A New Parameterization of Accretion Process of Cloud Water by Graupel and its Evaluation through Cloud and Precipitation Simulations

A new parameterization for the accretion process of cloud water by graupel is derived by analytically integrating the stochastic collection equation (SCE), where the collision efficiency of individual graupel particle-cloud droplet pairs is obtained using a particle trajectory model. The new parameterization is validated through box model simulations using the new parameterization, a bin-based direct SCE solver, and other accretion process parameterizations. The hydrometeor conversion time via accretion process predicted by the new parameterization is the closest to that predicted by the direct SCE solver in terms of the quantity and the dependence on the initial distribution of hydrometeors. Besides, the new parameterization predicts the slower decrease in the cloud droplet number concentration, which is also predicted by the direct SCE solver, compared to that in other bulk parameterizations. The new parameterization and other bulk parameterizations are implemented into a cloud-resolving model using a bulk microphysics scheme. In the idealized deep convective cloud simulations, the new parameterization predicts the largest accretion rate by graupel and the smallest accretion rate by snow, which overall enhances the rainfall via the largest graupel melting. The real-case simulations for a rainfall event over the middle Korean Peninsula indicated that the precipitation pattern predicted by the new parameterization is the closest to the observation provided by Korea Meteorological Administration among the bulk parameterizations. In this case, the new parameterization tends to reduce moderate rainfall and enhance heavy rainfall, compared to other bulk accretion parameterizations.