Cloud Microphysical Processes Research Articles

Understanding the role of cloud microphysical processes behind the Indian summer monsoon rainfall

Understanding the role of cloud microphysical processes behind the Indian summer monsoon rainfall

Vertical Structures Associated with Orographic Precipitation during Warm Season in the Sichuan Basin and Its Surrounding Areas at Different Altitudes from 8-Year GPM DPR Observations

Global precipitation measurement (GPM) is one of the effective means employed to observe orographic precipitation, and its inversed GPM DPR data can be used to study the microphysical structure of precipitation particles. This study considers statistics on convective precipitation (CP) and stratiform precipitation (SP) events over three types of terrain (plains, mountains, and high mountains) using the DPR onboard the GPM Core Observatory from May to September of 2014–2021 to analyze the vertical structure of heavy CP and SP. In mountain areas and high mountain areas, the updraft rendered by topography or seeder-feeder mechanism is not only conducive to the collision and merger of raindrops into large raindrops, but also increases the concentration of small drops, which is the main reason why the occurrence probability of not only large but also small raindrops increases and the horizontal distribution domain of mass weighted average raindrop diameter (Dm) widens. For heavy SP, the occurrence probability of medium-diameter precipitation particles below the freezing height (FzH) over high mountains is greater than those over plains. The precipitation particles above 10 km altitude of high mountains have characteristics, such as lower droplet number concentration and larger diameter, compared with those over plains. Furthermore, the study also analyzed the correlation between storm top altitude (STA) and Dm, water vapor and STA respectively. This study is helpful to further understand the effect of topography on heavy precipitation through cloud microphysical processes and the vertical structure of precipitation.

Numerical Simulation of a Typical Convective Precipitation and Its Cloud Microphysical Process in the Yushu Area, Based on the WRF Model

Cloud microphysical processes significantly impact the time variation and intensity of precipitation. However, due to the high altitude of the Tibetan Plateau (TP) and the lack of observational data, the understanding of cloud microphysical processes on the TP is relatively insufficient, affecting the accuracy of precipitation simulations around the TP. To further reveal the characteristics of convective precipitation and cloud microphysical structure over the TP, the mesoscale numerical model, WRF, and various observational data were used to simulate and evaluate typical convective precipitation in the Yushu area, which was recorded from 11 to 12 August 2020. The results showed that the combination of the Lin scheme in the WRF model could effectively reproduce this case’s characteristics and evolution process. In the simulation process, the particles of each phase were distributed at different altitudes, and their mass and density over time reflected the characteristics of surface precipitation changes. Among the particles mentioned above, rainwater contributed the most to the initiation and growth of graupel particles. Further research established that the initiation of graupel was mainly affected by the freezing effect of rainwater and cloud ice, while the growth of graupel was influenced primarily by the collision of graupel particles and rainwater. On the whole, from the evolution characteristics of microphysical processes over time, it was found that the ice phase process plays an essential role in this typical convective precipitation.

Process Studies of the Impact of Land-Surface Resolution on Convective Precipitation Based on High-Resolution ICON Simulations

This study investigated the relevant processes responsible for differences of convective precipitation caused by land-surface resolution. The simulations were performed with the ICOsahedral Nonhydrostatic model (ICON) with grid spacing of 156 m and Large Eddy Simulation physics. Regions of different orographic complexity, days with weak synoptic forcing and favourable convective conditions were selected. The resolution of land-surface properties (soil type, vegetation) and/or the orography was reduced from 156 to 5000 m. Analyses are based on backward trajectories (Lagrangian Analysis Tool (LAGRANTO)), heat budget and convective organisation potential (COP) calculations. On average, the relative difference of areal mean daily precipitation at 1250 and 5000 m land-surface resolutions compared to 156 m were 6% and 15%, respectively. No consistent dependency of precipitation on orography or land-surface properties was found. Both factors impact convective initiation over areas with embedded mesoscale-sized land-surface heterogeneities. The position of convective precipitation was often influenced by the resolution of orography. Coarsening from 156 to 5000 m considerably changed the location of wind convergence and associated convection initiation. It also affects the onset times of clouds (<20 min) and precipitation (≈1 h). Cloud aggregation and microphysical processes proved to be important for further development towards convective precipitation.

