The first approach uses the Advanced Regional Prediction System (ARPS). Incremental analysis updating (IAU) is used to introduce the analysis increments produced every five minutes by the ARPS three-dimensional variational (3DVAR) analysis and associated cloud and hydrometeor analysis. Data denial experiments are conducted to evaluate the impact of each non-conventional dataset on the accuracy of high-resolution analyses and forecasts of convective weather. The second approach is to conduct additional data denial experiments examining the impact of the different data sources using a GSI (Global Statistical Interpolation)-based EnKF (Ensemble Kalman Filter) data assimilation system coupled with the WRF-ARW (Advanced Research Weather Research and Forecasting) model. The structure, intensity, and timing of the meteorological features of interest seen in the model fields are compared with independent observations to determine the accuracy of individual forecasts. The effects of the increased observation density from these additional datasets on the high resolution analyses and forecasts produced by this system are then compared with that of the ARPS/3DVAR system. Using case studies from spring, 2015, the impact of the non-conventional observations on both specific severe weather events and over a month-long period are determined.