Removing the systematic errors of the model in operational oceanography forecasting system using data assimilation method

Abstract

Abstract For the operational oceanography forecast, the synoptic forecast error is partly from the long-term systematic bias of the model, which can be partly counteracted by adjusting the values of the physical parameters. To this end, a four-dimensional optimization system is implemented into the South China Sea operational oceanography forecasting system, to adjust the values of multi-parameters using data assimilation method. By assimilating Argo temperature profiles of 51 days in the model domain, five physical parameters (coefficients of horizontal/vertical diffusion/viscosity and linear bottom drag) of the model have been adjusted simultaneously, and then the optimal values are obtained. The RMSE of temperature simulations in the assimilation window decreases from 1.17 to 0.97 K, when using the optimal values. The validation of the freerun experiments shows that the temperature RMSE decreases from 0.97 to 0.88 K, which indicates that the optimal values are still valid in a longer and independent period. Finally, the validation of the hindcast experiments shows that at the synoptic scale the temperature RMSE decreases from 0.90 to 0.80 K and other variables also present improvements. It hints that it is feasible to reduce the synoptic forecast errors by adjusting the parameter values at the climatological scale to partly counteract the systematic bias of the model. Therefore, it also provides a potential pathway to improve the synoptic forecast skill for the operational oceanography forecasting system.