Assimilation of Argo temperature and salinity profiles using a bias-aware localized EnKF system for the Pacific Ocean

In this study, Argo profiles of temperature and salinity for the period from January 2005 to December 2007 are assimilated into a primitive equation model of the Pacific Ocean using a bias-aware localized ensemble Kalman filter (EnKF) with a sequence of 5-day assimilation cycles. Some other in situ observations, including XBT, TAO/TRITON and CTD profiles, used to supplement, are also assimilated into the model. To improve the assimilation performance, several strategies addressing the computational expense and model error statistics are incorporated into the assimilation scheme. Validation is performed by comparing the analyzed ocean states with independent data, including withheld Argo profiles, satellite remote sensing sea level height anomalies (SLA) and the NCEP ocean state re-analysis products. The results show that the assimilation system is capable of significantly reducing the bias and RMSE of ocean temperature and salinity compared with the control run. It can also improve the simulation of zonal currents and SLAs along the equator, especially during strong ENSO events. In addition, a hybrid coupled ENSO prediction model initialized by the assimilation analysis improves the ENSO prediction skill compared against that initialized by the control run without data assimilation.