Understanding the impact of ice nucleation on lightning and rainfall: A case study

A suitable parameterization of heterogeneous ice nucleation (HIN) is essential for the effective simulation of ice processes in climate models and comprehending their interactions with aerosols. In this study, the influence of HIN formulations in Morrison two-moment bulk cloud microphysical scheme of the Weather Research and Forecasting (WRF-ARW) model in simulating the thunderstorm event over Maharashtra, India, has been evaluated. The HIN parameterization is used to simulate the thunderstorm event is based on Fletcher (1962), Cooper (1986), and Mayers et al. (1992), along with the inclusion of measurements of ice nucleation from SPectrometer for Ice Nuclei at High Altitude Cloud Physics Laboratory, Indian Institute of Tropical Meteorology (IITM-SPIN). The results signify that different ice nucleation formulations are highly sensitive in simulating rainfall and lightning by the formation of cloud hydrometeors (e.g., cloud ice, snow, graupel, and rain). The HIN of IITM-SPIN and Fletcher (1962) simulate the better lightning flashes and rainfall compared to Cooper (1986) and Mayers et al. (1992). The results from a two-dimensional idealized simulation also corroborate the role of the HIN on cloud microphysical processes. Clouds and precipitation processes are influenced by the ice nucleation process, which modifies latent heat release and eventually modulates the dynamics. Therefore, the present work can provide a pathway in the selection of a better ice nucleation formulation for better portrayal of cloud dynamics.

The strong solutions to the primitive equations coupled with multi-phase moisture atmosphere

The occurrence and development of cloud and precipitation are the products of the combination of atmospheric dynamic, thermal processes and cloud microphysical processes. In order to understand the interaction between these influencing factors in more detail, in this paper we study a moisture model with multi-phase for warm clouds, which consists of the primitive equations and a set of humidity equations where water is present in the form of water vapor, rain water and cloud condensates. This model has been considered by Cao et al. in (2018), where the velocity field is a given function in L∞((0,t1)×M). In this paper, the velocity field is governed by the atmosphere dynamic equation, which is more in line with the actual situation. In order to overcome the difficulty caused by the physical range requirement for the humidity, we introduce a new penalized function which is different from that introduced in Cao et al. (2018). Then we obtain the global existence of both quasi-strong solutions and strong solutions.

Algorithmic Differentiation for Sensitivity Analysis in Cloud Microphysics

AbstractThe role of clouds for radiative transfer, precipitation formation, and their interaction with atmospheric dynamics depends strongly on cloud microphysics. The parameterization of cloud microphysical processes in weather and climate models is a well‐known source of uncertainties. Hence, robust quantification of this uncertainty is mandatory. Sensitivity analysis to date has typically investigated only a few model parameters. We propose algorithmic differentiation (AD) as a tool to detect the magnitude and timing at which a model state variable is sensitive to any of the hundreds of uncertain model parameters in the cloud microphysics parameterization. AD increases the computational cost by roughly a third in our simulations. We explore this methodology as the example of warm conveyor belt trajectories, that is, air parcels rising rapidly from the planetary boundary layer to the upper troposphere in the vicinity of an extratropical cyclone. Based on the information of derivatives with respect to the uncertain parameters, the ten parameters contributing most to uncertainty are selected. These uncertain parameters are mostly related to the representation of hydrometeor diameter and fall velocity, the activation of cloud condensation nuclei, and heterogeneous freezing. We demonstrate the meaningfulness of the AD‐estimated sensitivities by comparing the AD results with ensemble simulations spawned at different points along the trajectories, where different parameter settings are used in the various ensemble members. The ranking of the most important parameters from these ensemble simulations is consistent with the results from AD. Thus, AD is a helpful tool for selecting parameters contributing most to cloud microphysics uncertainty.

