Persistent biases in the energy budget over the Southern Ocean (SO) within climate simulations and reanalysis products have been linked to the poor representation of the unique clouds over the region, particularly in regions of shallow, post-frontal convection. In response to these challenges, the CAPRICORN field campaign was carried out to characterize the cloud, aerosol, precipitation and boundary layer properties over the SO. The Australian R/V Investigator undertook a 35-day cruise (March-April) in 2016 making observations from Hobart to the polar front. Two cases are examined in this study with a focus on shallow convective clouds. Shipborne measurements, Himawari-8 products, and high-resolution simulations using the Weather Research and Forecasting (WRF) model are integrated to investigate the dynamical and microphysical characteristics of the targeted cloud fields. In the first case (21-23 March), a rapid succession of two fronts were encountered, separating fields of shallow convective warm clouds. Light precipitation (~2 mm) originating from the pre-frontal shallow convection was recorded by the ship. This precipitation is underrepresented in the simulations, which is linked to a deficit of the low-cloud cover. The second case (26-28 March) focusses on a sustained period of open mesoscale cellular convection in a post-frontal environment. The observed cloud field in this case resided primarily below 2.5 km and in the sub-freezing temperature range (0 to -8°C), where mixed-phase cloud tops were suggested by both the shipborne and Himawari-8. Despite the relatively good representation of some surface meteorology, WRF simulations have difficulties in producing both the low-level cloud field, mixed-phase cloud tops, and surface precipitation. Sensitivity experiments with different physical parameterization schemes are performed to investigate the impact of boundary layer and microphysical processes on the simulations of the shallow convective clouds. Possible causes of the model deficiencies and possible pathways for model improvements will be discussed.