The ensemble transform Kalman filter (ETKF) ensemble forecast scheme is introduced and compared with both a simple and a masked breeding scheme. Instead of directly multiplying each forecast perturbation with a constant or regional rescaling factor as in the simple form of breeding and the masked breeding schemes, the ETKF transforms forecast perturbations into analysis perturbations by multiplying by a transformation matrix. This matrix is chosen to ensure that the ensemble based analysis error covariance matrix would be equal to the true analysis error covariance if the covariance matrix of the raw forecast perturbations were equal to the true forecast error covariance matrix and the data assimilation scheme were optimal. For small ensembles (~100), the computational expense of the ETKF ensemble generation is about the same as that of the masked breeding scheme. Comparisons performed on a global model indicate that the ETKF ensemble mean and covariance are both superior to those obtained via the masked breeding technique. In these comparisons, neither ensemble was centered on the control analysis. To address this issue, simplex methods of centering initial perturbations on the minimum error variance estimate of the atmospheric state are introduced and compared with the commonly used centering method of positive/negative perturbations. In these simplex methods, one linearly dependent perturbation is added to a set of linearly independent initial perturbations to ensure that the sum of the new initial perturbations equals zero; the covariance of the new initial perturbations is equal to that of the independent initial perturbations and all the new initial perturbations are equally likely. We call this type of ensemble a "spherical simplex ensemble". Comparisons performed on a global model indicate that ensemble means and covariances of K-member spherical simplex ensembles are more accurate than those of K-member ensembles centered using positive/negative perturbations.