Impacts of aerosols on the development and precipitation...

Impacts of aerosols on the development and precipitation

of a mesoscale squall line

Guohui Li,1 Yuan Wang,1 Keun-Hee Lee,1 Yiwei Diao,1 and Renyi Zhang1

Received 4 December 2008; revised 19 May 2009; accepted 15 June 2009; published 10 September 2009.

[1] The effects of aerosols on the development and precipitation for a mesoscale squallline occurring in the south plains of the United States have been investigated using acloud-resolving Weather Research and Forecasting (CR-WRF) model with a two-momentbulk microphysical scheme. Different aerosol scenarios are considered in the CR-WRFmodel experiments, including polluted continental aerosols with a mean concentration of2000 cm�3. The simulated temporal evolution of composite radar reflectivity and the 24-haccumulated precipitation in the polluted aerosol experiment are in agreement with themeasurements. The influence of aerosol concentrations is insignificant on the rainfalldistribution but is remarkable on the precipitation intensity. The CR-WRF experiment withthe polluted aerosol case predicts about 13% more precipitation and more locally intensiverainfall than do those with the clean aerosol case. Both the convection zone and the stormconvective strength are increased in the polluted aerosol experiment in response to theincrease in aerosol concentrations. The two-moment microphysical scheme is comparedwith three single-moment bulk schemes in the WRF model, including the Lin, WRFsingle-moment six-class, and Thompson schemes. Only the Thompson schemes reproducethe observed precipitation and radar reflectivity pattern, in agreement with the two-moment scheme with a leading convective line and a trailing stratiform precipitationregime. All of the single-moment schemes significantly overestimate the precipitation,especially with the Lin scheme, while the two-moment scheme yields the precipitationsimulation comparable with the measurement.

Citation: Li, G., Y. Wang, K.-H. Lee, Y. Diao, and R. Zhang (2009), Impacts of aerosols on the development and precipitation of a

mesoscale squall line, J. Geophys. Res., 114, D17205, doi:10.1029/2008JD011581.

1. Introduction

[2] The effect of aerosols is recognized as one of the keyissues in the climate system and the hydrological cycle.Atmospheric aerosols, formed from natural and anthropo-genic processes [Zhang et al., 2004a], cool the atmosphereby directly scattering a fraction of the incoming solarradiation back to space. While acting as cloud condensationnuclei (CCN) and ice nuclei (IN), aerosols play an importantrole in controlling cloud formation and development andprecipitation over local, regional, and global scales, which isoften referred to as the aerosol indirect effect [Ramanathanet al., 2001; Zhang et al., 2007]. Presently, the aerosolindirect effect represents the largest uncertainty in climatepredictions. Earlier studies indicate that an increase inanthropogenic aerosol concentrations likely results in adecrease of the cloud droplet size but an increase of thecloud droplet number concentration for a constant liquidwater content, enhancing the reflective properties of cloudsand the lifetime of clouds [e.g., Twomey, 1977]. However, ithas been recently suggested that anthropogenic pollution

increases both the relative dispersion and number concen-trations of cloud droplets and the two effects are counter-acting, leading to a smaller cooling effect by aerosols [Liuand Daum, 2002]. Most recently, it has been suggested thatthe cloud droplet effective radius may increase or decreasewith aerosol loading, depending on cloud dynamic andthermodynamic conditions and aerosol properties [Yuan etal., 2008].[3] The influence of aerosols on precipitation processes

also remains highly uncertain, because of the complexity ofcloud processes determined by intricate thermodynamic,dynamical, and microphysical processes and their interac-tions [Hobbs, 1993; Levin and Cotton, 2007]. Observationshave demonstrated that the effect of aerosols on rainfall isdependent on both the type of aerosols and precipitatingenvironments. Using satellite observations from TropicalRainfall Measuring Mission (TRMM), Rosenfeld [1999,2000] suggested a suppressed precipitation due to over-seeding from both smoke over Indonesia and urban pollu-tion over Australia. On the other hand, precipitationenhancement has been documented around heavily pollutedcoastal urban areas [Ohashi and Kida, 2002; Shepherd andBurian, 2003]. On the basis of statistical analysis, Cervenyand Balling [1998] suggested that air pollution enhancesprecipitation in near-coastal Atlantic Ocean areas. Lin et al.[2006] found that high biomass burning aerosols are corre-

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1Department of Atmospheric Sciences, Texas A&M University, CollegeStation, Texas, USA.

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lated with more rainfall over the Amazon basin throughanalyzing multiplatform satellite data. Bell et al. [2008]suggested a midweek increase in precipitation over thesoutheast United States using TRMM precipitation meas-urements and attributed to invigoration of rainstorms by airpollution. Most recently, a decadal increase in precipitationover the north Pacific in winter has been revealed from theanalysis of precipitation measurements from the GlobalPrecipitation Climatology Project (GPCP) and linked tothe Asian pollution outflow [Li et al., 2008a].[4] Numerous model studies have been conducted to

evaluate the role of aerosols on cloud development andprecipitation by using cloud-resolving models (CRMs) andregional models with fine resolutions. Most of the previousmodel studies are confined to isolated clouds in two- orthree-dimensional (2-D or 3-D) models or cloud systems in2-D models. For example, Khain and Pokrovsky [2004]employed a 2-D CRM with a spectral bin microphysics(SBM) model to investigate effects of different size distri-butions of CCN on the evolution of deep convective cloudsunder dry unstable thermodynamic conditions and demon-strated a significant decrease in accumulated precipitation insmoky air. Using the same model, Khain et al. [2005]simulated a deep convective cloud under dry continentalconditions and found that the precipitation response toaerosols depends on environmental conditions and in awet atmosphere an increase in the aerosol concentrationcan lead to an increase in precipitation. They found thatwind shear has a dual role and in the case of isolated cloudsan increase in aerosol concentrations can result in a decreasein precipitation with an increase in the wind shear. Also,aerosols can intensify cloud systems by fostering, forexample, the formation of squall lines. Cui et al. [2006]obtained similar results when applying an axisymmetriccloud model with a sectional treatment of aerosol andhydrometeor microphysical processes to study an isolatedcloud in an environment with low wind shear and relativelydry air in the midtroposphere and upper troposphere. Tellerand Levin [2006] used a 2-D CRM with spectral micro-physics [Reisin et al., 1998] to investigate the aerosol effecton a winter convective cloud in the eastern Mediterraneanregion. Their results indicated that larger number concen-trations of CCN decrease accumulated precipitation. How-ever, simulations using a 3-D CRM with a two-momentbulk microphysical scheme by Wang [2005] and a 2-DCRM with a SBM scheme by Fan et al. [2007a, 2007b]suggested an increased total amount of precipitation withincreasing aerosol concentrations at relatively lower aerosolloading over the tropics and coastal regions, respectively.Li et al. [2008b] employed a cloud-resolving WeatherResearch and Forecasting (CR-WRF) model with a two-moment bulk microphysics scheme to examine the aerosoleffects in a convective cumulus cloud event and showed anonmonotonic response of precipitation to the increase ofaerosol concentrations. Thus, aerosol impacts on precipita-tion are likely dependent on environmental factors, such ashumidity and atmospheric instability [Williams et al., 2002].Khain et al. [2008] proposed a classification of aerosoleffects on precipitation for clouds of different types withhigh freezing levels, on the basis of results from simulationsusing 2-D CRM with a SBM scheme.

