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Influence of Ice Nuclei Parameterization Schemes on the Hail Process

Influence of Ice Nuclei Parameterization Schemes on the Hail Process Hindawi Advances in Meteorology Volume 2018, Article ID 4204137, 17 pages https://doi.org/10.1155/2018/4204137 Research Article Influence of Ice Nuclei Parameterization Schemes on the Hail Process 1,2 3 4 5 Xiaoli Liu , Ye Fu, Zhibin Cao, and Shuanglong Jin Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China Xianyang Meteorology Bureau, Xianyang 712000, China Purple Mountain Observatory, Nanjing 210008, China State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing 100192, China Correspondence should be addressed to Xiaoli Liu; liuxiaoli2004y@nuist.edu.cn and Shuanglong Jin; jinshuanglong@epri.sgcc.com.cn Received 14 August 2017; Revised 7 December 2017; Accepted 21 December 2017; Published 20 February 2018 Academic Editor: Bin Yong Copyright © 2018 Xiaoli Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Ice nuclei are very important factors as they significantly aeff ct the development and evolvement of convective clouds such as hail clouds. In this study, numerical simulations of hail processes in the Zhejiang Province were conducted using a mesoscale numerical model (WRF v3.4). eTh effects of six ice nuclei parameterization schemes on the macroscopic and microscopic structures of hail clouds were compared. eTh effect of the ice nuclei concentration on ground hailfall is stronger than that on ground rainfall. There were significant spatiotemporal, intensity, and distribution differences in hailfall. Changes in the ice nuclei concentration caused different changes in hydrometeors and directly aeff cted the ice crystals, and, hence, the spatiotemporal distribution of other hydrometeors and the thermodynamic structure of clouds. An increased ice nuclei concentration raises the initial concentration of ice crystals with higher mixing ratio. In the developing and early maturation stages of hail cloud, a larger number of ice crystals competed for water vapor with increasing ice nuclei concentration. This effect prevents ice crystals from maturing into snow particles and inhibits the formation and growth of hail embryos. During later maturation stages, updraft in the cloud intensified and more supercooled water was transported above the 0 C level, benett fi ing the production and growth of hail particles. An increased ice nuclei concentration therefore favors the formation of hail. 1. Introduction are affected by changes in the atmospheric thermodynamic environment through artificially propagating IN that can Statistics show that over 50% of mid-latitude precipitation is grow into ice crystals. This consequently aeff cts the latent heat caused by the melting of large ice particles produced during release rates, dynamic processes of strong convective clouds, the ice-phase transformation process; this ratio is slightly and resultant precipitation particles [5]. lower (∼30%) in the tropics [1]. The microphysical process Giventhefact that eeff ctofINonstrongconvective of ice-phase transformation in clouds plays a distinct role in clouds is extremely complex, there are no definite conclusions the formation of precipitation particles. u Th s, the ice crystal abouttheneteeff ctsoftheconcentrationofINonstrong nucleation processisveryimportant [2–4]. Manyscholars convective clouds [6]. Some studies suggest that an increase have studied the distribution characteristics of ice nuclei (IN) in the IN concentration favors ground precipitation. For example, a study focusing on the period between the 1940s and their effects on clouds and precipitation using a com- bination of observations and numerical simulation results. and the 1960s found that the concentration of ice crystals eTh results showed that microphysical processes in clouds in cloud with a large volume of aerosols (especially IN) 2 Advances in Meteorology transported by upstream winds had increased, leading to be more suitably described by supersaturation with respect increasing rainfall and hail in Indiana, which was aeff cted to the ice surface [22–24]. This means that, under different by urban air pollution during this period [7]. Khain et supersaturation conditions, IN have different surface prop- al. [8] stated that the latent heat released by frozen small erties and the same types of IN can only simultaneously cloud droplets enhances the convection in clouds as the IN nucleate under the same environmental conditions. concentration increases in mixed-phase convection clouds, The heterogeneous ice-phase nucleation process is not resulting in increased precipitation. However, sensitivity tests simply determined by the temperature or ice surface super- of the IN concentration of strong convective clouds showed saturation. In 1985, Vali [25] proposed for the first time four opposite effects. van den Heever et al. [9] and Carri oe ´ t different mechanisms of heterogeneous ice-phase nucleation al. [10] discussed the eeff cts of IN on strong convective including deposition, immersion-freezing, condensation- clouds in Florida using the Regional Atmospheric Modeling freezing, and contact-freezing nucleation. However, the rel- System (RAMS), a highly versatile numerical code developed ative importance of these mechanisms has not been deter- by scientists at Colorado State University to simulate and mined. Relevant studies suggest that [26, 27] immersion- forecast meteorological phenomena [11]. eTh results showed freezing and contact-freezing nucleation are most important thatthenumberofgraupel particlesdecreases andthatofhail for mixed-phase clouds, while deposition nucleation is more particles increases when the aerosol concentration increases. important in ice clouds above the troposphere. Other studies In addition, an increased IN concentration in the early stages showed that immersion-freezing and condensation-freezing of convective clouds leads to increased ground precipitation. nucleation play a dominant role in the freezing mechanism However, in the later stages, the aerosol concentration is the [28]. lowest when the accumulated precipitation reaches its max- eTh prediction of the IN concentration in the model is imum. Connolly et al. [12] argued that the “frozen indirect very complex and difficult due to the lack of understanding of effect” produced by the increase in the IN concentration is the relative importance of the four heterogeneous ice crystal very small. nucleation mechanisms. Meyers et al. [11] first developed Note that the inu fl ence of the IN concentration on empirical relationships for different nucleation mechanisms, convective precipitation is very complex and may differ from which are still widely used in models [9, 10]. one location and/or case to another. Observatory IN param- The deposition and condensation-freezing processes are eterization reflects the natural distribution and properties of related to the surface supersaturation relative to ice. Water IN under certain conditions and locations, based on which vapor molecules attach to IN and then form ice crystals the ice phases in convective clouds initiate and evolve. u Th s, through the deposition process, that is, the heterogeneous it is meaningful to use observatory IN parameterization deposition nucleation process. If the aerosol has both char- to explore the role of IN in the development of severe acteristics of cloud condensation nuclei and IN, water vapor precipitation. molecules rfi st attach to the aerosol and form liquid water droplets, which then freeze into ice crystals, that is, the het- erogeneous condensation-freezing nucleation process. eTh 2. Observation and Brief Review of empiricalfittingequations forthetwoprocesses canbe Parameterization Schemes of Ice Nuclei expressed as Several IN parameterization schemes have been developed to 𝑁 = exp{𝑎+𝑏 [100(𝑆 −1)]}, (1) 𝑖𝑑 𝑖 describe the IN concentration and heterogeneous nucleation process of ice crystals. eTh tfi ting equation for the IN where 𝑁 is the concentration of the deposition and 𝑖𝑑 concentration obtained from observations is widely used in condensation-freezing nuclei per liter of air,𝑆 is the satura- the model because of its simplicity. In the 1960s, Fletcher tion relative to the ice surface,𝑎 = −0.639, 𝑏 = 0.1296,and [13] used the static filter method for the rfi st time and ∘ ∘ the applicable temperature range is−5 Cto−20 C. obtained an empirical equation for the IN concentration No separate observations on contact-freezing nuclei con- that is only relatedtotemperature.However,the nucleation centration had been obtained until Cooper and Saunders [15] process treated by this diagnostic framework overestimates conducted an observation, estimating the concentration of the concentration of ice crystals and lacks sensitivity to contact-freezing nuclei. Based on Cooper’s observation, Mey- changes in saturation. Several studies also suggested that ers et al. developed a tfi ting equation for the concentration the nucleation capacity of IN is temperature-dependent of contact-freezing nuclei, which are temperature-dependent [14–16]. Relevant research discussing the atmospheric IN [11]: concentration and its variation has also been conducted in China. You et al. [17–19] observed the concentrations of IN 𝑁 = exp[𝑎+𝑏 (273.15−𝑇 )]. (2) 𝑖𝑐 𝑐 and ice crystals in Beijing and Jilin, respectively, and obtained an empirical tt fi ing equation for the correlation between the If the aerosol diameter is 0.1𝜇m, 𝑇 is the cloud droplet IN concentration and temperature. The characteristics of IN temperature (K),𝑎 = −2.80, 𝑏 = 0.262,and theunitof 𝑁 𝑖𝑐 −1 and their influence on the ice crystal concentration were also is L . discussed. Following Meyers, the IN parameterization has been Many observations have proven that the concentration developed [29, 30]. China’s meteorological researchers car- of ice crystals in clouds is not strictly determined by the ried out a series of IN observation experiments in the upper temperature [20, 21]. eTh deposition nucleation process can reaches of the Yellow River [31, 32]. It was believed that Advances in Meteorology 3 the concentration of IN at different locations showed large based on the Ziegler framework [43], which can be used uc fl tuations, leading to changes in the empirical relationship to predict the average graupel particle density. eTh particles of atmospheric IN concentrations [33–36]. ranging from frozen drops to low-density hail embryos In 2007, Phillips et al. [37] developed an improved IN are collectively called graupels. Other physical processes concentration tfi ting equation based on Meyers’ research were selected as follows: the Monin–Obukhov surface layer by adding the empirical equation developed by DeMott scheme, the Mellor–Yamada–Janjic (MYJ) boundary layer et al. [38] using the continuous-flow diffusion chamber scheme,theNoahlandsurfacescheme,therapidandaccurate (CFDC) observed at the top of Mount Werner, Colorado, radiative transfer model (RRTM) for longwave radiation and extending the applicable temperature range of IN acti- scheme, and the Dudhia for shortwave radiation scheme. vation. This tfi ting showed that the IN concentration in the The Kain–Fritsch (new Eta) scheme was used for cumulus continental boundary layer calculated by Meyers’ empirical convection parameterization in the D01 area, while the D02 formula is significantly higher than that of the interior of the area was closed. troposphere (especially at high elevations far from the land source). 