| Improvement of short-termforecasting in the northwest Pacific through assimilating Argo data into initial fields |
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| 作者姓名： | FU Hongli CHU Peter C HAN Guijun HE Zhongjie LI Wei ZHANG Xuefeng |
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| 作者单位： | Key Laboratory of State Oceanic Adminstration forMarine Environmental Information Technology, NationalMarine Data and Information Service, State Oceanic Administration, Tianjin 300171, China;Naval Ocean Analysis and Prediction Laboratory, Naval Postgraduate School, Monterey, CA, USA;Key Laboratory of State Oceanic Adminstration forMarine Environmental Information Technology, NationalMarine Data and Information Service, State Oceanic Administration, Tianjin 300171, China;Key Laboratory of State Oceanic Adminstration forMarine Environmental Information Technology, NationalMarine Data and Information Service, State Oceanic Administration, Tianjin 300171, China;Key Laboratory of State Oceanic Adminstration forMarine Environmental Information Technology, NationalMarine Data and Information Service, State Oceanic Administration, Tianjin 300171, China;Key Laboratory of State Oceanic Adminstration forMarine Environmental Information Technology, NationalMarine Data and Information Service, State Oceanic Administration, Tianjin 300171, China |
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| 基金项目： | The National Natural Science Foundation of China under contract Nos 41030854, 41106005, 41176003, and 41206178; the National Science and Technology Support Program of China under contract No. 2011BAC03B02-01-04. |
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| 摘 要： | The impact of assimilating Argo data into an initial field on the short-term forecasting accuracy of temper- ature and salinity is quantitatively estimated by using a forecasting system of the western North Pacific, on the base of the Princeton ocean model with a generalized coordinate system （POMgcs）. This system uses a sequential multigrid three-dimensional variational （3DVAR） analysis scheme to assimilate observation da- ta. Two numerical experiments were conducted with and without Argo temperature and salinity profile data besides conventional temperature and salinity profile data and sea surface height anomaly （SSHa） and sea surface temperature （SST） in the process of assimilating data into the initial fields. The forecast errors are estimated by using independent temperature and salinity profiles during the forecasting period, including the vertical distributions of the horizontally averaged root mean square errors （H-RMSEs） and the horizontal distributions of the vertically averaged mean errors （MEs） and the temporal variation of spatially averaged root mean square errors （S-RMSEs）. Comparison between the two experiments shows that the assimila- tion of Argo data significantly improves the forecast accuracy, with 24% reduction of H-RMSE maximum for the temperature, and the salinity forecasts are improved more obviously, averagely dropping of 50% for H-RMSEs in depth shallower than 300 m. Such improvement is caused by relatively uniform sampling of both temperature and salinity from the Argo drifters in time and space.
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| 关 键 词： | Argo资料 短期预测 数据同化 西北太平洋 初始场 均方根误差 普林斯顿海洋模式 海表温度 |
| 收稿时间： | 2012/2/17 0:00:00 |
| 修稿时间： | 2012/11/13 0:00:00 |
Improvement of short-termforecasting in the northwest Pacific through assimilating Argo data into initial fields |
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| FU Hongli,CHU Peter C,HAN Guijun,HE Zhongjie,LI Wei,ZHANG Xuefeng.Improvement of short-termforecasting in the northwest Pacific through assimilating Argo data into initial fields[J].Acta Oceanologica Sinica,2013,32(7):57-65. |
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| Authors: | FU Hongli CHU Peter C HAN Guijun HE Zhongjie LI Wei and ZHANG Xuefeng |
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| Institution: | 1.Key Laboratory of State Oceanic Adminstration forMarine Environmental Information Technology, NationalMarine Data and Information Service, State Oceanic Administration, Tianjin 300171, China2.Naval Ocean Analysis and Prediction Laboratory, Naval Postgraduate School, Monterey, CA, USA |
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| Abstract: | The impact of assimilating Argo data into an initial field on the short-term forecasting accuracy of temperature and salinity is quantitatively estimated by using a forecasting system of the western North Pacific, on the base of the Princeton ocean model with a generalized coordinate system (POMgcs). This system uses a sequential multigrid three-dimensional variational (3DVAR) analysis scheme to assimilate observation data. Two numerical experiments were conducted with and without Argo temperature and salinity profile data besides conventional temperature and salinity profile data and sea surface height anomaly (SSHa) and sea surface temperature (SST) in the process of assimilating data into the initial fields. The forecast errors are estimated by using independent temperature and salinity profiles during the forecasting period, including the vertical distributions of the horizontally averaged root mean square errors (H-RMSEs) and the horizontal distributions of the vertically averaged mean errors (MEs) and the temporal variation of spatially averaged root mean square errors (S-RMSEs). Comparison between the two experiments shows that the assimilation of Argo data significantly improves the forecast accuracy, with 24% reduction of H-RMSE maximum for the temperature, and the salinity forecasts are improved more obviously, averagely dropping of 50% for H-RMSEs in depth shallower than 300 m. Such improvement is caused by relatively uniform sampling of both temperature and salinity from the Argo drifters in time and space. |
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| Keywords: | data assimilation Argo data western North Pacific ocean prediction |
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