Utilizing ground-based aerosol observations collected from 19 August 2024 to 19 August 2025, we analyzed the characteristics of aerosol particle size distributions (PSDs) at the summit of Mount Liupan during non-precipitation periods. The average aerosol number concentration spectrum exhibited a trimodal distribution, characterized by the presence of coarse particles exceeding 1000 nm. Distinct diurnal and monthly variations were observed in the number concentrations across the nucleation (5–20 nm), Aitken (20–100 nm), accumulation (100–1000 nm), and coarse (>1000 nm) modes. Generally, diurnal variations were characterized by higher concentrations during the daytime and lower levels at night, although the specific evolutionary patterns varied among different modes. The nucleation mode particles exhibited a unimodal distribution; their number concentration increased rapidly after sunrise and peaked at 14:00 Beijing Time (BJT). The peak for the Aitken and accumulation modes lagged behind that of the nucleation mode. Furthermore, the changes in aerosol PSD under varying precipitation intensities were analyzed, revealing that the reductions in aerosol particle number concentrations generally became more pronounced as precipitation intensity increased. Finally, aerosol PSDs across different times, months, and precipitation stages (before, during, and after precipitation) were fitted using multiple log-normal distributions. Overall, the multiple log-normal fits showed good performance. Except for the during- and post-heavy-rain spectra, which yielded R2 values of 0.8258 and 0.8879, respectively, the R2 values of all other fitted spectra ranged from 0.9121 to 0.9997. This provides localized aerosol spectral parameters as foundational references for numerical simulations in this region.

Recent studies have shown that smartphone barometric pressure observations (SPO) can capture mesoscale surface pressure features associated with convective storms; however, the potential of these data to enhance weather forecasting skill remains uncertain. This study investigates the impact of assimilating SPO data and traditional weather station (TWS) pressure data on the 30 June 2021 Beijing hailstorm using an hourly updated 3DVAR (three-dimensional variational data assimilation) system in the WRF (Weather Research and Forecasting) model. Three assimilation experiments were performed: a TWS-based experiment (STA); an SPO-based experiment with constant observation error variance (PHO-C); and an SPO-based experiment with real-time observation error variance (PHO-R). Compared to the control run without data assimilation (CNTL), all assimilation experiments yielded a more accurate representation of the hail spatial distribution and improved the simulation of the cold pool and gust front. The fractions skill scores for radar-derived MESH (maximum estimated size of hail) increased by 14

Accurate descriptions of cloud droplet spectra from aerosol activation to vapor condensation using microphysical parameterization schemes are crucial for numerical simulations of precipitation and climate change in weather forecasting and climate prediction models. Hence, the latest activation and triple-moment condensation schemes were combined to simulate and analyze the evolution characteristics of a cloud droplet spectrum from activation to condensation and compared with a high-resolution Lagrangian bin model and the current double-moment condensation schemes, in which the spectral shape parameter is fixed or diagnosed by an empirical formula. The results demonstrate that the latest schemes effectively capture the evolution characteristics of the cloud droplet spectrum during activation and condensation, which is in line with the performance of the bin model. The simulation of the latest activation and condensation schemes in a parcel model shows that the cloud droplet spectrum gradually widens and exhibits a multimodal distribution during the activation process, accompanied by a decrease in the spectral shape and slope parameters over time. Conversely, during the condensation process, the cloud droplet spectrum gradually narrows, resulting in increases in the spectral shape and slope parameters. However, these double-moment schemes fail to accurately replicate the evolution of the cloud droplet spectrum and its multimodal distribution characteristics. Furthermore, the latest schemes were coupled into a 1.5D cumulus model, and an observation case was simulated. The simulations confirm that the cloud droplet spectrum appears wider at the supersaturated cloud base and cloud top due to activation, while it becomes narrower at the middle altitudes of the cloud due to condensation growth.

