An Ensemble Kalman Filter for Numerical Weather Prediction Based on Variational Data Assimilation: VarEnKF

Abstract

everal NWP centers currently employ a variational data assimilation approach for initializing deterministic forecasts and a separate ensemble Kalman filter (EnKF) system both for initializing ensemble forecasts and for providing ensemble background error covariances for the deterministic system. This study describes a new approach for performing the data assimilation step within a perturbed-observation EnKF. In this approach, called VarEnKF, the analysis increment is computed with a variational data assimilation approach both for the ensemble mean and for all of the ensemble perturbations. To obtain a computationally efficient algorithm, a much simpler configuration is used for the ensemble perturbations, whereas the configuration used for the ensemble mean is similar to that used for the deterministic system. Numerous practical benefits may be realized by using a variational approach for both deterministic and ensemble prediction, including improved efficiency for the development and maintenance of the computer code. Also, the use of essentially the same data assimilation algorithm would likely reduce the amount of numerical experimentation required when making system changes, since their impacts in the two systems would be very similar. The variational approach enables the use of hybrid background error covariances and may also allow the assimilation of a larger volume of observations. Preliminary tests with the Canadian global 256-member system produced significantly improved ensemble forecasts with VarEnKF as compared with the current EnKF and at a comparable computational cost. These improvements resulted entirely from changes to the ensemble mean analysis increment calculation. Moreover, because each ensemble perturbation is updated independently, VarEnKF scales perfectly up to a very large number of processors.