[5] Previous studies have been conducted to investigatethe aerosol-cloud interaction for mesoscale convective sys-tems (MCSs) or large-scale meteorological systems. How-ever, most of those studies employed 2-D CRMs andregional models with fine resolutions to evaluate aerosoleffects on cloud systems [Lee et al., 2008a, 2008b; Li et al.,2009; Seifert and Beheng, 2006; Tao et al., 2007]. Only afew studies have been performed using 3-D models with alarge domain to explore the response of cloud systems to thechange of aerosol concentrations. Lynn et al. [2005a,2005b] implemented a SBM into the three-dimensionalfifth-generation Pennsylvania State University–NationalCenter for Atmospheric Research (NCAR) MesoscaleModel (MM5) to simulate a squall line that developed overFlorida. Their results demonstrated that the use of a conti-nental CCN concentration results in an overall delay in thegrowth of hourly rainfall and formation of a stronger squallline with higher updraft maxima, and the accumulatedprecipitation is larger with a lower aerosol concentration.Van den Heever et al. [2006] employed the RegionalAtmospheric Modeling System (RAMS) and a two-momentbulk microphysical scheme to investigate the sensitivity of athunderstorm over the peninsula of Florida to variations inthe concentrations of nucleating aerosols, showing thatdifferent combinations of CCN, giant CCN, and IN havedifferent impacts on microphysical and dynamical charac-teristics and the accumulated surface rainfall. Throughsimulations using the CR-WRF model with a two-momentbulk microphysical scheme, Zhang et al. [2007] concludedthat polluted aerosols transported from the Asian continentintensify storms and cause more precipitation over the northPacific. Iguchi et al. [2008] implemented a bin-basedmicrophysics scheme into a 3-D nonhydrostatic model toverify the influence of aerosols on cloud microphysicalproperties in the East Asia region; their sensitivity studieswith respect to condensation nuclei (CN) concentrationshowed that the simulated area and amount of precipitationare slightly affected by the CN concentration.[6] In this paper, we report an investigation of the effects

of aerosols on the development and precipitation of a squallline occurring in the south plain of the United States from0000 UTC, 31 October 2005, to 0600 UTC, 1 November2005, using the CR-WRF model with a two-moment bulkmicrophysical scheme developed by Li et al. [2008b].Simulations with different aerosol conditions are carriedout to evaluate the response of cloud microphysics tochanges in initial aerosol concentrations. The model resultsare compared with the available observational radar andprecipitation measurements. Additional simulations are con-ducted using three one-moment microphysical bulkschemes in the WRF model to compare with the two-moment scheme to evaluate the performance of differentcategories of microphysical schemes.

2. Numerical Model and Experiment Setup

2.1. Case Description

[7] A middle-latitude squall line that developed over thesouth plains of the United States from 0000 UTC, 31October 2005, to 0600 UTC, 1 November 2005, is consid-ered in the present study. The squall line convection initiallydeveloped along a long, well-defined cold front and subse-

D17205 LI ET AL.: AEROSOL EFFECTS ON SQUALL LINE

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quently moved away from the front, yielding its ‘prefrontal’character. At its peak a line of convection extended all theway from Illinois to the southwest Texas.[8] At 0000 UTC, 31 October 2005, a large-scale surface

cold front extended from Quebec, Canada, to the TexasPanhandle with a low-pressure center located at the inter-section of New Mexico and Colorado (Figure 1a). Thesurface circulation showed an anticyclonic northerly coldcurrent associated with a high-pressure center over thenorthwestern United States and a warm moist southerlyflow ahead of the cold front, resulting in a convergencezone along the front. At 500 hPa, there was a baroclinic

trough corresponding to the surface front, with the baseof the trough located southwest of the surface front(Figure 1b). At 300 hPa, an upper level jet streak waspresent, which appeared to provide a favorable environmentfor the development of deep convection on the basis of themass momentum adjustment argument by Uccellini andJohnson [1979] (Figure 1c). This prefrontal environmentprovided favorable conditions for the development of con-vections 6 h later. During the following 24 h, the surfacefront system along with the 500 hPa trough moved towardsoutheast, extending to the western Texas. The deep con-vections started to develop over the western Oklahoma at0600 UTC. At 1000 UTC, the radar composite analysisindicated that the majority of the precipitation was occurringover a zone located in advance of the surface cold front,aligned along the orientation of the surface cold front. From1000 to 2200 UTC, the squall line moved toward southeasttogether with the surface cold front, causing intensiverainfall over Texas.[9] Figure 2 shows the relevant sound data (located at

97.30�W, 32.83�N) at 1200 UTC, 31 October 2005. Thevertical temperature and dew point profiles reveal a mod-erate instability in the atmosphere, with a convectiveavailable potential energy (CAPE) of 850 J kg�1 integratedfrom the surface. The surface temperature is about 19.4�C.The relative humidity is about 78% at the surface and over94% at about 1.5 km. The magnitude of low-level windshear from the sounding is about 10 m s�1.

2.2. Model Configuration

[10] Amass coordinate version (version 2.2) of theWeatherResearch and Forecasting (WRF) model [Skamarock etal., 2005] is integrated for a 30-h period from 0000 UTC,31 October 2005, to 0600 UTC, 1 November 2005, over thedomain shown in Figure 3. The CR-WRF model is config-ured with three one-way nested grids with spacing of 12 km(231 � 231 grid points) for domain 1, 3 km (401 � 401 gridpoints) for domain 2, and 750 m (401 � 401 grid points) fordomain 3. The domain 1 is centered at 31�N and 97�E. Thesimulations using the domain 2 are primarily utilized toanalyze the aerosol effects, while the results using domain 3are used to evaluate the impacts of increasing horizontalresolution on the precipitation distribution. Thirty-five ver-tical levels are used in a stretched vertical grid with spacingranging from 100 m near the surface, 500 m at 2.5 km AGL,to 1 km above 14 km. The modeling system employs theYonsei University (YSU) PBL scheme [Noh et al., 2001],the thermal diffusion land-surface scheme based on theMM5 five-layer soil temperature model, a longwave radia-tion parameterization [Mlawer et al., 1997], and a shortwaveradiation parameterization [Dudhia, 1989]. Kain-Fritschcumulus parameterization scheme [Kain and Fritsch,1990, 1993] is used in the 12-km domain, and no cumulusparameterization is used in the 3-km and 750-m domain.The initial condition and lateral boundary conditions(updated every 3 h) are obtained from Eta operational modelanalyses.[11] In order to account for the impact of aerosols on

clouds, a two-moment bulk microphysical scheme devel-oped by Li et al. [2008b] is employed in the simulations.The two-moment bulk microphysical scheme predicts time-dependent bulk mass mixing ratios and bulk number con-

Figure 1. Synoptic analysis at (a) the surface, (b) 500 mbar,and (c) 300 mbar at 0000 UTC, 31 October 2005.


