The forward simulation of radar reflectivity requires details of clouds and precipitation from general circulation models (GCMs). But such details are represented as sub-grid processes that involve parameterizations and assumptions about the spatial coverage and thus depend on the GCM. In this research, we propose the use of a statistical method to generate sub-grid precipitation for generic use. In addition, the proposed method can be used to provide uncertainty estimates on the signals. The sub-grid variability is obtained from simulation with a global storm-resolving model called NICAM (non-hydrostatic icosahedral atmospheric model). The proposed method first generates precipitation probabilities for the possible scenarios and then sub-grid precipitation rates are generated from the generalized gamma distribution for the given cloud fraction and grid-scale precipitation rates. Compared to the standard method (which neglects the probabilities) that overestimates the precipitation fraction, our method well reproduces the NICAM dataset profiles of both the precipitation fraction and the radar-based cloud fraction. The in-cloud signal frequencies are also reproduced, although less accurately over a tropical region. Inclusion of sub-grid variability in precipitation rates was particularly important for the tropical region to obtain agreement of the precipitation fraction. Application of the two methods to a GCM shows it to have a robust bias for low-level liquid clouds. The proposed method can be used to identify uncertainty in the signals associated with sub-grid variability in the precipitation processes, indicating an effective way to use a global storm-resolving model to evaluate conventional GCMs.

Toward the achievement of reliable global kilometer-scale (k-scale) climate simulations, we improve the Nonhydrostatic ICosaherdral Atmospheric Model (NICAM) by focusing on moist physical processes. A goal of the model improvement is to establish a configuration that can simulate realistic fields seamlessly from the daily-scale variability to the climatological statistics. Referring to the two representative configurations of the present NICAM, of which each has been used for climate-scale and sub-seasonal-scale experiments, we try to find the appropriate partitioning of fast/local and slow/global-scale circulations. In a series of sensitivity experiments at 14-km horizontal mesh, (1) the tuning of terminal velocities of rain, snow, and cloud ice, (2) the implementation of turbulent diffusion by the Leonard term, and (3) enhanced vertical resolution are tested. These tests yield reasonable convection triggering and convection-induced tropospheric moistening, and result in better performance than in previous NICAM climate simulations. In the mean state, double Intertropical Convergence Zone bias disappears, and the zonal contrast of equatorial precipitation, top-of-atmosphere radiation balance, vertical temperature profile, and position/strength of subtropical jet are dramatically better reproduced. Variability such as equatorial waves and the Madden–Julian oscillation (MJO) is spontaneously realized with appropriate spectral power balance, and the Asian summer monsoon, boreal-summer MJO, and tropical cyclone (TC) activities are more realistically simulated especially around the western Pacific. Meanwhile, biases still exist in the representation of low-cloud fraction, TC intensity, and precipitation diurnal cycle, suggesting that both finer spatial resolutions and the further model development are warranted.

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.