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The Ensemble-based Reduced Dimension Variational (ERDVar) assimilation method can not only reduce the computational cost without solving the tangential model and adjoint model but also provide the “follow dependent” background error covariance matrix. The NMC (National Meteorogical Center, USA) perturbation method and Regional ERDVar (R-ERDVar) are proposed to resolve the initial perturbation and localization in this article. Finally, ERDVar has been applied to the Global Medium-range Numerical Weather Prediction Model T106L19. The results show that: (1) It is effective to obtain higher accuracy in assimilation using ERDVar, as the information of true innovations is extracted. (2) The NMC initial perturbations reflect the structure of forecast errors and cannot decay easily during forecast subsequently, with at least 10% reduction on forecast errors in ERDVar experiments. (3) Compared with the global ERDVar experiments, there is a 14% reduction for all variable RMSE on average in R-ERDVar experiments, with smaller computational cost. Farther more, the combination use of the R-ERDVar method and NMC perturbation samples can make improvements more stable.