Assimilating OSTIA SST into regional modeling systems for the Yellow Sea using ensemble methods
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摘要
The effects of sea surface temperature(SST) data assimilation in two regional ocean modeling systems were examined for the Yellow Sea(YS). The SST data from the Operational Sea Surface Temperature and Sea Ice Analysis(OSTIA) were assimilated. The National Marine Environmental Forecasting Center(NMEFC) modeling system uses the ensemble optimal interpolation method for ocean data assimilation and the Kunsan National University(KNU) modeling system uses the ensemble Kalman filter. Without data assimilation, the NMEFC modeling system was better in simulating the subsurface temperature while the KNU modeling system was better in simulating SST. The disparity between both modeling systems might be related to differences in calculating the surface heat flux, horizontal grid spacing, and atmospheric forcing data. The data assimilation reduced the root mean square error(RMSE) of the SST from 1.78°C(1.46°C) to 1.30°C(1.21°C) for the NMEFC(KNU) modeling system when the simulated temperature was compared to Optimum Interpolation Sea Surface Temperature(OISST) SST dataset. A comparison with the buoy SST data indicated a 41%(31%) decrease in the SST error for the NMEFC(KNU) modeling system by the data assimilation. In both data assimilative systems, the RMSE of the temperature was less than 1.5°C in the upper 20 m and approximately 3.1°C in the lower layer in October. In contrast, it was less than 1.0°C throughout the water column in February. This study suggests that assimilations of the observed temperature profiles are necessary in order to correct the lower layer temperature during the stratified season and an ocean modeling system with small grid spacing and optimal data assimilation method is preferable to ensure accurate predictions of the coastal ocean in the YS.
The effects of sea surface temperature(SST) data assimilation in two regional ocean modeling systems were examined for the Yellow Sea(YS). The SST data from the Operational Sea Surface Temperature and Sea Ice Analysis(OSTIA) were assimilated. The National Marine Environmental Forecasting Center(NMEFC) modeling system uses the ensemble optimal interpolation method for ocean data assimilation and the Kunsan National University(KNU) modeling system uses the ensemble Kalman filter. Without data assimilation, the NMEFC modeling system was better in simulating the subsurface temperature while the KNU modeling system was better in simulating SST. The disparity between both modeling systems might be related to differences in calculating the surface heat flux, horizontal grid spacing, and atmospheric forcing data. The data assimilation reduced the root mean square error(RMSE) of the SST from 1.78°C(1.46°C) to 1.30°C(1.21°C) for the NMEFC(KNU) modeling system when the simulated temperature was compared to Optimum Interpolation Sea Surface Temperature(OISST) SST dataset. A comparison with the buoy SST data indicated a 41%(31%) decrease in the SST error for the NMEFC(KNU) modeling system by the data assimilation. In both data assimilative systems, the RMSE of the temperature was less than 1.5°C in the upper 20 m and approximately 3.1°C in the lower layer in October. In contrast, it was less than 1.0°C throughout the water column in February. This study suggests that assimilations of the observed temperature profiles are necessary in order to correct the lower layer temperature during the stratified season and an ocean modeling system with small grid spacing and optimal data assimilation method is preferable to ensure accurate predictions of the coastal ocean in the YS.
The effects of sea surface temperature(SST) data assimilation in two regional ocean modeling systems were examined for the Yellow Sea(YS). The SST data from the Operational Sea Surface Temperature and Sea Ice Analysis(OSTIA) were assimilated. The National Marine Environmental Forecasting Center(NMEFC) modeling system uses the ensemble optimal interpolation method for ocean data assimilation and the Kunsan National University(KNU) modeling system uses the ensemble Kalman filter. Without data assimilation, the NMEFC modeling system was better in simulating the subsurface temperature while the KNU modeling system was better in simulating SST. The disparity between both modeling systems might be related to differences in calculating the surface heat flux, horizontal grid spacing, and atmospheric forcing data. The data assimilation reduced the root mean square error(RMSE) of the SST from 1.78°C(1.46°C) to 1.30°C(1.21°C) for the NMEFC(KNU) modeling system when the simulated temperature was compared to Optimum Interpolation Sea Surface Temperature(OISST) SST dataset. A comparison with the buoy SST data indicated a 41%(31%) decrease in the SST error for the NMEFC(KNU) modeling system by the data assimilation. In both data assimilative systems, the RMSE of the temperature was less than 1.5°C in the upper 20 m and approximately 3.1°C in the lower layer in October. In contrast, it was less than 1.0°C throughout the water column in February. This study suggests that assimilations of the observed temperature profiles are necessary in order to correct the lower layer temperature during the stratified season and an ocean modeling system with small grid spacing and optimal data assimilation method is preferable to ensure accurate predictions of the coastal ocean in the YS.
