Small shallow cumulus clouds (< 1 km) over the tropical oceans appear to possess the ability to self-organise 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 Large Eddy Simulation (DALES) 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.

Global cloud resolving models (GCRMs) are a new type of general circulation model that explicitly calculates the growth of cloud systems with fine spatial resolutions and more than 10 GCRMs have been developed at present. This chapter of the monograph reviews cloud microphysics schemes used in GCRMs with introductions to the recent progress and researches with GCRMs. Especially, research progress using a pioneer of GCRMs, Nonhydrostatic ICosahedral Atmospheric Model (NICAM), is focused. Since GCRMs deal with climatology and meteorology, it is a challenging issue to establish cloud microphysics schemes for GCRMs. A brief history of the development of cloud microphysics schemes and cloud-radiation coupling in NICAM is described. In addition, current progress in analytical techniques using satellite simulators is described. The combined use of multi-optical sensors enables us to constrain uncertain processes in cloud microphysics without artificial tuning. As a result, cloud microphysics schemes used in the NICAM naturally represent cloud systems, and hence, the radiative budget is well balanced with little optimization. Finally, a new satellite and a ground validation campaign are introduced for future work.

Typhoon Faxai hit Japan in 2019 and severely damaged the Tokyo metropolitan area. To mitigate such damages, a good track forecast is necessary even before the typhoon formation. To investigate the predictability of the genesis and movement of a precursor vortex and its relationship with the synoptic-scale flow, 1600-member ensemble simulations of Typhoon Faxai were performed using a 14-km mesh global nonhydrostatic atmospheric model, which started from 16 different initial days (i.e., 1600 members in total). The results show that the model could predict an enhanced risk of a Faxai-like vortex heading toward Japan two weeks before landfall, which was up to 70%. The reason for the enhancement was a rapid increase in the members reproducing a precursor vortex from 15 to 12 days before landfall in Japan. In addition, the upper-tropospheric trough played an essential role in the track simulation of Faxai.

NICAM (Nonhydrostatic Icosahedral Atmospheric Modeling) has been used to conduct global storm resolving simulations with a mesh size of O(km) over the globe (Satoh, M. et al. 2017). Using the supercomputer “Fugaku”, we explore studies in the following directions: 1. Large-ensemble simulations (1000 members), 2. longer-duration simulations (100 years: HighResMIP; Kodama, C. et al. 2021), 3. higher-resolution simulations (less than a kilometer dx; Miyamoto et al. 2013), 4. high-resolution atmosphere-ocean coupled model simulations (atmosphere 3.5 km × ocean 0.1 deg: NICOCO; Miyakawa, T. et al. 2017), and 5. large ensemble data assimilations with NICAM-LETKF (Yashiro, H. et al. 2020). In this talk, we first review the current activities of NICAM on Fugaku. As the most uncertain component of atmospheric models in general, we intercompared the cloud properties of the DYAMOND simulation data (Stevens, B. et al. 2019; Roh, W. et al. 2021). We found that the domain averaged outgoing long-wave radiation is relatively similar across the models, but the net shortwave radiation at the top of the atmosphere shows significant differences among the models (Figure). The vertical profiles of cloud concentration are widely divergent among models, and cloud water content exhibits larger intermodel differences than cloud ice. This result implies more focused evaluations of clouds are required for improving the global storm resolving models. The forthcoming satellite “EarthCARE” (Illingworth, A. et al. 2015) provides a comprehensive dataset for cloud evaluations of atmospheric models, particularly by the first cloud Doppler radar from space. We present possible strategies for the new era of satellite collaboration studies with global storm resolving models.