Sensitivity of Modeled Microphysics to Stochastically Perturbed Parameters

AbstractThis study examines the characteristics of several model parameter perturbation methodologies for ensemble simulations of cloud microphysical processes in convection. A simplified 1D model is used to focus the results on cloud microphysics without the complication of feedbacks to the dynamics and environment. Several parameter perturbation methods are tested, including non‐stochastic and stochastic with various distributions and parameter covariance. We find that an ensemble comprised of different time‐invariant parameters (non‐stochastic) exhibits little bias, but small spread. In addition, its behavior does not respect the time evolution of convection through its various phases. Stochastic parameter (SP) methods in which no inter‐parameter covariance is applied produce greater spread, but significant bias. The bias is particularly large for lognormal parameter perturbation distributions. The ensemble spread is retained and the bias reduced when time‐varying parameter covariance is applied. In this case, the SP scheme is able to adapt to the time and state‐dependent covariance structures and produce ensemble characteristics that are consistent with the specific microphysical processes operating at any given time. The results suggest that SP schemes would benefit from inclusion of parameter covariances, and specifically those that vary with the state of the system. It also suggests that a Normal or LogNormal SP scheme with no covariance may significantly impact the ensemble bias. Finally, the results indicate that high temporal and spatial resolution observations may be needed to characterize the variability in parameter values and covariance.

Mineral snowflakes on exoplanets and brown dwarfs

Context. Brown dwarfs and exoplanets provide unique atmospheric regimes that hold information about their formation routes and evolutionary states. Cloud particles form through nucleation, condensation, evaporation, and collisions, which affect the distribution of cloud particles in size and throughout these atmospheres. Cloud modelling plays a decisive role in understanding these regimes. Aims. Modelling mineral cloud particle formation in the atmospheres of brown dwarfs and exoplanets is a key element in preparing for missions and instruments like CRIRES+, JWST, and ARIEL, as well as possible polarimetry missions like PolStar. The aim is to support the increasingly detailed observations that demand greater understanding of the microphysical cloud processes. Methods. We extend our kinetic cloud formation model that treats nucleation, condensation, evaporation, and settling of mixed material cloud particles to consistently model cloud particle-particle collisions. The new hybrid code Hybrid moments (Ls) and Size (HyLandS) is then applied to a grid of Drift-Phoenix (Tgas, pgas) profiles. Effective medium theory and Mie theory are used to investigate the optical properties. Results. Turbulence proves to be the main driving process of particle-particle collisions, with collisions becoming the dominant process in the lower atmosphere (p > 10−4 bar) at the cloud base. Particle-particle collisions produce one of three outcomes for brown dwarf and gas-giant atmospheres: fragmenting atmospheres (log10(g[cms−2])=3.0) coagulating atmospheres (log10(g)=5.0), Teff ≤1800K) or condensational growth dominated atmospheres (log10(g) = 5.0, Teff > 1800 K). Cloud particle opacity slope at optical wavelengths (Hubble) is increased with fragmentation, as are the silicate features at JWST NIRSpec, JWST MIRI, and ARIEL AIRS wavelengths. Conclusions. The hybrid moment-bin method HyLandS demonstrates the feasibility of combining a moment and a bin method for cloud modelling, whilst assuring element conservation. It provides a powerful and fast tool for capturing general trends of particle collisions, consistently with other microphysical growth processes. Collisions are an important process in exoplanet and brown dwarf atmospheres, but cannot be assumed to be hit-and-stick only. The spectral effects of cloud particle collisions in both optical and mid-infrared wavelengths complicate inferences of cloud particle size and material composition from observational data.

Modeling Performance of SCALE‐AMPS: Simulations of Arctic Mixed‐Phase Clouds Observed During SHEBA

AbstractThe Advanced Microphysics Prediction System (AMPS), which adopts a two‐moment hybrid‐bin habit‐predicting scheme, was previously developed to study cloud microphysical processes that depend on ice habit; however, only one particular atmospheric model, the University of Wisconsin‐Nonhydrostatic Modeling System, has been used to test the AMPS. In this study, AMPS is implemented into the Scalable Computing for Advanced Library and Environment (SCALE) large‐eddy simulation model. The AMPS Eulerian advection scheme for non‐mass characteristic variables of ice particles, such as axis lengths, is refined to minimize numerical artifacts. The resulting SCALE‐AMPS model successfully reproduces features of mixed‐phase clouds observed during the Surface Heat Budget of the Arctic campaign, including liquid water path (LWP), ice particle size distributions, and ice habits, when ice particle number concentrations (Nice) are reproduced. Sensitivity studies show that increases in Nice result in reductions of LWP that are generally consistent with previous results. Interestingly, LWP reductions lead to changes in ice habits through increases in cloud temperature due to weaker cloud top radiative cooling. Furthermore, aspect ratios of precipitating particles also change following LWP reductions, because in Bigg's immersion freezing scheme, adopted in this study, the aspect ratios depend on the initial size of the ice particles and freezing rates depend on both temperature and droplet size. Because habits of ice particles affect their growth rates, fall speeds, and collision rates, the results obtained in this study reveal possible feedback processes of Arctic mixed‐phase clouds operating through ice habits.