Ice nuclei of some bacterial origin as ice catalysts can initiate ice nucleation at temperatures as warm as –2°C in certain laboratory experiments. The ice nucleation activities of airborne bacteria in the real atmosphere may be different from those experiments. To estimate the impact of typical atmospheric pollutants including monocarboxylic acids (MCAs), dicarboxylic acids (DCAs) and ammonia sulfate on ice nucleation activity of P.syringae pv lachrymans and P.syringae pv.panici, we have conducted some experiments by means of the modified Vali’s droplet freezing testing method in the immersion freezing mode with the mixture of the pure water and the polluted water. Our results show that the ice nucleation activity of bacterial origins can be regulated by such pollutant compounds even though the onset freezing temperatures of water droplets mainly depend on the concentrations of ice nucleation-active bacteria. Atmospheric acids can decrease ice nucleation activity of P.syringae pv lachrymans and P.syringae pv.panici. However, the onset freezing temperatures of water droplets immersed with ice nucleation-active P.syringae pv lachrymans will be enhanced by the low concentration of such atmospheric pollutants.

With advances in numerical models and improved computational capabilities, significant progress has been made in understanding the impact of aerosol particles on thunderstorm clouds. However, many of these studies have concentrated on using bulk microphysics models to explain the impact of aerosol particles serving as cloud condensation nuclei (CCN) on electrical activity. Developing this further, we established a three-dimensional high-resolution cloud–aerosol bin thunderstorm model with electrification and lightning to provide more accurate microphysics and dynamic fields for studying electrical activities.To examine the effects of aerosol particles as CCN on thunderstorm properties in a continental thundercloud, aerosols from clean and polluted continental backgrounds were chosen. The simulated cloud properties show that droplets develop a narrower spectrum in polluted continental conditions, and weakened ice crystal growth increases small ice crystal numbers compared to clean conditions. Smaller droplets and ice crystals result in less effective riming and decreased graupel concentration and mass. In polluted conditions, frozen droplet numbers decrease significantly, but their mass ratio increases and their size grows due to the higher liquid content promoting collisions with supercooled water. In accordance with the noninductive charging model, a significant decrease in the presence of large ice particles results in a weakened charge separation process. Consequently, this phenomenon contributes to decreased lightning frequency and delays in the overall lightning process.

Progress in numerical models and improved computational capabilities have significantly advanced our comprehension of how aerosol particles impact thunderstorm clouds. Yet, much of this research has focused on employing bulk microphysics models to explain the impacts of aerosol particles acting as cloud condensation nuclei (CCN) on electrical activities in thunderstorm clouds. The bulk thunderstorm models use mean sizes of particles and terminal-fall velocities. This causes calculation deviation in the electrification simulation, which in turn leads to deviations in the simulation of lightning processes. Developing this further, we established a three-dimensional high-resolution cloud–aerosol bin thunderstorm model with electrification and lightning to provide more accurate microphysics and dynamic fields for studying electrical activities. For evaluating the impacts of aerosol particles, specifically CCN, on the properties of continental thunderclouds, aerosols from both clean and polluted continental environments were selected. Cloud simulations indicate that droplets develop a narrower spectrum in polluted continental conditions, and weakened ice crystal growth increases the number of small ice crystals compared to clean conditions. Smaller droplets and ice crystals result in less effective riming and decreased graupel concentration and mass. Consequently, a significant decrease in large ice particles leads to a weakened process of charge separation under conditions of pollution. As a direct result, there is about a 43% reduction in lightning frequency and a delay of approximately 5 min in the lightning process under polluted conditions.

The shape parameter of the Gamma size distribution plays a key role in the evolution of the cloud droplet spectrum in the bulk parameterization schemes. However, due to the inaccurate specification of the shape parameter in the commonly used bulk double-moment schemes, the cloud droplet spectra cannot reasonably be described during the condensation process. Therefore, a newly-developed triple-parameter condensation scheme with the shape parameter diagnosed through the number concentration, cloud water content, and reflectivity factor of cloud droplets can be applied to improve the evolution of the cloud droplet spectrum. The simulation with the new parameterization scheme was compared to those with a high-resolution Lagrangian bin scheme, the double-moment schemes in a parcel model, and the observation in a 1.5D Eulerian model that consists of two cylinders. The new scheme with the shape parameter varying with time and space can accurately simulate the evolution of the cloud droplet spectrum. Furthermore, the volume-mean radius and cloud water content simulated with the new scheme match the Lagrangian analytical solutions well, and the errors are steady, within approximately 0.2%.

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 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.

