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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(En KF). 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 En KF assimilation scheme can significantly reduce the root mean square error(RMSE) of oceanic T and S compared with the control run and traditional En KF. The system can also improve the simulation of circulations and SLA.
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 (En KF). An adaptive observational error strategy In the meantime, aiming at the limited improvement in some sites caused by the T and S biases in the model, a TS constraint scheme is adopted to improve the assimilation performance, where T and S are optionally updated at these locations. Validation is performed 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 En KF assimilation scheme can significantly reduce the The root mean square error (RMSE) of oceanic T and S compared with the control run and traditional En KF. The system can also improve the simulation of circulations and SLA.