Collision-coalescence Process Research Articles

Can pollen affect precipitation?

Abstract. Large primary bioparticles such as pollen can be abundant in the atmosphere; for example near-surface pollen concentrations above 10 000 particles per cubic metre can occur during intense pollination periods. On one hand, due to their large size (10–100 µm), pollen can act as giant cloud condensation nuclei and enhance the collision–coalescence process in clouds that leads to drizzle formation. On the other hand, in humid conditions pollen is known to rupture and release many fine particles that can increase the cloud stability by reducing the droplet size. Additionally, both whole pollen grains and the sub-pollen particles released by pollen rupture are known to act as ice-nucleating particles (INPs). Due to these complex interactions, the role of pollen in modulating the cloud cover and precipitation remains uncertain. We used the UCLALES-SALSA (UCLA Large-Eddy Simulation Code and Sectional Aerosol module for Large-Scale Applications) large eddy simulator for simulating birch pollen effects on liquid- and mixed-phase clouds. Our simulations show that the pollen concentrations observed during the most intense pollination seasons can locally enhance precipitation from both liquid- and mixed-phase clouds, while more commonly encountered pollen concentrations are unlikely to cause a noticeable change. The liquid precipitation enhancement depended linearly on the emitted pollen flux in both liquid- and mixed-phase clouds; however, the slope of this relationship was case-dependent. Ice nucleation happened at a relevant degree only if the process of rupturing pollen producing large number of fine ice-nucleating particles was included in the simulations. The resulting precipitation saturated for the highest INP concentrations. Secondary ice formation by rime splintering had only a minor effect in the considered 1 d timescale.

Microphysical characteristics of torrential predecessor rain events over the Yangtze River Delta Area and the related tropical cyclones

The precipitation structures and microphysical characteristics of predecessor rain events (PREs) over the Yangtze River Delta area and related tropical cyclones (TCs) from 2014 to 2019 were investigated using Dual-frequency Precipitation radar data from Global Precipitation Measurement (GPM) for drop size distributions (DSDs). Results showed that the total mean rain rate of PREs was larger than that of TCs, primarily due to higher convective and stratiform rain rates, with enhanced fractional coverage of convective rain in PREs. Examination of microphysical characteristics revealed that a greater quantity of small-sized droplets and a smaller quantity of medium- as well as large-sized droplets contributed towards PREs in comparison with TCs. The conclusion still holds when partitioning DSDs based on different precipitation rate categories. Further investigation of DSDs using gamma functions illustrated that precipitation in PREs had lower average mass-weighted diameters (Dm) and enhanced normalized number concentration (Nw) compared with TCs; partitioning precipitation into convective and stratiform components illustrated a larger Dm and lower Nw in TCs than PREs. The analysis of microphysical and thermodynamical processes using the reanalysis data indicates that relatively intense convective activity with drier conditions may be favorable to enhancing raindrop growth through collision-coalescence processes, as a result of larger Dm in TCs than PREs. The empirical relations (Z–R algorithms) applied in different rain regimes (stratiform, convective, and total PREs) revealed significant diversities, relying on weather conditions and geographical locations. Plain language summaryThe Yangtze River Delta area is an important economic belt in China. Under climate change, observed frequent occurrences of weather extremes of heavy rainfall and tropical cyclones (TCs) exert adverse effects on economic development in this region. Thus, a deep understanding of the mechanism of TCs torrential rainfall in the Yangtze River Delta area is urgently necessary. In this study, we investigated the precipitation patterns and microphysical characteristics of predecessor rain events (PREs) in the Yangtze River Delta region, and their association with TCs in the South China Sea-Western North Pacific Ocean (SCS-WNPO) area from 2014 to 2019. We found that PREs had a higher total mean precipitation rate than TCs. Further examination using gamma functions demonstrated that PREs exhibited lower average mass-weighted diameters (Dm) and higher normalized intercept parameters (Nw) than TCs. This pattern persisted when distinguishing between convective and stratiform precipitation components. We believe that our study makes a significant contribution to the literature because these results provide valuable insights into the distinct precipitation characteristics of PREs and TCs in the study region and contribute to a better understanding of tropical cyclone-related rainfall patterns, and act as a scientific basis for disaster mitigation.