A Lagrangian advection scheme (LAS) for solving cloud drop diffusion growth was previously proposed (in 2020) and validated with simulations of cloud droplet spectra with a one-and-a-half dimensional (1.5D) cloud bin model for a deep convection case. The simulation results were improved with the new scheme over the original Eulerian scheme. In the present study, the authors simulated rain embryo formation with the LAS for a maritime shallow cumulus cloud case from the RICO (Rain in Cumulus over the Ocean) campaign. The model used to simulate the case was the same 1.5D cloud bin model coupled with the LAS. Comparing the model simulation results with aircraft observation data, the authors conclude that both the general microphysical properties and the detailed cloud droplet spectra are well captured. The LAS is robust and reliable for the simulation of rain embryo formation.

The effects of radiation heating and cooling on cumulus cloud development have been the focus of considerable attention for many years. However, it is still not clear how radiation impacts cloud droplet growth. Since cloud inhomogeneity has a great influence on radiation transmission, we coupled the 3D atmospheric radiative transfer model using the spherical harmonic discrete ordinate method with WRF-LES, which can improve the simulation accuracy of the inhomogeneous effect of clouds on radiation compared with that of the 1D radiation method. The shortwave and longwave radiation fluxes for upward and downward directions were simulated with different solar zenith angles. The comparison of 1D and 3D radiative solvers for deep convective cloud cases shows that the 3D radiative solver provides an accurate structure of solar and thermal radiation characteristics and the spatial distribution field. The solar radiation heating is likely to increase perpendicular to the solar incidence direction. For longwave radiation, the cooling effect on the cloud top and the heating effect on the cloud base are both more intense in the 3D radiation model. This study focuses on 3D cloud-radiative interactions in an inhomogeneous cloud field in a large eddy simulation, and the results suggest that compared with the widely used 1D radiative solver in WRF, the 3D radiation model can provide a precise description of the radiation field in an inhomogeneous atmosphere.

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.

In this two-part paper, influences from environmental factors on lightning in a convective storm are assessed with a model. In Part I, an electrical component is described and applied in the Aerosol–Cloud model (AC). AC treats many types of secondary (e.g., breakup in ice–ice collisions, raindrop-freezing fragmentation, rime splintering) and primary (heterogeneous, homogeneous freezing) ice initiation. AC represents lightning flashes with a statistical treatment of branching from a fractal law constrained by video imagery.The storm simulated is from the Severe Thunderstorm Electrification and Precipitation Study (STEPS; 19/20 June 2000). The simulation was validated microphysically [e.g., ice/droplet concentrations and mean sizes, liquid water content (LWC), reflectivity, surface precipitation] and dynamically (e.g., ascent) in our 2017 paper. Predicted ice concentrations (~10 L−1) agreed—to within a factor of about 2—with aircraft data at flight levels (−10° to −15°C). Here, electrical statistics of the same simulation are compared with observations. Flash rates (to within a factor of 2), triggering altitudes and polarity of flashes, and electric fields, all agree with the coincident STEPS observations.The “normal” tripole of charge structure observed during an electrical balloon sounding is reproduced by AC. It is related to reversal of polarity of noninductive charging in ice–ice collisions seen in laboratory experiments when temperature or LWC are varied. Positively charged graupel and negatively charged snow at most midlevels, charged away from the fastest updrafts, is predicted to cause the normal tripole. Total charge separated in the simulated storm is dominated by collisions involving secondary ice from fragmentation in graupel–snow collisions.

观测和分档方案的数值模拟都证明气溶胶的谱分布特征对云滴谱的演变有直接影响继而作用于降水的发展.目前广泛使用的总体双参数云滴谱方案因为表征云滴谱的预报量不足,在凝结过程中云滴谱呈不正常的拓宽现象.因此在参数化方案中,气溶胶谱对云滴谱的影响未有明确结论.中科学院大气物理研究所(IAP)云降水物理与强风暴重点实验室(LACS)新研发的三参数方案(IAP-LACS)通过增加的预报量克服了云滴谱的拓宽问题,提高了云滴谱模拟的准确性.为了研究在参数化方案中气溶胶谱分布特征对云滴谱的影响,本文采用新方案进行WRF(Weather Research and Forecasting mode)大涡理想性试验,验证了新方案中气溶胶对数正态谱函数中数浓度、几何半径和标准差3个参量对云滴谱演变的影响.针对3个参量的敏感性试验表明新的气溶胶活化方案和三参数云滴凝结增长方案能够描述气溶胶谱对云滴谱演变的影响规律:气溶胶数浓度对云滴谱影响最显著,数浓度越高活化生成的云滴数量越多,云滴半径越小,云滴谱趋向窄谱,气溶胶数浓度低时,云滴数量少、半径大.较大的几何半径使气溶胶谱向大粒径移动,导致大云滴生成,标准差对云滴谱的影响最不显著.

