Using data from the 6 July 2015 PECAN case study, this paper provides the first objective assessment of how the assimilation of ground-based remote sensing profilers affects the forecasts of bore-driven convection. To account for the multiscale nature of the phenomenon, data impacts are examined separately with respect to (i) the bore environment, (ii) the explicitly resolved bore, and (iii) the bore-initiated convection. The findings from this work suggest that remote sensing profiling instruments provide considerable advantages over conventional in situ observations, especially when the retrieved data are assimilated at a high temporal frequency. The clearest forecast improvements are seen in terms of the predicted bore environment where the assimilation of kinematic profilers reduces a preexisting bias in the structure of the low-level jet. Data impacts with respect to the other two forecast components are mixed in nature. While the assimilation of thermodynamic retrievals from the Atmospheric Emitted Radiance Interferometer (AERI) results in the best convective forecast, it also creates a positive bias in the height of the convectively generated bore. Conversely, the assimilation of wind profiler data improves the characteristics of the explicitly resolved bore, but tends to further exacerbate the lack of convection in the control forecasts. Various dynamical diagnostics utilized throughout this study provide a physical insight into the data impact results and demonstrate that a successful prediction of bore-driven convection requires an accurate depiction of the internal bore structure as well as the ambient environment ahead of it.

A novel object-based algorithm capable of identifying and tracking convective outflow boundaries in convection-allowing numerical models is presented in this study. The most distinct feature of the proposed algorithm is its ability to seamlessly analyze numerically simulated density currents and bores, both of which play an important role in the dynamics of nocturnal convective systems. The unified identification and classification of these morphologically different phenomena is achieved through a multivariate approach combined with appropriate image processing techniques. The tracking component of the algorithm utilizes two dynamical constraints, which improve the object association results in comparison to methods based on statistical assumptions alone. Special attention is placed on some of the outstanding challenges regarding the formulation of the algorithm and possible ways to address those in future research. Apart from describing the technical details behind the algorithm, this study also introduces specific algorithm applications relevant to the analysis and prediction of bores. These applications are illustrated for a retrospective case study simulated with a convection-allowing ensemble prediction system. The paper highlights how the newly developed algorithm tools naturally form a foundation for understanding the initiation, structure, and evolution of bores and convective systems in the nocturnal environment.

A long-lived supercell developed in Northwest Bulgaria on 15 May 2018 and inflicted widespread damage along its track. The first part of this article presents a detailed overview of the observed storm evolution. Doppler radar observations reveal that the storm acquired typical supercellular signatures and maintained reflectivity values in excess of 63 dBZ for more than 4 h. The thunderstorm was also analyzed through lightning observations that highlighted important characteristics of the overall supercell dynamics. In its second part, the study investigates the predictability of the severe weather outbreak. In the medium forecast ranges, the global European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble indicated the presence of favorable conditions for the development of deep moist convection 4 days prior to the event. A set of three convection-allowing ensemble simulations also demonstrated that the practical predictability of the supercell was approximately 12 h, which is considerably higher than some previously reported estimates. Nevertheless, the skill of the convective forecasts appears to be limited by the presence of typical model errors, such as the timing of convection initiation and the development of spurious convective activity. The relevance of these errors to the optimal ensemble size and to the design of future convection-allowing numerical weather prediction (NWP) systems is further discussed.