Simulation of a convective cloud from a Lagrangian cloud model, and its application to the parameterization of cloud microphysics

Yign Noh
Yonsei University

A Lagrangian cloud model (LCM), in which the flow field is simulated by LES, and the droplets are treated as Lagrangian particles responding to the simulated flow field, is applied to simulate a precipitating convective cloud. The LCM is based on the concept of an ensemble of super-droplets with each super-droplet representing a large number of real droplets of a given size. A statistical method is used to calculate the collision/coalescence of droplets. It is shown that the model is capable of reproducing the general features of the cloud and precipitation process, including the evolutions of mixing ratio, relative humidity, vertical velocity, dissipation rate, droplet spectrum, and the initiation of precipitation. The LCM is then applied to study the impact of turbulent collision/coalescence on the precipitation process, revealing that precipitation starts earlier and becomes stronger, if the effect of turbulence is included in the collection kernel. Furthermore, it has been demonstrated that the LCM provides various information on the motion of droplets, which plays a crucial role in the parameterization of cloud microphysics, such as the vertical velocity of droplets, the raindrop size distribution, and the precipitation flux.

Besides, two other topics from the lecturer’s previous works for NWP are briefly introduced; the parameterization of PBL and the production of SST from satellite data assimilation to a NWP-ocean mixed layer coupled model.