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centrations of cloud water, rainwater, ice crystals, snowflakes, and graupels, as well as the aerosol mass mixingratio, surface area and number concentration. The sizedistributions of the five types of hydrometeors are repre-sented by the gamma function,

Nx Dð Þ ¼ N0xDax

x exp �lxDxð Þ; ð1Þ

where x 2 [c, r, i, s, g] refers to cloud, rain, ice crystal,snow, and graupel, respectively, N0x is the intercept, lx isthe slope, and ax is the shape parameter of the distribution.[12] A three-moment approach is used to represent the

aerosol size distribution. We do not include the aerosolsources such as sulfur chemistry, emissions, and releasefrom cloud droplet evaporation or ice crystal sublimation.Except for the activation process in clouds, no other aerosolsink is considered in the present simulation. For the CCNnucleation, the aerosol spectrum is divided into 92 sections

from 0.002 to 2.5 mm. The critical radius of dry aerosols iscalculated from the Kohler theory using water supersatura-tion predicted from the CR-WRF model [Roger and Yau,1989; Pruppacher and Klett, 1997]. When aerosols in asection are activated, the mass of water condensing on CCNis calculated under the equilibrium assumption (Kohlerequation), if the radius (ra) of dry aerosols is less than0.03 mm; if the radius is greater than 0.03 mm, the mass ofwater condensing on these CCN at zero supersaturation iscalculated as mw = K4

3pra

3rw, where 3 < K < 8 [Khain et al.,2000].[13] For the warm rain process, the analytic solutions to

the stochastic collection equation (SCE) by a polynomialapproximation for the collection kernel are used accordingto Cohard and Pinty [2000]. The autoconversion parame-terization developed by Liu and Daum [2004] is applied inthe simulations.[14] Several processes are considered for ice nucleation in

the mixed phase of the cloud development [Williams et al.,1991; Demoz et al., 1993]. The number of ice crystalsformed from deposition nucleation is related to temperatureand ice supersaturation according to Pruppacher and Klett[1997]:

Ni ¼ Ni0 exp �a T � T0ð Þf g Qv � Qsi

Qsi

� �b1

r

� �; ð2Þ

where Ni is the number of ice crystals, Ni0 = 10-2/m3, T istemperature, Qv is saturation of water vapor, Qvi is thesupersaturation of water vapor with respect to ice, a = 0.6K-1,b = 5.0, and r is air density. The number production rate ofnewly nucleated ice crystals at a time step and a certain gridpoint, Rnuc, is calculated from

Rnuc ¼dNi

dt¼ �aNi

dT

dt: ð3Þ

The rate of drop freezing follows the immersion-freezingparameterization based on the stochastic hypothesis for-mulated by Bigg [1953] and homogeneous freezing by

Figure 3. Nested grid domains. The blue dotsrepresent the monitoring sites with hourly precipitationmeasurements.

Figure 2. Atmospheric soundings at 97.30�W and32.83�N and 1200 UTC, 31 October 2005: (a) temperatureand dew point, (b) relative humidity, and (c) horizontal windvelocities.


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DeMott et al. [1994]. Contact freezing of drops is fromMeyers et al. [1997] and is negligible for temperatureswarmer than �10�C.[15] For the secondary ice crystal production, the rime-

splintering mechanism by Hallett and Mossop [1974] isused. At T = �5�C, 250 collisions of droplets having radiiexceeding 24 mm with graupel particles lead to the forma-tion of one ice splinter. In order to obtain the number ofcollisions, the number of cloud droplets with radii greaterthan 24 mm is calculated by integrating the cloud dropletspectrum function from 24 mm and the ratio of clouddroplets with radii greater than 24 mm to the total dropletsis obtained. The collisions of droplets having radii exceed-ing 24 mm with graupel particles are calculated using theratio to scale the collisions of all cloud droplets with graupelparticles. Ice particles grow through deposition growth,aggregation among ice crystals, riming of supercooleddroplets [Lou et al., 2003; Li et al., 2008b]. A heavilyrimed ice crystal is transferred to a graupel. When all iceparticles fall below the freezing level, melting occurs byinstantaneous conversion of these particles into liquid dropsof equal mass. The mass-dimension relationship for solidprecipitation particles by Mitchell et al. [1990] is used.[16] To evaluate the role of aerosols in the development

of MCSs, different aerosol scenarios have been considered.The initial aerosol distributions are interpolated from thedaily average ammonium sulfate output of modeling ozoneand related chemical tracers (MOZART), a global chemicaltransport model [Tie et al., 2001; Zhang et al., 2003]. Themean initial aerosol number concentration is about2000 cm�3 at the surface. The aerosol distribution for thiscase is consistent with those previously measured andsimulated from photochemical oxidation of inorganic andorganic compounds under polluted conditions in the Hous-ton area (referred to as the P-case) [Fan et al., 2005, 2006;Zhang et al., 2004b; Zhao and Zhang, 2004; Li et al., 2005].In addition, a clean background case (C-case) is considered,and this case is assumed to contain ammonium sulfate witha number concentration of 200 cm�3 at the surface for theinitial condition. An exponential decrease is assumed forthe height dependence of the aerosol concentration in theC-case [Fan et al., 2007a, 2008]. Figure 4 displays thevertical profile of the domain average initial aerosol numberconcentrations in the C-case and P-case. The decreasingtendency of the aerosol number concentrations with heightbelow 4 km does not differ significantly in the two cases.

Above 4-km level, the number concentration in the P-casedecreases slowly with height, but there is a sharp decreaseof the number concentration in the C-case. At the lateralboundaries, zero horizontal gradients are assumed in theC-case [Khain and Pokrovsky, 2004; Lynn et al., 2005a,2005b]. In the P-case, a temporally variable lateral bound-ary condition is interpolated from the MOZART output[Iguchi et al., 2008].[17] Several single-moment bulk microphysical schemes

have been developed to represent cloud processes in theWRF model. To evaluate the difference between one-moment and two-moment bulk parameterization schemesin the representation of cloud microphysics, three types ofone-moment microphysical schemes are chosen to comparewith the two-moment scheme [Li et al., 2008b], includingLin scheme [Lin et al., 1983; Rutledge and Hobbs, 1984],Thompson scheme [Thompson et al., 2006], and the WRFsingle-moment six-class (WSM6) graupel scheme [Hong etal., 2004], all of which predict bulk mass mixing ratios butnot the number concentrations of hydrometeors.

3. Model Results

3.1. Comparisons With Radar and PrecipitationMeasurements

[18] Composite radar reflectivity (the maximum reflectiv-ity in a vertical column) represents a common postprocess-ing product of both research and operational models, withthe advantages to compare with readily available, observedcomposite equivalent reflectivity products [Koch et al.,2005]. The total equivalent radar reflectivity factor (Zet inmm6 m�3) is calculated from the sum of the reflectivities forall hydrometeor species except for the cloud water [Ferrier,1994]:

Zet ¼ Zer þ Zei þ Zes þ Zeg; ð4Þ

where the reflectivity for each hydrometeor category (x 2 [r,i, s, g]) using equation (1) is

Zex ¼ 1012N0x

Z 1

0

Kj j2xKj j2w

D6þax

x e�lxDxdDx

¼ 1012N0x

Kj j2xKj j2w

G 7þ axð Þl� 7þaxð Þx ; ð5Þ

where jKjx2 and jKjw2 correspond to ice species and liquidwater, respectively, and G is the gamma function. For

raindrops,Kj j2rKj j2w

= 1, and, for ice particles,Kj j2iKj j2w

= 0.224 [Smith,

1984]. In order to calculate the radar reflectivity factor inmicrophysics schemes, the three parameters of the sizedistribution function need to be determined. In one-momentmicrophysical schemes, one moment of the hydrometeorsize distribution, proportional to the mass content, ispredicted and lx is the prognostic parameter, while N0x

and ax are held constant. In the two-moment scheme, themass and the total number concentration of the hydrometeorcategories are independently predicted, with l and N0 beingthe independent prognostic variables and a being aconstant. In the calculation of radar reflectivity factor forice particles, several factors can cause errors, such as the

Figure 4. Domain-averaged vertical profiles of aerosols.