3.2. Introduction of Ice Nuclei Parameterization Schemes Phillips et al. [39] considered the effects of the phys- 3.2.1. Ice Crystal Nucleation Process in the NSSL Framework. ical and chemical properties and surface area of IN and In NSSL,three typesofnucleationoficecrystals viaINare developed a new IN concentration equation. Connolly et al. considered: deposition, condensation-freezing, and contact- [12] developed a correlation equation between the density freezing (heterogeneous ice-phase nucleation). of nuclei surface activation sites and IN concentration by studying the immersion-freezing nucleation of dust particles. Recently, Chinese meteorological researchers developed a (1) Deposition and Condensation-Freezing Nucleation. There new empirical tfi ting equation for IN concentration using are two scenarios for calculating the IN concentration of the observation data from Su [40] and Yang et al.[41]. deposition and condensation-freezing processes. First, when the temperature is less than−5 C, the empirical relationship of Meyers et al. [11] is used. Second, when the temperature is 3. Introduction of the Case Study and greaterthanorequal to−5 C, the equation given in Cotton et Simulation Framework al. [44] is used: 3.1. Introduction of the Case Study and Models. Strong con- 4.5 (𝑞 −𝑞 ) { V is ∘ vective weather occurred in a large area of the Zhejiang 𝑛 [ ] exp(−0.6𝑇 ),𝑇≥−5 C 𝑖𝑜 𝑁 = (3) (𝑞 −𝑞 ) Province on November 9, 2009. From 06:00 China Standard 𝑖 { ws is Time (CST) to 12:00 CST, thick fog appeared in some areas exp[12.96(𝑆 −1)−0.639], 𝑇<−5 C, of eastern, northern, and central Zhejiang and lasted for up where𝑞 is the water vapor mixing ratio,𝑞 and𝑞 are the V is ws to six hours. under Th storms and strong winds occurred aer ft saturation ratios relative to ice and water, respectively, and 15:00 CST throughout the province, representing the most 𝑆 =𝑞 /𝑞 is the saturation relative to the ice surface. 𝑖 V is extensive and strongly convective weather that occurred in theautumnofthatyear. In addition,hailfellat15:45CST (2) Contact-Freezing Nucleation. The parameter 𝑁 is the in thePujiangCounty andother places.Thehaildiameter −3 activated contact nuclei concentration (unit: m ) expressed reached 1.5 cm. as𝑁 = exp(4.11−0.262𝑇 ) and𝑇 is the cloud droplet In this paper, numerical simulations using the WRF v3.4 𝑖 𝑐 𝑐 temperature. mesoscale model were conducted. Six-hourly global reanaly- ∘ ∘ In most of the two-moment microphysical schemes, the sis data (1×1 ) from the National Centers for Environmental IN concentration is used to predict the concentration and Prediction (NCEP) were selected as the initial field and mixing ratio of ice crystals. eTh calculation of the IN con- boundary data. eTh integration time of the hail process in centration is based on simple empirical equations, as shown themodelrangedfrom 08:00to20:00CSTonNovember above, and the initial ice crystal concentrations produced by 9, 2009. eTh integration time step was 36 s. eTh latitude and ∘ ∘ heterogeneous nucleation are obtained by calculation. longitude of the simulation center were 29.60 Nand119.80 E, respectively. Double bidirectional nesting was adopted in the model. From the coarse to ne fi D01 and D02 areas, the grid 3.2.2. Sensitivity Test of the Ice Crystal Nucleation Process. To resolution was 6.2 km and the numbers of grid points were better understand the effect of IN on cloud microphysical 150× 160 and 262× 280, respectively. Twenty-seven layers processes, vfi e additional IN parameterization schemes were were used vertically. eTh microphysical cloud scheme was employed in this study in addition to the original one. an NSSL two-moment scheme [42] developed by the US Through comparative experiments, the effects of the six National Severe Storms Laboratory (NSSL) in 2010, which is IN parameterizations on the spatiotemporal distribution of ahybridphase cloudframework.Withrespect to microphys- ice crystals and other hydrometeors were discussed. The ical cloud processes, the model predicts the specific water effects of different IN parameterizations on microphysical content (Qc, Qr, Qi, Qs, Qg,and Qh) and concentrations processes of hail clouds and the occurrence of hailstorms (Nc, Nr, Ni, Ns, Ng,and Nh)ofsix typesofhydrometeors were discussed. eTh following presents an overview of the (cloud water, rainwater, ice crystals, snow, graupel, and hail vfi eINschemesproposed to besimulatedand theapplicable particles). eTh NSSL framework is an improved framework conditions. 4 Advances in Meteorology 2 3 10 10 −20.0 −19.8 −19.6 −1 −2 −3 −40 −36 −32 −28 −24 −20 −16 −12 −8 −4 0 5 1015202530 Temperature ( C) Ice supersaturation (%) Original (T< −5 C) Fletcher (−27 ≤ T ≤ 0 C) Phillips (−30 ≤ T < −5 C) Cooper (−40 ≤ T ≤ 0 C) Phillips (−80 < T < −30 C) Chi_Mt. Hu (−25 ≤ T ≤ −10 C) Chi_NJ (−25 ≤ T ≤ −10 C) (a) (b) −1 ∘ Figure 1: Variation of the ice nuclei concentration (unit: L ) depending on the activation temperature; (a)𝑇1 and𝑇2 groups (unit: C) and ice surface supersaturation; (b)𝑆 group (unit: %). (1) eTh Fletcher [13] Scheme (4) eTh Huang Mountain Observation (Chi Mt. H) Scheme [40]. As differences in the IN concentration are significant −5 (4) 𝑁 =10 × exp(−0.6×𝑇 ). 𝑖 due to the influence of regional and meteorological factors, the empirical relationship of the Chinese IN observation The parameter 𝑁 isthenumberofactivated IN in the was introduced. Su [40] observed IN at the top of the −1 unit volume (L ),𝑇 is the corresponding temperature when Huang Mountain in Anhui, China, using a 5 L Bigg mixing the IN is activated, and the applicable temperature ranges cloud chamber. eTh observation period was May to October ∘ ∘ from−27 Cto0 C. The concentration of IN calculated by 2011 and the total IN concentration equation (temperature this scheme is lower than the value observed at higher ∘ ∘ range from−25 Cto−10 C) was developed by tfi ting, con- temperatures and the calculated concentration is higher when sidering four types of nucleation mechanisms (deposition, the temperature is relatively low (−25 C). condensation-freezing, contact-freezing, and immersion- freezing): (2) eTh Cooper [15] Scheme 𝑁 =0.0046× exp(−0.388×𝑇 ). (7) 𝑁 =0.005× exp(−0.304×𝑇 ). (5) 𝑖 ∘ ∘ eTh applicable temperature ranges from −40 Cto0 C. (5) eTh Nanjing Observation (Chi NJ) Scheme [41]. Yang et al. [41] performed observations using a 5 L Bigg mixed (3) eTh Phillips et al. [39] Scheme. Phillips et al. [39] inte- cloud chamber in Nanjing, China. The observation period grated and improved the Meyers [11] and DeMott schemes was summer 2011 and a total ice nucleation concentration ∘ ∘ [38]. eTh empirical correction factor Ψ was added and the equation (temperature ranging from−25 Cto−10 C) based applicable temperature range of ice nucleation activation was on the four nucleation mechanisms was developed: extended: 𝑁 =0.0049× exp(−0.388×𝑇 ). (8) To understand the microphysical characteristics of clouds (6) exp[−0.639+12.96(𝑆 −1)]×Ψ, −30≤𝑇<−5 and the response of precipitation to variations in the IN { 0.3 concentration, the six ice nucleation schemes were divided {exp[12.96(𝑆 −1.1)]} , −80<𝑇<−30, into three groups according to the calculation method of where the empirical correction factor Ψ = 0.06 and 𝑆 the IN concentration (Figure 1) and the temporal and spatial represents the surface saturation relative to ice. hydrometeor evolution were compared. Ice-nucleus concentration (/m ) Ice-nucleus concentrations (/m ) Advances in Meteorology 5 (b) ∘ (a) ∘ 31 N 31 N 65 65 ∘ ∘ 30 N 30 N 45 45 ∘ ∘ 29 N 29 N 25 25 ∘ ∘ 28 N 28 N 5 5 ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ 118 E 119 E 120 E 121 E 122 E 123 E 118 E 119 E 120 E 121 E 122 E 123 E (a) (b) Figure 2: Six-hour cumulative precipitation in Zhejiang at 20:00 CST on November 9; (a) observed and (b) simulated (unit: mm). eTh black dot represents the Pujiang area and the black triangle represents the Hangzhou area. In the rfi st group, the IN concentration is temperature- 65 dBz (Figure 3). Subsequently, this strong convective cloud dependent, which is the classical tfi (𝑇1 group) suitable for the continuedtomoveeastwards whilemerging withcellsinthe deposition and condensation-freezing nucleation processes front and strengthening. At 13:42 CST, a multicellular storm including the Fletcher and Cooper schemes. eTh second composed of convection cells at different development stages group is also temperature-dependent (mixed-phase cloud formed in the Hangzhou area. It was hook-shaped and the chamber measurements in China). It refers to the total IN echo top was 11 km high. At 15:23 CST, the multicellular storm concentration equation based on all nucleation mechanisms continued to develop and new cells were constantly generated (𝑇2 group) including the Chi NJ and Chi Mt. H schemes. to the northeast and merged into the storm. Ultimately, a eTh third group is related to the supersaturation relative to vault convection belt was formed in Hangzhou and strong the ice surface (𝑆 group) andincludes thePhilips andoriginal thunderstorms and winds occurred in the Hangzhou area. Meyers schemes. In the Pujiang County area at the southern end of the echo zone, another strong convection cell developed and moved eastwards with an intensity reaching 65 dBz. eTh top 4. Analysis of Numerical Simulation Results of the echo was 16 km and the convection was strong. At 4.1. Simulation and Analysis of Ground Rainfall. The main approximately 15:45 CST, ground hail occurred in Pujiang. precipitation area of this severe convective weather process At 17:42 CST, the multicellular storm scattered into several is northern and western Zhejiang Province and the rainfall strong convection cells with wide coverage and formed a bow belt follows a northeast–southwest direction. eTh six hours of echo. Its intensity was 60 dBz, and the maximum height of cumulative precipitation, beginning at 9:00 CST on Novem- the echo reached 14 km, corresponding to the thunderstorm ber 9, reached 67 mm (Figure 2(a)). A secondary precipitation weather occurring at 18:00 CST. Subsequently, the echo belt center was located in the Hangzhou area, where the cumula- weakened and moved eastwards into the sea, ending the tive rainfall reached 44 mm. The precipitation area and time strong convective weather in the Zhejiang Province. of occurrence of the simulated 6-h cumulative precipitation The simulated precipitation area and intensity of the (Figure 2(b)) were close to the actual condition. The precipita- maximum echo are consistent with the observation; both tion can be divided into two northeastern–southwestern rain showed eastward movement, which clearly indicates the belts located in central-northern and southeastern Zhejiang. evolution characteristics of the strong convection cell such as The northern precipitation belt was close to the actual formation, development, and mature stages. Slight differences situation and the location of the precipitation center was of the observation are a larger echo range and slower consistent. eTh maximum cumulative rainfall was 70 mm, simulateddevelopmentspeed ofthemulticellular storm. eTh closetothatobserved. eTh cumulative precipitationofthe simulationslaggedbehindthe actualstormbyapproximately largest precipitation center in Hangzhou was 70 mm, which one hour. was higher than that observed. 