The evolution of the ECMWF hybrid data assimilation system

The trend towards using flow‐dependent, ensemble‐based estimates of background‐error covariances has been one of the main themes of atmospheric data assimilation research and development in recent years. In this work it is documented how flow‐dependent ensemble information from the ECMWF ensemble of data assimilations (EDA) has gradually been incorporated into the B model which describes the background‐error covariance matrix at the start of the ECMWF 4D‐Var assimilation window. Starting with background‐error variances for the balanced part of the control vector and observation quality control, the current article extends the flow‐dependency to background‐error variances for the unbalanced part of the control vector and for background‐error correlation structures. The correlations are determined either online from previous days or from a hybrid of climatological and current cycle estimates. Each of these changes is shown to improve both the realism of the modelled B and the accuracy of the analysis and forecast fields produced by the 4D‐Var assimilation cycle which makes use of the improved B . Finally, increasing the resolution at which the EDA 4D‐Vars are run is shown to reduce the underestimation of the EDA‐based error estimates.