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Silver iodide seeding impact on the microphysics and dynamics of

convective clouds in the high plains

Baojun Chen a,, Hui Xiao b

a Key Laboratory of Mesoscale Severe Weather/MOE, School of Atmospheric Sciences, Nanjing University, Nanjing 210093, Chinab Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

a r t i c l e i n f o a b s t r a c t

Article history:

Accepted 6 April 2009Theconcept of dynamicseeding is a physically plausible hypothesis buthas notyet beenconfirmed

by observations or numerical simulations. To verify the hypothesis of dynamic seeding, a three-

dimensional nonhydrostatic cloud model with two-moment bulk microphysics scheme has been

used to investigate the effects of silver iodide seeding on cloud microphysics, dynamics and

precipitation of convective storms. Eight species of water are included in the model: vapor, cloud

water, rain, cloud ice,snow, graupel, frozen drops andhail. A HighPlains hailstorm casedeveloped

on 1 August 1981 during the Cooperative Convective Precipitation Experiment season is used for

the simulations.

The simulated cloud system consists of two cloud cycles during the period of integration. The

second-cycle clouds are the dominant precipitation producer of the simulation, contributes

about 80% of the total surface precipitation, which are caused by interactions of the downdraft

outflow induced by falling precipitation from thefirst cycle cloud in the boundary layer and the

southeasterly relative infl

ow at low level.The model results show that the cold microphysical processes dominate the hydrometeor

production in the simulated storms. The ice hydrometeors account for ~70% of the total

hydrometeor mass. Accretion of cloud water is the dominant growth mechanism for precipitating

ice hydrometeors. Melting of graupel and accretion of cloud water by rain are the major sources of

rain water. Conversion of graupel is the largest source of hail formation, contributing about 80%.

Four seeding tests have been carried out to investigate the effects of seeding at a different release

mode (instantaneous or continuous, one grid point or several grid points), and with different

amounts of the seeding agent. All of cases are seeded in the region of the strongest updraft when

the model cloud top was passing the 10 C level at 10 min, and produce significant effects. Thecloud seeding results in substantial increases in accumulated precipitation at the surface in all

seeded cases (by 2030%). Moreover, both rainfall and hailfall have increased due to seeding. The

most important contribution to the increase in hail is due to conversion of graupel to hail and

accretion of cloud water by hail. Increase of graupel melting and subsequent accretion of cloud

water by rain contribute mostly to rain enhancement.

Theseeding enhancesthe unloading effectof precipitationmassmainly in theform of graupel,leads

to a stronger downdraft outflow and enhanced convergence in the boundary layer, further causes

thesecondary clouds toform earlier andgrowlarger. Theenhanced updraftincreases theinflowand

causes the cloud to process more water vapor and thereby cloud water, resulting in increase of

accretional growth of cloud water by precipitating particles, finally the precipitation enhancement.

These results indicate that silver iodide seeding could significantly influence the cloud

dynamics, microphysics and further precipitation of convective storms in the High Plains. The

simulation notonly supports the hypothesis of dynamic seeding, butalso demonstratesthat the

convective cloud with a cold base but a long lifetime has dynamic seeding potential as well.

2009 Elsevier B.V. All rights reserved.

Keywords:

Convective clouds

Precipitation enhancement

Dynamic seeding

Weather modification

High Plains hailstorm

Atmospheric Research 96 (2010) 186207

Corresponding author. School of Atmospheric Sciences, Nanjing University, 22 Hankou Road, Nanjing 210093, China. Tel.: +86 25 83592575.

E-mail address: [email protected] (B. Chen).

0169-8095/$

see front matter 2009 Elsevier B.V. All rights reserved.doi:10.1016/j.atmosres.2009.04.001

Contents lists available at ScienceDirect

Atmospheric Research

j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / a t m o s
mailto:[email protected]://dx.doi.org/10.1016/j.atmosres.2009.04.001http://www.sciencedirect.com/science/journal/01698095http://www.sciencedirect.com/science/journal/01698095http://dx.doi.org/10.1016/j.atmosres.2009.04.001mailto:[email protected]


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1. Introduction

In many places around the world, cloud seeding has been

widely used for rain enhancement and hail suppression.

Among precipitating cloud systems, convective clouds are

some of the major targets for cloud seeding. There are two

major methods of convective cloud seeding: glaciogenic

seeding and hygroscopic seeding. The scientific basis for

increasing precipitation from convective clouds by glaciogenic

seeding rests on two concepts, namely, the static and dynamic

seeding concepts (Braham, 1986).

The fundamental concept of the static seeding is to

increase the efficiency of precipitation formation in clouds

by introducing an optimum concentration of ice crystal by

cloud seeding. While the main object of the dynamic seeding

is to enhance the vertical motion in clouds and thereby

vertically process more water through the clouds resulting in

increased precipitation.

