Intercomparison of Variational Data Assimilation and the Ensemble Kalman Filter for Global Deterministic NWP. Part II: One-Month Experiments with Real Observations

An intercomparison of the Environment Canada variational and ensemble Kalman filter (EnKF) data assimilation systems is presented in the context of producing global deterministic numerical weather forecasts. Five different variational data assimilation approaches are considered including four-dimensional variational data assimilation (4D-Var) and three-dimensional variational data assimilation (3D-Var) with first guess at the appropriate time (3D-FGAT). Also included among these is a new approach, called Ensemble-4D-Var (En-4D-Var), that uses 4D ensemble background-error covariances from the EnKF. A description of the experimental configurations and results from single-observation experiments are presented in the first part of this two-part paper. The present paper focuses on results from medium-range deterministic forecasts initialized with analyses from the EnKF and the five variational data assimilation approaches for the period of February 2007. All experiments assimilate exactly the same full set of meteorological observations and use the same configuration of the forecast model to produce global deterministic medium-range forecasts. The quality of forecasts in the short (medium) range obtained by using the EnKF ensemble mean analysis is slightly degraded (improved) in the extratropics relative to using the 4D-Var analysis with background-error covariances similar to those used operationally. The use of the EnKF flow-dependent error covariances in the variational system (4D-Var or 3D-FGAT) leads to large (modest) forecast improvements in the southern extratropics (tropics) as compared with using covariances similar to the operational system (a gain of up to 9 h at day 5). The En-4D-Var approach leads to (i) either improved or similar forecast quality when compared with the 4D-Var experiment similar to the currently operational system, (ii) slightly worse forecast quality when compared with the 4D-Var experiment with EnKF error covariances, and (iii) generally similar forecast quality when compared with the EnKF experiment.