TY - GEN
AU - Massimo Bonavita
AU - E. Hólm
AU - L. Isaksen
AU - Mike Fisher
AB - 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 EDA has gradually been incorporated into the B model which describes the background error covariance matrix at the start of the ECMWF 4DVar assimilation window. Starting with background error variances for the balanced part of the control vector and observation quality control (Bonavita et al., 2012) the current paper 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 4DVar assimilation cycle which makes use of the improved B. Finally, increasing the resolution at which the EDA 4DVars are run is shown to reduce the under-dispersiveness of the EDA-based error estimates
BT - ECMWF Technical Memoranda
DA - 12/2014
DO - 10.21957/uri3603hp
LA - eng
M1 - 743
N2 - 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 EDA has gradually been incorporated into the B model which describes the background error covariance matrix at the start of the ECMWF 4DVar assimilation window. Starting with background error variances for the balanced part of the control vector and observation quality control (Bonavita et al., 2012) the current paper 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 4DVar assimilation cycle which makes use of the improved B. Finally, increasing the resolution at which the EDA 4DVars are run is shown to reduce the under-dispersiveness of the EDA-based error estimates
PB - ECMWF
PY - 2014
T2 - ECMWF Technical Memoranda
TI - The evolution of the ECMWF hybrid data assimilation system
UR - https://www.ecmwf.int/node/8271
ER -