Practical Ensemble-Based Approaches to Estimate Atmospheric Background Error Covariances for Limited-Area Deterministic Data Assimilation

High-resolution flow-dependent background error covariances can allow for a better usage of dense observation networks in applications of data assimilation for numerical weather prediction. The generation of high-resolution ensembles, however, can be computationally cost prohibitive. In this study, practical and low-cost ensemble generation methods are presented and compared against both global and regional ensemble Kalman filters (G-EnKF and R-EnKF, respectively). The goal is to provide limited-area deterministic assimilation schemes with higher-resolution flow-dependent background error covariances that perform at least as well as those from the G-EnKF when assimilating the same observations. The low-cost methods are based on short-range regional ensemble forecasts initialized from 1) deterministic analysis plus balanced perturbations (filter free approach) and 2) a simplified ensemble square root filter (S-EnSRF), centered on deterministic analyses. The resulting ensembles from the different approaches are used within a 4D ensemble-variational (4D-EnVar) assimilation system covering most of Canada and the northern United States. Diagnostic results show that the mean is an important component of the ensembles. Results also show that the persistence of the homogeneous characteristics of the perturbations in the filter free approach makes this method unsuited for short assimilation time windows since some error structures take longer to develop. The S-EnSRF approach overcomes this limitation by recycling part of the prior perturbations. Results from 1-month assimilation experiments show that the S-EnSRF and R-EnKF experiments provide forecasts of similar quality to those from G-EnKF. Furthermore, results from precipitation verification indicate that the R-EnKF experiment provides the best precipitation accumulation predictions over 24-h periods.