The evolution of the planetary boundary layer (PBL), as well as the development of clouds and precipitation, is tightly coupled to land surface properties. Land-atmosphere (L-A) interactions are a continued source of uncertainty in numerical weather prediction (NWP) models due to the non-linear feedbacks in the L-A system, which make identifying and isolating deficiencies in physics parameterizations challenging. The purpose of this study is to identify the source of modeled biases in PBL evolution using observations from Bondville, IL in 2024. To examine the processes that contribute to PBL evolution from sunrise through the early afternoon hours, we use a mixing diagram framework to quantify the relative contributions of surface fluxes, advection, radiation, and entrainment to PBL evolution based on observational data and model output from the Common Community Physics Package single column model (CCPP SCM) using the High-Resolution Rapid Refresh (HRRR) physics suite. Compared to observations, the operational HRRR shows persistent biases: negative (positive) biases in surface temperatures during the day (night), positive (negative) biases in sensible (latent) heat fluxes as well as soil temperature and moisture. We have chosen several cases from the end of April through the beginning of October to examine the evolution of model biases throughout the growing season, and here we highlight one case that is explored in greater detail.