5. Comparative Experiment on the Effect of Ice 4.2. Characteristics and Simulation of Radar Echo Evolution. In this paper, the S-band of the newly generated Doppler Nuclei Parameterizations on the Convective weather radar (CINRAD/SA) data from the Hangzhou Sta- Precipitation Process tion was used. At 11:43 CST on November 9, a convection storm cell formed in the Huangshan area. At 12:17 CST, the 5.1. Comparison of Cumulative Ground Rainfall. Figure 4 convection cell moved northeast and the intensity reached shows the simulated 6-h cumulative ground precipitation at 6 Advances in Meteorology dBZ 31 N 30 N ∘ 65 29 N >75 Figure 3: Comparison of the radar reflectivity factor (top) between 12:00 and 18:00 CST on November 9 and the simulated maximum reflectivity (bottom; unit: dBz). ∘ ∘ ∘ 31 N 31 N 31 N ∘ ∘ ∘ 30 N 30 N 30 N ∘ ∘ ∘ 29 N 29 N 29 N ∘ ∘ ∘ 28 N 28 N 28 N Original Cooper Chi_NJ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ 118 E 119 E 120 E 121 E 122 E 123 E 118 E 119 E 120 E 121 E 122 E 123 E 118 E 119 E 120 E 121 E 122 E 123 E ∘ ∘ ∘ 31 N 31 N 31 N ∘ ∘ ∘ 30 N 30 N 30 N ∘ ∘ ∘ 29 N 29 N 29 N ∘ ∘ ∘ 28 N 28 N 28 N Phillips Fletcher Chi_Mt.H ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ 118 E 119 E 120 E 121 E 122 E 123 E 118 E 119 E 120 E 121 E 122 E 123 E 118 E 119 E 120 E 121 E 122 E 123 E Figure 4: Simulation of the 6-h cumulative precipitation at 20:00 CST on November 9 in the Zhejiang Province based on different ice nuclei parameterizations (unit: mm). 20:00 CST on November 9 based on the six IN parame- simulated with the Fletcher, Philips, and original schemes terizations. Compared with the observed precipitation (Fig- were the most accurate. Overall, the Fletcher scheme was ure 2(a)), all parameterizations simulated the trends and closest to the actual situation (followed by the original range of rainfall, but the rainfall intensity and heavy rainfall scheme) in terms of the simulated range, trend, and rainfall center of the rain belt were different. For the heavy rainfall intensity of the rain belt and the locations of the two centers, the results of the Fletcher, Chi Mt. H, and original precipitation centers matched the actual locations. schemes were the closest to the actual situation and the intensities were greater than 65 mm. In addition, the rainfall 5.2. Comparative Analysis of Ground Cumulative Hailfall. intensity predicted by the Chi NJ program was smaller, while According to the cumulative hailfall intensity and distribu- that by the Cooper scheme was larger. For the secondary tion, the simulation results of the Cooper scheme (Figure 5) precipitation center in Hangzhou, the intensity and location weretheleastaccurateandthecenterpositionofthestrongest 118 E 119 E 120 E 121 E 122 E 118 E 119 E 120 E 121 E 122 E 118 E 119 E 120 E 121 E 122 E 118 E 119 E 120 E 121 E 122 E Advances in Meteorology 7 ∘ ∘ ∘ 30 N 30 N 30 N ∘ ∘ ∘ 29.8 N 29.8 N 29.8 N Chi_NJ Original Cooper ∘ ∘ ∘ 29.6 N 29.6 N 29.6 N ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ 119 E 119.4 E 119.8 E 119 E 119.4 E 119.8 E 119 E 119.4 E 119.8 E ∘ ∘ ∘ 30 N 30 N 30 N ∘ ∘ ∘ 29.8 N 29.8 N 29.8 N Phillips Chi_Mt.H Fletcher ∘ ∘ ∘ 29.6 N 29.6 N 29.6 N ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ 119 E 119.4 E 119.8 E 119 E 119.4 E 119.8 E 119 E 119.4 E 119.8 E 0.25 0.5 1 1.5 2 2.5 3 3.5 Figure 5: Case study 09: Simulated ground cumulative hail volume in the Pujiang area at 17:00 CST based on different simulations (unit: mm). hailfall deviated to the west of Pujiang. eTh hail intensity temperature and therefore the highest concentration of initial 3 −1 in Pujiang was the smallest with respect to the other vfi e ice crystals was produced (110 × 10 L )and thewater −1 schemes. The Chi Mt. H scheme simulation results were also content was the highest (21 mg⋅kg ). In comparison, the IN not ideal, showing a deviation in the location of maximum concentration of the Fletcher scheme was the smallest and hailfall. eTh maximum hailfall locations simulated by the the applicable temperature range was smaller than that of the other schemes appeared in the Pujiang area. eTh original Cooper scheme. er Th efore, the number of initial ice crystals 3 −1 scheme showed the highest hail amount, while the Fletcher in this scheme was the smallest (51× 10 L )andthewater −1 scheme showed the smallest. content was the lowest (13 mg⋅kg ). The differences in the In summary, the effect of the IN concentration on ground concentration and water content of ice crystals between these hail is significantly stronger than that on ground rainfall. eTh 3 −1 −1 two schemes were 59× 10 L and 8 mg⋅kg ,respectively. IN influences the formation and growth of large ice particles by changing the ice crystals, which directly contributes to 5.3.2. Inu fl ence on the Vertical Distribution of Ice Crystals. The ground hail. For rainfall, only the strong precipitation center differences in the vertical distribution of ice crystals in each location and rainfall amount dieff r slightly; the difference scheme are due to the maximum values and water content does not exceed 5 mm. (Figure 6(c)). The Cooper scheme has the highest height of maximum value and water content of ice crystal. The Fletcher 5.3. Influence of Ice Nuclei Parameterizations on scheme has the lowest height of maximum value (8.5 km) the Microphysical Structure of Convective Clouds and water content of ice crystal. This difference is due to the Cooper scheme having a wider temperature range; there 5.3.1. Influence on the Spatial and Temporal Distribution of are more IN at lower temperatures and, in contrast, less IN Initial Ice Crystals. Figure 6 shows that the variation of the IN in the low temperature ranges in the Fletcher scheme. us, Th concentration directly aeff cts the spatiotemporal distribution the height at which ice crystals are present is related to the oftheinitial icecrystals(Figures6(a) and6(b)).Theice temperature range of ice nucleation activation. crystals generatedinthe hail cloudformation period are used as initial ice crystals and the average ice crystal water content and number concentration characteristics (Figures 5.3.3. Eeff cts of Dieff rent Ice Nuclei Parameterizations on the 6(a) and 6(b)) and vertical distributions of ice crystal water Spatial and Temporal Distribution of Other Hydrometeors. content (Figure 7) were analyzed. eTh results show that the The variation of the IN concentration leads to different average ice water content and number concentrations during changes of different types of hydrometeors. Ice crystals, as the period before 14:00 CST are as follows: (1) 𝑇1 group: the most basic ice-phase particles, change the temporal and Cooper > Fletcher; (2) 𝑇2 group: Chi NJ > Chi Mt. H; spatial distribution of other hydrometeors and the macrode- and (3)𝑆 group: the original program> Philips. Based on velopment and structure of the cloud by participating in the combination with the IN concentration (Figure 1), the the ice-phase transformation process. On the other hand, distribution of ice crystals is related to the IN distribution. these changes will in turn aeff ct the temporal and spatial A greater number of IN leads to a greater number of initial distribution of ice crystals through microphysical processes. ice crystals with higher water content. For example, the To understand the evolution of microphysical processes in ∘ ∘ applicable temperature range (from −40 Cto0 C) of ice the cloud, the spatiotemporal distributions of the airflow nucleation activation in the Cooper scheme is the largest. structureandhydrometeors inthecloudareanalyzedin The IN concentration increased rapidly with a decrease of detail. 8 Advances in Meteorology 11Z 12Z 13Z 14Z 15Z 16Z 11Z 12Z 13Z 14Z 15Z 16Z Time (h) Time (h) Original Fletcher Original Fletcher Phillips Chi_NJ Phillips Chi_NJ Cooper Chi_Mt.H Cooper Chi_Mt.H (a) (b) 0 20 40 60 80 100 120 140 −1 QICE (mg·kg ) Original Fletcher Phillips Chi_NJ Cooper Chi_Mt.H (c) ∘ ∘ ∘ ∘ −1 Figure 6: Variation of the regional (118.7 E–120.2 E, 29.4 N–30.4 N) average mixing ratio ((a) unit: mg⋅kg ) and the number concentrations 3 −1 ((b) unit: 10 L ) of ice crystals with time and the variation of the regional average mixing ratio with height from the hail cloud generation −1 to the extinction period ((c) unit: mg⋅kg ). (1) Influence on the Temporal and Spatial Distribution of Snow overtime wasanalyzed(Figure 8).Thepeakofsnowwater Particles. In combination with the temporal evolution of ice occurs slightly later than the peak of ice crystals. The lag of crystals, the macroscopic variation of the snow water content snow water indicates that initial ice crystals were formed first −1 QICE (mg·kg ) Height (km) −1 QNICE (g ) Advances in Meteorology 9 16 14:20 15:00 15:40 15:50 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (a) 16 13:40 15:00 15:30 14:40 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (b) T1 16 13:50 14:30 14:40 15:00 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (c) T1 16 13:30 15:20 15:50 16:00 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (d) Figure 7: Continued. Height (km) Height (km) Height (km) Height (km) 10 Advances in Meteorology T2 16 13:50 14:40 15:20 15:10 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (e) T2 16 13:50 14:40 15:30 15:40 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (f) −1 −1 Figure 7: Cross section of the simulated cloud water mixing ratio (solid line, minimum 0.1 g⋅kg ,interval1g⋅kg ), ice crystal mixing −1 −1 ratio (black dotted line, minimum 0.05 g⋅kg , interval 0.1 g⋅kg ), and updraft (colored) and isothermal line (dashed gray line) in latitudinal direction: (a) original; (b) Philips; (c) Cooper; (d) Fletcher; (e) Chi NJ; and (f) Chi Mt.H. andthengrewtoacertainsizethrough deposition, Bergeron, (2)Inufl enceontheTemporalandSpatialDistributionofCloud Water and Rainwater. The modified IN parameterization and collision processes and eventually transformed into snow schemes have the least eeff ct on the cloud water and rainwater particles. content. The cloud water content (Figure 7) during the initial The spatial distribution characteristics of snow particles stage is in the order of original program < Philips and at different stages were analyzed (Figure 9). In the develop- Cooper< FletcherbecauseagreaternumberofINeventually ing and early-mature stages of hail cloud, the snow water result in an increased number of ice crystals, thus raising the content is in the following order: (1)𝑇1 group: Cooper< consumption of liquid water in the cloud. Fletcher; (2) 𝑇2 group: Chi NJ < Chi Mt. H; and (3) 𝑆 group: original< Philips. This is opposite to the distribution