引文
Derber J,Rosati A.1989.A global oceanic data assimilation system.Journal of Physical Oceanography,19(9):1333-1347
Donlon C J,Martin M,Stark J,et al.2012.The operational sea surface temperature and sea ice analysis(OSTIA)system.Remote Sensing of Environment,116:140-158
Evensen G.1994.Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics.Journal of Geophysical Research,99(C5):10143-10162
Evensen G.2003.The ensemble Kalman filter:theoretical formulation and practical implementation.Ocean Dynamics,53(4):343-367
Fairall C W,Bradley E F,Rogers D P,et al.1996.Bulk parameterization of air-sea fluxes for tropical ocean-global atmosphere Coupled-Ocean Atmosphere Response Experiment.Journal of Geophysical Research,101(C2):3747-3764
Gandin L S.1965.Objective analysis of meteorological fields.Jerusalem:Israel Program for Scientific Translations
Haidvogel D B,Arango H G,Hedstrom K,et al.2000.Model evaluation experiments in the North Atlantic Basin:simulations in nonlinear terrain-following coordinates.Dynamics of Atmospheres and Oceans,32(3-4):239-281
Hamill T M,Whitaker J S,Snyder C.2001.Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter.Monthly Weather Review,129(11):2776-2790
Kara A B,Wallcraft A J,Martin P J,et al.2008.Performance of mixed layer models in simulating SST in the equatorial Pacific Ocean.Journal of Geophysical Research,113(C2):C02020
Kwon K M,Choi B J,Lee S H,et al.2011.Coastal current along the eastern boundary of the Yellow Sea in summer:numerical simulations.The Sea,16(4):155-168
Li Wei,Xie Yuanfu,He Zhongjie,et al.2008.Application of the multigrid data assimilation scheme to the china seas’temperature forecast.Journal of Atmospheric and Oceanic Technology,25(11):2106-2116
Lin Xiaopei,Yang Jiayan.2011.An asymmetric upwind flow,Yellow Sea Warm Current:2.Arrested topographic waves in response to the northwesterly wind.Journal of Geophysical Research,116(C4):C04027
Liu Guimei,Chai Fei.2009.Seasonal and interannual variability of primary and export production in the South China Sea:a threedimensional physical-biogeochemical model study.ICESJournal of Marine Science,66(2):420-431
Liu Guimei,Wang Hui,Sun Song,et al.2003a.Numerical study on density residual currents of the Bohai Sea in summer.Chinese Journal of Oceanology and Limnology,21(2):106-113
Liu Guimei,Wang Hui,Sun Song,et al.2003b.Numerical study on the velocity structure around tidal fronts in the Yellow Sea.Advances in Atmospheric Sciences,20(3):453-460
Lyu Guokun,Wang Hui,Zhu Jiang,et al.2014.Assimilating the along-track sea level anomaly into the regional ocean modeling system using the ensemble optimal interpolation.Acta Oceanologica Sinica,33(7):72-82
Mellor G L,Yamada T.1982.Development of a turbulence closure model for geophysical fluid problems.Reviews of Geophysics,20(4):851-875
Naimie C E,Blain C A,Lynch D R.2001.Seasonal mean circulation in the Yellow Sea-a model-generated climatology.Continental Shelf Research,21(6-7):667-695
Oke P R,Sakov P,Corney S P.2007.Impacts of localisation in the EnKF and En OI:experiments with a small model.Ocean Dynamics,57(1):32-45
Park K S,Heo K Y,Jun K,et al.2015.Development of the operational oceanographic system of Korea.Ocean Science Journal,50(2):353-369
Reynolds R W,Smith T M,Liu Chunying,et al.2007.Daily high-resolution-blended analyses for sea surface temperature.Journal of Climate,20(22):5473-5496
Senju T,Enomoto H,Matsuno T,et al.2006.Interannual salinity variations in the Tsushima Strait and its relation to the Changjiang discharge.Journal of Oceanography,62(5):681-692
Seo S N.2008.Digital 30sec gridded bathymetric data of Korea marginal Seas-Korbathy30s.Journal of Korean Society of Coastal and Ocean Engineers,20(1):110-120
Seo G H,Cho Y K,Choi B J.2014.Variations of heat transport in the northwestern Pacific marginal seas inferred from high-resolution reanalysis.Progress in Oceanography,121:98-108
