Simulations of reacting multiphase flows tend to display an inhomogeneously distributed computational intensity over the spatial and temporal domains. The time-to-solution of chemical reaction rates can span multiple orders of magnitude due to the emergence of combustible kernels and thin turbulent reaction zones. Similarly, the time to solve the equation of state (EoS) for non-ideal fluid mixtures deviates substantially between the grid cells. These effects result in a performance profile that is unbalanced and rapidly changing for transient simulations, and therefore beyond the capabilities of traditional (quasi-)static mesh partitioning methods. We analyse this loss of parallel efficiency for large-eddy simulations of the ECN Spray-A benchmark with the multi-physics solver INCA and propose to mitigate the problem by introducing two independent repartitioning stages in addition to the classic domain decomposition for fluid transport: one for the EoS and one for chemical reactions. We explore various scalable repartitioning strategies in this context and observe that rebalancing computational load yields a significant speedup that is robust for various mesh resolutions and process numbers. The dynamic multistage load-balancing thus effectively removes obstacles towards good parallel scaling of INCA and similar solvers for reacting and/or multiphase flows.

Small shallow cumulus clouds (less-than 1 km) over the tropical oceans appear to possess the ability to self-organize into mesoscale (10–100 km) patterns. To better understand the processes leading to such self-organized convection, we present Cloud Botany, an ensemble of 103 large-eddy simulations on domains of 150 km, produced by the Dutch Atmospheric Large Eddy Simulation model on supercomputer Fugaku. Each simulation is run in an idealized, fixed, larger-scale environment, controlled by six free parameters. We vary these over characteristic ranges for the winter trades, including parameter combinations observed during the EUREC4A (Elucidating the role of clouds–circulation coupling in climate) field campaign. In contrast to simulation setups striving for maximum realism, Cloud Botany provides a platform for studying idealized, and therefore more clearly interpretable causal relationships between conditions in the larger-scale environment and patterns in mesoscale, self-organized shallow convection. We find that any simulation that supports cumulus clouds eventually develops mesoscale patterns in their cloud fields. We also find a rich variety in these patterns as our control parameters change, including cold pools lined by cloudy arcs, bands of cross-wind clouds and aggregated patches, sometimes topped by thin anvils. Many of these features are similar to cloud patterns found in nature. The published data set consists of raw simulation output on full 3D grids and 2D cross-sections, as well as post-processed quantities aggregated over the vertical (2D), horizontal (1D) and all spatial dimensions (time-series). The data set is directly accessible from Python through the use of the EUREC4A intake catalog.