Complex interplay of sulfate aerosols and meteorology conditions on precipitation and latent heat vertical structure

An eight-year satellite observation dataset reveals that sulfate aerosols significantly influence the vertical structure of precipitation and latent heat (LH) in the Beijing-Tianjin-Hebei (BTH) region during summer. In this period, prevalent sulfate aerosols combine with warm, humid southerly winds and elevated convective available potential energy (CAPE), influencing precipitation dynamics. Under polluted conditions with specific CAPE and precipitation top temperature (PTT) ranges, precipitation particles experience accelerated growth within the mixed-phase layer, delineated by the −5 °C to 2 °C isotherms, compared to pristine environments. This results in a marked increase in both the intensity and height at which the maximum LH is released. Subsequent analysis reveals that hygroscopic sulfate aerosols, acting as cloud condensation nuclei (CCN), amplify the collision-coalescence process within the mixed layer amid high cloud water content, propelling rapid precipitation particle growth and elevating the PTT. This warming effect surpasses the cooling contribution from robust CAPE, culminating in a net elevation of PTT under polluted scenarios compared to pristine ones. Additionally, quantification of PTT sensitivity to both CAPE and aerosol optical depth (AOD) unveils a high consistency between satellite-detected PTT responses to CAPE and those predicted by cloud-resolving model simulations. The study deduces that the role of aerosols as CCN in either invigorating or diminishing the collision-coalescence process is contingent on the available cloud water.

Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5)

Abstract. Machine learning (ML) algorithms can be used in Earth system models (ESMs) to emulate sub-grid-scale processes. Due to the statistical nature of ML algorithms and the high complexity of ESMs, these hybrid ML ESMs require careful validation. Simulation stability needs to be monitored in fully coupled simulations, and the plausibility of results needs to be evaluated in suitable experiments. We present the coupling of SuperdropNet, a machine learning model for emulating warm-rain processes in cloud microphysics, with ICON (Icosahedral Nonhydrostatic) model v2.6.5. SuperdropNet is trained on computationally expensive droplet-based simulations and can serve as an inexpensive proxy within weather prediction models. SuperdropNet emulates the collision–coalescence of rain and cloud droplets in a warm-rain scenario and replaces the collision–coalescence process in the two-moment cloud microphysics scheme. We address the technical challenge of integrating SuperdropNet, developed in Python and PyTorch, into ICON, written in Fortran, by implementing three different coupling strategies: embedded Python via the C foreign function interface (CFFI), pipes, and coupling of program components via Yet Another Coupler (YAC). We validate the emulator in the warm-bubble scenario and find that SuperdropNet runs stably within the experiment. By comparing experiment outcomes of the two-moment bulk scheme with SuperdropNet, we find that the results are physically consistent and discuss differences that are observed in several diagnostic variables. In addition, we provide a quantitative and qualitative computational benchmark for three different coupling strategies – embedded Python, coupler YAC, and pipes – and find that embedded Python is a useful software tool for validating hybrid ML ESMs.

Revealing the Precipitation Characteristics of Tropical Cyclone Ockhi from Polarimetric Doppler Weather Radar in Conjunction with Disdrometer Observations

Abstract This paper describes the rainfall and microphysical structure of precipitation associated with Tropical Cyclone Ockhi using polarimetric Doppler weather radar (PDWR) products. The study reports the statistical analysis of precipitation types of tropical cyclone cloud systems over the north Indian Ocean, by combining the observations of the PDWR and disdrometer for the first time. There are few studies that mention initial DWR observations of TC over this low-latitude region below 23.5°N. We tried to further carry out a statistical analysis of precipitating clouds in a cyclone from its depression stage to severe cyclonic stage. This study tries to classify and quantify the contribution of convective and stratiform rain to the total TC rainfall. Precipitating clouds have been classified into convective and stratiform based on reflectivity measurements. The vertical profiles (VPRs) of the radar reflectivity Z and the differential reflectivity ZDR obtained for the stratiform and the convective events are compared. A study of the VPR of the convective events reveals that the Z and ZDR parameters tend to increase as the raindrops descend toward the ground owing to enhanced collision–coalescence processes. The VPR of stratiform rain shows signatures of the bright band (BB). The drop size distribution (DSD) parameters and rainfall rate pertaining to the two different precipitation regimes are estimated from the radar data and have been compared. The Joss–Waldvogel Disdrometer measurements have been used to derive DSD parameters and polarimetric rain-rate relation.

