Sensitivity of regional ensemble data assimilation spread to perturbations of lateral boundary conditions

The implementation of a regional ensemble data assimilation and forecasting system requires the specification of appropriate perturbations of lateral boundary conditions (LBCs), in order to simulate associated errors. The sensitivity of analysis and 6-h forecast ensemble spread to these perturbations is studied here formally and experimentally by comparing three different LBC configurations for the ensemble data assimilation system of the ALADIN-France limited-area model (LAM). While perturbed initial LBCs are provided by the perturbed LAM analyses in each ensemble, the three ensemble configurations differ with respect to LBCs used at 3- and 6-h forecast ranges, which respectively correspond to: (1) perturbed LBCs provided by the operational global ensemble data assimilation system (GLBC), which is considered as a reference configuration; (2) unperturbed LBCs (ULBC) obtained from the global deterministic model; (3) perturbed LBCs obtained by adding random draws of an error covariance model (PLBC) to the global deterministic system. A formal analysis of error and perturbation equations is first carried out, in order to provide an insight of the relative effects of observation perturbations and of LBC perturbations at different ranges, in the various ensemble configurations. Horizontal variations of time-averaged ensemble spread are then examined for 6-h forecasts. Despite the use of perturbed initial LBCs, the regional ensemble ULBC is underdispersive not only near the lateral boundaries, but also in approximately one-third of the inner area, due to advection during the data assimilation cycle. This artefact is avoided in PLBC through the additional use of non-zero LBC perturbations at 3-and 6-h ranges, and the sensitivity to the amplitude scaling of the covariance model is illustrated for this configuration. Some aspects of the temporal variation of ensemble spread and associated sensitivities to LBC perturbations are also studied. These results confirm the importance of LBC perturbations for regional ensemble data assimilation. They also indicate that perturbing initial LBC is not sufficient to obtain realistic ensemble spread, whereas this can be achieved approximately by using random covariance draws for simulating LBC errors during the forecast and associated data assimilation cycling.