Improvement of cloud microphysical parameterization and its advantages in simulating precipitation along the Sichuan-Xizang Railway

This study is led by the Nanjing Joint Institute for Atmospheric Sciences, the Institute of Plateau Meteorology of China Meteorological Administration, and the Nanjing University of Information Science & Technology. The research team enhanced the cloud microphysics scheme by modifying four cloud microphysical processes with uncertainty, including cloud droplet activation, cloud-rain autoconversion, accretion between cloud droplets and raindrops, and the entrainment-mixing process. They conducted simulations on six large-scale precipitation events during the flood season along the Sichuan-Xizang Railway in 2021 and highlighted the advantages of the improved scheme.

The research indicated that the default scheme significantly overestimated the spatial extent and amount of precipitation, while the modified scheme enabled a more accurate determination of precipitation center location and trend, leading to a substantial reduction in root-mean-square error of six precipitation processes by 22%. Moreover, the modified scheme enhanced the precipitation scores and effectively mitigated the original scheme's tendency to overestimate precipitation along the Sichuan-Xizang Railway.

Researchers identified an inconsistency in commonly used schemes with fixed cloud droplet number concentration employed by current operational departments. Specifically, different cloud water contents corresponded to an identical cloud droplet concentration, which deviated from reality and resulted in excessively large cloud droplet size as well as related physical process rates. This introduced uncertainty into both cloud microphysics and precipitation modeling.

To address this concern, researchers have made several improvements to microphysical processes. Firstly, they introduced a cloud droplet activation process and updated cloud droplet number concentration to achieve a more reasonable distribution in terms of number concentration and size. Secondly, they refined the formula of cloud-rain autoconversion as well as accretion between cloud droplets and raindrops to align them more closely with stochastic collection equations. Lastly, they implemented entrainment-mixing parameterization to better simulate cloud microphysics within clouds. These improvements resulted in a more realistic simulation of various quantities related to clouds while reducing simulation deviations for liquid water path and droplet radius from 2 times to less than 1 time.

###

See the article:

Improvement of cloud microphysical parameterization and its advantages in simulating precipitation along the Sichuan-Xizang Railway. Science China Earth Sciences, 67(3): 856–873, https://doi.org/10.1007/s11430-023-1247-2