Variability of Index of Coalescence Activity (ICA) over a rain-shadow region during monsoon and its role in cloud seeding programs in India

The Index of Coalescence Activity (ICA) is an indicator of the microphysical state of the cloud. ICA is related to cloud base temperature and buoyancy of cloud at 500 hPa level. It is a useful parameter for making a decision on the type of seeding to be carried out. During Cloud Aerosol Interaction and Precipitation Enhancement EXperiment (CAIPEEX) over the Indian region, aircraft operations for in situ measurements of aerosols and clouds were conducted from the base station Hyderabad to cover parts of north peninsular India falling under the rain-shadow region. Special Radiosonde flights were conducted in the afternoon hours prior to aircraft operations from the locations Hyderabad and Mahabubnagar in addition to regular India Meteorological Department (IMD) Radiosonde flights at 00 UTC from Hyderabad. The parameters like cloud base height and buoyancy at 500 hPa level obtained from Radiosonde flights have been used to estimate ICA values during the period of the experiment (June to October 2009–2011). The variability of ICA has been studied using morning and afternoon sounding profiles over the rain-shadow region. Morning hours showed highly negative ICA values, as compared to noon hours. During noon time, positive ICA values are observed before monsoon onset (in 2009, monsoon onset was 21st June over the study area) and on few occasions during post-monsoon (October). Positive ICA values represent that collision-coalescence (CC) process is not fully developed and hence, hygroscopic seeding is the proper decision. While, negative ICA values indicate active CC process, suggesting glaciogenic seeding. The soundings conducted just before the cloud seeding operations provide information about the actual thermodynamic and microphysical state of the cloud. Hence, ICA information is useful for providing guidelines to the operational cloud seeding programs.

The impact of aerosols and model grid spacing on a supercell storm from Swabian MOSES 2021

AbstractThe supercell storm that occurred in southwestern Germany on June 23, 2021, had an exceptionally long lifetime of 7.5 hr, travelled a distance of nearly 190 km, and produced large amounts of hail. During that summer, the Swabian MOSES field campaign was held in that area, and several hydro‐meteorological measurements are available, as the storm passed directly over the main observation site. We present hindcasts of this event with the Icosahedral Non‐hydrostatic model using two horizontal grid spacings (i.e., 2 km, 1 km) with a single‐moment and an advanced double‐moment microphysics scheme. Numerical results show that all 2 km model realizations do not simulate convective precipitation at the correct location and time. For the 1 km grid spacing, changes in aerosol concentration resulted in large changes in convective precipitation. Only the 1 km run assuming a low cloud condensation nuclei (CCNs) concentration is able to realistically capture the storm, whereas no supercell is simulated in the more polluted scenarios. Observed aerosol particle concentrations indicate that CCNs values were the lowest of the month, which suggests that the low aerosol concentration is a reasonable assumption. The thermodynamic structure of the pre‐convective environment, as well as other observations, showed the best agreement to this model run as well, indicating that the good representation of the supercell was obtained for the right reason. The automatic tracking of individual clouds revealed that more convective cells with longer lifetimes are simulated at finer resolution. We also find a negative aerosol–precipitation effect that is not only due to a reduced collision–coalescence process, but also to weaker cold‐rain processes. These findings demonstrate the benefits of using an aerosol‐aware double‐moment microphysics scheme for convective‐scale predictability and that the use of different CCNs concentrations can determine whether a supercell is successfully simulated or not.

