Abstract
Abstract. The space spanned by the background ensemble provides a basis for
correcting forecast errors in the ensemble Kalman filter. However, the
ensemble space may not fully capture the forecast errors due to the limited
ensemble size and systematic model errors, which affect the assimilation
performance. This study proposes a new algorithm to generate pseudomembers
to properly expand the ensemble space during the analysis step. The
pseudomembers adopt vectors orthogonal to the original ensemble and are
included in the ensemble using the centered spherical simplex ensemble
method. The new algorithm is investigated with a six-member ensemble Kalman
filter implemented in the 40-variable Lorenz model. Our results suggest that
the ensemble singular vector, the ensemble mean vector, and their orthogonal
components can serve as effective pseudomembers for improving the analysis
accuracy, especially when the background has large errors.