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Korea Institute of Atmospheric Prediction Systems(KIAPS) is currently developing a numerical weather prediction model, including a data assimilation system, to replace the Unified Model(UM). The KIAPS Integrated Model(KIM) consists of a spectral element-based non-hydrostatic dynamical core using a finite-volume method and physics packages. The data assimilation system adopted a hybrid 4D-EnVAR. 4D-EnVAR means that combined KIM VARiational data assimilation system(KVAR) and Local Ensemble Transform Kalman Filter(LETKF) data assimilation technique. Ensemble members currently uses 50 members. To evaluate the performance of the KIM, it is one of the important factors to understand the performance of system by operating and combining the individually developed systems. KIM Operational System(KOS) constructed a cycle experiments using the cylc meta-scheduler, which is widely used by various operational agencies and research laboratories. The cyclical experiments involves a data assimilation process every 6 hours, including KIM Package for Observation Processing(KPOP). The cyclical experiments were performed DA systems at 4 times a day, and 10-day forecasts are execute at 00 and 12 UTC due to model verification. Each task was written by python scripts and was configured to efficiently parallelize by using the cylc meta scheduler. Each task was configured pre- and post-processing progress and can be executed independently. Post processing contains visualization and remap from cubed-sphere grid to lat/lon grid. In addition, the grid remapped lat/lon grid data is used for visualization and displayed on the monitoring system for performance verification and stable operation of the numerical forecasting system. All of procedure are carried out automatically without any operation by user.

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