Evaluation of several model error schemes in the EnKF assimilation: Applied to Argo profiles in the Pacific Ocean

Abstract

[1] The efficacy of several model error schemes in the Ensemble Kalman Filter (EnKF) data assimilation is investigated through a series of sensitivity experiments, in which the Argo and other in situ temperature and salinity profiles are assimilated into an ocean general circulation model (OGCM) for the Pacific Ocean. Different schemes for combining the additive inflation, multiplicative inflation, one-step bias correction and two-stage bias correction are evaluated in the framework of the EnKF. Experimental results indicate that the additive inflation is the key technique that can maintain ensemble spread in an adequate range. When sufficient observations are available, the assimilation system with additive inflation scheme can efficiently reduce both model bias and random errors. The combination of additive inflation and multiplicative inflation can further improve the performance of the assimilation system, in particular when the additive inflation underestimates model error. The bias correction schemes, the one-step method and the persistent bias method are effective in reducing the model bias only within a relatively short initial assimilation period and in some regions. Further improvement from the bias correction schemes is not evident as the assimilation period increases.