Effects of cloud condensation nuclei concentration on the evolution of severe convective storms

Idealized three-dimensional storm simulations were created to investigate the effects of varying initial cloud condensation nuclei (CCN) concentrations (CCNCs) ahead of the severe convective storm using the Weather Research and Forecasting (WRF) model with the National Severe Storms Laboratory (NSSL) double-moment microphysics scheme. The simulations showed that increased initial CCNC ahead of the storm (CCN mainly entrains from the boundary layer) increased the rain- and hailfall intensities. Multiple strong centers of rain- and hailfall appeared at the surface along the moving direction of the convective storm. The increased initial CCNC ahead of the storm played an important role in the warm cloud processes. As the CCNC increased, a large amount of CCN enter the cloud and was activated, which decreased the equivalent sphere radius of the cloud droplets and increased the content of cloud droplets. Thus, the equivalent sphere radius of the raindrops increased due to the enhanced collection process of cloud droplets by raindrops. Furthermore, the collection processes of supercooled cloud droplets and raindrops by hail were also enhanced as the CCNC increased, resulting in an increase in the content of hail. These processes further influenced the cold cloud processes. The increase in CCNC ahead of the storm accelerated the downdraft by the cloud microphysical processes and formed a stronger cold pool and front outflow. These thermodynamic and dynamic processes were favorable for increasing the updraft and thus enhancing the cloud microphysical processes. Therefore, the increase in the CCNC ahead of the storm created a positive feedback mechanism to the thermodynamic and dynamic processes by the cloud microphysical processes, which promoted the development and maintenance of the storm and increased rain- and hailfall intensities at the surface.

Assessing the potential for simplification in global climate model cloud microphysics

Abstract. Cloud properties and their evolution influence Earth's radiative balance. The cloud microphysical (CMP) processes that shape these properties are therefore important to represent in global climate models. Historically, parameterizations in these models have grown more detailed and complex. However, a simpler formulation of CMP processes may leave the model results mostly unchanged while enabling an easier interpretation of model results and helping to increase process understanding. This study employs sensitivity analysis of an emulated perturbed parameter ensemble of the global aerosol–climate model ECHAM-HAM to illuminate the impact of selected CMP cloud ice processes on model output. The response to the perturbation of a process serves as a proxy for the effect of a simplification. Autoconversion of ice crystals is found to be the dominant CMP process in influencing key variables such as the ice water path and cloud radiative effects, while riming of cloud droplets on snow has the most influence on the liquid phase. Accretion of ice and snow and self-collection of ice crystals have a negligible influence on model output and are therefore identified as suitable candidates for future simplifications. In turn, the dominating role of autoconversion suggests that this process has the greatest need to be represented correctly. A seasonal and spatially resolved analysis employing a spherical harmonics expansion of the data corroborates the results. This study introduces a new application for the combination of statistical emulation and sensitivity analysis to evaluate the sensitivity of a complex numerical model to a specific parameterized process. It paves the way for simplifications of CMP processes leading to more interpretable climate model results.

Influence of the terminal velocity of graupel on the simulation of a convective system over Beijing

The terminal velocity of graupel (Vg) affects the development of strong convective systems, but there are large uncertainties in the description of Vg in numerical models. We examined the effects of Vg on the structure and evolution of a strong convective system in Beijing using the Advanced Research Weather Research and Forecasting Model (ARW-WRF). Multiple simulations were performed to test the sensitivity of the model to different fitting equations of Vg in the WDM6 microphysics scheme. We found that a larger value of Vg significantly increased the precipitation rate. With respect to the distribution of hydrometeors, a larger value of Vg reduced the centroid and peak value of snow, graupel and cloud water above the freezing layer, while the centroid of rainwater decreased and the peak value increased. This promoted the conversion of ice particles into rainwater. As a result of the complexity of heat exchange in cloud microphysical processes, the effects of Vg on the heat budget of precipitation particles within clouds have strong nonlinear characteristics.

