The Sensitivity Analyses of Initial Condition and Data Assimilation for a Fog Event using the Mesoscale Meteorological Model

摘要: The accurate simulation of micro-scale weather phenomena such as fog using the mesoscale meteorological models is a very complex task. Especially, uncertainty arisen from initial input data numerical has decisive effect on accuracy models. assimilation required to reduce data. In this study, limitation model was verified by WRF (Weather Research and Forecasting) for summer event around Nakdong river in Korea. sensitivity analyses were conducted two different boundary conditions: KLAPS (Korea Local Analysis Prediction System) LDAPS (Local Data Assimilation addition, improvement performance FDDA (Four-Dimensional Assimilation) observational AWS (Automatic Weather investigated. result analysis showed that simulated air temperature, dew point relative humidity with higher than those KLAPS, but wind speed lower KLAPS. Significant difference found case where RMSE (Root Mean Square Error) 15.7 35.6%, respectively. speed, improved approximately , 2.2%, respectively after incorporating FDDA.