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assumption of the size distribution function of ice particles,the assumption of dry ice particles above the freezing level,and the geometry of ice particles.[19] Figure 5 shows the observed and simulated compos-

ite radar reflectivity at 1800 and 2100 UTC, 31 October2005, and 0000 UTC, 1 November 2005. At 1800 UTC, 31October, the observed squall line lies in the central Texasand the southeast of Oklahoma, in a southwest-northeastdirection. The squall line consists of a leading convectiveline approximately 30–40 km wide and a trailing stratiformprecipitation region. After 3 h, the stratiform precipitationregion becomes wider and more remarkable. AT 0000 UTC,1 November, the convective line reaches the coastline ofMexico Gulf in Texas. Compared with the measurement,both the P-case and C-case track well the development ofthe squall line. For example, an evident leading convectiveline and a stratiform precipitation regime are successfullyreproduced in the simulations in the P-case and C-case. Themagnitude of the radar reflectivity is also reasonable incomparison with the measurement. However, the develop-ment of the squall line is slightly delayed (about 1 h) in thesimulations. Also, the modeled squall line is considerably

wider and longer than the measurement. It is worthy notingthat there are several limitations to compare the width of thesquall line between composite radar reflectivity calculatedfrom a model grid point and that measured by scanningradar, because scanning radars cannot detect hydrometeorsin the lower atmosphere owing to the earth’s curvatureeffect and numerous other considerations. The pattern anddevelopment of the squall line in terms of composite radarreflectivity are similar in the P-case and C-case, but theP-case yields a larger leading convective line. Additionally,the propagation of the squall line is slower in the P-casethan the C-case. At 0000 UTC on November 1, the leadingconvective line in the P-case does not reach the Houstonarea, but in the C-case it passes through the Houston area.[20] The rainfall distribution measurement used in the

present study for comparisons with the simulations isobtained from the Global Energy and Water Cycle Exper-iment (GEWEX) Continental-scale International ProjectEnhanced Observing Period (GCIP/EOP) Surface Precipi-tation: National Centers for Environmental PredictionEnvironmentalModeling Center (NCEP/EMC) 4kmGriddedData (GRIB) Stage IV Data (http://data.eol.ucar.edu/codiac/

Figure 5. Composite radar reflectivity from (top) observation, (middle) simulation for C-case, and(bottom) simulation for P-case at 1800 and 2100 UTC, 31 October 2005, and 0000 UTC, 1 November2005.


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dss/id = 21.093). The observed and simulated spatial dis-tributions of accumulated 1-h precipitation from 1700–1800 UTC and 2000–2100 UTC, 31 October, and from2300 UTC, 31 October, to 0000 UTC, 1 November, aredisplayed in Figure 6. Compared to the measurement, boththe P-case and C-case reproduce well the temporal variationof precipitation distribution, but the timing of the simulatedprecipitation distribution is delayed. In terms of the inten-sive precipitation with the rainfall rate greater than 25 mmh�1, the P-case shows a better agreement with the measure-ment than the C-case. From 1700 to 1800 UTC, 31 October,and from 2300 UTC, 31 October, to 0000 UTC, 1 Novem-ber, the P-case yields the intensive precipitation more than35 mm h�1 and is comparable to the measurement, but theC-case fails to produce precipitation greater than 25 mmh�1. However, the P-case predicts more precipitation by15 mm h�1 than the C-case and the measurement. Theobserved and simulated spatial distributions of accumulatedprecipitation from 0600 UTC, 31 October, to 0600 UTC,1 November, are displayed in Figure 7. The observed heavyrainfall is mainly concentrated in the west Texas andOkalahoma, with three main precipitation centers located

in the west of Okalahoma, in the northwest of Texas, andaround the Houston area. The measured maximal accumu-lated rainfall amount is more than 80 mm. The pattern of thesimulated cumulative precipitation is in agreement with theobservation in both the P-case and C-case, but the simu-lations produce more rainfall at certain locations. Theaverage accumulated rainfall amount over the wholedomain is 14.4 mm in the P-case and 12.7 mm in the C-case,greater than the observed value of 10.4 mm. Compared withthe measurement, the P-case and C-case overestimate thedomain-averaged precipitation by about 38% and 22%,respectively. Therefore, in terms of the total domain precip-itation, the C-case performs better than the P-case. It shouldbe pointed out that the rainfall distribution measurements,based on hourly radar precipitation estimates from theWSR-88D radars and the automated gauge reports trans-mitted via the GOES Data Collection Platform, also containuncertainties. Figure 8 presents the temporal variation ofmodeled and observed rainfall rate averaged over 72 mon-itoring sites in Texas. In both the P-case and C-case, theCR-WRF model tracks reasonably well the rainfall ratevariability with time. For example, the increase of rainfall

Figure 6. The 1-h precipitation from (top) observation, (middle) simulation for C-case, and (bottom)simulation for P-case at 1700–1800 UTC and 2000–2100 UTC, 31 October 2005, and from 2300 UTC,31 October 2005, to 0000 UTC, 1 November 2005.


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rate from 1200 UTC to 1800 UTC, 31 October, is repro-duced, and the falloff of rainfall rate from 2100 UTC, 31October, to 0000 UTC, 1 November, is simulated. However,the simulated rainfall rate in the P-case has a betteragreement with measurement than that in the C-case duringthe period with intensive precipitation (1700–2100 UTC,31 October) except that C-case has a perfect agreement withobservation at 1900 UTC, whereas the C-case shows abetter performance than the P-case from 1300–1600 UTCand from 2100–2300 UTC, 31 October. Note that, therainfall rate shown in Figure 8 is averaged over all the 72monitoring sites with hourly precipitation measurements, sothe intensive precipitation rate corresponds to the period thatthe squall line covers more monitoring sites.

3.2. Response of Cloud Propertiesto Changes in Aerosols

3.2.1. Number Concentrationsand Sizes of Hydrometeors[21] The influence of the initial aerosol concentrations on

the microphysics of MCSs is reflected in the change of thecloud droplet number concentration (CDNC) and cloudwater contents (CWC). Figure 9 illustrates the impact ofthe initial aerosol concentration on the maximal CDNC andCWC between the P-case and C-case at 2100 UTC,31 October. A dramatic increase in the maximal CDNCoccurs in the P-case, in response to the increase in theinitial aerosol concentrations. The maximal CDNC along theleading convective line in P-case ranges from 50 � 106 to

1000 � 106 kg�1, about 3–8 times larger that in the C-case.The enhancement of the maximal CWC in the P-case is notremarkable in comparison with that in the C-case, owing tothe increase of the maximal CDNC. Figure 10 shows thetemporal variation of the averaged total column CWC andliquid water path (LWP) in the P-case and C-case. Theaverage LWP in the P-case is significantly enhanced com-pared to that in the C-case, with an increase of about 70%.[22] In order to evaluate the overall response of the

modeled cloud properties in MCSs to changes in the aerosol

Figure 8. Comparison of temporal variation of the rainfallrate averaged over 72 monitoring sites (as shown in Figure 3)from observation and model simulations.

Figure 9. Simulated maximal cloud droplet numberconcentration and maximal cloud water in the P-case andthe C-case at 2100 UTC, 31 October 2005.

Figure 7. The 24-h precipitation from (a) observation, (b) simulation for C-case, and (c) simulation forP-case from 0600 UTC, 31 October 2005, to 0600 UTC, 1 November 2005.


