One limitation of convective-scale NWP ensemble modeling systems is the inadequate representation of the pre-convective and near-convective environment in the model, particularly in the lower level (0–2 km) of the atmospheric boundary layer (BL). This limitation is largely due to the unavailability of thermodynamic and kinematic BL observations with high spatial and temporal resolutions. This study aims to assess the impact of assimilating the emerging Uncrewed Aerial System (UAS) lower atmospheric BL measurements in convective-scale ensemble modeling systems. The case used to conduct the study is the violent EF4 24–25 March 2023 Rolling Fork tornado event. The event was sampled using various instrumentation during the PERiLS 2023 field campaign, including three CopterSonde UAS instruments located at three separate, stationary profiling sites near the Arkansas/Louisiana/Mississippi border. The CopterSondes collected lower atmospheric BL observations every 15–30 minutes, with cadence depending on storm motion and other environmental factors. Several retrospective experiments are conducted using the National Severe Storm Laboratory’s Warn-on-Forecast (WoFS) data assimilation and modeling system to assimilate temperature, dewpoint temperature, u-, and v-wind components from the CopterSonde in addition to all other routinely available observations in the baseline WoFS. Results indicate that assimilating the CopterSonde observations more accurately predict the evolution of the Rolling Fork tornadic supercell and its shape, intensity, location, and associated low-level updraft helicity (UH). Assimilating those observations more realistically represented the extent of low-level moisture advection and the strength of horizontal winds as they evolve throughout the observation period, thus limiting northward and westward displacement and better locating the most intense parts of the supercell. Even with the limited dataset, the CopterSonde UAS observations show promise in analyzing the storm environment and predicting the probability of severe thunderstorms with higher accuracy in the model. However, while the results from this single case study with only three profiling sites give us glimmers of hope, the results cannot be generalized. More studies need to be performed to assess the robustness of these results by assimilating a network of UAS BL measurements over a diverse set of cases in a convective-scale ensemble DA and prediction system.