Assimilating temperature and salinity profiles using Ensemble Kalman Filter with an adaptive observation error and <i>T-S</i> constraint

LIU Danian; SHI Ping; SHU Yeqiang; YAO Jinglong; WANG Dongxiao; SUN Lu

doi:10.1007/s13131-016-0793-1

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Article Navigation > Acta Oceanologica Sinica > 2016 > 35(1): 30-37

LIU Danian, SHI Ping, SHU Yeqiang, YAO Jinglong, WANG Dongxiao, SUN Lu. Assimilating temperature and salinity profiles using Ensemble Kalman Filter with an adaptive observation error and T-S constraint[J]. Acta Oceanologica Sinica, 2016, 35(1): 30-37. doi: 10.1007/s13131-016-0793-1

Citation:

LIU Danian, SHI Ping, SHU Yeqiang, YAO Jinglong, WANG Dongxiao, SUN Lu. Assimilating temperature and salinity profiles using Ensemble Kalman Filter with an adaptive observation error and T-S constraint[J]. Acta Oceanologica Sinica, 2016, 35(1): 30-37. doi: 10.1007/s13131-016-0793-1

Citation:

LIU Danian, SHI Ping, SHU Yeqiang, YAO Jinglong, WANG Dongxiao, SUN Lu. Assimilating temperature and salinity profiles using Ensemble Kalman Filter with an adaptive observation error and T-S constraint[J]. Acta Oceanologica Sinica, 2016, 35(1): 30-37. doi: 10.1007/s13131-016-0793-1

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Assimilating temperature and salinity profiles using Ensemble Kalman Filter with an adaptive observation error and T-S constraint

doi: 10.1007/s13131-016-0793-1

1.
State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China
2.
South China Sea Monitoring Center, South China Sea Branch, State Oceanic Administration, Guangzhou 510301, China

Received Date: 2015-01-23
Rev Recd Date: 2015-05-11

Abstract

Abstract

Temperature (T) and salinity (S) profiles from conductivity-temperature-depth data collected during the Northern South China Sea Open Cruise from August 16 to September 13, 2008 are assimilated using Ensemble Kalman Filter (EnKF). An adaptive observational error strategy is used to prevent filter from diverging. In the meantime, aiming at the limited improvement in some sites caused by the T and S biases in the model, a T-S constraint scheme is adopted to improve the assimilation performance, where T and S are separately updated at these locations. Validation is performed by comparing assimilated outputs with independent in situ data (satellite remote sensing sea level anomaly (SLA), the OSCAR velocity product and shipboard ADCP). The results show that the new EnKF assimilation scheme can significantly reduce the root mean square error (RMSE) of oceanic T and S compared with the control run and traditional EnKF. The system can also improve the simulation of circulations and SLA.
- Ensemble Kalman Filter,
- adaptive observation error,
- T-S constraint

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