This paper proposes an inverse model for raindrop size distribution (DSD) retrieval with polarimetric radar variables. In this method, a forward operator is first developed based on the simulations of monodisperse raindrops using a T-matrix method, and then approximated with a polynomial function to generate a pseudo training dataset by considering the maximum drop diameter in a truncated Gamma model for DSD. With the pseudo training data, a nearest-neighborhood method is optimized in terms of mass-weighted diameter and liquid water content. Finally, the inverse model is evaluated with simulated and real radar data, both of which yield better agreement with disdrometer observations compared to the existing Bayesian approach. In addition, the rainfall rate derived from the DSD by the inverse model is also improved when compared to the methods using the power-law relations.

Observations of large concentrations of ice particles in the dissipating stage of warm-based precipitating shallow cumulus clouds point to the limitations of scientists’ understanding of the physics of such clouds and the possible role of cloud dynamics. The most commonly accepted mechanisms of ice splinter production in the riming process have limitations to properly explain the rapid production of ice bursts. A more detailed description of the temporal and spatial evolution of hydrometeors and their interaction with cloud condensation nuclei and ice nuclei is needed to understand this phenomenon. A cloud model with bin-resolved microphysics can describe the time-dependent evolution of liquid droplets and ice particles and provide insights into how the physics and dynamics and their interaction may result in ice initiation and ice multiplication. The authors developed a 1.5-dimensional nonhydrostatic convective cloud and aerosol interaction model with spectral (bin) microphysics. The number and mass concentrations of aerosols, including ice nuclei and cloud condensation nuclei, were explicitly followed. Since both in situ observations of bioaerosols and laboratory experiments pointed to efficient nucleation capabilities at relative warm temperatures, it was assumed that ice-nucleating bioaerosols are involved in primary ice particle formation in condensation and immersion modes. Results show that bioaerosols can be the source of primary ice pellets, which in turn lead to high ice concentrations.

Double-moment schemes cannot accurately describe the evolution of the cloud droplet spectrum during condensation. Hence, a new triple-moment condensation scheme is developed to describe the evolution of cloud droplet spectra. In this scheme, a three-parameter gamma distribution function of the cloud droplet mass is adopted, and the prognostic equations of the spectral shape parameter and slope parameter are derived by means of the number concentration, cloud water content, and reflectivity factor of cloud droplets. The new parameterization scheme is compared with high-resolution Lagrangian and Eulerian bin schemes, double-moment schemes, and existing triple-moment schemes by performing simulations under different supersaturation values. The new scheme can reduce the cloud spectral error in the cloud water content and reflectivity factor caused by the fixed shape parameter in some bulk schemes. The spectra simulated with the new scheme match the Lagrangian analytical solutions well, with errors within approximately 1% in the cloud water content and reflectivity factor. The effects of curvature and solution on condensation growth are also tested using the new scheme, and a method of using multiple gamma distribution functions to characterize the multimodal spectrum of cloud droplets is proposed in the new condensation scheme. Ultimately, the formation of rain embryos from giant aerosols can be simulated via the new scheme.

Cloud drop diffusion growth is a fundamental microphysical process in warm clouds. In the present work, a new Lagrangian advection scheme (LAS) is proposed for solving this process. The LAS discretizes cloud drop size distribution (CDSD) with movable bins. Two types of prognostic variable, namely, bin radius and bin width, are included in the LAS. Bin radius is tracked by the well-known cloud drop diffusion growth equation, while bin width is solved by a derived equation. CDSD is then calculated with the information of bin radius, bin width, and prescribed droplet number concentration. The reliability of the new scheme is validated by the reference analytical solutions in a parcel cloud model. Artificial broadening of CDSD, understood as a by-product of numerical diffusion in advection algorithm, is strictly prohibited by the new scheme. The authors further coupled the LAS into a one-and-half dimensional (1.5D) Eulerian cloud model to evaluate its performance. An individual deep cumulus cloud studied in the Cooperative Convective Precipitation Experiment (CCOPE) campaign was simulated with the LAS-coupled 1.5D model and the original 1.5D model. Simulation results of CDSD and microphysical properties were compared with observational data. Improvements, namely, narrower CDSD and accurate reproduction of particle mean diameter, were achieved with the LAS-coupled 1.5D model.

Cloud droplet nucleation is classically defined as a droplet growing to a size such that its ambient supersaturation exceeds its surface equilibrium water vapor pressure. Unactivated particles are always in equilibrium with the ambient vapor pressure. Further studies showed that such an equilibrium assumption leads to many more cloud droplets being nucleated due to neglecting kinetic growth limitations, including the inertial mechanism, evaporation mechanism, and deactivation mechanism. Moreover, the inertial mechanism results in great discrepancy between the actual size and the critical size of nucleation for large aerosol particles. These issues complicate cloud droplet nucleation parameterization for applications in cloud modeling. To establish a physically based nucleation scheme, we established a highly size-resolved Lagrangian parcel model. Vapor diffusion and heat conduction were calculated according to Maxwell theory, and the surface vapor density and temperature were explicitly simulated. The surface temperature variation of a droplet with its size was considered. The surface supersaturation of a droplet, taking into account the surface temperature variation, is different from its equilibrium supersaturation at its large sizes. The nucleation simulation showed that the inertial and deactivation mechanisms can impact droplet nucleation. Moreover, very large nuclei can trigger rain embryo formation in a short time period. Even though there are kinetic limitations, the classical equilibrium assumption can be applied to determine the primary nucleation number of cloud droplets. Meanwhile, a regression formula for the size of a nucleated droplet and its dry aerosol size was established.