Comparison of initial perturbation methods for ensemble prediction at convective scale

Convective-scale ensemble prediction systems (EPSs) are often initialized with downscaled initial condition perturbations (ICPs) from a global coarser EPS. Although downscaled ICPs have been shown to have a positive impact at short ranges, they cannot represent the uncertainty at small scales. Hence, there is a spin-up of around 9-12 h until the forecast perturbations develop realistic small-scale structures. On the other hand, ensemble data assimilation (EDA) is a common approach to obtain initial perturbations at all scales resolved by the numerical model. However, the high computational cost of EDA systems severely limits their size and their resolution. An alternative cheaper method to derive small-scale ICPs is considered here, based on a random sampling of the model background-error covariances. This article provides an evaluation of random and EDA-based IC perturbation methods against the baseline downscaling approach, in the framework of the pre-operational convective-scale EPS developed at Meteo-France with the AROME-France model at a 2.5 km horizontal resolution. Small-scale IC perturbation methods are shown to significantly improve the short-range EPS performance for surface weather variables. For 2 m temperature and 10 m wind speed, random ICPs give as good results as the EDA, owing to the very short spin-up of random perturbations. Precipitation forecasts are also strongly improved during the first six forecast hours, especially when initial humidity perturbations are included. The sensitivity of the EPS performance to the EDA size, horizontal resolution and representation of model errors is discussed. It is found that a large fraction of the initial uncertainty can be properly described with an EDA of reasonable size and at a slightly coarser resolution than the EPS.