Seo G H,Choi B J,Cho Y K,et al.2010.Assimilation of sea surface temperature in the Northwest Pacific Ocean and its marginal seas using the ensemble Kalman filter.Ocean Science Journal,45(4):225-242
Seo G H,Choi B J,Cho Y K,et al.2015.Evaluation of a regional ocean reanalysis system for the East Asian marginal seas based on the ensemble Kalman filter.Ocean Science Journal,50(1):29-48
Shchepetkin A F,Mc Williams J C.2005.The regional oceanic modeling system(ROMS):a split-explicit,free-surface,topographyfollowing-coordinate oceanic model.Ocean Modelling,9(4):347-404
Wang Fan,Meng Qingjia,Tang Xiaohui,et al.2013.The long-term variability of sea surface temperature in the seas east of China in the past 40 a.Acta Oceanologica Sinica,32(3):48-53
Xia Changshui,Qiao Fangli,Yang Yongzeng,et al.2006.Three-dimensional structure of the summertime circulation in the Yellow Sea from a wave-tide-circulation coupled model.Journal of Geophysical Research,111(C11):C11S03,doi:10.1029/2005JC003218
Xiao Xianjun,Wang Dongxiao,Yan Changxiang,et al.2008.Evaluation of a 3d VAR system for the South China Sea.Progress in Natural Science,18(5):547-554
Xie Jiping,Zhu Jiang.2010.Ensemble optimal interpolation schemes for assimilating Argo profiles into a hybrid coordinate ocean model.Ocean Modelling,33(3-4):283-298
Xie Jiping,Zhu Jiang,Li Yan.2008.Assessment and inter-comparison of five high-resolution sea surface temperature products in the shelf and coastal seas around China.Continental Shelf Research,28(10-11):1286-1293
Zheng Peng,Chen Xueen.2013.Numerical simulation to 3-D structure of water temperature of Bohai Sea in summer.Periodical of Ocean University of China(in Chinese),43(11):9-16
Zhu Jiang,Zhou Guangqing,Yan Changxiang,et al.2006.A three-dimensional variational ocean data assimilation system:scheme and preliminary results.Science in China Series D:Earth Sciences,49(11):1212-1222
Donlon C J,Martin M,Stark J,et al.2012.The operational sea surface temperature and sea ice analysis(OSTIA)system.Remote Sensing of Environment,116:140-158
Evensen G.1994.Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics.Journal of Geophysical Research,99(C5):10143-10162
Evensen G.2003.The ensemble Kalman filter:theoretical formulation and practical implementation.Ocean Dynamics,53(4):343-367
Fairall C W,Bradley E F,Rogers D P,et al.1996.Bulk parameterization of air-sea fluxes for tropical ocean-global atmosphere Coupled-Ocean Atmosphere Response Experiment.Journal of Geophysical Research,101(C2):3747-3764
Gandin L S.1965.Objective analysis of meteorological fields.Jerusalem:Israel Program for Scientific Translations
Haidvogel D B,Arango H G,Hedstrom K,et al.2000.Model evaluation experiments in the North Atlantic Basin:simulations in nonlinear terrain-following coordinates.Dynamics of Atmospheres and Oceans,32(3-4):239-281
Hamill T M,Whitaker J S,Snyder C.2001.Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter.Monthly Weather Review,129(11):2776-2790
Kara A B,Wallcraft A J,Martin P J,et al.2008.Performance of mixed layer models in simulating SST in the equatorial Pacific Ocean.Journal of Geophysical Research,113(C2):C02020
Kwon K M,Choi B J,Lee S H,et al.2011.Coastal current along the eastern boundary of the Yellow Sea in summer:numerical simulations.The Sea,16(4):155-168
Li Wei,Xie Yuanfu,He Zhongjie,et al.2008.Application of the multigrid data assimilation scheme to the china seas’temperature forecast.Journal of Atmospheric and Oceanic Technology,25(11):2106-2116
Lin Xiaopei,Yang Jiayan.2011.An asymmetric upwind flow,Yellow Sea Warm Current:2.Arrested topographic waves in response to the northwesterly wind.Journal of Geophysical Research,116(C4):C04027
