One of the main challenges presented by a limited‐area model ensemble prediction system (LAMEPS) concerns the limited capacity for its initial condition perturbations to correctly represent large‐scale flow uncertainties due to its limited‐size domain and deficiencies in formulating lateral boundary conditions. In addition, a mismatch between LAMEPS (initial condition) and host EPS lateral boundary perturbations can form spurious gravity waves at the boundaries. In the present work, an ensemble Jk blending method is proposed for improving representation of large‐scale uncertainties and for addressing consistent initial conditions and lateral boundary perturbations. Our approach involves employing Jk blending within a framework of three‐dimensional variation (3D‐Var) ensemble data assimilation (EDA). In such a system, small‐scale perturbations are generated from 3D‐Var EDA, while large‐scale perturbations are generated from the host ensemble via Jk blending. The ensemble Jk method is implemented in the C‐LAEF (Convection‐permitting Limited‐Area Ensemble Forecasting) system and is compared to the standard perturbed‐observation EDA approach, i.e. perturbed‐observation EDA without large‐scale constraint. The comparison shows that the ensemble Jk method gives a more skilful and reliable EPS, especially for upper‐air variables. In addition, positive effects on the surface pressure and precipitation of large‐scale perturbations are shown. Finally, the ensemble Jk method's capacity to alleviate perturbation mismatches is demonstrated.

The evaluation of several climatological background-error covariance matrix (defined as the B matrix) estimation methods was performed using the ALADIN limited-area modeling data-assimilation system at a 4 km horizontal grid spacing. The B matrices compared were derived using the standard National Meteorological Center (NMC) and ensemble-based estimation methods. To test the influence of lateral boundary condition (LBC) perturbations on the characteristics of ensemble-based B matrix, two ensemble prediction systems were established: one used unperturbed lateral boundary conditions (ENS) and another used perturbed lateral boundary conditions (ENSLBC). The characteristics of the three B matrices were compared through a diagnostic comparison, while the influence of the different B matrices on the analysis and quality of the forecast were evaluated for the ENSLBC and NMC matrices. The results showed that the lateral boundary condition perturbations affected all the control variables, while the smallest influence was found for the specific humidity. The diagnostic comparison showed that the ensemble-based estimation method shifted the correlations toward the smaller spatial scales, while the LBC perturbations gave rise to larger spatial scales. The influence on the analysis showed a smaller spatial correlation for the ensemble B matrix compared to that of the NMC, with the most pronounced differences for the specific humidity. The verification of the forecast showed modest improvement for the experiment with the ensemble B matrix. Among the methods tested, the results suggest that the ensemble-based data-assimilation method is the favorable approach for background-error covariance calculation in high-resolution limited-area data assimilation systems.

C‐LAEF (Convection‐permitting Limited‐Area Ensemble Forecasting) has been developed at the Austrian national weather service ZAMG (Zentralanstalt für Meteorologie und Geodynamik) and has been running operationally at the European Centre for Medium Range Weather Forecasts (ECMWF) supercomputer since November 2019. It includes (a) an ensemble 3D variational blending technique to deal with atmospheric initial uncertainties, (b) an ensemble of land surface data assimilation to account for uncertainties in the initial land surface conditions, (c) a hybrid stochastic physics perturbation scheme to treat model uncertainties in the different physics parametrization schemes, and (d) a coupling with the global ensemble system IFS‐ENS (Integrated Forecasting System‐ENSemble) to consider uncertainties in the lateral boundary conditions. C‐LAEF has a horizontal resolution of 2.5 km and consists of 16 perturbed members plus one unperturbed control run. It runs four times a day and provides probabilistic forecasts up to 60 hr on a domain covering the whole Alpine area. This article describes the C‐LAEF system in detail and evaluates the relative contributions of the different perturbation techniques. The ensemble variational blending technique and the ensemble surface data assimilation provide additional spread in the first forecast hours, while the hybrid stochastically perturbed parametrization scheme improves the performance of C‐LAEF during the whole forecast range. The performance of C‐LAEF is evaluated extensively over one summer and one winter period and compared with its mesoscale counterpart ALADIN‐LAEF (Aire Limitee Adaptation dynamique Developpement InterNational – Limited‐Area Ensemble Forecasting) and the IFS‐ENS. State‐of‐the‐art probabilistic measures indicate that C‐LAEF is able to outperform ALADIN‐LAEF for all considered upper‐air variables. At the surface, C‐LAEF outperforms ALADIN‐LAEF and IFS‐ENS according to most conventional measures, particularly for wind and precipitation. C‐LAEF benefits from the higher resolution and the explicit treatment of deep convection and can provide more accurate probabilistic information for weather warnings.

The main goal of this study is to assess the performance of the analog‐based post‐processing method applied to the Austrian ALADIN‐LAEF wind speed ensemble predictions through a set of sensitivity experiments. Evaluation of several analog‐based configurations using various meteorological variables as predictors is therefore conducted. The results of those experiments are compared to the ensemble model output statistics (EMOS) baseline model. The hypothesis further investigated is that using summarized measures, such as mean and standard deviation of an ensemble for several meteorological variables, is comparable to the analog post‐processing using all of the ensemble members. Results show that both analog‐based and EMOS experiments considerably improve the raw model forecast. Even though the improvement over raw model forecast is evident, large differences among nearby stations are noticed in the highly complex terrain. The processes at lower stations seem to be better represented by the raw model, which leads to a better input forecast to the post‐processing and a better overall result than for the mountain stations. The analog‐based method is overall comparable to or even outperforms the EMOS. Assessing the post‐processing performance for high wind speeds shows that the analog experiments can improve the raw forecast, exhibiting significantly higher skill than the EMOS. The difference among all analog experiments is less pronounced, especially the experiment using all of the raw model ensemble members and the one using summarized measures. Furthermore, it is demonstrated that the usage of summarized ensemble measures is an optimal way to improve the forecast skill, compared to the other analog‐based experiments and the EMOS model. Therefore, it is suggested that it is not necessary to increase the computational costs by using the full input spectrum of a raw probabilistic model, that is, all ALADIN‐LAEF members as predictors, as the summarized metric suffices.

To deal with the land surface physics uncertainties, a stochastic scheme based on stochastic perturbation of physics tendencies is implemented and tested. The impact of land surface physics uncertainties and their relative importance to land surface initial uncertainties are investigated in the regional ensemble forecasting system ALADIN‐LAEF (Aire Limitée Adaptation Dynamique Développement InterNational – Limited Area Ensemble Forecasting). The land surface initial perturbation is generated by using an ensemble of land surface data assimilation; and the land surface physics uncertainties by applying the idea of stochastically perturbed parametrization tendencies (SPPT) scheme. Three experiments are conducted and compared with the reference ensemble over a 2‐month period. The results show the introduction of land surface stochastic physics increases the ensemble spread, reduces the ensemble bias, and keeps neutral in deterministic forecast skill of the ensemble, its impact strongly depending on the quality of ensemble initial conditions. The ensemble land surface data assimilation has stronger positive impact on the ALADIN‐LAEF than the land surface stochastic physics for screen‐level temperature and humidity. There is not much impact on 10 m wind and precipitation. Best results are obtained when both the ensemble land surface data assimilation and land surface stochastic physics are used simultaneously; it gives a more reliable and statistically consistent forecast, which is contributed mainly by ensemble land surface data assimilation in the first forecast hours and largely by land surface stochastic physics in the later forecast hours.