Motivation for CLEO

In light of such attractive properties, a natural question to raise is whether SDM can be used to model warm rain more accurately than conventional models. The discrepancies between bulk models and observations of tropical warm rain is well documented, for example as discussed in Schulz and Stevens 2023 [SS23] and vanZanten et al. 2011 [vSN+11]. Already SDM has been used to improve our understanding of how precipitation formation depends on small scale influences, for example the choice of turbulent scheme, strength of entrainment and mixing, and the CCN size and concentration. Such analysis uses domains with horizontal extents up to O(10km) and resolutions no coarser than O(100m). What remains unclear is the role that cloud microphysics has at larger scales, for example due to the interplay between precipitation and mesoscale circulations O(100km).

By applying SDM to simulations in regional domains O(100km) with realistic boundary conditions and large scale forcings, we would, for the first time, have an alternative to conventional Eulerian perspectives on the role of microphysical processes at these scales. The comparison between such fundamentally different models gives us a new tool for assessing the behaviour of bulk models, and showing how their use is influencing climate simulations. Furthermore, when these simulations are weighed up with observations, we could quantify the extent to which SDM is a better representation not only warm rain formation, but also cloud organisation and evolution. The recent EUREC4A campaign, has provided a multitude of exceptionally high quality measurements which could be used for such an assessment.

It therefore seems apparent that a new implementation of SDM is required; capable of modelling warm rain in LES with realistic boundary conditions and large scale forcings, and capable of application in large regional domains, with horizontal extents O(100km). CLEO is an attempt to build such a SDM. It strives to be a library for SDM to model warm clouds with exceptional computational performance.