Evaluation of convective cloud microphysics in numerical weather prediction models with dual-wavelength polarimetric radar observations: methods and examples

Abstract. The representation of cloud microphysical processes contributes substantially to the uncertainty of numerical weather simulations. In part, this is owed to some fundamental knowledge gaps in the underlying processes due to the difficulty of observing them directly. On the path to closing these gaps, we present a setup for the systematic characterization of differences between numerical weather model and radar observations for convective weather situations. Radar observations are introduced which provide targeted dual-wavelength and polarimetric measurements of convective clouds with the potential to provide more detailed information about hydrometeor shapes and sizes. A convection-permitting regional weather model setup is established using five different microphysics schemes (double-moment, spectral bin (“Fast Spectral Bin Microphysics”, FSBM), and particle property prediction (P3)). Observations are compared to hindcasts which are created with a polarimetric radar forward simulator for all measurement days. A cell-tracking algorithm applied to radar and model data facilitates comparison on a cell object basis. Statistical comparisons of radar observations and numerical weather model runs are presented on a data set of 30 convection days. In general, simulations show too few weak and small-scale convective cells. Contoured frequency by altitude diagrams of radar signatures reveal deviations between the schemes and observations in ice and liquid phase. Apart from the P3 scheme, high reflectivities in the ice phase are simulated too frequently. Dual-wavelength signatures demonstrate issues of most schemes to correctly represent ice particle size distributions, producing too large or too dense graupel particles. Comparison of polarimetric radar signatures reveals issues of all schemes except the FSBM to correctly represent rain particle size distributions.

Processes associated with extremely heavy precipitation in the Meghalaya Plateau region: A case modelling study

AbstractThe Meghalaya Plateau (MP) located in northeast India is one of the rainiest regions in the world. On 18–19 August 2015, Mawsynram on the southern slope of MP received 745 mm of precipitation in 24 hr. This study investigates the dynamical, thermodynamical and cloud microphysical processes associated with this event through numerical simulations with fine horizontal resolutions (1 and 1/3 km). The control (CNTL) simulation with 1 km grid spacing successfully reproduces the observed spatial pattern of accumulated precipitation. A simulation without MP (noMP) is carried out to examine the role of MP in this precipitation. From 1500 LST 18 to 0000 LST 19 (P1) when the low‐level jet carrying warm and moist air towards MP is relatively weak, the upslope region receives a moderate amount of precipitation which is initiated over this region due to the orographic lifting, while almost no precipitation is received there in the noMP simulation. Warm microphysical processes play dominant roles in the precipitation in P1. From 0000 to 0900 LST 19 (P2) when the low‐level jet is enhanced, the CNTL simulation shows very heavy precipitation in the upslope region, much heavier than that in the noMP simulation. Deep convective systems developed upwind of MP move towards MP. These convective systems merge together and strengthen over the upslope region. The accretion process is substantially enhanced by the vigorous updraughts at low levels over the steep slope of MP, resulting in heavy precipitation. The 1/3 km resolution simulation shows much heavier precipitation in the upslope region than the CNTL simulation. The increased horizontal resolution makes the slopes steeper, resulting in further intensification of the updraughts over this region. This increase in simulated precipitation reduces the deviation from the rain‐gauge observation, implying the importance of very high horizontal resolutions in simulating extremely heavy precipitation in MP.

Comparing 3DVAR and hybrid radar data assimilation methods for heavy rain forecast

Radar data are assimilated into numerical prediction models to improve precipitation forecasts using three-dimensional (3D) variational (VAR) and hybrid data assimilation (DA) methods. The effect of radar DA (RDA) and prediction accuracy according to DA methods are evaluated by conducting experiments of three heavy rainfall events and analyzing the spatial pattern of accumulated precipitation, profiles of hydrometeors, and cloud microphysical processes. Increments of the initial analysis revealed that the hybrid method simulated the convective band, wind convergence in front of the cold front and a higher amount of water vapor mixing ratio compared to 3DVAR. Based on numerical experiments, the root mean square error of the total cumulative precipitation in 3DVAR was 9.68 mm lower than that of the no-RDA experiment (CTRL), whereas that of the Hybrid was 11.12 mm lower than that of CTRL. RDA improved precipitation forecasts, and Hybrid showed better precipitation forecast accuracy than 3DVAR. Although RDA indirectly changed the water vapor, rain, snow, and graupel mixing ratios, the change in the water vapor amount was the major factor affecting microphysical processes. The increase (decrease) in the water vapor mixing ratio had the greatest impact on precipitation formation (dissipation), whereas the effect of snow, rain, and graupel mixing ratios was relatively small. The results indicate that it is critical to create an environment in which water vapor can transform into precipitating hydrometeors and the hydrometeors can grow through cloud microphysical processes. The accuracy of heavy rain forecast can be improved by hybrid DA method, which can consider the model error in real time, making it more adaptable to the day-to-day changing weather conditions.