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concentrations, the population mean (p-mean hereinafter) ofa given variable over all qualified grid boxes in the studydomain and during a given integration period is used, in away similar to that of Wang [2005]. The p-mean is definedas

Cp ¼ 1

PT2t¼T1

N tð Þ

XT2t¼T1

Xq>qminn>nmin

c x; y; tð Þ; ð6Þ

where c represents a given quantity. The calculation usingequation (6) applies to grid points where both the massconcentration q and number concentration n of a hydro-meteor exceed a minimal. The total number of the gridpoints at a given output time step t is represented by N(t). T1and T2 are the start and end output steps, respectively.[23] Figure 11 illustrates the p-mean of number concen-

trations and effective sizes of five types of hydrometeors inthe P-case and C-case. The effective size is defined as

Dex ¼RD3þax

x e�lxDxdDxRD2þax

x e�lxDxdDx

: ð7Þ

The P-case predicts much more cloud droplets but less rainand graupel particles than the C-case. In the P-case, theenhancement of CDNC is correspondent to the high-levelaerosol concentrations, which also hampers the conversionof cloud water to raindrops to decrease the formation ofraindrops. The reduction of graupel concentrations is

explained by the inefficient immersion ice nucleation dueto the formation of large concentrations of cloud droplets inthe P-case. The number concentrations of ice crystals andsnow flakes in the P-case and the C-case do not differsignificantly, although there are slightly more ice crystalsand snow flakes in the P-case than in the C-case(Figure 11a). The effective radius of cloud droplets exhibitsa large reduction from the C-case to the P-case, consistentwith the Twomey effect [Twomey, 1977]. In contrast to thecase of cloud droplets, the characteristic size of raindropsand graupels in the P-case is larger than that in the C-case,likely caused by an enhanced collection of smaller liquidparticles from an increased CDNC in the P-case. However,the size increases of snow flakes, raindrops, and graupelsare not remarkable in the P-case compared to that in theC-case, likely attributable to the decrease of the collectionefficiency for both cloud droplets and ice particle rimingcaused by more numerous small droplets in the P-case.3.2.2. Precipitation[24] A comparison between P-case and C-case indicates

that the initial aerosol concentrations have an insignificantimpact on the precipitation pattern (Figure 7). Both the P-case and the C-case reproduce the three major precipitationcenters compared with the measurement. The principalcharacteristics of the precipitation distribution in a large-scale squall line are mainly determined by meteorologicalconditions, which play key roles in the transport of watervapor and when/where precipitation occurs. In addition, it ispossible that the spatial scale of the squall line is rather largeand a difference in precipitation distribution under differentaerosol conditions may be difficult to be identified. How-ever, large variations in precipitation intensity exist, asshown in the surface rainfall difference between the P-caseand the C-case (Figure 12). The precipitation difference ismore than 10 mm d-1 in most of the areas where heavyrainfall occurs. The area with increased precipitation islarger than that with reduced precipitation. The total rainfallin the P-case is increased by about 13% compared with thatin the C-case, indicating that the polluted aerosols have apositive contribution to precipitation. Our results of precip-itation enhancement are consistent with several previousstudies. For example, it has been documented that moreprecipitation occurs around heavily polluted coastal urbanareas such as Houston [Shepherd and Burian, 2003] andTokyo [Ohashi and Kida, 2002]. Also, it has been suggestedthat air pollution enhances precipitation in near-coastal

Figure 10. Temporal evolution of domain-averaged liquidwater path.

Figure 11. Histograms of p-mean of (a) number concentrations and (b) effective sizes of hydrometeors.Red is for the C-case, and green is for the P-case.


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Atlantic Ocean areas [Cerveny and Balling, 1998]. Highbiomass burning derived aerosols have been found to becorrelated with the more rainfall over the Amazon basin[Lin et al., 2006]. In addition, model studies have alsodemonstrated that polluted aerosols increase precipitation ina moist, unstable atmosphere [Wang, 2005; Zhang et al.,2007; Fan et al., 2007b; Li et al., 2009; Khain andPokrovsky, 2004; Khain et al., 2008; Lynn et al., 2005b;Lynn and Khain, 2007; Phillips et al., 2007].[25] Figure 13a depicts the temporal evolution of the

modeled surface rainfall rate averaged over the study domain.The averaged rainfall rate in the P-case is greater than thatin the C-case during the entire period from 0600 UTC,31 October, to 0600 UTC, 1 November. Especially, theenhancement of the averaged rainfall rate in the P-case ismore significant when the heavy rainfall occurs. Theenhanced precipitation with increasing aerosols from theC-case to the P-case is explained by the suppressedconversion of cloud droplets to raindrops but enhancedconvective strength due to more latent heat release in theprolonged condensation process and the delayed down-draft, which causes less efficient warm rain process butmore efficient mixed-phase processes. The precipitatingparticles (raindrops and graupels) grow to sufficiently largesizes to survive evaporation after falling to a subsaturatedcondition below the cloud base (Figure 11b).[26] The ratio of grids with the rainfall rate greater than

16 mm h-1 (the lower limit of heavy rain) to the total grids inthe study domain is shown in Figure 13b. Compared to theC-case, the increase of the area with more than 16 mm h-1

rain rate is remarkable in the P-case. Totally, the area isincreased by more than 100%, even if the total rainfall in thestudy domain is enhanced by about 13%. This implies thataerosol induced precipitation enhancement is not distributedevenly but concentrated in isolated locations. In addition,the simulated maximal rainfall rate in the P-case is alsolarger than that in the C-case during the entire simulationperiod (Figure 13c).

[27] In order to evaluate the variation of precipitation dueto the increase in aerosol concentrations, we divide thedaily precipitation amount into five categories: trace(<2.54 mm d�1), light (2.54–12.7 mm d�1), moderate(12.7–25.4 mm d�1), heavy (25.4–50.8 mm d�1), andextreme (>50.8 mm d�1). The proportion of precipitationgrid cells falling in each category is calculated for the cleanand polluted cases. Table 1 presents the percentage of totalprecipitation in each of the five precipitation categoriesbetween the P-case and the C-case. A remarkable enhance-ment occurs in the proportion of precipitation for theextreme precipitation category caused by deep convectiveclouds. However, the proportion of precipitation for traceand light precipitation categories decreases in the P-casecompared to the C-case, which is associated principally withsmall cumulus clouds located far from the squall line.3.2.3. Convective Strength[28] As an indicator of the largest local latent heat release,

the maximal updraft velocity is often considered in cloud

Figure 12. Comparison of the 24-h cumulative precipita-tion between the P-case and the C-case.

Figure 13. Temporal evolution of (a) domain-averagedrainfall rate, (b) area percentage with rainfall rate greaterthan 16 mm h�1, and (c) maximal rainfall rate.