Liu Guimei,Chai Fei.2009.Seasonal and interannual variability of primary and export production in the South China Sea:a threedimensional physical-biogeochemical model study.ICESJournal of Marine Science,66(2):420-431
Liu Guimei,Wang Hui,Sun Song,et al.2003a.Numerical study on density residual currents of the Bohai Sea in summer.Chinese Journal of Oceanology and Limnology,21(2):106-113
Liu Guimei,Wang Hui,Sun Song,et al.2003b.Numerical study on the velocity structure around tidal fronts in the Yellow Sea.Advances in Atmospheric Sciences,20(3):453-460
Lyu Guokun,Wang Hui,Zhu Jiang,et al.2014.Assimilating the along-track sea level anomaly into the regional ocean modeling system using the ensemble optimal interpolation.Acta Oceanologica Sinica,33(7):72-82
Mellor G L,Yamada T.1982.Development of a turbulence closure model for geophysical fluid problems.Reviews of Geophysics,20(4):851-875
Naimie C E,Blain C A,Lynch D R.2001.Seasonal mean circulation in the Yellow Sea-a model-generated climatology.Continental Shelf Research,21(6-7):667-695
Oke P R,Sakov P,Corney S P.2007.Impacts of localisation in the EnKF and En OI:experiments with a small model.Ocean Dynamics,57(1):32-45
Park K S,Heo K Y,Jun K,et al.2015.Development of the operational oceanographic system of Korea.Ocean Science Journal,50(2):353-369
Reynolds R W,Smith T M,Liu Chunying,et al.2007.Daily high-resolution-blended analyses for sea surface temperature.Journal of Climate,20(22):5473-5496
Senju T,Enomoto H,Matsuno T,et al.2006.Interannual salinity variations in the Tsushima Strait and its relation to the Changjiang discharge.Journal of Oceanography,62(5):681-692
Seo S N.2008.Digital 30sec gridded bathymetric data of Korea marginal Seas-Korbathy30s.Journal of Korean Society of Coastal and Ocean Engineers,20(1):110-120
Seo G H,Cho Y K,Choi B J.2014.Variations of heat transport in the northwestern Pacific marginal seas inferred from high-resolution reanalysis.Progress in Oceanography,121:98-108
Seo G H,Choi B J,Cho Y K,et al.2010.Assimilation of sea surface temperature in the Northwest Pacific Ocean and its marginal seas using the ensemble Kalman filter.Ocean Science Journal,45(4):225-242
Seo G H,Choi B J,Cho Y K,et al.2015.Evaluation of a regional ocean reanalysis system for the East Asian marginal seas based on the ensemble Kalman filter.Ocean Science Journal,50(1):29-48
Shchepetkin A F,Mc Williams J C.2005.The regional oceanic modeling system(ROMS):a split-explicit,free-surface,topographyfollowing-coordinate oceanic model.Ocean Modelling,9(4):347-404
Wang Fan,Meng Qingjia,Tang Xiaohui,et al.2013.The long-term variability of sea surface temperature in the seas east of China in the past 40 a.Acta Oceanologica Sinica,32(3):48-53
Xia Changshui,Qiao Fangli,Yang Yongzeng,et al.2006.Three-dimensional structure of the summertime circulation in the Yellow Sea from a wave-tide-circulation coupled model.Journal of Geophysical Research,111(C11):C11S03,doi:10.1029/2005JC003218
Xiao Xianjun,Wang Dongxiao,Yan Changxiang,et al.2008.Evaluation of a 3d VAR system for the South China Sea.Progress in Natural Science,18(5):547-554
Xie Jiping,Zhu Jiang.2010.Ensemble optimal interpolation schemes for assimilating Argo profiles into a hybrid coordinate ocean model.Ocean Modelling,33(3-4):283-298
Xie Jiping,Zhu Jiang,Li Yan.2008.Assessment and inter-comparison of five high-resolution sea surface temperature products in the shelf and coastal seas around China.Continental Shelf Research,28(10-11):1286-1293
Zheng Peng,Chen Xueen.2013.Numerical simulation to 3-D structure of water temperature of Bohai Sea in summer.Periodical of Ocean University of China(in Chinese),43(11):9-16
Zhu Jiang,Zhou Guangqing,Yan Changxiang,et al.2006.A three-dimensional variational ocean data assimilation system:scheme and preliminary results.Science in China Series D:Earth Sciences,49(11):1212-1222