HarmonEPS is the limited-area, short-range, convection-permitting ensemble prediction system developed and maintained by the HIRLAM consortium as part of the shared ALADIN–HIRLAM system. HarmonEPS is the ensemble realization of HARMONIE–AROME, used for operational short-range forecasting in HIRLAM countries. HarmonEPS contains a range of perturbation methodologies to account for uncertainties in the initial conditions, forecast model, surface, and lateral boundary conditions. This paper describes the state of the system at the version labeled cycle 40 and highlights some directions for further development. The different perturbation methods available are evaluated and compared where appropriate. Several institutes have operational or preoperational implementations of HarmonEPS, such as MEPS (Finland, Norway, and Sweden), COMEPS (Denmark), IREPS (Ireland), KEPS (the Netherlands), AEMET-γSREPS (Spain), and RMI-EPS (Belgium), and these systems are briefly described and compared with the ensemble prediction system (IFS ENS) from the European Centre for Medium-Range Weather Forecasts (ECMWF).

Abstract.A hybrid variational ensemble data assimilation has been developed on top of the HIRLAM variational data assimilation. It provides the possibility of applying a flowdependent background error covariance model during the data assimilation at the same time as full rank characteristics of the variational data assimilation are preserved. The hybrid formulation is based on an augmentation of the assimilation control variable with localised weights to be assigned to a set of ensemble member perturbations (deviations from the ensemble mean). The flow-dependency of the hybrid assimilation is demonstrated in single simulated observation impact studies and the improved performance of the hybrid assimilation in comparison with pure 3-dimensional variational as well as pure ensemble assimilation is also proven in real observation assimilation experiments. The performance of the hybrid assimilation is comparable to the performance of the 4-dimensional variational data assimilation. The sensitivity to various parameters of the hybrid assimilation scheme and the sensitivity to the applied ensemble generation techniques are also examined. In particular, the inclusion of ensemble perturbations with a lagged validity time has been examined with encouraging results.

To fill the gap in the observation system for humidity, the HIRLAM–ALADIN Research on Mesoscale Operational NWP in Euromed (HARMONIE) limited-area high-resolution kilometer-scale model has been prepared for assimilation of Global Navigation Satellite System (GNSS) zenith total delay (ZTD) observations. The observation-processing system includes data selection, bias correction, quality control, and a GNSS observation operator for data assimilation. A large part of the bias between observations and model equivalents comes from the relatively low model top used in the HARMONIE experiments. The functionality of the different observation-processing components was investigated in detail as was the overall performance of the GNSS ZTD data assimilation. This paper contains an extensive description of the GNSS ZTD observation-processing system and a comparison of a newly introduced variational bias correction for GNSS ZTD data with an alternative static bias correction, as well as a detailed analysis of the impact of GNSS ZTD data, both in terms of statistical evaluations over a longer period and in terms of individual case studies. Assimilation of the GNSS ZTD observations with a variational bias correction has improved the quality of short-range weather forecasts for the moisture-related parameters in particular, both in a statistical sense and in individual case studies. The paper also discusses further improvements in the HARMONIE variational data-assimilation system that are needed to fully utilize the potential of high-resolution GNSS ZTD observations.

Abstract. A four-dimensional ensemble variational (4D-En