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 m...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.展开更多
基金supported by the Provincial Science and Technology Development Program of Shandong under Grant No.2008GG10008001Key Technology Integration and Application Program of China Meteorological Administration,under Grant No.CMAGJ2011M32+1 种基金Forecaster Research Program of China Meteorological Administration,under Grant No.CMAYBY2012-031Science and Technology Research Programs of Shandong Provincial Meteorological Bureau,under Grant Nos.2012sdqxz03,2012sdqxz01,2010sdqxz01
文摘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.
