Numerical simulation of the cloud seeding operation of a convective rainfall event occurred in Beijing

Abstract

Observation-validated cloud seeding simulation is valuable in assisting in evaluating seeding effect, but its sensitivity to microphysics schemes and cloud seeding parameterizations is rarely investigated. In this research three cloud seeding parameterizations (the Hsie, Demott and Xue parameterizations) are coupled with two microphysics schemes (the Thompson and Milbrandt schemes), to perform simulations of the rainfall suppression cloud seeding operation on a convective rainfall event occurred in North China on 1 July 2021, aiming to evaluate the seeding effect and investigate its sensitivity to microphysics schemes and seeding parameterizations. The differences of rainfall suppression effect between three seeding parameterizations are smaller than those between two microphysics schemes. The rainfall suppression ratios produced by the simulations configured with the Milbrandt scheme range from 16.3% to 24.7%, while those with the Thompson scheme range from 0.47% to 1.38%. The difference of the seeding effect between these two microphysics schemes arises from their different method in parameterizing the snow deposition growth process. In addition, in the seeding simulations with the Milbrandt scheme, the surface rainfall decrease region is followed by a rainfall increase region. This rainfall decrease-increase pattern is caused by the fact that the reduced graupel particles caused by cloud seeding falls to the ground earlier with its larger fall velocity, while the seeding-increased snow particle falls to the ground later due to its smaller fall velocity. This result suggests there is an ephemeral cloud seeding window for rainfall suppression operation.