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simulations to determine the impacts of aerosols on theconvective intensity [Khain et al., 2005; Lynn et al., 2005a,2005b; Fan et al., 2007a; Tao et al., 2007]. When anisolated cloud is simulated, it is reasonable to use thisindicator to evaluate the convective strength. However, ifa mesoscale cloud system is considered, the maximalupdraft velocity may not be a suitable indicator to reflectthe convective strength due to the different timings ofconvective cells. Figure 14 displays the temporal evolutionof the maximal vertical velocity in the study domain in theP-case and the C-case. As is expected, no consistentconclusion can be drawn to verify the aerosol effect onthe convective strength from the comparison of the maximalupdraft between the P-case and the C-case.[29] A useful approach to examine the convective strength

is to compare the probability density function (PDF) ofvertical velocity (W) between the P-case and C-case. Table 2summarizes the relative frequency of W in the intervalsfrom 1 m s�1 to 10 m s�1 with 1 m s�1 increase and greaterthan 10 m s�1. The percentage of the interval of 1–2 m s�1

is larger in the C-case than the P-case. When W is more than2 m s�1, the relative frequency of W in the P-case is greaterthan that in the C-case, indicating that the convection in theP-case is more intensive than that in the C-case.[30] To better evaluate the convective strength of modeled

cloud systems,Wang [2005] suggested the application of thep-mean updraft and downdraft in the core area with theabsolute vertical wind speed greater than 1 m s�1 and totalcondensed water mixing ratio greater than 0.01 g kg�1. Thetime sequences of the percentage of the core updraft area inthe study domain in the P-case and C-case are displayed inFigure 15a. The core updraft area is increased considerablyin the P-case, indicating that polluted aerosols increase theconvection zone in the squall line. In the calculation of thep-mean updraft and downdraft using equation (6), T1 is setequal to T2 to account for temporal variation of theconvective strength in the study domain (Figures 15b and15c). The core downdraft is not sensitive to changes in the

initial aerosol concentrations, with a negligible differencebetween the P-case and the C-case. This is perhaps causedby the insignificant difference of the precipitating particlesbetween the P-case and C-case. The core updraft isenhanced significantly in the P-case at most of timescompared to the C-case. Hence, the convection is intensifiedwith the increase of the initial aerosol concentrations.[31] Another approach to determine the convective

strength is to compare the radar reflectivity. The mostintense convection is defined with the radar reflectivity

Table 1. Percentage of Total Precipitation in Each of the Five

Precipitation Categoriesa

Precipitation Category

Trace Light Moderate Heavy Extreme

P-case 1.4 10.4 24.0 46.3 17.9C-case 1.9 13.2 27.1 45.8 12.0

aTrace, <2.54 mm d�1; light, 2.54–12.7 mm d�1; moderate, 12.7–25.4 mm d�1; heavy, 25.4–50.8 mm d�1; and extreme, >50.8 mm d�1.

Figure 14. Time evolution of maximal vertical velocity.

Table 2. Relative Frequency of W Calculated in the Entire

Domain From 0600 UTC, 31 October 2005, to 0600 UTC,

1 November 2005a

W Interval (m s�1)

1–2 2–3 3–4 4–5 5–6 6–7 7–8 8–9 9–10 >10

P-case 59.61 16.35 8.15 4.80 3.18 2.13 1.49 1.10 0.81 2.38C-case 61.90 16.12 7.60 4.44 2.75 1.88 1.35 0.99 0.74 2.23

aFor relative frequencies of W that are greater than 1 m s�1.

Figure 15. Time evolution of (a) core area percentage,(b) core updraft, and (c) core downdraft.


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greater than 57 dbZ. Figure 16 shows time sequences of theratio of the area with the most intense convection to thestudy domain in the P-case and C-case. There are moreintense convections in the P-case than in the C-case,demonstrating that polluted aerosols also increase intensiverainfall locally.3.2.4. Cold Pool Response[32] Previous simulations of squall lines show the surface-

based cold pool and environmental vertical wind shearas critical factors in determining their structure and evolu-tion [e.g., Hane, 1973; Nicholls et al., 1988; Robe andEmanuel, 2001; James et al., 2005]. Rotunno et al. [1988](hereinafter referred to as RKW) and Weisman et al. [1988]advanced a theory based on numerical simulations, theoret-ical reasoning, and an observational review. RKW noticedthat the simulated squall line is maintained through theregeneration of new cells along a line of cold outflowproduced during the decay of old cells. RKW found thatthe cold surface outflow of an old cell to lift environmentalboundary layer air to the level of free convection to producea new cell is enhanced with low-level shear and concludedthat the cold pool–shear interaction is a central element inunderstanding the maintenance of strong squall lines, espe-cially in the absence of significant external forcing features.Because of the critical importance of the cold pool incontributing to the structure and evolution of squall lines,it is necessary to investigate the response of cold poolbehavior to the change of aerosol concentrations in squallline simulations, in order to further explore the impact ofaerosols on storm dynamics.[33] The cold pool can be quantified by an quantity, C,

defined as the theoretical propagation speed of a two-dimensional density current in an infinitely deep, unstrati-fied environment [Benjamin, 1968],

C2 ¼ 2

Z H

0

�Bð Þdz; ð8Þ

where H is the cold pool depth, B is buoyancy,

B ¼ gq� q

qþ 0:61 qv � qvð Þ � qc � qr

� �ð9Þ

and q is potential temperature. The overbars indicate themodel’s base state, and qv, qc, and qr are the mixing ratios ofwater vapor, cloud water, and rainwater, respectively. An

optimal condition for deep lifting at the leading edge of thecold pool occurs when C/DU = 1, where DU is theenvironmental vertical shear through the depth of the coldpool oriented normal to its leading edge. For C/DU < 1,updrafts are predicted to tilt downshear, which inhibitsupscale growth and organization, while C/DU > 1 predictsthe upshear tilt commonly observed in the mature-to-dissipating stage of squall lines.[34] For the calculations of C representative of the mature

phase of the squall line at 0000 UTC, 1 November 2005, thebuoyancy field, B, is averaged in a region along the leadingedge of the cold pool with 50 km, extending 20 km behindthe leading edge and with a representative cold pool depth,H (defined as the level at which B vanishes). The environ-mental wind shears are calculated at the point with a 100 kmahead of the leading edge, in order to mitigate the possibleinfluence of anvil circulations on upper tropospheric winds[Trier et al., 2005]. Table 3 reveals that the C value in theP-case is slightly less than that in the C-case, since the largerprecipitating particles in the P-case correspond to lessevaporation due to faster sedimentation in the cold pool,reducing the potential temperature perturbation. FollowingRKW, we calculate the ground-relative cold pool speed,Cgr = C + U0, by adding an environmental ground-relativeretarding wind U0. Assuming that U0 is the same in theP-case and C-case, the propagation of the cold pool in theC-case is slightly faster than that in the P-case, as is evidentfrom the movement of the convective leading line inFigure 5. The values of C/DU in both the P-case andC-case are greater than 2, similar to that found by Coniglioand Stensrud [2001] for a long-lived simulated derecho, butthe value of C/DU in the P-case is less than that in theC-case.

3.3. Comparison With Other Bulk MicrophysicalSchemes in the WRF Model

[35] In the current version of the WRF model, severalone-moment bulk microphysical schemes are available topredict the mass mixing ratios of cloud water, rain, icecrystal, snow flake, and graupel, including the Lin scheme,the Thompson scheme, and the WSM6 scheme. The Lin andWSM6 scheme are mainly based on the work of Lin et al.[1983] and Rutledge and Hobbs [1984], but the WSM6scheme has revised ice microphysics proposed by Hong etal. [2004]. The most prominent feature of Hong et al.’s[2004] work is the assumption that the IN number concen-tration is dependent on temperature and the amount of ice.The associated ice processes are varied correspondingly.The saturation adjustments are based on the work of Tao etal. [2003] and treat separately the ice and water saturationprocesses. A detailed description of the WSM6 scheme withall the source/sink terms and the computational procedurescan be found in the work of Hong and Lim [2006]. Incontrast to the other two schemes, the Thompson scheme[Thompson et al., 2006] predicts additionally the ice number

Figure 16. Time evolution of the area percentage withradar reflectivity greater than 57 dbZ.

