Impact of Model Bias Correction on a Hybrid Data Assimilation System

Hybrid data assimilation combines a conventional 3-D or 4-D variational system with background error covariance (BEC) generated from ensemble forecast systems. In order to achieve better BEC, three perturbation schemes, namely, the random combination of multiple physical paramterization schemes (referred to as MP), the MP plus stochastical perturbation on physical process tendencies (MP-SPPT), and the unified perturbation of stochastic physics with bias correction (UPSB, proposed by the authors of this paper in a previous work), were first used in a regional ensemble model, i.e., the Global and Regional Assimilation and Prediction System-Regional Ensemble Prediction System (GRAPES-REPS), and the BECs thus obtained were compared for 7-day ensemble forecasts. The results show that UPSB, which is in fact an MP-SPPT but with the systematic model bias removed, has a better consistency, i.e., the ratio between root-mean-square error (RMSE) and ensemble spread is much closer to 1, especially at low model levels, compared to the other two schemes. Moreover, the BEC derived from UPSB captured more reasonable distributions of forecast errors. Second, performance of a hybrid data assimilation system (the GRAPES-MESO hybrid En-3DVar) was evaluated by using the BECs from the three perturbation schemes for 7-day hybrid data assimilation forecasts, and thus disclosing the effect of the model bias correction (assuming that the random stocastical features are in general offset in the three perturbation schemes) on the hybrid system forecasts. A covariance weight of 0.8 was prescribed, and this value was determined through sensitivity experiments. The forecast results from the hybrid data assimilation system show that UPSB reduced the false correlation between distant points. The quality of analysis fields of the UPSB scheme shows visible improvement, i.e., the analysis fields produced by UPSB have much smaller RMSEs than those of the other two schemes, at all vertical model levels. The quality of the hybrid data assimilation forecast fields was also improved by this scheme. Furthermore, the improvement was much greater in the early stage of the assimilation cycle than in the late stage. Generally, the quality of the hybrid data assimilation of GRAPES-MESO hybrid En-3DVar could be efficiently improved by the model bias correction in the UPSB scheme.