Statistical characteristics of convective and stratiform precipitation during the rainy season over South China based on GPM-DPR observations

South China is one of the wettest regions in the world, and is prone to precipitation-related disasters. The GPM-DPR product is utilized in this study to investigate the vertical structure and drop size distribution (DSD) characteristics of different precipitation types, i.e., stratiform precipitation (SP) and convective precipitation (CP) in South China. The precipitation frequency and accumulation in South China are dominated by SP and deep CP, respectively. Shallow CP has comparable occurrence ratio to deep CP, but the mean intensity is much lower. As such, it contributes <5% to the total rainfall. Vertical features differ among precipitation types and intensities. Higher storm top altitude (STA) is roughly related to more intense precipitation, especially for CPs. As surface PR increases in deep CP, the contribution of increase of PR (ΔPR) in liquid-phase region to total PR also increases, from approximately 10% for 4 mm/h to 55% for 100 mm/h. The intensity of shallow CP is generally low, but ΔPR in the liquid-phase region is significant. For SP and shallow CP, higher precipitation intensity is often caused by higher hydrometer concentration. Differently, for deep CP with PR >15 mm/h, both the increases in hydrometer size and concentration contribute to the increase in PR. The collision-coalescence process is dominant in the low-level layers in SP without bright band and CP, and approximately four fifth of shallow CP samples are influenced by collision-coalescence process. Compared with inland mountainous region, collision-coalescence process plays more important role in intense precipitation in coastal region. The results of this study indicate that it is important to consider the vertical evolution and DSD characteristics of different precipitation types for improving the accuracy of near surface quantitative precipitation estimate.

The Kinematic and Microphysical Characteristics of Extremely Heavy Rainfall in Zhengzhou City on 20 July 2021 Observed with Dual-Polarization Radars and Disdrometers

In this study, we utilized dual-polarization weather radar and disdrometer data to investigate the kinematic and microphysical characteristics of an extreme heavy rainfall event that occurred on 20 July 2021, in Zhengzhou. The results are as follows: FY-2G satellite images showed that extremely heavy rainfall mainly occurred during the merging period of medium- and small-scale convective cloud clusters. The merging of these cloud clusters enhanced the rainfall intensity. The refined three-dimensional wind field, as retrieved by the multi-Doppler radar, revealed a prominent mesoscale vortex and convergence structure at the extreme rainfall stage. This led to echo stagnation, resulting in localized extreme heavy rainfall. We explored the formation mechanism of the notable ZDR arc feature of dual-polarization variables during this phase. It was revealed that during the record-breaking hourly rainfall event in Zhengzhou (20 July 2021, 16:00–17:00 Beijing Time), the warm rain process dominated. Effective collision–coalescence processes, producing a high concentration of medium- to large-sized raindrops, significantly contributed to heavy rainfall at the surface. From an observational perspective, it was revealed that raindrops exhibited significant collision interactions during their descent. Moreover, a conceptual model for the kinematic and microphysical characteristics of this extreme rainfall event was established, aiming to provide technical support for monitoring and early warning of similar extreme rainfall events.

Comparison of Simulated Warm‐Rain Microphysical Processes in a Record‐Breaking Rainfall Event Using Polarimetric Radar Observations

AbstractDuring 6–7 May 2017, a record‐breaking nocturnal rainfall event occurred in Guangzhou, China, and it was a typical warm‐sector heavy rainfall event under weak synoptic forcing. A prior observational study by the authors revealed that warm‐rain microphysical processes were dominant and responsible for the record‐breaking precipitation. In this study, the double‐moment Morrison, Thompson and NSSL microphysics schemes in WRF are evaluated against polarimetric radar observations in their ability of reproducing observed microphysical characteristics. The Thompson scheme shows the greatest fidelity to the observed raindrop size distribution (RSD) median value, corresponding to the most amount of precipitation forecast. While the Morrison and NSSL simulations overestimate (underestimate) the raindrop size (number concentration), exhibiting continental‐type convective precipitation. The three experiments slightly overestimate differential reflectivity (ZDR), but significantly underestimate specific differential phase (KDP) and liquid water content by about 30%–50%, implying the undervaluation of number of medium‐sized raindrops. Examinations of the occurrence frequencies of ZDR, KDP, mass‐weighted diameter, and logarithmic normalized intercept parameter for rain suggest that all three schemes fail to reproduce the full variability of observed RSD for the extreme rainfall. The vertical variations of RSD parameters and the Kumjian‐Ryzhkov parameter space suggest that the collision–coalescence is the dominant warm‐rain microphysical process but the simulated process is too weak. This may be attributed to the misrepresented RSD near the melting layer, where the raindrops with lower number concentration and larger sizes cannot grow through the collision–coalescence process as actively.

