| FU Hongli,CHU Peter C,HAN Guijun,HE Zhongjie,LI Wei,ZHANG Xuefeng. 2013. Improvement of short-termforecasting in the northwest Pacific through assimilating Argo data into initial fields. Acta Oceanologica Sinica, 32(7):57－65 |
| Improvement of short-termforecasting in the northwest Pacific through assimilating Argo data into initial fields |
| Improvement of short-termforecasting in the northwest Pacific through assimilating Argo data into initial fields |
| Received:February 17, 2012 Revised:November 13, 2012 |
| DOI：10.1007/s13131-013-0332-2 |
| Key words:data assimilation Argo data western North Pacific ocean prediction |
| 中文关键词: data assimilation Argo data western North Pacific ocean prediction |
| 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. |
| Author Name | Affiliation | E-mail | | FU Hongli | Key Laboratory of State Oceanic Adminstration forMarine Environmental Information Technology, NationalMarine Data and Information Service, State Oceanic Administration, Tianjin 300171, China | | | CHU Peter C | Naval Ocean Analysis and Prediction Laboratory, Naval Postgraduate School, Monterey, CA, USA | | | HAN Guijun | Key Laboratory of State Oceanic Adminstration forMarine Environmental Information Technology, NationalMarine Data and Information Service, State Oceanic Administration, Tianjin 300171, China | gjhan@mail.nmdis.gov.cn | | HE Zhongjie | Key Laboratory of State Oceanic Adminstration forMarine Environmental Information Technology, NationalMarine Data and Information Service, State Oceanic Administration, Tianjin 300171, China | | | LI Wei | Key Laboratory of State Oceanic Adminstration forMarine Environmental Information Technology, NationalMarine Data and Information Service, State Oceanic Administration, Tianjin 300171, China | | | ZHANG Xuefeng | Key Laboratory of State Oceanic Adminstration forMarine Environmental Information Technology, NationalMarine Data and Information Service, State Oceanic Administration, Tianjin 300171, China | |
|
| Hits: 1799 |
| Download times: 1911 |
| Abstract: |
| The impact of assimilating Argo data into an initial field on the short-termforecasting 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 experimentswere conductedwith andwithout 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 verticaldistributions of the horizontally averaged rootmean 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 fromthe Argo drifters in time and space. |
| 中文摘要: |
| The impact of assimilating Argo data into an initial field on the short-termforecasting 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 experimentswere conductedwith andwithout 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 verticaldistributions of the horizontally averaged rootmean 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 fromthe Argo drifters in time and space. |
|
HTML
View Full Text
View/Add Comment Download reader |
| Close |
|
|
|
