A comparison of WRF-based 3DVAR, the ensemble Kalman filter (EnKF), and E3DVAR over East Asia for one-month period

As importance of regional reanalysis with higher resolution emerged, Yang and Kim (2017) developed regional reanalysis in East Asia based on the Unified Model (UM) for the 2-yr period of 2013-14 to prepare for a future, long-term data. While Yang and Kim (2017) assimilated observations with 4DVAR assimilation method, this study focuses on ensemble-based assimilation method to include information about uncertainties based on ensemble forecasts.

To develop an ensemble-based regional reanalysis system for East Asia, the best, state-of-the-art data assimilation method should be examined over East Asia and its optimization is required. In this study, East Asia regional reanalysis system is developed based on the Weather Research and Forecasting (WRF, v3.7.1) model. To decide the most appropriate assimilation method for East Asia, three different WRF-based data assimilation methods, which are 3DVAR, the ensemble Kalman filter (EnKF), and E3DVAR, are developed and compared with each other over East Asia for one-month period. For ensemble-based data assimilation methods, 40 ensemble members are used.

The domain for East Asia regional reanalysis is the same as Yang and Kim (2017). The horizontal resolution is 12-km and 50 vertical levels up to 5 hPa from the surface are used. The 5th ECMWF ReAnalysis (ERA5) data are used as an initial condition at the first cycle and used as lateral boundary conditions for every cycle. For each experiment, analysis fields are produced every 6-h (00, 06, 12, 18 UTC) by assimilating conventional observation data with a 6-h assimilation window. The evaluation results will be presented at the meeting.

Acknowledgments

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the South Korean government (Ministry of Science and ICT) (Grant 2017R1E1A1A03070968) and the Korea Polar Research Institute (KOPRI, PN17081).