A Modified Double-Moment Bulk Microphysics Scheme Geared toward the East Asian Monsoon Region

Representation of cloud microphysical processes is one of the key aspects of numerical models. An improved double-moment bulk cloud microphysics scheme (named IMY) was created based on the standard Milbrandt-Yau (MY) scheme in the Weather Research and Forecasting (WRF) model for the East Asian monsoon region (EAMR). In the IMY scheme, the shape parameters of raindrops, snow particles, and cloud droplet size distributions are variables instead of fixed constants. Specifically, the shape parameters of raindrop and snow size distributions are diagnosed from their respective shape-slope relationships. The shape parameter for the cloud droplet size distribution depends on the total cloud droplet number concentration. In addition, a series of minor improvements involving detailed cloud processes have also been incorporated. The improved scheme was coupled into the WRF model and tested on two heavy rainfall cases over the EAMR. The IMY scheme is shown to reproduce the overall spatial distribution of rainfall and its temporal evolution, evidenced by comparing the modeled results with surface gauge observations. The simulations also successfully capture the cloud features by using satellite and ground-based radar observations as a reference. The IMY has yielded simulation results on the case studies that were comparable, and in ways superior to MY, indicating that the improved scheme shows promise. Although the simulations demonstrated a positive performance evaluation for the IMY scheme, continued experiments are required to further validate the scheme with different weather events.

Automated summary

The representation of cloud microphysical processes is crucial in numerical models. This study improves the existing scheme by introducing variable shape parameters and incorporating minor improvements in cloud processes. The improved scheme successfully reproduces the spatial distribution and temporal evolution of rainfall, as well as captures cloud features, showcasing its promise for future use.