With respect to the rainwater content (Figure 9), schemes of the ice crystal content in the previous section. eTh with more IN correspond to less rainwater below the 0 Clevel distribution and variation characteristics of the concentration before the maturation stage (e.g., Chi NJ< Chi Mt. H). As of snow particles are similar to those of the water content the IN concentration increases, many ice crystals compete (Figure 8(b)). for liquid water, consequently inhibiting the growth of ice particles. This is not conducive to the growth of large-sized ice The above-mentioned results show that the increase of particles and formation of raindrops and results in less rain. ice crystal caused by increased IN is not conducive to the In addition, the increased intensity of the updraft in the cloud formation and growth of snow particles. In case of a con- leadstoareducedliquidwater contentbelow the0 Clayer. stantamountofwater vapor,thewater vaporconsumption However, this trend is less pronounced aeft r the maturation increases with increasing formation of ice crystals. Many ice of the hail cloud. crystals compete for limited water vapor and cannot grow quickly to transform into snow crystals, thus inhibiting the production and growth of snow particles, resulting in a lower (3) Inuence fl on the Spatial and Temporal Distribution of Hail amount of snow particles. Embryos (Graupel) and Hail. The relationship between ice Height (km) Height (km) Advances in Meteorology 11 3.0 2.4 1.8 1.2 20 0.6 0.0 11Z 12Z 13Z 14Z 15Z 16Z 11Z 12Z 13Z 14Z 15Z 16Z Time (h) Time (h) Original Fletcher Original Fletcher Phillips Chi_NJ Phillips Chi_NJ Cooper Chi_Mt.H Cooper Chi_Mt.H (a) (b) ∘ ∘ ∘ ∘ −1 Figure 8: Variation of the regional average (118.7 E–120.2 E, 29.4 N–30.4 N) mixing ratio ((a) unit: mg⋅kg ) and concentration ((b) unit: 3 −1 10 L ) of snow particles with time. nuclei and graupel/hail is even more complex. Hail embryos concentration corresponds to a larger hail water content in (graupel)formedinthehail clouddevelopingstage andthen the maturation stage and greater amount of hail falling to the enlarged and developed into hail. eTh hail then grew to a large ground. enough size and started falling in the early-mature stage of the eTh increase of the IN concentration has a certain hail cloud. After hail fell on the ground, the hail cloud entered inhibitory effect on the growth of hail embryo (graupel; in the later maturation stage. the development and early maturity stages). However, when The variation of graupel in each group is similar to that hail grew to the hail precipitation stage (later period of the of snow particles from hail embryo formation to hailfall mature stage), the scheme in which the hail content was (Figure 10). For example, in the𝑇2 group Chi NJ< Chi Mt. higher corresponds to a higher IN concentration, indicating H; that is, the graupel water content of the scheme cor- that an increased IN concentration is favorable for later hail responding to more abundant IN is smaller than that of growth. In the hail embryo formation and growth stages, the the scheme corresponding to fewer IN. With increasing IN increased IN concentration is not conducive to the growth concentration, the early formation of graupel is inhibited and of hail embryos due to reasons similar to those presented the stronger updraft in the later stages of hail cloud may for the above-mentioned snow particle suppression; that is, promote the transformation of snow to graupel particles. a large number of small ice particles compete for water As a result, the snow water content in the mature stage resources, therefore making it more difficult to grow into is noticeablyreducedsuchasintheCooper andChi NJ large ice particles, leading to lower graupel water contents. schemes (Figure 10). More importantly, the amount of snow particles is relatively eTh hail particles in each group show a variation trend small. Therefore, the corresponding graupel transformed by opposite to that of above-mentioned graupel particles in the snow particle growth decreases. However, at the same time, early or later periods of the mature stage (Figure 11). This is the intensity of updraft in the cloud increases, which may be consistent with the results of van den Heever et al. [9] and duetosignicfi antlatentheatreleasedbyphasetransformation Carrio et al. [10] who studied the eeff cts of the increase of IN (such as deposition, freezing), consequently strengthening on graupel and hail in a severe precipitation case in Florida. the convection in clouds [5, 45]. In this study, the hail water content in the𝑆 group is in the When graupel grows into hail, the increase in the updraft order of original> Philipsschemeinthe earlystage ofhail velocity will transport more supercooled water above the cloud maturation. In the later maturation stage of the hail 0 C level and lead to the growth of graupel and hail by cloud, the hail content in the𝑇1 and𝑇2 groups showed the collision with supercooled water, which is indicated by the following order: Cooper> Fletcher and Chi NJ> Chi Mt. apparent supercooled water consumption in the Fletcher H. The results demonstrate that the scheme with higher IN scheme (Figure 7). Otherwise, the updraft is strong enough to −1 QSNOW (mg·kg ) −1 QNSNOW (g ) 12 Advances in Meteorology 16 14:20 15:00 15:40 15:50 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (a) 13:40 14:40 15:00 15:30 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (b) T1 16 13:50 14:30 14:40 15:00 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (c) T1 16 13:30 15:20 15:50 16:00 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (d) Figure 9: Continued. Height (km) Height (km) Height (km) Height (km) Advances in Meteorology 13 T2 16 13:50 14:40 15:10 15:20 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (e) T2 16 13:50 14:40 15:30 15:40 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (f) −1 −1 Figure 9: Cross section of the simulated rain water mixing ratio (solid line, minimum 0.1 g⋅kg ,interval2.5g⋅kg ), snow water mixing −1 −1 ratio (black dotted line, minimum 0.1 g⋅kg , interval 0.3 g⋅kg ), and updraft (colored) and isothermal line (dashed gray line) in latitudinal direction: (a) original; (b) Philips; (c) Cooper; (d) Fletcher; (e) Chi NJ; and (f) Chi Mt. H. allowhailtohavemoretimetoencountersupercooledwater occurred was determined by the temperature range of the ice droplets, allowing hail particles to grow. nucleation activation. eTh effect of the IN concentration on the cloud water and rainwater content was relatively small. As the IN concentration increases, a large number of ice 6. Summary and Discussion crystals is generated, which compete for water vapor, and the (1)Withrespect to thehailprocess simulatedinthiswork, rapid growth of ice crystals through the Bergeron process theinufl enceofthe IN concentrationongroundhailfallis consumes a large number of supercooled water droplets, significantly stronger than that on ground rainfall. er Th e which indirectly aeff cts the variation of the cloud water and were significant differences in hailfall region and intensity. rainwater content. For rainfall, it only led to slightly different locations of (2)Inthiscase,theinufl enceofvaryingINconcentrations the strong precipitation center and rainfall amount and the at different stages of the hail cloud also differ. The IN changes difference of rainfall did not exceed 5 mm. The changes of the temporal and spatial distribution of other hydrometeors the IN concentration caused different changes of hydrome- and dynamic structure in the cloud by ice crystal variation. teors in the cloud. The effect on ice crystals was the most Generally, in the developing and early maturation stages of signicfi ant. eTh larger the amount of IN is, the greater hail development, a larger number of ice crystals compete for the initial concentration of ice crystals and water content limitedwater vaporandcloudwater astheINconcentration of ice crystals is. The difference of the ice crystal water increases, therefore preventing the growth of ice crystals content, calculated based on the Cooper scheme with the and slowing down their transformation to snow crystals. highest IN concentration and the Fletcher scheme with the This indirect dependency has an inhibitory eeff ct on the −1 smallest IN concentration, was 9 mg⋅kg . eTh difference in formation and growth of snow/hail embryos. During the later 3 −1 the ice crystal concentration was 95× 10 L . In addition, maturation stage, updraft in the cloud increases, transporting theheightatwhichthemaximum icecrystalwatercontent more supercooled water above the 0 C level. On the other Height (km) Height (km) 14 Advances in Meteorology 14:20 15:00 15:40 15:50 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 (a) 13:40 14:40 15:00 15:30 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 (b) T1 13:50 14:30 14:40 15:00 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 (c) T1 13:30 15:20 15:50 16:00 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 (d) T2 13:50 14:40 15:10 15:20 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 (e) Figure 10: Continued. Height (km) Height (km) Height (km) Height (km) Height (km) Advances in Meteorology 15 T2 13:50 14:40 15:30 15:40 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 (f) −1 −1 Figure 10: Cross section of the simulated graupel water mixing ratio (solid line, minimum 0.5 g ⋅kg ,interval3g⋅kg ), UW wind vector, and 0 C line (thin solid line) in latitudinal direction based on the different sch emes: (a) the original scheme; (b) Philips; (c) Cooper; (d) Fletcher; (e) Chi NJ; and (f) Chi Mt. H. S S 16 16 14 14 12 12 10 10 8 8 6 6 4 4 2 2 0 0 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 (a) (b) T1 T1 16 16 14 14 12 12 10 10 8 8 6 6 4 4 2 2 0 0 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 (c) (d) T2 T2 16 16 14 14 12 12 10 10 8 8 6 6 4 4 2 2 0 0 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 (e) (f) −1 −1 Figure 11: Cross section of the simulated hail water mixing ratio (solid line, minimum 0.1 g⋅kg ,interval1g⋅kg ), UW wind vector, and 0 C line (thin solid line) in latitudinal direction based on the different sch emes: (a) original scheme; (b) Philips; (c) Cooper; (d) Fletcher; (e) Chi NJ; and (f) Chi Mt. H. Height (km) Height (km) Height (km) Height (km) Height (km) Height (km) Height (km) 16 Advances in Meteorology hand,thisallowsmoretimeforhailparticlestogrowvia [10] G. G. Carrio, ´ S. C. van den Heever, and W. R. 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Influence of Ice Nuclei Parameterization Schemes on the Hail Process

Advances in Meteorology , Volume 2018: 17 – Feb 20, 2018

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Copyright © 2018 Xiaoli Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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10.1155/2018/4204137
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Abstract

