Forming proper ensemble forecast initial members with four-dimensional variational data assimilation method

A method has been presented to improve ensemble forecast by utilizing these initial members generated by four-dimensional variational data assimilation (4-D VDA), to conquer limitation of those initial members generated by Monte Carlo forecast (MCF) or lagged average forecast (LAF). This method possesses significant statistical characteristic of MCF, and by virtue of LAP that contains multi-time information and its initial members are harmonic with the dynamic model, six groups of numerical control and contrast experiments were performed with T42 spectral 4-D VDA system. The results show that in dekad range ensemble forecast with initial members generated by this method is prior to that by LAP. The anomaly correlation coefficient (ACC) of 500 hPa height in 10-d average by employing this method is 0.01-0.04 larger than by LAP, and root mean square error (RMSE) less than 0.2-0.4 dagpm.