To study quantitatively the effect of cloud condensation nuclei (CCN) on the precipitation of clouds, we developed a hybrid cloud microphysical model. The maximum value of supersaturation, which each air mass has experienced, is the important factor to decide the number concentration of cloud droplets. This maximum value can not be estimated by values on the grid points with a interval larger than tens meters. This model estimates these maximum values by using the parcel model in the grid model.

In our hybrid microphysical cloud model, each grid point has a parcel model to estimate the activation of nuclei. In the case that the relative humidity of the grid point reaches 100% for the first time, or the case that relative humidity of the grid point is larger than 100% and cloud water on the windward side of the point does not exist, air parcel including CCN and vapor starts to rise from the windward side of the point. In each parcel, the condensation growth of CCN is estimated in Lagrangian framework using the microphysical model developed by Takeda and Kuba (1982). When droplets condensed on CCN grow enough to be distinguished from embryo, which can not become cloud droplets, the cloud droplets size distribution, the mixing ratio of vapor and potential temperature in the parcel are used to give variables on the grid points. The cloud droplet size distribution on the grid point is formulated using bins of fixed radii, and their growth by condensation and coalescence is calculated in the Eulerian framework with special attention to prevent numerical diffusion of cloud droplet size distribution (Kuba et al., 2002).

Using this model, we studied the difference in the production of rain water resulting from the difference in CCN. Our results showed that the increase in CCN number concentration makes the rain water, rainfall rate, rain drop number concentration reduced remarkably and that giant particles CCN do not play an important role for the precipitation in maritime air masses. The spatial distributions of rain water derived by using our model are definitely different from those derived by using bulk model in which rain drops fall at the same terminal velocity. This implies that the difference in terminal velocity among rain drops can not be ignored to estimate the precipitation. Based on our results, we would like to make proposals for improvement of bulk model.