A stochastic physics scheme is tested in the Application of Research to Operations at Mesoscale (AROME) short-range convection-permitting ensemble prediction system. It is an adaptation of ECMWF’s stochastic perturbation of physics tendencies (SPPT) scheme. The probabilistic performance of the AROME model ensemble is found to be significantly improved, when verified against observations over two 2-week periods. The main improvement lies in the ensemble reliability and the spread–skill consistency. Probabilistic scores for several weather parameters are improved. The tendency perturbations have zero mean, but the stochastic perturbations have systematic effects on the model output, which explains much of the score improvement. Ensemble spread is an increasing function of the SPPT space and time correlations. A case study reveals that stochastic physics do not simply increase ensemble spread, they also tend to smooth out high-spread areas over wider geographical areas. Although the ensemble design lacks surface perturbations, there is a significant end impact of SPPT on low-level fields through physical interactions in the atmospheric model.

Flow-dependent background-error variances can be estimated by means of an ensemble of assimilations. However, the finite size of the ensemble implies a sampling noise, which is detrimental for the variance estimation. This article presents a filtering procedure for ensemble-estimated variance fields, which relies on an estimate of spectral signal/noise ratios.It is first demonstrated that the sampling noise covariance can be expressed analytically as a simple function of the background-error covariance. The resulting formula shows in particular that the spatial structure of the sampling noise is closely related to the spatial structure of background error (i.e. to its correlation function). It is then explained how this relation can be used to calculate an objective filter.Investigations are first conducted in a highly idealized 1D framework, to show that the proposed filter is able to remove most of the sampling noise, while extracting the signal of interest. Application to an ensemble of Météo-France Arpège forecasts is then considered. This objective filter reveals a vertical-level dependence, with a larger signal/noise ratio near the surface, and a scale separation between signal and noise which is more pronounced in altitude. The results also indicate that, after applying such an optimized filter, variance estimates obtained from a six-member ensemble have a residual estimation error variance around 10%.Some insights are then given into the spatio-temporal dynamics of the variance field. It is observed that the globally averaged background-error variance is fairly stable in time, while spatial patterns of the variance field are closely linked to the meteorological situation, with high values found in the vicinity of troughs.Finally, impact studies in the Arpège system show that the filtering of vorticity variances has a positive impact on the quality of the NWP system.

The AROME-EPS convection-permitting ensemble prediction system has been evaluated over the HyMeX-SOP1 period. Objective verification scores are computed using dense observing networks prepared for the HyMeX experiment. In probabilistic terms, the AROME-EPS ensemble performs better than the AROME-France deterministic prediction system, and a state-of-the-art ensemble at a lower resolution. The strengths and weaknesses of AROME-EPS are discussed. Here, impact experiments are used to study perturbation schemes for the initial conditions and the model surface. Both have a significant effect on the ensemble performance. The interactions between the perturbations of lateral boundaries, initial conditions and surface perturbations are studied. The consistency between initial and lateral perturbations is found to be unimportant from a meteorological point of view. Ensemble data assimilation is not as effective as a simpler surface perturbation scheme, and it is noted that both approaches could be usefully combined.