Improving initial condition perturbations in a convection-permitting ensemble prediction system

One of the main challenges presented by a limited-area model ensemble prediction system (LAMEPS) concerns the limited capacity for its initial condition perturbations to correctly represent large-scale flow uncertainties due to its limited-size domain and deficiencies in formulating lateral boundary conditions. In addition, a mismatch between LAMEPS (initial condition) and host EPS lateral boundary perturbations can form spurious gravity waves at the boundaries. In the present work, an ensemble Jk blending method is proposed for improving representation of large-scale uncertainties and for addressing consistent initial conditions and lateral boundary perturbations. Our approach involves employing Jk blending within a framework of three-dimensional variation (3D-Var) ensemble data assimilation (EDA). In such a system, small-scale perturbations are generated from 3D-Var EDA, while large-scale perturbations are generated from the host ensemble via Jk blending. The ensemble Jk method is implemented in the C-LAEF (Convection-permitting Limited-Area Ensemble Forecasting) system and is compared to the standard perturbed-observation EDA approach, i.e. perturbed-observation EDA without large-scale constraint. The comparison shows that the ensemble Jk method gives a more skilful and reliable EPS, especially for upper-air variables. In addition, positive effects on the surface pressure and precipitation of large-scale perturbations are shown. Finally, the ensemble Jk method's capacity to alleviate perturbation mismatches is demonstrated.