90 misc Parameters optimization misc Ensemble data assimilation misc Wave state parameters misc Ocean mixed layer misc Sea spray parameterization scheme Ensemble estimates of the wave state related parameters in a sea spray parameterization scheme |
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550 ASE 38.90 bkl Ensemble estimates of the wave state related parameters in a sea spray parameterization scheme Parameters optimization (dpeaa)DE-He213 Ensemble data assimilation (dpeaa)DE-He213 Wave state parameters (dpeaa)DE-He213 Ocean mixed layer (dpeaa)DE-He213 Sea spray parameterization scheme (dpeaa)DE-He213 |
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ddc 550 bkl 38.90 misc Parameters optimization misc Ensemble data assimilation misc Wave state parameters misc Ocean mixed layer misc Sea spray parameterization scheme |
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ddc 550 bkl 38.90 misc Parameters optimization misc Ensemble data assimilation misc Wave state parameters misc Ocean mixed layer misc Sea spray parameterization scheme |
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ddc 550 bkl 38.90 misc Parameters optimization misc Ensemble data assimilation misc Wave state parameters misc Ocean mixed layer misc Sea spray parameterization scheme |
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Ensemble estimates of the wave state related parameters in a sea spray parameterization scheme |
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Ensemble estimates of the wave state related parameters in a sea spray parameterization scheme |
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Zhang, Lianxin |
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Ocean dynamics |
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Zhang, Lianxin Wu, Xinrong Perrie, William Zhang, Xuefeng Guan, Changlong |
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550 ASE 38.90 bkl |
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Zhang, Lianxin |
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10.1007/s10236-019-01270-6 |
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ensemble estimates of the wave state related parameters in a sea spray parameterization scheme |
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Ensemble estimates of the wave state related parameters in a sea spray parameterization scheme |
| abstract |
Abstract Uncertainties of the wave state parameters in a sea spray parameterization scheme can be a major source of errors for air-sea turbulent (momentum, sensible and latent) fluxes parameterizations resulting in biases in numerical ocean simulations and forecasts. In this study, we explore applications of the ensemble adjustment Kalman filter (EAKF) data assimilation method to optimize the wave states parameters the 1-D POM ocean model for a full range of wind speed conditions. Thus, we assimilate sea surface temperature (SST) synthesized observations to improve our estimates of the SST analysis and prediction skill from low to high winds. Two types of experiments are conducted. In the first type, in a “twin” experiment framework, the SST “observations” generated by a “truth” model are assimilated into an imperfect, biased model to investigate the extent to which the parameters are able to be optimized, with respect to the “truth” values based on data from Station Papa and the Kuroshio Extension Observatory (KEO). In the second type, real SST observations from KEO are assimilated to obtain optimized parameters. With these optimized parameters, the SST analysis and prediction errors are significantly reduced, especially for high wind conditions. |
| abstractGer |
Abstract Uncertainties of the wave state parameters in a sea spray parameterization scheme can be a major source of errors for air-sea turbulent (momentum, sensible and latent) fluxes parameterizations resulting in biases in numerical ocean simulations and forecasts. In this study, we explore applications of the ensemble adjustment Kalman filter (EAKF) data assimilation method to optimize the wave states parameters the 1-D POM ocean model for a full range of wind speed conditions. Thus, we assimilate sea surface temperature (SST) synthesized observations to improve our estimates of the SST analysis and prediction skill from low to high winds. Two types of experiments are conducted. In the first type, in a “twin” experiment framework, the SST “observations” generated by a “truth” model are assimilated into an imperfect, biased model to investigate the extent to which the parameters are able to be optimized, with respect to the “truth” values based on data from Station Papa and the Kuroshio Extension Observatory (KEO). In the second type, real SST observations from KEO are assimilated to obtain optimized parameters. With these optimized parameters, the SST analysis and prediction errors are significantly reduced, especially for high wind conditions. |
| abstract_unstemmed |
Abstract Uncertainties of the wave state parameters in a sea spray parameterization scheme can be a major source of errors for air-sea turbulent (momentum, sensible and latent) fluxes parameterizations resulting in biases in numerical ocean simulations and forecasts. In this study, we explore applications of the ensemble adjustment Kalman filter (EAKF) data assimilation method to optimize the wave states parameters the 1-D POM ocean model for a full range of wind speed conditions. Thus, we assimilate sea surface temperature (SST) synthesized observations to improve our estimates of the SST analysis and prediction skill from low to high winds. Two types of experiments are conducted. In the first type, in a “twin” experiment framework, the SST “observations” generated by a “truth” model are assimilated into an imperfect, biased model to investigate the extent to which the parameters are able to be optimized, with respect to the “truth” values based on data from Station Papa and the Kuroshio Extension Observatory (KEO). In the second type, real SST observations from KEO are assimilated to obtain optimized parameters. With these optimized parameters, the SST analysis and prediction errors are significantly reduced, especially for high wind conditions. |
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| container_issue |
6 |
| title_short |
Ensemble estimates of the wave state related parameters in a sea spray parameterization scheme |
| url |
https://dx.doi.org/10.1007/s10236-019-01270-6 |
| remote_bool |
true |
| author2 |
Wu, Xinrong Perrie, William Zhang, Xuefeng Guan, Changlong |
| author2Str |
Wu, Xinrong Perrie, William Zhang, Xuefeng Guan, Changlong |
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| doi_str |
10.1007/s10236-019-01270-6 |
| up_date |
2024-07-04T01:08:12.178Z |
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| score |
7.3985004 |