A 3D ensemble variational data assimilation scheme for the limited-area AROME model: Formulation and preliminary results

This paper presents the formulation and preliminary results of a 3D ensemble-variational data assimilation algorithm (3DEnVar) for the AROME-France model at 3.8 km horizontal resolution. This algorithm is a deterministic, variational data assimilation scheme that uses background-error covariances sampled from an ensemble. Our ensemble is an ensemble of data assimilation (EDA) at convective scale, based on the same system with the same spatial resolutions. In ensemble schemes, localization of the covariances is necessary to filter sampling noise. Two different localization schemes have been implemented, one in spectral space and one in grid-point space. We also evaluate hybrid formulations, where the background-error covariances are a weighted linear combination of the sampled covariances with the climatological ones. Cycled experiments are performed over a five-week time period with 3 h updates. The 3DEnVar scheme largely outperforms standard 3D-Var in terms of forecast scores. The best experiment is the one with the grid-point localization scheme. A diagnostic of objective localization can provide guidance about the horizontal and vertical localization lengths that give best performance. The hybrid configuration with 80% of ensemble covariances and 20% of climatological ones performs also significantly better than the 3D-Var, but to a lesser extent than the best 3DEnVar configuration, although it has better balanced initial fields.