Table 3. Values of C, DU, and C/DU in the P-Case and the

C-Case at 0000 UTC, 1 November 2005

C (m s�1) DU (m s�1) C/DU

C-case 24.57 9.60 2.56P-case 23.74 9.65 2.46


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concentration. The intercept parameters are not constant andall hydrometeors are represented by a generalized gammadistribution instead of the purely exponential distribution.The intercept parameter of rain is diagnosed from rainmixing ratio and/or from equivalent melted snow/graupeldiameter relationships. The snow intercept parameter isdependent upon both temperature and snow water content.The graupel intercept parameter depends on its mixing ratioto allow the graupel category to mimic both graupel andhail. In addition, CDNC can be employed as an inputparameter of the autoconversion parameterization in theThompson scheme. According to the p-mean CDNC inthe P-case, a CDNC of 100 cm�3 is employed in the presentsimulations.3.3.1. Comparison of Radar Reflectivity[36] Figure 17 presents the modeled composite radar

reflectivity from the three single-moment schemes and thetwo-moment scheme that accounts for the aerosol effect(hereinafter referred as to the Aero scheme) at 2100 UTC,31 October. In general, the position of the squall line issuccessfully reproduced by the four types of microphysicalschemes compared with the measurement (Figure 4), indi-cating that the structure and evolution of MSCs are likelydetermined by large-scale meteorological conditions. Boththe Lin and WSM6 schemes predict a more pronouncedleading convective line, but a thinner stratiform precipita-tion regime than the observation. The radar reflectivity inthe convective zone is significantly overestimated by thetwo schemes. Those results are likely explained by morewarm rain but less ice particles produced by the twoschemes. Both schemes are based on similar microphysicalparameterizations [Lin et al., 1983; Rutledge and Hobbs,1984]. However, the stratiform precipitation regime in theWSM6 scheme is slightly broader than that in the Lin

scheme owing to smaller falling velocities of graupel anda different radiation feedback in the revised ice microphys-ics in the WSM6 scheme [Hong and Lim, 2006].[37] A leading convective line and a trailing stratiform

regime comparable to the observed are produced by theThompson scheme and the P-case and C-case using the Aeroscheme, but there is a noticeable discontinuation in theconvective line in the simulation of the Thompson scheme.The manifested improvement of the radar reflectivity sim-ulation in the Thompson scheme is attributable to thediagnosed intercept parameters which play an importantrole in determining the vertical velocity and microphysicalgrowth rates of hydrometeors. The snow size distribution isassumed to be dependent on both ice water content andtemperature (with larger intercepts at higher altitudes). Thesnow is also assumed to be nonspherical and represented bya sum of exponential and gamma distribution. Constants forthe snow terminal velocity are chosen to be consistent withvertically pointing radar data [Thompson et al., 2006].Therefore, the simulated snow is mainly distributed at highaltitude, producing a larger trailing stratiform region [Tao etal., 2007]. In the two-moment scheme, the sedimentation ofthe bulk mass and number of hydrometeors is accounted for.Since the bulk mass and the bulk number sediment atdifferent falling velocities and the mass-weighted fallingvelocity is always larger than the number concentration-weighted falling velocity, sedimentation results in a realisticgravitational size-sorting mechanism whereby the meansizes are redistributed in the vertical direction with larger(smaller) mean sizes appearing at relatively lower (higher)levels. A large amount of precipitating particles with smallerfall velocity is withheld at high levels to form the largerstratiform regime. Additionally, the leading line in the P-caseis more evident than that in the C-case, indicating that

Figure 17. Comparison of composite radar reflectivity distributions at 2100 UTC, 31 October 2005,from different microphysical schemes and observations.


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increasing aerosols enhance the convection of the squallline.3.3.2. Rainfall Comparison[38] Figure 18 shows the pattern of the simulated 24-h

precipitation from 0600 UTC, 31 October, to 0600 UTC,1 November, using the four different microphysicalschemes. Generally, the various schemes successfully pre-dict the precipitation pattern compared with the measure-ment (Figure 7a). The major intensive rainfall centers arereproduced by all schemes. However, all four schemesoverestimate the precipitation amount, especially in theLin and the WSM6 schemed. The most intensive cumula-tive precipitation is predicted by the Lin scheme, more than100 mm d�1. Compared with the three single-momentschemes, the cumulative precipitation produced by the two-moment scheme is in better agreement with themeasurement.[39] The performance of the two-moment scheme is

further evaluated from the temporal variation of the mod-eled and observed rainfall rate averaged over 72 monitoringsites in Texas in the P-case and the C-case (Figure 19). TheLin scheme significantly overestimates the rainfall rate. TheWSM6 and the Thompson schemes also considerablyoverestimate the rainfall rate, especially for the WSM6scheme. The simulation by the two-moment scheme in theP-case agrees well with observation, demonstrating theadvantage of the two-moment microphysical scheme torepresent the cloud processes. However, the C-case under-estimates the precipitation during the period with theintensive precipitation. The realistic gravitational size-sort-ing mechanism due to predictions of the bulk mass andnumber of hydrometeors in the two-moment microphysicalscheme leads to sedimentation of larger precipitating par-ticles but retains smaller precipitating particles at high

levels. The smaller precipitating particles are subject toevaporation with lower falling speed, which efficientlydecreases the surface precipitation. The diagnosed interceptparameters and the adjustable terminal velocity of snow inthe Thompson scheme also cause snow to be withheld athigh levels. The evaporation loss of snow during sedimen-tation contributes to the reduction of the surface precipita-tion. The simulation of the surface precipitation by theWSM6 scheme is improved compared with that by theLin scheme, because of a small but nevertheless importantdecrease of falling velocities of graupels in the WSM6scheme.[40] It is worthy to note that precipitation produced by the

single-moment bulk schemes is dependent on model param-eters which may partially be responsible for aerosol effects.For example, in the Thompson scheme, the CDNC can beemployed as an input parameter in the WRF model to

Figure 18. Comparison of 24-h precipitation distributions at 2100 UTC, 31 October 2005, fromdifferent microphysical schemes and observations.

Figure 19. Comparison of temporal variation of therainfall rate averaged over 72 monitoring sites from theobservations and different microphysical schemes.


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consider the aerosol-cloud interaction through changes theautoconversion rate, and the results of the Thompsonscheme can be dependent on the assumed droplet concen-tration. In the present study, according to the p-mean CDNCin the P-case, a CDNC of 100 cm�3 is utilized to comparewith other schemes. An additional sensitivity study has beenperformed with a CDNC of 300 cm cm�3 to verify theresponse of precipitation to the variation of CDNC. Con-trary to the results of the two-moment scheme, when theCDNC increases from 100 to 300 cm�3, the total domainprecipitation is decreased by about 2.7%, showing that theaerosol-cloud interaction cannot be reasonably evaluatedonly through modifying the autoconversion process in theone-moment scheme.[41] Although the two-moment scheme shows better

performance in precipitation than the four different micro-physical schemes, this scheme overestimates precipitationby roughly 38% in the P-case and 22% in the C-case overthe entire domain. A number of studies have showed thatthe 3-km grid spacing might not be sufficient to resolve deepconvection and that finer spacing is required [Bryan et al.,2003; van den Heever et al., 2006; Trier et al., 2005]. Forexample, Bryan et al. [2003] pointed out that adequatelyresolving deep convective cells, which are building blocksof MCSs, requires horizontal grid spacings of 1 km or less.In order to evaluate the impacts of horizontal grid spacingon the simulations of precipitation, additional simulationwith a 750-m horizontal resolution in the P-case has beenperformed in the domain 3. Figure 20 shows a comparisonof the precipitation distribution in the domain 3 with the 3-km and 750-m resolution. The difference in the precipitationbetween the 3-km and 750-m simulations is rather insignif-icant, except that the distribution in the 750-m simulation issmoother than that in the 3-km simulation. The total domainprecipitation in the 750-m simulation is slightly larger thanthat in the 3-km simulation, indicating that an increasinghorizontal resolution from 3 km to 750 m may not neces-sarily improve precipitation simulation in the case. Addi-tionally, varying vertical resolutions also influence theintensity and precipitation in numerical weather prediction(NWP) models [Lindzen and Fox-Rabinovitz, 1989; Pecnickand Keyser, 1989; Persson and Warner, 1991]. In this study,