Characterization of rain microphysics on the leeside of the Western Ghats

AbstractThis study evaluates the performance of four impact‐type disdrometers installed at the Indian Institute of Tropical Meteorology, Pune, located on the leeside of the Western Ghats (WG). Further, seasonal variation of rain microphysical properties is studied during premonsoon (March–May), monsoon (June–September), postmonsoon (October–November), and winter (December–February). The four disdrometers exhibit comparable raindrop size distribution (DSD) patterns, though negligible differences are found at smaller drop diameters. The DSD shows higher concentrations of smaller (large‐size) drops during monsoon (premonsoon and postmonsoon). Principal component analysis revealed three distinct modes of DSD characteristics. Monsoon DSDs are associated with a group of numerous smaller drops allied with shallow storm heights (warm rain). The premonsoon and postmonsoon DSDs are clustered in a group where ice‐based processes dominate, resulting in higher median drop diameters (D0) and smaller normalized intercept parameters (Nw). The fitted gamma DSD model indicates higher mass‐weighted mean diameter (Dm) during premonsoon, and higher intercept parameter during monsoon, whereas postmonsoon and winter have intermediate values. DSD stratified with rain rate shows that the Dm values increase with an increase in rain rate during winter, monsoon and postmonsoon, whereas in premonsoon, Dm increases initially and then decreases. Higher Dm and lower Nw are observed during convective rain in all seasons. The fitted slope–shape parameter relationships show a considerable seasonal variation. The DSD on the WG's leeside is notably different from the windward slopes and other WG locations. Different microphysical and dynamical mechanisms lead to seasonal differences in DSD characteristics. In monsoon, a considerable volume of water vapour advected from the Arabian Sea promotes the formation of raindrops through collision–coalescence processes, which may result in a higher proportion of smaller and midsized raindrops. The deeper clouds during premonsoon and postmonsoon indicate mixed‐phase processes, which lead to mid and large‐sized raindrops.

Development and evaluation of a bulk three-moment parameterization scheme incorporating the processes of sedimentation and collision-coalescence

There are a few three-moment schemes that consider other processes besides sedimentation. Thus, a performance assessment of these types of schemes due to the combined effect of sedimentation and other microphysical processes is a matter of interest. In this study, a warm rain bulk three-moment parameterized scheme was developed and evaluated through a detailed comparison with a bin microphysical scheme. To evaluate the impact of sedimentation and the combined effect of sedimentation and collision-coalescence on the droplet size distribution (DSD), a rain shaft model was applied to the DSD with different initial values of the shape parameter. For pure sedimentation, a good correspondence was obtained between the three-moment scheme and the explicit model, with a practically perfect coincidence of bulk quantities for larger values of the gamma distribution’s initial shape parameter and, in general, the three-moment parameterization scheme performing much better than the two-moment scheme. The simulations performed for this case confirm (as reported in previous studies) that for pure sedimentation, the three-moment parameterization schemes deliver a physically more complete representation of the evolution of droplet size distribution. The impact of the combined effect of sedimentation and collision-coalescence processes on DSD was also assessed. We could observe that certain differences arise between the parameterized scheme and the spectral model when the collision-coalescence process is incorporated, as the onset of precipitation occurs earlier in the three-moment parameterized scheme. It can be concluded that, in general, the three-moment warm rain bulk microphysics scheme is able to reproduce the results of the reference bin microphysical model.

