Performance of a hybrid gain ensemble data assimilation system based on the GRAPES_Meso model.

In this paper, a hybrid gain data assimilation (HGDA) method was introduced into the mesoscale version of the Global/Regional Assimilation and Prediction System (GRAPES_Meso) model. To further improve the forecasting performance of the GRAPES_Meso model, a scheme combining the HGDA and multiscale incremental analysis update (IAU) methods was first proposed in this work. To evaluate the performance of the HGDA method in the GRAPES_Meso model, different initialization schemes, including the GRAPES three-dimensional variational (3DVAR), HGDA and HGDA multiscale IAU schemes, were compared for a tropical cyclone (TC) case (Lupit, 2021). The results showed that the HGDA scheme outperformed the GRAPES-3DVAR scheme in forecasting the TC position and intensity. Using the optimal relaxation times, the HGDA multiscale IAU scheme attained the best performance in forecasting the TC position, intensity and precipitation. In addition, 3-month quasioperational comparative experiments were conducted with different assimilation schemes. Rainfall validation showed that the HGDA multiscale IAU method yielded a higher threat score (TS) and equitable threat score (ETS) and a lower bias score than did the 3DVAR multiscale IAU method. Especially in the first 18 h of the forecast, the HGDA scheme could increase the TS and ETS values by 11% and 6%, respectively, compared to the control and 3DVAR experiments. The results demonstrated that the HGDA method resulted in better prediction performance of the GRAPES_Meso model than did the GRAPES-3DVAR method. • A hybrid gain ensemble data assimilation (HGDA) scheme was adopted in the GRAPES_Meso model. • The HGDA performance in the prediction of Typhoon Lupit was better than that of the three-dimensional variational scheme. • A combined scheme of the HGDA and multiscale incremental analysis update (IAU) methods was first proposed in this work. [ABSTRACT FROM AUTHOR]