the vertical model resolution near cloud base is about 500 mand the spacing increases to 1 km toward the cloud toplevels. Zhang and Wang [2003] studied the effects ofvarying vertical resolution on the simulation of a hurricaneusing MM5 and concluded that increasing vertical resolu-tion tends to produce a deeper storm and more precipitation.[42] Another factor that influences the precipitation sim-

ulation in the two-moment scheme is the representation ofthe aerosol size distribution in the WRF model. The three-moment modal approach is employed in the present study.In a previous study by Zhang et al. [2002] on evaluation ofthe impact of the aerosol size representation on modelingaerosol-cloud interaction in shallow stratiform clouds, modalapproaches (two-moment and three-moment) yield results thatare generally within a few percentages compared to a high-resolution 108-section approach. According to Figure 19,the precipitation overestimation in the P-case is principallyattributable to the fact that the intensive precipitation occurs1 h earlier and the squall line moves more slowly than theobservation. The occurrence of earlier precipitation andslower movement of the squall line are also predicted fromthe one-moment schemes and the C-case, indicating thatdifferent microphysical schemes or varying aerosols in thetwo-moment scheme do not affect the timing of precipitation,which is likely determined by large-scale meteorologicalconditions. Additionally, the P-case predicts a higher precip-itation rate than the C-case because the increase of aerosolsenhances the precipitation efficiency in the P-case, as dis-cussed in section 3.2.2. When the squall line covers most ofthe monitoring sites from 1700–2100 UTC, 31 October,the P-case performs well in the rainfall rate in comparisonwith the measurement, but the C-case underestimatesthe rainfall. However, the precipitation enhancement in theP-case results in the overestimation of the rainfall ratecompared with the measurement from 1300–1600 UTCand from 2100–2300 UTC, 31 October, mainly caused bythe occurrence of earlier precipitation and slower movementof the squall line. In addition, the three-moment modalapproach used for the aerosol representation does not appearto have significant impacts on the aerosol effects comparedwith other studies [Wang, 2005; Zhang et al., 2007; Fan etal., 2007b; Li et al., 2009; Khain and Pokrovsky, 2004;Khain et al., 2008; Lynn et al., 2005b; Lynn and Khain,2007; Phillips et al., 2007].

4. Summary and Conclusions

[43] A squall line occurred in the south plains of theUnited States from 0000 UTC, 31 October, to 0600 UTC,1 November 2005, has been investigated using the CR-WRF model with a two-moment bulk microphysical schemedeveloped by Li et al. [2008b]. The effects of aerosolconcentrations on the evolution of the convective stormhave been evaluated through model experiments with dif-ferent aerosol scenarios, including continental aerosols fromthe MOZART ammonium sulfate output to quantitativelyrepresent the polluted aerosol condition.[44] Compared with the observed composite radar reflec-

tivity, the CR-WRF model with the two-moment micro-physical scheme well reproduces the development of thesquall line, with successful simulations of a leading con-vective line and a trailing stratiform precipitation regime.

Figure 20. Comparison of modeled 24-h precipitation forthe P-case from 0600 UTC, 31 October 2005, to 0600 UTC,1 November 2005, using (a) a 3-km horizontal resolutionand (b) a 750-m horizontal resolution in domain 3.


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The magnitude of the simulated radar reflectivity is alsocomparable to the observed. The pattern of the simulatedaccumulated rainfall is in good agreement with the obser-vation, but the CR-WRF model overestimates precipitationin the P-case over the whole domain. For the P-case, themodeled and observed rainfall rates averaged over 72monitoring sites in Texas are in good agreement.[45] With varying aerosol concentrations from the clean

background case to the polluted continental case, thenumber of activated aerosols increases significantly, leadingto a remarkable enhancement in the CDNC and a consid-erable reduction in the droplet size. More numerous smalldroplets decrease the collection efficiency of both clouddroplets and ice particle riming. The enhanced CDNC notonly hampers the conversion of cloud water to rainwater butalso the formation of graupels by suppressing immersion icenucleation. Correspondingly, the number concentrations ofraindrops and graupel particles are decreased in the pollutedaerosol case. Less efficient warm rain process with higherCDNC leads to a greater quantity of supercooled waterbeing thrust aloft and frozen (mostly by riming), yieldinggreater buoyancy that invigorates cloud dynamics [Seifertand Beheng, 2006; Khain and Pokrovsky, 2004; Zhang etal., 2005; Lynn et al., 2005a, 2005b; van den Heever et al.,2006; van den Heever and Cotton, 2007]. Furthermore, theprecipitating particles grow to large sizes because of pro-longed collection of smaller liquid particles with increasedCDNC. The aerosol concentrations have significant effectson the precipitation intensity, but a minor effect on therainfall pattern. The precipitation averaged in the entiredomain is increased by 13% in the P-case compared tothe C-case. This is explained since the raindrops with alarger size and a faster falling speed have larger probabil-ities to survive evaporation after falling to a subsaturatedcondition below the cloud base. More intensive rainfalloccurs in the P-case than in the C-case, consistent withthe increase of convection zone and convective strength inthe P-case. Aerosols intensify deep convective clouds, butdecrease the intensity and precipitation from small cumulusclouds located far from the squall line, consistent with theresults found by Lynn et al. [2005a, 2005b] and Lee et al.[2008a, 2008b]. Khain et al. [2008] and Khain [2009]presented a classification of aerosol effects on precipitationfrom convective clouds and convective systems, in whichthe relative humidity represents one of important parametersin determining the response of precipitation to aerosols andfor high relative humidity precipitation from convectiveclouds increases in polluted conditions. The transport ofwater vapor from the Gulf of Mexico provides high relativehumidity condition for the development of the squall line inthe present study. The response of precipitation to theincrease of aerosols in our study agrees with the classifica-tion defined by Khain et al. [2008] and Khain [2009],indicating that the two-moment bulk microphysics schemereproduces these features in precipitation.[46] The two-moment microphysical scheme has been

compared with three WRF single-moment bulk schemes(the Lin, WSM6, and Thompson schemes). All schemesreproduce the precipitation and radar reflectivity pattern.However, the Thompson scheme and the two-momentscheme are in better agreement with observations in termsof radar reflectivity, both showing a leading convective line

and a trailing stratiform precipitation regime. The two-moment scheme yields the precipitation simulation closerto the measurement, because the realistic gravitational size-sorting mechanism in predictions of the bulk mass andnumber of hydrometeors in the two-moment scheme facil-itates the sedimentation of larger precipitating particles butretains smaller precipitating particles at high levels. Thesmaller precipitating particles are subject to evaporationwith lower falling speed, which decreases the surfaceprecipitation.

[47] Acknowledgments. This study was supported by NSF (ATM-0424885). Additional support was provided by the Texas A&M UniversitySupercomputing Facilities. R.Z. acknowledges support from the NationalNatural Science Foundation of China (grant 40728006). The authors aregrateful to Naifang Bei for assistance in analysis of the meteorologicalpattern in the study.

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