A Square-Root, Dual-Resolution 3DEnVar for the AROME Model: Formulation and Evaluation on a Summertime Convective Period

Abstract

AbstractA three-dimensional ensemble-variational (3DEnVar) data assimilation algorithm has been developed for the high-resolution AROME NWP system. Building on previous work on 3DEnVar for AROME, we describe a formulation of the 3DEnVar that is based on the traditional square-root preconditioning. The localization may be performed either in gridpoint or spectral space, and allows for cross-covariances between surface pressure and the three-dimensional variables. The scheme has capacity for dual resolution, with the ensemble running at a lower 3.2-km spatial resolution than the deterministic AROME running at 1.3 km. This formulation is compatible with the variational bias correction scheme used in AROME. Hybrid covariances are implemented with climatological covariances at 1.3-km resolution being combined with ensemble perturbations that are interpolated to high resolution on the fly in the computation of the gradient. Hybrid 3DEnVar has an increased computational cost compared to 3DVar, which is partly mitigated by the use of dual resolution and the adoption of a flexible convergence criterion in the minimization. To get the full benefit from the ensemble scheme, it is recommended 1) to increase ensemble size from 25 to 50 members and 2) to decrease the localization length scale for the benefit of high-density radar observations. With those changes, the 3DEnVar outperforms the operational AROME-France 3DVar by a significant margin on the first 12 h of forecast range, as evidenced by a 3-month summer experiment. Finally, a case study reports on the improved prediction of heavy rainfall that frequently occurs in the Mediterranean region.