The approach taken in this work is to identify fields of cumulus clouds that are free of other cloud types, and then compare the statistics from the GOES observations with the cloud field simulated by the HRRR. The development of new image processing techniques, which take advantage of the typical spatial patterns associated with large-scale cumulus fields, results in a rapid, accurate, and automated method of cumulus field identification within GOES image retrievals. The fitting of a spatial ellipse within identified cumulus regions makes for the trivial computation of an associated “field centroid” within latitude and longitude space. A similar approach is used to identify resolved cumulus fields in the HRRR through the analysis of liquid water path (LWP) distributions. Fields of cumulus clouds that ‘overlap’ between the observations and HRRR can them be compared statistically to see if the model is getting the correct cloud fraction and LWP distribution in each field. This approach, when combined with other datasets (e.g., downwelling shortwave radiative flux from the HRRR and observations and overall environmental conditions), provides better insights on how well the HRRR is able to simulate the shallow convective processes that lead to fair weather cumulus clouds.