A homogeneous linear estimation method for system error in data assimilation

In this paper, a new bias estimation method is proposed and applied in a regional ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting (WRF) Model. The method is based on a homogeneous linear bias model, and the model bias is estimated using statistics at each assimilation cycle, which is different from the state augmentation methods proposed in previous literatures. The new method provides a good estimation for the model bias of some specific variables, such as sea level pressure (SLP). A series of numerical experiments with EnKF are performed to examine the new method under a severe weather condition. Results show the positive effect of the method on the forecasting of circulation pattern and meso-scale systems, and the reduction of analysis errors. The background error covariance structures of surface variables and the effects of model system bias on EnKF are also studied under the error covariance structures and a new concept 'correlation scale' is introduced. However, the new method needs further evaluation with more cases of assimilation.