Hindawi Advances in Meteorology Volume 2018, Article ID 4204137, 17 pages https://doi.org/10.1155/2018/4204137 Research Article Influence of Ice Nuclei Parameterization Schemes on the Hail Process 1,2 3 4 5 Xiaoli Liu , Ye Fu, Zhibin Cao, and Shuanglong Jin Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China Xianyang Meteorology Bureau, Xianyang 712000, China Purple Mountain Observatory, Nanjing 210008, China State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing 100192, China Correspondence should be addressed to Xiaoli Liu; liuxiaoli2004y@nuist.edu.cn and Shuanglong Jin; jinshuanglong@epri.sgcc.com.cn Received 14 August 2017; Revised 7 December 2017; Accepted 21 December 2017; Published 20 February 2018 Academic Editor: Bin Yong Copyright © 2018 Xiaoli Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Ice nuclei are very important factors as they significantly aeff ct the development and evolvement of convective clouds such as hail clouds. In this study, numerical simulations of hail processes in the Zhejiang Province were conducted using a mesoscale numerical model (WRF v3.4). eTh effects of six ice nuclei parameterization schemes on the macroscopic and microscopic structures of hail clouds were compared. eTh effect of the ice nuclei concentration on ground hailfall is stronger than that on ground rainfall. There were significant spatiotemporal, intensity, and distribution differences in hailfall. Changes in the ice nuclei concentration caused different changes in hydrometeors and directly aeff cted the ice crystals, and, hence, the spatiotemporal distribution of other hydrometeors and the thermodynamic structure of clouds. An increased ice nuclei concentration raises the initial concentration of ice crystals with higher mixing ratio. In the developing and early maturation stages of hail cloud, a larger number of ice crystals competed for water vapor with increasing ice nuclei concentration. This effect prevents ice crystals from maturing into snow particles and inhibits the formation and growth of hail embryos. During later maturation stages, updraft in the cloud intensified and more supercooled water was transported above the 0 C level, benett fi ing the production and growth of hail particles. An increased ice nuclei concentration therefore favors the formation of hail. 1. Introduction are affected by changes in the atmospheric thermodynamic environment through artificially propagating IN that can Statistics show that over 50% of mid-latitude precipitation is grow into ice crystals. This consequently aeff cts the latent heat caused by the melting of large ice particles produced during release rates, dynamic processes of strong convective clouds, the ice-phase transformation process; this ratio is slightly and resultant precipitation particles [5]. lower (∼30%) in the tropics [1]. The microphysical process Giventhefact that eeff ctofINonstrongconvective of ice-phase transformation in clouds plays a distinct role in clouds is extremely complex, there are no definite conclusions the formation of precipitation particles. u Th s, the ice crystal abouttheneteeff ctsoftheconcentrationofINonstrong nucleation processisveryimportant [2–4]. Manyscholars convective clouds [6]. Some studies suggest that an increase have studied the distribution characteristics of ice nuclei (IN) in the IN concentration favors ground precipitation. For example, a study focusing on the period between the 1940s and their effects on clouds and precipitation using a com- bination of observations and numerical simulation results. and the 1960s found that the concentration of ice crystals eTh results showed that microphysical processes in clouds in cloud with a large volume of aerosols (especially IN) 2 Advances in Meteorology transported by upstream winds had increased, leading to be more suitably described by supersaturation with respect increasing rainfall and hail in Indiana, which was aeff cted to the ice surface [22–24]. This means that, under different by urban air pollution during this period [7]. Khain et supersaturation conditions, IN have different surface prop- al. [8] stated that the latent heat released by frozen small erties and the same types of IN can only simultaneously cloud droplets enhances the convection in clouds as the IN nucleate under the same environmental conditions. concentration increases in mixed-phase convection clouds, The heterogeneous ice-phase nucleation process is not resulting in increased precipitation. However, sensitivity tests simply determined by the temperature or ice surface super- of the IN concentration of strong convective clouds showed saturation. In 1985, Vali [25] proposed for the first time four opposite effects. van den Heever et al. [9] and Carri oe ´ t different mechanisms of heterogeneous ice-phase nucleation al. [10] discussed the eeff cts of IN on strong convective including deposition, immersion-freezing, condensation- clouds in Florida using the Regional Atmospheric Modeling freezing, and contact-freezing nucleation. However, the rel- System (RAMS), a highly versatile numerical code developed ative importance of these mechanisms has not been deter- by scientists at Colorado State University to simulate and mined. Relevant studies suggest that [26, 27] immersion- forecast meteorological phenomena [11]. eTh results showed freezing and contact-freezing nucleation are most important thatthenumberofgraupel particlesdecreases andthatofhail for mixed-phase clouds, while deposition nucleation is more particles increases when the aerosol concentration increases. important in ice clouds above the troposphere. Other studies In addition, an increased IN concentration in the early stages showed that immersion-freezing and condensation-freezing of convective clouds leads to increased ground precipitation. nucleation play a dominant role in the freezing mechanism However, in the later stages, the aerosol concentration is the [28]. lowest when the accumulated precipitation reaches its max- eTh prediction of the IN concentration in the model is imum. Connolly et al. [12] argued that the “frozen indirect very complex and difficult due to the lack of understanding of effect” produced by the increase in the IN concentration is the relative importance of the four heterogeneous ice crystal very small. nucleation mechanisms. Meyers et al. [11] first developed Note that the inu fl ence of the IN concentration on empirical relationships for different nucleation mechanisms, convective precipitation is very complex and may differ from which are still widely used in models [9, 10]. one location and/or case to another. Observatory IN param- The deposition and condensation-freezing processes are eterization reflects the natural distribution and properties of related to the surface supersaturation relative to ice. Water IN under certain conditions and locations, based on which vapor molecules attach to IN and then form ice crystals the ice phases in convective clouds initiate and evolve. u Th s, through the deposition process, that is, the heterogeneous it is meaningful to use observatory IN parameterization deposition nucleation process. If the aerosol has both char- to explore the role of IN in the development of severe acteristics of cloud condensation nuclei and IN, water vapor precipitation. molecules rfi st attach to the aerosol and form liquid water droplets, which then freeze into ice crystals, that is, the het- erogeneous condensation-freezing nucleation process. eTh 2. Observation and Brief Review of empiricalfittingequations forthetwoprocesses canbe Parameterization Schemes of Ice Nuclei expressed as Several IN parameterization schemes have been developed to 𝑁 = exp{𝑎+𝑏 [100(𝑆 −1)]}, (1) 𝑖𝑑 𝑖 describe the IN concentration and heterogeneous nucleation process of ice crystals. eTh tfi ting equation for the IN where 𝑁 is the concentration of the deposition and 𝑖𝑑 concentration obtained from observations is widely used in condensation-freezing nuclei per liter of air,𝑆 is the satura- the model because of its simplicity. In the 1960s, Fletcher tion relative to the ice surface,𝑎 = −0.639, 𝑏 = 0.1296,and [13] used the static filter method for the rfi st time and ∘ ∘ the applicable temperature range is−5 Cto−20 C. obtained an empirical equation for the IN concentration No separate observations on contact-freezing nuclei con- that is only relatedtotemperature.However,the nucleation centration had been obtained until Cooper and Saunders [15] process treated by this diagnostic framework overestimates conducted an observation, estimating the concentration of the concentration of ice crystals and lacks sensitivity to contact-freezing nuclei. Based on Cooper’s observation, Mey- changes in saturation. Several studies also suggested that ers et al. developed a tfi ting equation for the concentration the nucleation capacity of IN is temperature-dependent of contact-freezing nuclei, which are temperature-dependent [14–16]. Relevant research discussing the atmospheric IN [11]: concentration and its variation has also been conducted in China. You et al. [17–19] observed the concentrations of IN 𝑁 = exp[𝑎+𝑏 (273.15−𝑇 )]. (2) 𝑖𝑐 𝑐 and ice crystals in Beijing and Jilin, respectively, and obtained an empirical tt fi ing equation for the correlation between the If the aerosol diameter is 0.1𝜇m, 𝑇 is the cloud droplet IN concentration and temperature. The characteristics of IN temperature (K),𝑎 = −2.80, 𝑏 = 0.262,and theunitof 𝑁 𝑖𝑐 −1 and their influence on the ice crystal concentration were also is L . discussed. Following Meyers, the IN parameterization has been Many observations have proven that the concentration developed [29, 30]. China’s meteorological researchers car- of ice crystals in clouds is not strictly determined by the ried out a series of IN observation experiments in the upper temperature [20, 21]. eTh deposition nucleation process can reaches of the Yellow River [31, 32]. It was believed that Advances in Meteorology 3 the concentration of IN at different locations showed large based on the Ziegler framework [43], which can be used uc fl tuations, leading to changes in the empirical relationship to predict the average graupel particle density. eTh particles of atmospheric IN concentrations [33–36]. ranging from frozen drops to low-density hail embryos In 2007, Phillips et al. [37] developed an improved IN are collectively called graupels. Other physical processes concentration tfi ting equation based on Meyers’ research were selected as follows: the Monin–Obukhov surface layer by adding the empirical equation developed by DeMott scheme, the Mellor–Yamada–Janjic (MYJ) boundary layer et al. [38] using the continuous-flow diffusion chamber scheme,theNoahlandsurfacescheme,therapidandaccurate (CFDC) observed at the top of Mount Werner, Colorado, radiative transfer model (RRTM) for longwave radiation and extending the applicable temperature range of IN acti- scheme, and the Dudhia for shortwave radiation scheme. vation. This tfi ting showed that the IN concentration in the The Kain–Fritsch (new Eta) scheme was used for cumulus continental boundary layer calculated by Meyers’ empirical convection parameterization in the D01 area, while the D02 formula is significantly higher than that of the interior of the area was closed. troposphere (especially at high elevations far from the land source). 