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 Sour-dimensional variational data assimilatio n (4-D VDA), to conquer limitation of those initial members generated by Mo nte Carte forecast (MCF) or lagged average forecast (LAF), This method poss esses significant statistical characteristic of MCF, and by virtue of LAF t hat contains multi-time information and its initial members are harmonic wi th the dynamic model, six groups of numerical control and contrast experime nts were performed with T42 spectral 4-D VDA system. The results show that in dekad range ensemble forecast with initial members generated by this met hod is prior to that by LAF. The anomaly correlation coefficient (ACC) of 5 00 hPa height in 10-d average by employing this method is 0.01-0.04 larger than by LAF, and root mean square error (RMSE) less than 0.2-0.4 dagpm.