The relative contributions of soil moisture heterogeneities, a stochastic boundary‐layer perturbation scheme and varied aerosol concentrations representing microphysical uncertainties on the diurnal cycle of convective precipitation and its spatial variability are examined conditional on the prevailing weather regime. To achieve this, separate perturbed‐parameter ensemble simulations are performed with the Consortium for Small‐scale Modeling (COSMO) model at convection‐permitting horizontal grid spacing for 10 days during a high‐impact weather episode in 2016 in Central Europe. We consider hourly precipitation amounts and their spatial distribution, focus on ensemble mean and spread aggregated over strong and weak forcing conditions, and employ spatial evaluation techniques. The convective adjustment time‐scale diagnostic is used to distinguish the different precipitation regimes. While the total amount of daily precipitation is hardly changed by the different perturbation approaches (less than 5%), the spatial variability of precipitation exhibits clear differences. Soil moisture heterogeneity primarily introduces variability during convection initiation causing a steeper increase in normalized rainfall spread prior to the onset of afternoon precipitation. The stochastic boundary‐layer perturbations lead to the largest spatial variability impacting precipitation from initial time onwards with an amplitude comparable to the operational ensemble spread. Similarly, perturbed aerosol concentrations impact spatial precipitation variability from the model start onwards, but to a smaller degree. Soil moisture heterogeneity shows the strongest weather regime dependence, with the greatest impact on convection during weak synoptic forcing. All types of perturbation increase dispersion of precipitation while maintaining the domain‐averaged precipitation rates.

We investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDA simulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale.

Deep convection represents a classic example of limited predictability on the convective scale. We investigate the potential impact of assimilating radar reflectivity and velocity observations on the predictive skill of precipitation in short-term forecasts (up to 6 hr) using the operational COSMO-KENDA ensemble data assimilation and forecasting system in an idealized set-up. Additionally, the role of a Gaussian-shaped mountain providing a permanent source of predictability for the location of convective precipitation is examined with and without data assimilation. Using a hierarchy of quality measures, we found a long-lasting beneficial impact of radar data assimilation throughout the entire forecast range of 6 hr. The up-scaled normalized RMS error and the Fractions Skill Score show that precipitation forecasts based on initial conditions including the assimilation of radar data are skilful on scales larger than 40 km at a lead time of 6 hr and thus are better than a reference ensemble without any data assimilation at lead times of less than 1 hr. The presence of orography strongly increases the predictability of precipitation throughout the forecast range, particularly within the immediate area and where no radar data are assimilated. This remarkable impact of radar data assimilation exceeding 6 hr is larger and longer-lasting than in many real modelling systems. While this is partly related to the idealized set-up assuming a perfect forecast model, perfect large-scale boundary conditions and a perfect radar forward operator, our study demonstrates the potential impact that could be achieved for radar data assimilation if the systematic model and operator deficiencies, as well as boundary condition errors, could be reduced. Furthermore, our results highlight the important role of orography in structuring the precipitation field, especially if no observations are assimilated.

Tropical cyclones are associated with a variety of significant social hazards, including wind, rain, and storm surge. Despite this, most of the model validation effort has been directed toward track and intensity forecasts. In contrast, few studies have investigated the skill of state-of-the-art, high-resolution ensemble prediction systems in predicting associated TC hazards, which is crucial since TC position and intensity do not always correlate with the TC-related hazards, and can result in impacts far from the actual TC center. Furthermore, dynamic models can provide flow-dependent uncertainty estimates, which in turn can provide more specific guidance to forecasters than statistical uncertainty estimates based on past errors. This study validates probabilistic forecasts of wind speed and precipitation hazards derived from the HWRF ensemble prediction system and compares its skill to forecasts by the stochastically-based operational Monte Carlo Model (NHC), the IFS (ECMWF), and the GEFS (NOAA) in use 2017-2019. Wind and Precipitation forecasts are validated against NHC best track wind radii information and the National Stage IV QPE Product. The HWRF 34 kn wind forecasts have comparable skill to the global models up to 60 h lead time before HWRF skill decreases, possibly due to detrimental impacts of large track errors. In contrast, HWRF has comparable quality to its competitors for higher thresholds of 50 kn and 64 kn throughout 120 h lead time. In terms of precipitation hazards, HWRF performs similar or better than global models, but depicts higher, although not perfect, reliability, especially for events over 5 in120h−1. Post-processing, like Quantile Mapping, improves forecast skill for all models significantly and can alleviate reliability issues of the global models.