3.2. Introduction of Ice Nuclei Parameterization Schemes Phillips et al. [39] considered the effects of the phys- 3.2.1. Ice Crystal Nucleation Process in the NSSL Framework. ical and chemical properties and surface area of IN and In NSSL,three typesofnucleationoficecrystals viaINare developed a new IN concentration equation. Connolly et al. considered: deposition, condensation-freezing, and contact- [12] developed a correlation equation between the density freezing (heterogeneous ice-phase nucleation). of nuclei surface activation sites and IN concentration by studying the immersion-freezing nucleation of dust particles. Recently, Chinese meteorological researchers developed a (1) Deposition and Condensation-Freezing Nucleation. There new empirical tfi ting equation for IN concentration using are two scenarios for calculating the IN concentration of the observation data from Su [40] and Yang et al.[41]. deposition and condensation-freezing processes. First, when the temperature is less than−5 C, the empirical relationship of Meyers et al. [11] is used. Second, when the temperature is 3. Introduction of the Case Study and greaterthanorequal to−5 C, the equation given in Cotton et Simulation Framework al. [44] is used: 3.1. Introduction of the Case Study and Models. Strong con- 4.5 (𝑞 −𝑞 ) { V is ∘ vective weather occurred in a large area of the Zhejiang 𝑛 [ ] exp(−0.6𝑇 ),𝑇≥−5 C 𝑖𝑜 𝑁 = (3) (𝑞 −𝑞 ) Province on November 9, 2009. From 06:00 China Standard 𝑖 { ws is Time (CST) to 12:00 CST, thick fog appeared in some areas exp[12.96(𝑆 −1)−0.639], 𝑇<−5 C, of eastern, northern, and central Zhejiang and lasted for up where𝑞 is the water vapor mixing ratio,𝑞 and𝑞 are the V is ws to six hours. under Th storms and strong winds occurred aer ft saturation ratios relative to ice and water, respectively, and 15:00 CST throughout the province, representing the most 𝑆 =𝑞 /𝑞 is the saturation relative to the ice surface. 𝑖 V is extensive and strongly convective weather that occurred in theautumnofthatyear. In addition,hailfellat15:45CST (2) Contact-Freezing Nucleation. The parameter 𝑁 is the in thePujiangCounty andother places.Thehaildiameter −3 activated contact nuclei concentration (unit: m ) expressed reached 1.5 cm. as𝑁 = exp(4.11−0.262𝑇 ) and𝑇 is the cloud droplet In this paper, numerical simulations using the WRF v3.4 𝑖 𝑐 𝑐 temperature. mesoscale model were conducted. Six-hourly global reanaly- ∘ ∘ In most of the two-moment microphysical schemes, the sis data (1×1 ) from the National Centers for Environmental IN concentration is used to predict the concentration and Prediction (NCEP) were selected as the initial field and mixing ratio of ice crystals. eTh calculation of the IN con- boundary data. eTh integration time of the hail process in centration is based on simple empirical equations, as shown themodelrangedfrom 08:00to20:00CSTonNovember above, and the initial ice crystal concentrations produced by 9, 2009. eTh integration time step was 36 s. eTh latitude and ∘ ∘ heterogeneous nucleation are obtained by calculation. longitude of the simulation center were 29.60 Nand119.80 E, respectively. Double bidirectional nesting was adopted in the model. From the coarse to ne fi D01 and D02 areas, the grid 3.2.2. Sensitivity Test of the Ice Crystal Nucleation Process. To resolution was 6.2 km and the numbers of grid points were better understand the effect of IN on cloud microphysical 150× 160 and 262× 280, respectively. Twenty-seven layers processes, vfi e additional IN parameterization schemes were were used vertically. eTh microphysical cloud scheme was employed in this study in addition to the original one. an NSSL two-moment scheme [42] developed by the US Through comparative experiments, the effects of the six National Severe Storms Laboratory (NSSL) in 2010, which is IN parameterizations on the spatiotemporal distribution of ahybridphase cloudframework.Withrespect to microphys- ice crystals and other hydrometeors were discussed. The ical cloud processes, the model predicts the specific water effects of different IN parameterizations on microphysical content (Qc, Qr, Qi, Qs, Qg,and Qh) and concentrations processes of hail clouds and the occurrence of hailstorms (Nc, Nr, Ni, Ns, Ng,and Nh)ofsix typesofhydrometeors were discussed. eTh following presents an overview of the (cloud water, rainwater, ice crystals, snow, graupel, and hail vfi eINschemesproposed to besimulatedand theapplicable particles). eTh NSSL framework is an improved framework conditions. 4 Advances in Meteorology 2 3 10 10 −20.0 −19.8 −19.6 −1 −2 −3 −40 −36 −32 −28 −24 −20 −16 −12 −8 −4 0 5 1015202530 Temperature ( C) Ice supersaturation (%) Original (T< −5 C) Fletcher (−27 ≤ T ≤ 0 C) Phillips (−30 ≤ T < −5 C) Cooper (−40 ≤ T ≤ 0 C) Phillips (−80 < T < −30 C) Chi_Mt. Hu (−25 ≤ T ≤ −10 C) Chi_NJ (−25 ≤ T ≤ −10 C) (a) (b) −1 ∘ Figure 1: Variation of the ice nuclei concentration (unit: L ) depending on the activation temperature; (a)𝑇1 and𝑇2 groups (unit: C) and ice surface supersaturation; (b)𝑆 group (unit: %). (1) eTh Fletcher [13] Scheme (4) eTh Huang Mountain Observation (Chi Mt. H) Scheme [40]. As differences in the IN concentration are significant −5 (4) 𝑁 =10 × exp(−0.6×𝑇 ). 𝑖 due to the influence of regional and meteorological factors, the empirical relationship of the Chinese IN observation The parameter 𝑁 isthenumberofactivated IN in the was introduced. Su [40] observed IN at the top of the −1 unit volume (L ),𝑇 is the corresponding temperature when Huang Mountain in Anhui, China, using a 5 L Bigg mixing the IN is activated, and the applicable temperature ranges cloud chamber. eTh observation period was May to October ∘ ∘ from−27 Cto0 C. The concentration of IN calculated by 2011 and the total IN concentration equation (temperature this scheme is lower than the value observed at higher ∘ ∘ range from−25 Cto−10 C) was developed by tfi ting, con- temperatures and the calculated concentration is higher when sidering four types of nucleation mechanisms (deposition, the temperature is relatively low (−25 C). condensation-freezing, contact-freezing, and immersion- freezing): (2) eTh Cooper [15] Scheme 𝑁 =0.0046× exp(−0.388×𝑇 ). (7) 𝑁 =0.005× exp(−0.304×𝑇 ). (5) 𝑖 ∘ ∘ eTh applicable temperature ranges from −40 Cto0 C. (5) eTh Nanjing Observation (Chi NJ) Scheme [41]. Yang et al. [41] performed observations using a 5 L Bigg mixed (3) eTh Phillips et al. [39] Scheme. Phillips et al. [39] inte- cloud chamber in Nanjing, China. The observation period grated and improved the Meyers [11] and DeMott schemes was summer 2011 and a total ice nucleation concentration ∘ ∘ [38]. eTh empirical correction factor Ψ was added and the equation (temperature ranging from−25 Cto−10 C) based applicable temperature range of ice nucleation activation was on the four nucleation mechanisms was developed: extended: 𝑁 =0.0049× exp(−0.388×𝑇 ). (8) To understand the microphysical characteristics of clouds (6) exp[−0.639+12.96(𝑆 −1)]×Ψ, −30≤𝑇<−5 and the response of precipitation to variations in the IN { 0.3 concentration, the six ice nucleation schemes were divided {exp[12.96(𝑆 −1.1)]} , −80<𝑇<−30, into three groups according to the calculation method of where the empirical correction factor Ψ = 0.06 and 𝑆 the IN concentration (Figure 1) and the temporal and spatial represents the surface saturation relative to ice. hydrometeor evolution were compared. Ice-nucleus concentration (/m ) Ice-nucleus concentrations (/m ) Advances in Meteorology 5 (b) ∘ (a) ∘ 31 N 31 N 65 65 ∘ ∘ 30 N 30 N 45 45 ∘ ∘ 29 N 29 N 25 25 ∘ ∘ 28 N 28 N 5 5 ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ 118 E 119 E 120 E 121 E 122 E 123 E 118 E 119 E 120 E 121 E 122 E 123 E (a) (b) Figure 2: Six-hour cumulative precipitation in Zhejiang at 20:00 CST on November 9; (a) observed and (b) simulated (unit: mm). eTh black dot represents the Pujiang area and the black triangle represents the Hangzhou area. In the rfi st group, the IN concentration is temperature- 65 dBz (Figure 3). Subsequently, this strong convective cloud dependent, which is the classical tfi (𝑇1 group) suitable for the continuedtomoveeastwards whilemerging withcellsinthe deposition and condensation-freezing nucleation processes front and strengthening. At 13:42 CST, a multicellular storm including the Fletcher and Cooper schemes. eTh second composed of convection cells at different development stages group is also temperature-dependent (mixed-phase cloud formed in the Hangzhou area. It was hook-shaped and the chamber measurements in China). It refers to the total IN echo top was 11 km high. At 15:23 CST, the multicellular storm concentration equation based on all nucleation mechanisms continued to develop and new cells were constantly generated (𝑇2 group) including the Chi NJ and Chi Mt. H schemes. to the northeast and merged into the storm. Ultimately, a eTh third group is related to the supersaturation relative to vault convection belt was formed in Hangzhou and strong the ice surface (𝑆 group) andincludes thePhilips andoriginal thunderstorms and winds occurred in the Hangzhou area. Meyers schemes. In the Pujiang County area at the southern end of the echo zone, another strong convection cell developed and moved eastwards with an intensity reaching 65 dBz. eTh top 4. Analysis of Numerical Simulation Results of the echo was 16 km and the convection was strong. At 4.1. Simulation and Analysis of Ground Rainfall. The main approximately 15:45 CST, ground hail occurred in Pujiang. precipitation area of this severe convective weather process At 17:42 CST, the multicellular storm scattered into several is northern and western Zhejiang Province and the rainfall strong convection cells with wide coverage and formed a bow belt follows a northeast–southwest direction. eTh six hours of echo. Its intensity was 60 dBz, and the maximum height of cumulative precipitation, beginning at 9:00 CST on Novem- the echo reached 14 km, corresponding to the thunderstorm ber 9, reached 67 mm (Figure 2(a)). A secondary precipitation weather occurring at 18:00 CST. Subsequently, the echo belt center was located in the Hangzhou area, where the cumula- weakened and moved eastwards into the sea, ending the tive rainfall reached 44 mm. The precipitation area and time strong convective weather in the Zhejiang Province. of occurrence of the simulated 6-h cumulative precipitation The simulated precipitation area and intensity of the (Figure 2(b)) were close to the actual condition. The precipita- maximum echo are consistent with the observation; both tion can be divided into two northeastern–southwestern rain showed eastward movement, which clearly indicates the belts located in central-northern and southeastern Zhejiang. evolution characteristics of the strong convection cell such as The northern precipitation belt was close to the actual formation, development, and mature stages. Slight differences situation and the location of the precipitation center was of the observation are a larger echo range and slower consistent. eTh maximum cumulative rainfall was 70 mm, simulateddevelopmentspeed ofthemulticellular storm. eTh closetothatobserved. eTh cumulative precipitationofthe simulationslaggedbehindthe actualstormbyapproximately largest precipitation center in Hangzhou was 70 mm, which one hour. was higher than that observed. 