Mesoscale ensemble 4DVAR and its comparison with EnKF and 4DVAR

Inter-comparisons between variational, ensemble-based and hybrid data assimilations (DA) are presented on a regional weather research and forecasting model (WRF) over the continental United States during an active summer month of June 2003. Four different data assimilation (DA) methods are considered, including three/four-dimensional variational methods (3D/4DVAR) with NMC-based background error covariance, ensemble Kalman filter (EnKF) with flow-dependent forecast error uncertainties and the hybrid approach that couples 4DVAR with EnKF (E4DVAR). In E4DVAR, the ensemble-based background error covariance is incorporated into the 4DVAR minimization via the alpha-control transform, while the ensemble perturbations are updated in EnKF but their mean is replaced by the 4DVAR analysis. Among those DA experiments, E4DVAR is more flexible, whose flow-dependent benefits can be addressed both on the explicit background error covariance estimated by EnKF ensembles and its implicit counterpart modeled by 4DVar trajectory, moreover the capability of 4DVAR on dealing with asynchronous and high-volume observations also gives the coupled E4DVAR method more flexibility, especially for those mesoscale weather systems.

For the experiments, various conventional observations are assimilated over the North America region during June 2003, and comparisons of DA performances are demonstrated by the forecast error verifications against radiosonde measurements. Based on the month-long statistical results, E4DVAR significantly outperformed all the other DA methods for a 48-h lead time, whose root mean square errors kept in a lower level than the others during the whole month. The monthly mean results also show that E4DAVR had the lowest forecast errors for both dynamical and thermal variables, followed by EnKF, 4DVAR and 3DVAR in sequence.