Handout (2.9 MB)

The activation of CCN and initial condensational growth are computed in a Lagrangian particle framework using a parcel model. The solute effect of CCN is taken into account even after the activation. Because the maximum supersaturation experienced by an air parcel is estimated accurately, the number of cloud droplets that can be activated is also estimated accurately. This method precludes numerical diffusion of the droplet size distribution. A time step of 0.05 s is adopted for the parcel model to calculate CCN activation and the consequent condensational growth of droplets. This hybrid cloud microphysical model also uses a two-moment bin method based on that of Chen and Lamb (1994) in a 2-D grid model to estimate condensation and coalescence with a semi-Lagrangian framework and to estimate sedimentation and advection with an Eulerian spatial framework. The cloud droplet size distribution estimated by the parcel model is used as the initial cloud droplet size distribution for the two-moment bin method.

To properly estimate multi-coalescence in one time step, two schemes are used. One is a general stochastic coalescence scheme for rare lucky coalescence between droplets, and the other is a continuous coalescence scheme for frequent coalescence of a large drop and numerous small droplets (numerous small droplets are evenly shared by large drops) following the method reported in the doctoral dissertation of Dr. Jen-Ping Chen (1992). We distinguish rare lucky coalescence and frequent coalescence using the predicted frequency of collision in one time step. If only the general stochastic coalescence scheme is used, a very short time step such as 0.01 s is needed to avoid underestimation of coalescence growth caused by the underestimation of multiple coalescences. This method using both continuous and stochastic schemes with a time step of 3.0 s leads to the same results as the method using only the stochastic scheme with a time step of 0.01 s. However, in this study, a 0.5 s time step is adopted for the bin method considering other conditions. We use 73 bins to express a range of radii (from 1µm to 4 mm) for activated cloud droplets and raindrops. In addition, we adopt the coalescence efficiency proposed by Seifert et al. (2005) and a breakup scheme based on that of Fiengold et al. (1988) to estimate the collision-breakup of raindrops.

The kinematic framework of this study is based on that used by Szumowski et al. (1998) to test the warm rain microphysical model. The kinematic cloud model prescribes an evolving flow and performs 2-D advection of temperature and water variables (domain: 9 km x 3 km, dx and dz: 50 m, dt: 3 s). The flow pattern shows low-level convergence, upper-level divergence, and a narrow updraft located in the center of the domain. The magnitude, vertical structure, width, and tilt of the flow through the central updraft are all prescribed using simple analytical functions. This kinematic framework with a microphysical scheme predicts temporal and spatial evolution of water vapor, hydrometeors, and potential temperature explicitly by using the prescribed flow field and initial and boundary conditions of water vapor content and potential temperature. The advection scheme is a modified version of that of Smolarkiewicz (1984). The bulk microphysical scheme incorporated in Szumowski's original model is replaced with our hybrid microphysical model.

The following conclusions for a moderate continental air mass (an air mass with a large number of background CCN).

(1) Seeding can hasten the onset of surface rainfall and increase the accumulated amount of surface rainfall if the amount and radius of seeding particles are appropriate.

(2) The optimal radius of monodisperse particles to increase rainfall becomes larger with the increase in the total mass of seeding particles.

(3) Seeding with salt micro-powder can hasten the onset of surface rainfall and increase the accumulated amount of surface rainfall if the amount of seeding particles is sufficient.

(4) Seeding by a hygroscopic flare decreases rainfall in the case of large updraft velocity (shallow convective cloud) and increases rainfall slightly in the case of small updraft velocity (stratiform cloud).

(5) Seeding with hygroscopic flares including ultra-giant particles (r > 5µm) hastens the onset of surface rainfall but may not significantly increase the accumulated surface rainfall amount.

(6) Hygroscopic seeding increases surface rainfall by two kinds of effects: the competition effect by which large soluble particles prevent the activation of smaller particles and the raindrop embryo effect in which giant soluble particles can immediately become raindrop embryos. In some cases, one of the effects works, and in other cases, both effects work, depending on the updraft velocity and the amount and size of seeding particles.