The initiation and intensity of warm rain are processes dominated by the evolution of cloud droplet spectra. To treat the cloud condensation process properly is a fundamental step for the simulation of warm rain formation. Double-moment bulk schemes with a limited number of prognostic variables cannot simulate the evolution of droplet spectra properly. A triple-moment bulk scheme, however, should overcome the problem of spurious cloud droplet spectrum broadening induced by double-moment schemes. To compare the effects of a newly developed triple-moment scheme with double-moment schemes on warm rain formation, the authors conducted WRF-LES numerical simulations to investigate the impacts of the two types of condensation scheme on rain initiation and intensity. In the early stage of raindrop formation, the simulation with the triple-moment scheme delays the raindrop initiation and produces droplet spectra with smaller average radii than those with the double-moment scheme. In the developing stage, the triple-moment scheme reduces the raindrop water content at the precipitation center. However, the further triple-moment scheme for raindrop is needed to simulate the development of warm rain accurately.

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.

In Part I of this two-part paper, a formulation was developed to treat fragmentation in ice-ice collisions. In the present Part II, the formulation is implemented in two microphysically advanced cloud models simulating a convective line observed over the U.S. high plains. One model is 2D with a spectral bin microphysics scheme. The other has a hybrid bin-two-moment bulk microphysics scheme in 3D. The case consists of cumulonimbus cells with cold cloud bases (near 0 degrees C) in a dry troposphere.Only with breakup included in the simulation are aircraft observations of particles with maximum dimensions > 0.2 mm in the storm adequately predicted by both models. In fact, breakup in ice-ice collisions is by far the most prolific process of ice initiation in the simulated clouds (95%-98% of all nonhomogeneous ice), apart from homogeneous freezing of droplets. Inclusion of breakup in the cloud-resolving model (CRM) simulations increased, by between about one and two orders of magnitude, the average concentration of ice between about 0 degrees and -30 degrees C. Most of the breakup is due to collisions of snow with graupel/hail. It is broadly consistent with the theoretical result in Part I about an explosive tendency for ice multiplication.Breakup in collisions of snow (crystals >similar to 1mm and aggregates) with denser graupel/hail was the main pathway for collisional breakup and initiated about 60%-90% of all ice particles not from homogeneous freezing, in the simulations by both models. Breakup is predicted to reduce accumulated surface precipitation in the simulated storm by about 20%-40%.

大气数值模式普遍采用一维辐射传输模型,无法表征有云大气中的三维辐射传输过程,从而影响数值模拟乃至数值天气预报的准确性.为评估这种不确定性,研究了水平云体间的三维辐射相互作用及其改变云体热力结构的规律,力求为改进数值模式辐射计算方案提供理论依据.选取I3RC PhaseⅡ的典型积云场和层积云场作为试验对象,将云场中心区域云体视为目标云体,周围云体为邻近云体,采用宽带三维辐射传输模式SHDOM模拟长波和短波辐射变温率的空间分布,定量阐明邻近云体对目标云体热力结构的影响.结果表明,邻云在长波区域对目标云体主要起辐射保温的作用,目标云体增温区域集中于邻云一侧的云体表面层,增温强度与云覆盖率和云间距离倒数成正比,最高可达3.08 K/h,云体增温的厚度与目标云体液态水含量成反比;在短波区域,邻云同时起散射增温和遮蔽降温的作用,太阳垂直入射时,散射增温效应较弱,变温率空间差异小;当太阳天顶角增大后,遮蔽降温效应逐步起主导作用,造成目标云体被邻云遮挡一侧的表面层明显降温,峰值可达-1.72 K/h,数值上甚至超过邻云长波增温效应.总之,邻近云体可以明显改变目标云体的变温率空间分布,引入三维邻云辐射效应对改进大气数值模式辐射计算方案具有重要意义.