Toward a Numerical Benchmark for Warm Rain Processes

Abstract The Kinematic Driver-Aerosol (KiD-A) intercomparison was established to test the hypothesis that detailed warm microphysical schemes provide a benchmark for lower-complexity bulk microphysics schemes. KiD-A is the first intercomparison to compare multiple Lagrangian cloud models (LCMs), size bin-resolved schemes, and double-moment bulk microphysics schemes in a consistent 1D dynamic framework and box cases. In the absence of sedimentation and collision–coalescence, the drop size distributions (DSDs) from the LCMs exhibit similar evolution with expected physical behaviors and good interscheme agreement, with the volume mean diameter (Dvol) from the LCMs within 1%–5% of each other. In contrast, the bin schemes exhibit nonphysical broadening with condensational growth. These results further strengthen the case that LCMs are an appropriate numerical benchmark for DSD evolution under condensational growth. When precipitation processes are included, however, the simulated liquid water path, precipitation rates, and response to modified cloud drop/aerosol number concentrations from the LCMs vary substantially, while the bin and bulk schemes are relatively more consistent with each other. The lack of consistency in the LCM results stems from both the collision–coalescence process and the sedimentation process, limiting their application as a numerical benchmark for precipitation processes. Reassuringly, however, precipitation from bulk schemes, which are the basis for cloud microphysics in weather and climate prediction, is within the spread of precipitation from the detailed schemes (LCMs and bin). Overall, this intercomparison identifies the need for focused effort on the comparison of collision–coalescence methods and sedimentation in detailed microphysics schemes, especially LCMs.

A study of rain drop size distributions and associated rain microphysical processes over a subtropical station in the Northeast India

A study of rain Drop Size Distributions (DSDs) and associated microphysical processes is carried out during the premonsoon and monsoon seasons at Kohima (25.67° N, 94.08° E) in the Patkai hill range of the Northeast India. The study is carried out with the help of Micro Rain Radar (24.1 GHz). The rainfall is classified in three categories namely, Bright Band (BB), No Bright Band Low rain rate (R) i.e. 0.10 mm h−1< R ≤ 10 mm h−1 (NBBL) and No Bright Band High rain rate i.e. 10 mm h−1 <R ≤ 20 mm h−1 (NBBH). During the premonsoon and monsoon, for R ≤ 20 mm h−1, rainfall occurrence time constitute around 96% and 98% respectively, whereas rainfall accumulation constitute around 51%. and 72% respectively. The BB height is found to be lower during the premonsoon (∼2.2 km from surface level) compared to monsoon (∼3.6 km from surface level) season. The observational results suggest that, during the premonsoon as well monsoon seasons, BB rain is predominantly associated with collision–coalescence–breakup process with varying proportion, whereas NBBL and NBBH rain are predominantly associated with collision–coalescence process with varying proportion. In each season, collision-coalescence process is most dominant for NBBH type of rain. Seasonally, collision-coalescence process is found to be stronger during the premonsoon season. For the falling rain drops, there is a transition in the microphysical process from number controlled (collision-coalescence-breakup) to size-controlled (collision-coalescence) process with varying proportion. For each season, for R ≤ 10 mm h−1, larger rain drops are found for BB type of rain compared to NBBL rain. Overall, relatively larger raindrops are found to be associated with NBBH rain. Seasonally, larger drops are found during the premonsoon compared to the monsoon season. The rain DSDs and associated microphysical processes during the premonsoon are found to be associated with stronger convection compared to the monsoon season.

How uncertain are precipitation and peak flow estimates for the July 2021 flooding event?

