The Lagrangian approach of MPIC has a number of advantages: correlations between thermodynamic properties are naturally conserved, an explicit representation of subgrid-scale mixing is possible, and the flow is not damped by numerical diffusion. Moreover, the properties carried by parcels do not need to be advected separately, which will be a large advantage for studies where a prognostic representation of cloud droplet size and aerosol transport is needed.
For the ascent of a moist thermal, MPIC is compared against Large-Eddy Simulations using the Met Office NERC Cloud (MONC) model. Dynamical features evolve similarly throughout the development of the thermal in both models. The convergence of bulk properties, such as liquid water profiles and root-mean-square velocities, is also studied. Most of these properties converge rapidly in MPIC: the model thus provides an alternative for the simulation of moist physics and dynamics that compares well to MONC, but could be significantly cheaper for a given effective resolution. However, the probability function of liquid water is too narrow at low resolution in MPIC. Strategies to address this issue are discussed.
Subgrid-scale properties of small eddies captured by the MPIC model can be explicitly reconstructed on a fine grid, as in the figure. This allows investigation of additional scales that are captured by the model. MPIC is shown to retain a large amount of variability in the humidity field on scales near the grid spacing. During the development of the cloud, the fractal dimension of the cloud interface is higher than has been suggested in previous work on moist convection, though this may be related to the way in which the thermal is initiated. Finally, the Lagrangian parcels in MPIC are used to study the origin of cloud air in a consistent manner. The development of a massively-parallel version of the model, which is currently being undertaken, will make it more attractive to adapt MPIC for future studies.