Representation of the autoconversion from cloud to rain using a weighted ensemble approach: a case study using WRF v4.1.3

Abstract. Cloud and precipitation processes remain among the largest sources of uncertainties in weather and climate modelling, and considerable attention has been paid to improving the representation of the cloud and precipitation processes in numerical models in the last several decades. In this study, we develop a weighted ensemble (named EN) scheme by employing several widely used autoconversion (ATC) schemes to represent the ATC from cloud water to rainwater. One unique feature of the EN approach is that the ATC rate is a weighted mean value based on the calculations from several ATC schemes within a microphysics scheme with a negligible increase in computation cost. The EN scheme is compared with the several commonly used ATC schemes by performing real case simulations. In terms of accumulated rainfall and extreme hourly rainfall rate, the EN scheme provides better simulations than by using the single Berry–Reinhardt scheme, which was originally used in the Thompson scheme. It is worth emphasizing, in the present study, that we only pay attention to the ATC process from cloud water into rainwater with the purpose of improving the modelling of the extreme rainfall events over southern China. Actually, any (source and sink) term in a cloud microphysics scheme can be treated with the same approach. The ensemble method proposed herein appears to have important implications for developing cloud microphysics schemes in numerical models, especially for the models with variable grid resolution, which would be expected to improve the representation of cloud microphysical processes in the weather and climate models.

Subgrid-scale horizontal and vertical variation of cloud water in stratocumulus clouds: a case study based on LES and comparisons with in situ observations

Abstract. Stratocumulus clouds in the marine boundary layer cover a large fraction of ocean surface and play an important role in the radiative energy balance of the Earth system. Simulating these clouds in Earth system models (ESMs) has proven to be extremely challenging, in part because cloud microphysical processes such as the autoconversion of cloud water into precipitation occur at scales much smaller than typical ESM grid sizes. An accurate autoconversion parameterization needs to account for not only the local microphysical process (e.g., the dependence on cloud water content qc and cloud droplet number concentration Nc) but also the subgrid-scale variability of the cloud properties that determine the process rate. Accounting for subgrid-scale variability is often achieved by the introduction of a so-called enhancement factor E. Previous studies of E for autoconversion have focused more on its dependence on cloud regime and ESM grid size, but they have largely overlooked the vertical dependence of E within the cloud. In this study, we use a large-eddy simulation (LES) model, initialized and constrained with in situ and surface-based measurements from a recent airborne field campaign, to characterize the vertical dependence of the horizontal variation of qc in stratocumulus clouds and the implications for E. Similar to our recent observational study (Zhang et al., 2021), we found that the inverse relative variance of qc, an index of horizontal homogeneity, generally increases from cloud base upward through the lower two-thirds of the cloud and then decreases in the uppermost one-third of the cloud. As a result, E decreases from cloud base upward and then increases towards the cloud top. We apply a decomposition analysis to the LES cloud water field to understand the relative roles of the mean and variances of qc in determining the vertical dependence of E. Our analysis reveals that the vertical dependence of the horizontal qc variability and enhancement factor E is a combined result of condensational growth throughout the lower portion of the cloud and entrainment mixing at cloud top. The findings of this study indicate that a vertically dependent E should be used in ESM autoconversion parameterizations.

Analysis of Raindrop Size Distribution and Estimation of Cloud Microphysical Processes Using Ground-Based Observations at Mountain Sites in Korea and at Other Sites in the East Asian Region

Analysis of Raindrop Size Distribution and Estimation of Cloud Microphysical Processes Using Ground-Based Observations at Mountain Sites in Korea and at Other Sites in the East Asian Region

Influence of Non-Uniform Atmospheric Saturation on Extreme Hourly Precipitation Cloud Microphysical Processes in a Heavy Rainfall Case in Zhengzhou

Influence of Non-Uniform Atmospheric Saturation on Extreme Hourly Precipitation Cloud Microphysical Processes in a Heavy Rainfall Case in Zhengzhou