Abstract. The disastrous July 2021 flooding event made us question the ability of current hydrometeorological tools in providing timely and reliable flood forecasts for unprecedented events. This is an urgent concern since extreme events are increasing due to global warming, and existing methods are usually limited to more frequently observed events with the usual flood generation processes. For the July 2021 event, we simulated the hourly streamflows of seven catchments located in western Germany by combining seven partly polarimetric, radar-based quantitative precipitation estimates (QPEs) with two hydrological models: a conceptual lumped model (GR4H) and a physically based, 3D distributed model (ParFlowCLM). GR4H parameters were calibrated with an emphasis on high flows using historical discharge observations, whereas ParFlowCLM parameters were estimated based on landscape and soil properties. The key results are as follows. (1) With no correction of the vertical profiles of radar variables, radar-based QPE products underestimated the total precipitation depth relative to rain gauges due to intense collision–coalescence processes near the surface, i.e., below the height levels monitored by the radars. (2) Correcting the vertical profiles of radar variables led to substantial improvements. (3) The probability of exceeding the highest measured peak flow before July 2021 was highly impacted by the QPE product, and this impact depended on the catchment for both models. (4) The estimation of model parameters had a larger impact than the choice of QPE product, but simulated peak flows of ParFlowCLM agreed with those of GR4H for five of the seven catchments. This study highlights the need for the correction of vertical profiles of reflectivity and other polarimetric variables near the surface to improve radar-based QPEs for extreme flooding events. It also underlines the large uncertainty in peak flow estimates due to model parameter estimation.

A Comparative Study on the Vertical Structures and Microphysical Properties of a Mixed Precipitation Process over Different Topographic Positions of the Liupan Mountains in Northwest China

A field campaign in Liupan Mountains was carried out by the Weather Modification Center of the China Meteorological Administration to study the impact of terrain on precipitation in Northwest China. The vertical structures and microphysical characteristics of a mixed cloud and precipitation process, which means stratiform clouds with embedded convection, over three topographic positions of the Liupan Mountains, namely, the Longde (LD, located on the windward slope), Liupan (LP, located on the mountain top), and Dawan sites (DW, located on the leeward slope), are compared using measurements from ground-based cloud radar (CR), micro rain radar (MRR), and disdrometer (OTT). The 17 h process is classified into cumulus mixed (1149 min), shallow (528 min), and stratiform (570 min) cloud and precipitation stages. Among them, the vertical structures over the three sites are relatively similar in the third stage, while the differences, mainly in cloud-top heights (CTHs) and rain rates (Rs), are significant in the second stage due to the strong instability. Overall, the characteristics of higher concentrations and smaller diameters of raindrops are found in this study, especially at the LP site. Topographic forcing makes the microphysical and dynamic processes of mountaintop clouds and precipitation more intense. The updrafts are the strongest at the LP, caused by orographic uplifting, and the DW is dominated by the downdrafts due to the topography impact on the dynamic structure. Meanwhile, particle falling velocities (Vts) and downdrafts rapidly increase within 0.6 km near the ground over the LP, forming positive feedback, and the collision–coalescence process is dominant.

Observation and simulation study on the macro–microphysical characteristics of a coastal fog offshore Zhejiang Province of China

A coastal fog patch occurred offshore Zhejiang Province of China on 1 April 2021, and was investigated by using observations from automatic weather stations, a millimeter–wave radar and a fog droplet spectrometer and the Weather Research and Forecasting model simulations. The macro–microphysical characteristics of the fog in five stages, i.e., light fog, formation, burst, mature and dissipation, have been studied. The millimeter–wave radar displays that the spatial scale of the fog episode is about 6 km, with vertical thickness and duration <700 m and 3 h, respectively. Cloud base lowering is essential for the fog burst reinforcement. Condensational growth is the main microphysical process for the fog formation and burst, however, the collision–coalescence process is important as well, with stronger intensity in the burst stage. Both observations and simulations indicate that the fog formed because of moisture convergence related to sea breeze developing along the Zhejiang coast. The convergence is not only favorable for the fog formation, but also for weak ascent and low cloud formation, which in turn intensifies the inversion layer by latent heat releasing, then further enhances descent in the boundary layer. A sufficiently moist subcloud layer capped by a strong inversion, together with downward motion are important factors for the cloud base lowering. The top–cooling entrainment is the main reason for the fog dissipation. Finally the macro–microphysical processes of the fog episode are summarized.