The hypothesized chain of physical responses to dynamic

seeding in earlier experiments has been summarized by

Woodley et al. (1982). But few of the hypothesized steps in

the chain of events have been measured in experiments or

verified and validated by numerical models (Orville, 1996). To

explain the less-than-expected increases in cloud-top heights,

Rosenfeld and Woodley (1993, hereafter RW93) proposed a

modified conceptual model of dynamic seeding. The revised

conceptual model gives much more attention to microphysi-

cal processes than before. It involves the production and

sustenance of greater precipitation mass at and above the

seeded region, which allows more time for continued growth

of the cloud. The subsequent unloading of this enhanced

water mass increases the downdraft and precipitation while

at the same time allowing for additional growth in the region

that retains some of the previously released latent heat.Furthermore, RW93 note that the modified conceptual model

applies to convective clouds in which the coalescence process

is active to produce rain drops in the supercooled region.

However, this is also a proposed hypothesis of dynamic

seeding that has not been verified.

Although the concept of dynamic seeding is a physically

plausible hypothesis that offers the opportunity to increase

rainfall by much larger amounts than simply enhancing the

precipitation efficiency of a cloud, this method remains as yet

an unproven technology for increasing rainfall for water

resources (Bruintjes, 1999; Silverman, 2001). It is still a critical

issue in weather modification that the hypothesis of dynamic

seeding needs to be validated and supported (NRC, 2003).Numerical cloud models are important tool for weather

modification research (Orville, 1996; Garstang et al., 2005).

Orville (1996) provided a comprehensive overview of the use

of cloud models in the field of weather modification. During

the past decades, great progress has been made in the field of

cloud modeling. Guo and Huang (2002) simulated the hail

formation and growth mechanism in a multicellular storm

using a three-dimensional hail category model. Farley et al.

(2004a,b) examined the relative influence of warm rain

process on precipitation development and hail formation in

thunderstorms and seeding effects using a two-dimensional

hail category model. Lin et al. (2005) investigated the

differences in microphysical structures of summer thunder-storms in the humid subtropics versus High Plains using a

three-dimensional cloud model. Kuhlman et al. (2006) madea

successful simulation of the 29 June 2000 tornadic supercell

storm using a three-dimensional cloud model with electrifica-

tion mechanisms. uri et al. (2006, 2007, 2008) investigated

thedispersal of seeding agent within convective cloud and the

effects of silver-iodide seeding on precipitation using a cloud-

resolving mesoscale model. And their simulations showed

that a silver-iodide seeding resulted in enhanced precipitationover 100 km downwind from the initial point of release.

Despite significant advances in numerical simulation of

convective cloud structures and seeding effects, modeling

studies with respect to verifying the hypothesis of dynamic

seeding in three-dimensional cloud model have not be done

yet.

In this work we investigate the effect of Silver Iodide (AgI)

seeding on the cloud microphysics, dynamics and precipita-

tion of convective clouds in the High Plains using a three-

dimensional cloud model with bulk microphysical scheme.

The primary purpose of this study is 1) to examine whether

cloud seeding can increase the precipitation of convective

clouds with a cold base in the high plains region; 2) to verifyand validate the hypothesis of dynamic seeding. Generally,

the demand for fresh water resources is very urgent in some

high plains region around the world, for instance, in the west

of the United States and northwest of China. Previous studies

indicated that the High Plains convective clouds might be not

suitable for static-mode seeding due to the limited lifetime

and low liquid water content of clouds (Cooper and Lawson,

1984; Smith and Coauthors,1984). Such clouds may also have

no dynamic seeding potential because of the lack of abundant

supercooled raindrops as pointed out by RW93. Note that the

retention of increased precipitation mass in the cloud is an

important aspect of RW93 conceptual model. If the cloud

lifetime is long enough to allow the seeding-increased iceparticles growing in the supercooled region longer, resulting

in the retention of precipitation mass in the cloud, can the

dynamic seeding effect be accomplished?

The paper is organized as follows. Section 2 describes the

cloud model used for the present study. The results of

numerical simulation including the unseeded and seeded

runs are presented in Section 3. Discussions of the results and

conclusions are presented in Sections 4 and 5, respectively.

2. Model description

2.1. Convective cloud model

A three-dimensional compressible nonhydrostatic cloud

model with two-moment bulk microphysics scheme (Kong et

al., 1990; Hong and Fan, 1999; Xiao et al., 2004) was used to

simulate the hailstorm. The nineteen predictive variables

include the three Cartesian velocity components, the Exner

function, potential temperature, mixing ratios of water vapor

(v), cloud water (c), rain water (r), cloud ice (i), snow (s),

graupel (g), frozen drop (f) and hail (h), and number

concentrations of raindrop, cloud ice, snow, graupel, frozen

drop and hail. The basic equations in standard Cartesian

coordinates (x, y, z) are:

