Despite the performance enhancements, the ensemble is still under-dispersive, i.e. lacking spread and spread growth. To overcome this difficulty, we design and implement a stochastic forcing model to add stochastic forcing to the tendencies of the important prognostic variables at all the grid points. One of the advantages of this model is the ability to generate the stochastic forcing with the desired statistical characteristics such as the spatial, temporal de-correlation scales and the amplitudes of the forcing in order to target various uncertainties with different scales. The stochastic forcing model with different spatial and temporal scales and amplitude has been tested and compared, including the ensemble without stochastic forcing. The results including issues, future opportunities and research areas will also be discussed and addressed.