Evaluation of the raindrop size distribution representation of microphysics schemes in typhoon lekima using disdrometer network observations

Raindrop size distribution (RSD) is a crucial linkage between the precipitation characteristics and related microphysical processes. However, its representation in the numerical weather model still has significant uncertainties. In this study, by using the Weather Research and Forecasting (WRF) model with three widely-used bulk microphysics schemes: The Morrison, Thompson, and WRF double-moment 6-class (WDM6) microphysics schemes, the simulated RSD characteristics in the eyewall and spiral rainbands of Typhoon Lekima (2019) are evaluated against a dense disdrometer network observation. Verifications generally show the overestimated mass-weight mean diameter (Dm) but the underestimated normalized number concentration (Nw) by three microphysics schemes. In comparison with the other two schemes, the RSD characteristics simulated by the Thompson scheme are more consistent with the observation. Furthermore, simulations produced less and somewhat different RSD variability among the eyewall and rainbands than observations. From the outer rainband to the eyewall, the probability density function (PDF) curve of observed Dm shows a left-skewed distribution with a flatter shape and decreasing peakedness, whereas the simulations show a narrower and right-skewed PDF of Dm. The discrepancy between the model and observations is primarily attributed to more intense tendencies of Dm growth and Nw reduction in simulations during raindrops descend to the ground. Examinations of vertical profiles for rain-related microphysical processes indicate that this discrepancy likely originates with the over-predicted collision-coalescence process.

Cloud Vertical Structure of Stratiform Clouds with Embedded Convections Occurring in the Mei-Yu Front

Cloud Vertical Structure (CVS) plays a crucial role in determining atmospheric circulation and the hydrological cycle. We analyzed the CVS in Stratiform Clouds with Embedded Convection (SCEC) occurring in the mei-yu front over central-eastern China based on the conjunction of the S-band Doppler weather radar, the C-band Frequency Modulation Continuous Wave (C-FMCW) rad ar, and the Microrain Radar (MRR). Our results showed that both the melting layers and the rain rate were unevenly distributed in the three SCEC cases, and there was a thicker melting layer and a larger rain rate in the embedded convection. In the stratiform regions, the vertical velocity of particles in the upper region of the melting layer was generally in the range of 0–4 m·s−1, and increased rapidly to 4–12 m·s−1 near the bottom of the melting layer. In the case of June 28, due to the vigorous development of embedded convection, the cloud particles in the upper layer showed upward movement, and the growth rate of the particles in this region was faster than that in the surrounding stratiform regions. The vertical distributions of Drop Spectrum Distributions (DSDs) showed that the average concentration of drops larger than 3 mm increased as they fell from 3 km to 1 km, and the collision–coalescence process of drops in the embedded convection was stronger.

Collision Fluctuations of Lucky Droplets with Superdroplets

Abstract It was previously shown that the superdroplet algorithm for modeling the collision–coalescence process can faithfully represent mean droplet growth in turbulent clouds. An open question is how accurately the superdroplet algorithm accounts for fluctuations in the collisional aggregation process. Such fluctuations are particularly important in dilute suspensions. Even in the absence of turbulence, Poisson fluctuations of collision times in dilute suspensions may result in substantial variations in the growth process, resulting in a broad distribution of growth times to reach a certain droplet size. We quantify the accuracy of the superdroplet algorithm in describing the fluctuating growth history of a larger droplet that settles under the effect of gravity in a quiescent fluid and collides with a dilute suspension of smaller droplets that were initially randomly distributed in space (“lucky droplet model”). We assess the effect of fluctuations upon the growth history of the lucky droplet and compute the distribution of cumulative collision times. The latter is shown to be sensitive enough to detect the subtle increase of fluctuations associated with collisions between multiple lucky droplets. The superdroplet algorithm incorporates fluctuations in two distinct ways: through the random spatial distribution of superdroplets and through the Monte Carlo collision algorithm involved. Using specifically designed numerical experiments, we show that both on their own give an accurate representation of fluctuations. We conclude that the superdroplet algorithm can faithfully represent fluctuations in the coagulation of droplets driven by gravity.