During the Verification of the Origins of Rotation in Tornadoes EXperiment-Southeast project (VORTEX USA) and the Propagation, Evolution, and Rotation in Linear Storms (PERiLS), held in the Southeast United States, many in-situ and ground-based remote sensing platforms were deployed to monitor the dynamic and thermodynamic state of the atmosphere to explore the conditions that make tornadoes and quasi-linear convective systems especially dangerous in that area. In this study, data collected at one of the VORTEX USA/PERiLS sites in particular (Courtland, AL) by a radar wind profiler (RWP) with associated radio acoustic sounding system (RASS), a ceilometer, and various surface observations are used to assess two operational numerical weather prediction models: the High Resolution Rapid Refresh (HRRR version 4; 3 km horizontal grid spacing) and the Rapid Refresh (RAP version 5; 13 km horizontal grid spacing). The RWP, RASS, and surface observation datasets cover the period from the end of 2019 to present, with additional information on cloud-base height available during 2022-2025 from the ceilometer at Courtland. Datasets collected at other sites in Louisiana and Mississippi, although less comprehensive, were used to support the findings from the Courtland site. The HRRR and RAP model errors of surface variables and wind speed, direction, and virtual temperature within the boundary layer are evaluated as a function of the year, season, month, daily cycle, and forecast horizon over a multi-year period. While the daily cycle and vertical structure of wind speed and temperature in the boundary layer are well captured by the models, this study finds a distinct diurnal signature in the errors of surface variables such as mixing ratio, temperature, wind speed, and pressure. The errors for surface parameters are also represented as a function of cloud characteristics. Clouds are not found to strongly impact forecast errors of surface parameters, except for the existence of larger errors for surface wind speed and pressure in rainy conditions.