Development of a Physically Based Autoconversion Parameterization and Its Application to Cloud Modeling

In this study, a new autoconversion process parameterization is derived by analytically integrating the stochastic collection equation (SCE). A Lagrangian particle model is employed to obtain the collision efficiency between cloud droplets. The new parameterization proposed in this study is validated against a bin-based direct SCE solver and compared to other autoconversion process parameterizations using a box model. The time required for 10% of the initial cloud water mass to be converted into rainwater mass is employed for the validation. The result of the new parameterization agrees well with that of the direct SCE solver. Moreover, the dependency of the autoconversion rate on drop number concentration in the new parameterization is similar to that in the direct SCE solver, whereas some other autoconversion process parameterizations show somewhat large dependency on drop number concentration. In shallow warm cloud simulations using a cloud-resolving model, the new parameterization tends to yield the moderate autoconversion rate among the autoconversion process parameterizations, as in the box model. When cloud optical thickness, cloud fraction, and surface precipitation amount are selected for comparison, the results of the new parameterization are generally the closest to those of the bin microphysics scheme. The autoconversion process parameterizations that yield the small (large) autoconversion rate tend to produce large (small) cloud optical thickness, small (large) cloud fraction, and small (large) surface precipitation amount. The new autoconversion process parameterization is expected to give good performances in weather and climate prediction.