5. Comparative Experiment on the Effect of Ice 4.2. Characteristics and Simulation of Radar Echo Evolution. In this paper, the S-band of the newly generated Doppler Nuclei Parameterizations on the Convective weather radar (CINRAD/SA) data from the Hangzhou Sta- Precipitation Process tion was used. At 11:43 CST on November 9, a convection storm cell formed in the Huangshan area. At 12:17 CST, the 5.1. Comparison of Cumulative Ground Rainfall. Figure 4 convection cell moved northeast and the intensity reached shows the simulated 6-h cumulative ground precipitation at 6 Advances in Meteorology dBZ 31 N 30 N ∘ 65 29 N >75 Figure 3: Comparison of the radar reflectivity factor (top) between 12:00 and 18:00 CST on November 9 and the simulated maximum reflectivity (bottom; unit: dBz). ∘ ∘ ∘ 31 N 31 N 31 N ∘ ∘ ∘ 30 N 30 N 30 N ∘ ∘ ∘ 29 N 29 N 29 N ∘ ∘ ∘ 28 N 28 N 28 N Original Cooper Chi_NJ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ 118 E 119 E 120 E 121 E 122 E 123 E 118 E 119 E 120 E 121 E 122 E 123 E 118 E 119 E 120 E 121 E 122 E 123 E ∘ ∘ ∘ 31 N 31 N 31 N ∘ ∘ ∘ 30 N 30 N 30 N ∘ ∘ ∘ 29 N 29 N 29 N ∘ ∘ ∘ 28 N 28 N 28 N Phillips Fletcher Chi_Mt.H ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ 118 E 119 E 120 E 121 E 122 E 123 E 118 E 119 E 120 E 121 E 122 E 123 E 118 E 119 E 120 E 121 E 122 E 123 E Figure 4: Simulation of the 6-h cumulative precipitation at 20:00 CST on November 9 in the Zhejiang Province based on different ice nuclei parameterizations (unit: mm). 20:00 CST on November 9 based on the six IN parame- simulated with the Fletcher, Philips, and original schemes terizations. Compared with the observed precipitation (Fig- were the most accurate. Overall, the Fletcher scheme was ure 2(a)), all parameterizations simulated the trends and closest to the actual situation (followed by the original range of rainfall, but the rainfall intensity and heavy rainfall scheme) in terms of the simulated range, trend, and rainfall center of the rain belt were different. For the heavy rainfall intensity of the rain belt and the locations of the two centers, the results of the Fletcher, Chi Mt. H, and original precipitation centers matched the actual locations. schemes were the closest to the actual situation and the intensities were greater than 65 mm. In addition, the rainfall 5.2. Comparative Analysis of Ground Cumulative Hailfall. intensity predicted by the Chi NJ program was smaller, while According to the cumulative hailfall intensity and distribu- that by the Cooper scheme was larger. For the secondary tion, the simulation results of the Cooper scheme (Figure 5) precipitation center in Hangzhou, the intensity and location weretheleastaccurateandthecenterpositionofthestrongest 118 E 119 E 120 E 121 E 122 E 118 E 119 E 120 E 121 E 122 E 118 E 119 E 120 E 121 E 122 E 118 E 119 E 120 E 121 E 122 E Advances in Meteorology 7 ∘ ∘ ∘ 30 N 30 N 30 N ∘ ∘ ∘ 29.8 N 29.8 N 29.8 N Chi_NJ Original Cooper ∘ ∘ ∘ 29.6 N 29.6 N 29.6 N ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ 119 E 119.4 E 119.8 E 119 E 119.4 E 119.8 E 119 E 119.4 E 119.8 E ∘ ∘ ∘ 30 N 30 N 30 N ∘ ∘ ∘ 29.8 N 29.8 N 29.8 N Phillips Chi_Mt.H Fletcher ∘ ∘ ∘ 29.6 N 29.6 N 29.6 N ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ 119 E 119.4 E 119.8 E 119 E 119.4 E 119.8 E 119 E 119.4 E 119.8 E 0.25 0.5 1 1.5 2 2.5 3 3.5 Figure 5: Case study 09: Simulated ground cumulative hail volume in the Pujiang area at 17:00 CST based on different simulations (unit: mm). hailfall deviated to the west of Pujiang. eTh hail intensity temperature and therefore the highest concentration of initial 3 −1 in Pujiang was the smallest with respect to the other vfi e ice crystals was produced (110 × 10 L )and thewater −1 schemes. The Chi Mt. H scheme simulation results were also content was the highest (21 mg⋅kg ). In comparison, the IN not ideal, showing a deviation in the location of maximum concentration of the Fletcher scheme was the smallest and hailfall. eTh maximum hailfall locations simulated by the the applicable temperature range was smaller than that of the other schemes appeared in the Pujiang area. eTh original Cooper scheme. er Th efore, the number of initial ice crystals 3 −1 scheme showed the highest hail amount, while the Fletcher in this scheme was the smallest (51× 10 L )andthewater −1 scheme showed the smallest. content was the lowest (13 mg⋅kg ). The differences in the In summary, the effect of the IN concentration on ground concentration and water content of ice crystals between these hail is significantly stronger than that on ground rainfall. eTh 3 −1 −1 two schemes were 59× 10 L and 8 mg⋅kg ,respectively. IN influences the formation and growth of large ice particles by changing the ice crystals, which directly contributes to 5.3.2. Inu fl ence on the Vertical Distribution of Ice Crystals. The ground hail. For rainfall, only the strong precipitation center differences in the vertical distribution of ice crystals in each location and rainfall amount dieff r slightly; the difference scheme are due to the maximum values and water content does not exceed 5 mm. (Figure 6(c)). The Cooper scheme has the highest height of maximum value and water content of ice crystal. The Fletcher 5.3. Influence of Ice Nuclei Parameterizations on scheme has the lowest height of maximum value (8.5 km) the Microphysical Structure of Convective Clouds and water content of ice crystal. This difference is due to the Cooper scheme having a wider temperature range; there 5.3.1. Influence on the Spatial and Temporal Distribution of are more IN at lower temperatures and, in contrast, less IN Initial Ice Crystals. Figure 6 shows that the variation of the IN in the low temperature ranges in the Fletcher scheme. us, Th concentration directly aeff cts the spatiotemporal distribution the height at which ice crystals are present is related to the oftheinitial icecrystals(Figures6(a) and6(b)).Theice temperature range of ice nucleation activation. crystals generatedinthe hail cloudformation period are used as initial ice crystals and the average ice crystal water content and number concentration characteristics (Figures 5.3.3. Eeff cts of Dieff rent Ice Nuclei Parameterizations on the 6(a) and 6(b)) and vertical distributions of ice crystal water Spatial and Temporal Distribution of Other Hydrometeors. content (Figure 7) were analyzed. eTh results show that the The variation of the IN concentration leads to different average ice water content and number concentrations during changes of different types of hydrometeors. Ice crystals, as the period before 14:00 CST are as follows: (1) 𝑇1 group: the most basic ice-phase particles, change the temporal and Cooper > Fletcher; (2) 𝑇2 group: Chi NJ > Chi Mt. H; spatial distribution of other hydrometeors and the macrode- and (3)𝑆 group: the original program> Philips. Based on velopment and structure of the cloud by participating in the combination with the IN concentration (Figure 1), the the ice-phase transformation process. On the other hand, distribution of ice crystals is related to the IN distribution. these changes will in turn aeff ct the temporal and spatial A greater number of IN leads to a greater number of initial distribution of ice crystals through microphysical processes. ice crystals with higher water content. For example, the To understand the evolution of microphysical processes in ∘ ∘ applicable temperature range (from −40 Cto0 C) of ice the cloud, the spatiotemporal distributions of the airflow nucleation activation in the Cooper scheme is the largest. structureandhydrometeors inthecloudareanalyzedin The IN concentration increased rapidly with a decrease of detail. 8 Advances in Meteorology 11Z 12Z 13Z 14Z 15Z 16Z 11Z 12Z 13Z 14Z 15Z 16Z Time (h) Time (h) Original Fletcher Original Fletcher Phillips Chi_NJ Phillips Chi_NJ Cooper Chi_Mt.H Cooper Chi_Mt.H (a) (b) 0 20 40 60 80 100 120 140 −1 QICE (mg·kg ) Original Fletcher Phillips Chi_NJ Cooper Chi_Mt.H (c) ∘ ∘ ∘ ∘ −1 Figure 6: Variation of the regional (118.7 E–120.2 E, 29.4 N–30.4 N) average mixing ratio ((a) unit: mg⋅kg ) and the number concentrations 3 −1 ((b) unit: 10 L ) of ice crystals with time and the variation of the regional average mixing ratio with height from the hail cloud generation −1 to the extinction period ((c) unit: mg⋅kg ). (1) Influence on the Temporal and Spatial Distribution of Snow overtime wasanalyzed(Figure 8).Thepeakofsnowwater Particles. In combination with the temporal evolution of ice occurs slightly later than the peak of ice crystals. The lag of crystals, the macroscopic variation of the snow water content snow water indicates that initial ice crystals were formed first −1 QICE (mg·kg ) Height (km) −1 QNICE (g ) Advances in Meteorology 9 16 14:20 15:00 15:40 15:50 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (a) 16 13:40 15:00 15:30 14:40 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (b) T1 16 13:50 14:30 14:40 15:00 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (c) T1 16 13:30 15:20 15:50 16:00 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (d) Figure 7: Continued. Height (km) Height (km) Height (km) Height (km) 10 Advances in Meteorology T2 16 13:50 14:40 15:20 15:10 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (e) T2 16 13:50 14:40 15:30 15:40 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (f) −1 −1 Figure 7: Cross section of the simulated cloud water mixing ratio (solid line, minimum 0.1 g⋅kg ,interval1g⋅kg ), ice crystal mixing −1 −1 ratio (black dotted line, minimum 0.05 g⋅kg , interval 0.1 g⋅kg ), and updraft (colored) and isothermal line (dashed gray line) in latitudinal direction: (a) original; (b) Philips; (c) Cooper; (d) Fletcher; (e) Chi NJ; and (f) Chi Mt.H. andthengrewtoacertainsizethrough deposition, Bergeron, (2)Inufl enceontheTemporalandSpatialDistributionofCloud Water and Rainwater. The modified IN parameterization and collision processes and eventually transformed into snow schemes have the least eeff ct on the cloud water and rainwater particles. content. The cloud water content (Figure 7) during the initial The spatial distribution characteristics of snow particles stage is in the order of original program < Philips and at different stages were analyzed (Figure 9). In the develop- Cooper< FletcherbecauseagreaternumberofINeventually ing and early-mature stages of hail cloud, the snow water result in an increased number of ice crystals, thus raising the content is in the following order: (1)𝑇1 group: Cooper< consumption of liquid water in the cloud. Fletcher; (2) 𝑇2 group: Chi NJ < Chi Mt. H; and (3) 𝑆 group: original< Philips. This is opposite to the distribution With respect to the rainwater content (Figure 9), schemes of the ice crystal content in the previous section. eTh with more IN correspond to less rainwater below the 0 Clevel distribution and variation characteristics of the concentration before the maturation stage (e.g., Chi NJ< Chi Mt. H). As of snow particles are similar to those of the water content the IN concentration increases, many ice crystals compete (Figure 8(b)). for liquid water, consequently inhibiting the growth of ice particles. This is not conducive to the growth of large-sized ice The above-mentioned results show that the increase of particles and formation of raindrops and results in less rain. ice crystal caused by increased IN is not conducive to the In addition, the increased intensity of the updraft in the cloud formation and growth of snow particles. In case of a con- leadstoareducedliquidwater contentbelow the0 Clayer. stantamountofwater vapor,thewater vaporconsumption However, this trend is less pronounced aeft r the maturation increases with increasing formation of ice crystals. Many ice of the hail cloud. crystals compete for limited water vapor and cannot grow quickly to transform into snow crystals, thus inhibiting the production and growth of snow particles, resulting in a lower (3) Inuence fl on the Spatial and Temporal Distribution of Hail amount of snow particles. Embryos (Graupel) and Hail. The relationship between ice Height (km) Height (km) Advances in Meteorology 11 3.0 2.4 1.8 1.2 20 0.6 0.0 11Z 12Z 13Z 14Z 15Z 16Z 11Z 12Z 13Z 14Z 15Z 16Z Time (h) Time (h) Original Fletcher Original Fletcher Phillips Chi_NJ Phillips Chi_NJ Cooper Chi_Mt.H Cooper Chi_Mt.H (a) (b) ∘ ∘ ∘ ∘ −1 Figure 8: Variation of the regional average (118.7 E–120.2 E, 29.4 N–30.4 N) mixing ratio ((a) unit: mg⋅kg ) and concentration ((b) unit: 3 −1 10 L ) of snow particles with time. nuclei and graupel/hail is even more complex. Hail embryos concentration corresponds to a larger hail water content in (graupel)formedinthehail clouddevelopingstage andthen the maturation stage and greater amount of hail falling to the enlarged and developed into hail. eTh hail then grew to a large ground. enough size and started falling in the early-mature stage of the eTh increase of the IN concentration has a certain hail cloud. After hail fell on the ground, the hail cloud entered inhibitory effect on the growth of hail embryo (graupel; in the later maturation stage. the development and early maturity stages). However, when The variation of graupel in each group is similar to that hail grew to the hail precipitation stage (later period of the of snow particles from hail embryo formation to hailfall mature stage), the scheme in which the hail content was (Figure 10). For example, in the𝑇2 group Chi NJ< Chi Mt. higher corresponds to a higher IN concentration, indicating H; that is, the graupel water content of the scheme cor- that an increased IN concentration is favorable for later hail responding to more abundant IN is smaller than that of growth. In the hail embryo formation and growth stages, the the scheme corresponding to fewer IN. With increasing IN increased IN concentration is not conducive to the growth concentration, the early formation of graupel is inhibited and of hail embryos due to reasons similar to those presented the stronger updraft in the later stages of hail cloud may for the above-mentioned snow particle suppression; that is, promote the transformation of snow to graupel particles. a large number of small ice particles compete for water As a result, the snow water content in the mature stage resources, therefore making it more difficult to grow into is noticeablyreducedsuchasintheCooper andChi NJ large ice particles, leading to lower graupel water contents. schemes (Figure 10). More importantly, the amount of snow particles is relatively eTh hail particles in each group show a variation trend small. Therefore, the corresponding graupel transformed by opposite to that of above-mentioned graupel particles in the snow particle growth decreases. However, at the same time, early or later periods of the mature stage (Figure 11). This is the intensity of updraft in the cloud increases, which may be consistent with the results of van den Heever et al. [9] and duetosignicfi antlatentheatreleasedbyphasetransformation Carrio et al. [10] who studied the eeff cts of the increase of IN (such as deposition, freezing), consequently strengthening on graupel and hail in a severe precipitation case in Florida. the convection in clouds [5, 45]. In this study, the hail water content in the𝑆 group is in the When graupel grows into hail, the increase in the updraft order of original> Philipsschemeinthe earlystage ofhail velocity will transport more supercooled water above the cloud maturation. In the later maturation stage of the hail 0 C level and lead to the growth of graupel and hail by cloud, the hail content in the𝑇1 and𝑇2 groups showed the collision with supercooled water, which is indicated by the following order: Cooper> Fletcher and Chi NJ> Chi Mt. apparent supercooled water consumption in the Fletcher H. The results demonstrate that the scheme with higher IN scheme (Figure 7). Otherwise, the updraft is strong enough to −1 QSNOW (mg·kg ) −1 QNSNOW (g ) 12 Advances in Meteorology 16 14:20 15:00 15:40 15:50 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (a) 13:40 14:40 15:00 15:30 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (b) T1 16 13:50 14:30 14:40 15:00 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (c) T1 16 13:30 15:20 15:50 16:00 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (d) Figure 9: Continued. Height (km) Height (km) Height (km) Height (km) Advances in Meteorology 13 T2 16 13:50 14:40 15:10 15:20 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (e) T2 16 13:50 14:40 15:30 15:40 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 −1 Wind (GM ) −12 −8 −4 0 4 8 12 16 20 24 28 32 (f) −1 −1 Figure 9: Cross section of the simulated rain water mixing ratio (solid line, minimum 0.1 g⋅kg ,interval2.5g⋅kg ), snow water mixing −1 −1 ratio (black dotted line, minimum 0.1 g⋅kg , interval 0.3 g⋅kg ), and updraft (colored) and isothermal line (dashed gray line) in latitudinal direction: (a) original; (b) Philips; (c) Cooper; (d) Fletcher; (e) Chi NJ; and (f) Chi Mt. H. allowhailtohavemoretimetoencountersupercooledwater occurred was determined by the temperature range of the ice droplets, allowing hail particles to grow. nucleation activation. eTh effect of the IN concentration on the cloud water and rainwater content was relatively small. As the IN concentration increases, a large number of ice 6. Summary and Discussion crystals is generated, which compete for water vapor, and the (1)Withrespect to thehailprocess simulatedinthiswork, rapid growth of ice crystals through the Bergeron process theinufl enceofthe IN concentrationongroundhailfallis consumes a large number of supercooled water droplets, significantly stronger than that on ground rainfall. er Th e which indirectly aeff cts the variation of the cloud water and were significant differences in hailfall region and intensity. rainwater content. For rainfall, it only led to slightly different locations of (2)Inthiscase,theinufl enceofvaryingINconcentrations the strong precipitation center and rainfall amount and the at different stages of the hail cloud also differ. The IN changes difference of rainfall did not exceed 5 mm. The changes of the temporal and spatial distribution of other hydrometeors the IN concentration caused different changes of hydrome- and dynamic structure in the cloud by ice crystal variation. teors in the cloud. The effect on ice crystals was the most Generally, in the developing and early maturation stages of signicfi ant. eTh larger the amount of IN is, the greater hail development, a larger number of ice crystals compete for the initial concentration of ice crystals and water content limitedwater vaporandcloudwater astheINconcentration of ice crystals is. The difference of the ice crystal water increases, therefore preventing the growth of ice crystals content, calculated based on the Cooper scheme with the and slowing down their transformation to snow crystals. highest IN concentration and the Fletcher scheme with the This indirect dependency has an inhibitory eeff ct on the −1 smallest IN concentration, was 9 mg⋅kg . eTh difference in formation and growth of snow/hail embryos. During the later 3 −1 the ice crystal concentration was 95× 10 L . In addition, maturation stage, updraft in the cloud increases, transporting theheightatwhichthemaximum icecrystalwatercontent more supercooled water above the 0 C level. On the other Height (km) Height (km) 14 Advances in Meteorology 14:20 15:00 15:40 15:50 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 (a) 13:40 14:40 15:00 15:30 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 (b) T1 13:50 14:30 14:40 15:00 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 (c) T1 13:30 15:20 15:50 16:00 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 (d) T2 13:50 14:40 15:10 15:20 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 (e) Figure 10: Continued. Height (km) Height (km) Height (km) Height (km) Height (km) Advances in Meteorology 15 T2 13:50 14:40 15:30 15:40 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 (f) −1 −1 Figure 10: Cross section of the simulated graupel water mixing ratio (solid line, minimum 0.5 g ⋅kg ,interval3g⋅kg ), UW wind vector, and 0 C line (thin solid line) in latitudinal direction based on the different sch emes: (a) the original scheme; (b) Philips; (c) Cooper; (d) Fletcher; (e) Chi NJ; and (f) Chi Mt. H. S S 16 16 14 14 12 12 10 10 8 8 6 6 4 4 2 2 0 0 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 (a) (b) T1 T1 16 16 14 14 12 12 10 10 8 8 6 6 4 4 2 2 0 0 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 (c) (d) T2 T2 16 16 14 14 12 12 10 10 8 8 6 6 4 4 2 2 0 0 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 119.0 119.5 120.0 120.5 (e) (f) −1 −1 Figure 11: Cross section of the simulated hail water mixing ratio (solid line, minimum 0.1 g⋅kg ,interval1g⋅kg ), UW wind vector, and 0 C line (thin solid line) in latitudinal direction based on the different sch emes: (a) original scheme; (b) Philips; (c) Cooper; (d) Fletcher; (e) Chi NJ; and (f) Chi Mt. H. Height (km) Height (km) Height (km) Height (km) Height (km) Height (km) Height (km) 16 Advances in Meteorology hand,thisallowsmoretimeforhailparticlestogrowvia [10] G. G. Carrio, ´ S. C. van den Heever, and W. R. Cotton, “Impacts of nucleating aerosol on anvil-cirrus clouds: A modeling study,” thecollectionofice,snowparticles,andsupercooledwater Atmospheric Research,vol.84,no.2,pp. 111–131,2007. droplets. [11] M. P. Meyers, P. J. Demott, and W. R. Cotton, “New primary In conclusion, the development of hail storms and their ice-nucleation parameterizations in an explicit cloud model,” dependency on IN are very complex. In the future, the Journal of Applied Meteorology and Climatology,vol.31,no.7, temporal and spatial variations of IN should be considered pp.708–721,1992. inthesimulationofsevereprecipitation basedonhigh- [12] P. J. Connolly, O. Mohler ¨ , P. R. Field et al., “Studies of het- precision observations of aerosol characteristics such as the erogeneous freezing by three different desert dust samples,” physical and chemical properties of aerosol particles and IN Atmospheric Chemistry and Physics,vol.9,no.8, pp.2805–2824, activation properties of aerosols. 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