du

dt + CpvAV

Ax = Du 1

187B. Chen, H. Xiao / Atmospheric Research 96 (2010) 186207


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dv

dt+ Cpv

AV

Ay= Dv 2

dw

dt+ Cpv

AV

Az= g V

+ 0:608q Vv

qc + qr + qi + qs + qg + qf + qh

+ Dw3

dV

dt+

C2

Cp2

v

AvujAxj

= RdCv

VAujAxj

+C2

Cp2v

dvdt

+ D 4

d

dt= Qlv + Qil + Qvi + D 5

dqxdt

= Sqx + Dqx +1

A

AzqxVx 6

dNxdt

= SNx + DNx +1

A

AzNxVx 7

dXsdt

= DXs + source + sink 8

Where u, v, w, ', , v and Xs are the three velocity

components, the perturbation Exner function, potential

temperature, virtual potential temperature and the mixing

ratio of the seeding agent, respectively. qx is one of mixing

ratio of water vapor, cloud water, rain water, cloud ice, snow,

graupel, frozen drop and hail. Nx is number concentration of

raindrop, cloud ice, snow, graupel, frozen drop and hail,

respectively. Du, Dv, Dw, D, D, Dqx, DNx and DXs are the

turbulent fluxes of u, v, w, ', , qx, Nx and Xs. Qlv, Qil and Qviare the latent heating/cooling terms due to condensation/

evaporation, freezing/melting, and sublimation/deposition

produced by microphysical processes, respectively. Vx is the

terminal velocity of hydrometeor x but omitted for watervapor and cloud water. Sqx and SNx are denoted as cloud

microphysical processes. source in Eq. (8) represents the

seeding rate of seeding agent added at the seeding time. The

total sink term for the seeding agent is given by

sink = Sbc + Sic + Sbr + Sir + Sdv; 9

where Sbc, Sic, Sbr and Sir are Brownian and inertial impact

collection rates due to cloud droplets and raindrops, respectively.

Sdv is the activated agent particles which work as deposition

nuclei. These sink terms represent the effects of contact freezing

and condensationfreezing/deposition nucleation.

The model domain is on a standard spatially staggered

mesh system. A conventional time-splitting integration

technique, the same as that proposed by Klemp and

Wilhelmson (1978), is also used in this model. The large

time step is 5 s, while the small time step is 1 s. The spatial

difference terms are of second-order accuracy except for the

advection term that has fourth-order accuracy. All other

derivatives are evaluated with second-order centered differ-

ences. The radiation boundary conditions of Klemp and

Wilhelmson (1978) are used for the lateral boundaries

while the top and bottom boundaries are assumed as a rigid

wall. A Rayleigh friction zone is also used to absorb vertically

propagating gravity waves near the top of the domain. The

model includes a conventional first-order closure for subgrid

turbulence and a diagnostic surface boundary layer based on

Monin-Obukhov similarity theory.

2.2. Model microphysics

Cloud ice and rain are assumed to follow gamma size

distribution. While all precipitating ice particles including

snow, graupel, frozen drops and hail, are assumed to follow

inverse exponential size distributions (Hong and Fan, 1999).

Cloud droplet is initiated by condensation and assumed to

be monodisperse. The collision and coalescence of cloud

droplets to form raindrops is parameterized following Lin

et al. (1983), but the relative dispersion is calculated from the

prescribed concentration of cloud droplets, following Grabow-

ski (1999). Herein, the number concentration of cloud droplets

is set to 1000 cm3, a typical value for cold-based convective

clouds of the High Plains region (e.g., Kubesh et al., 1988).

The natural cloud ice is produced through depositional

nucleation and homogeneous freezing of cloud droplets below

40 C. Once formed, cloud ice grows via the depositional and

riming processes. Ice multiplication, or the secondary ice

generation mechanism, for riming of snow and graupel/frozen

drop at temperatures between 3 C and 8 C is based onHallet and Mossop (1974) and is parameterized following Hu

and He (1988).

In the model, two categories of hail embryos are

simulated: frozen drops and graupel. Frozen drop can be

initiated by probabilistic freezing of raindrops, collisions

between rain and cloud ice, snow or active AgI particles

only when the raindrop diameter is greater than 1 mm. If the

raindrop diameter is smaller than 1 mm, frozen raindrop is

converted to graupel. Graupel may be also created via a

parameterized form of the Bergeron process, or by aggrega-

tion of ice crystals and snowflakes. The autoconversion rate

coefficient for cloud ice toformsnow, snowto form graupel, is

based on Lin et al. (1983).

Graupel and frozen drop convert to hail when their

diameters are greater or equal to 5 mm via autoconversion

(Hu and He, 1988). All of frozen drop, graupel and hail grow

byaccretion of cloudwater, cloud ice, rain and snow, or can be

melted or sublimated.

Snow can be formed by the BergeronFindeisen process

and autoconversion of cloud ice to snow. Production terms for

snow include various accretion terms (collisions of snow with

cloud ice, cloud water, raindrops, and hail/graupel/frozen

drop), snow melting and sublimation/deposition.Rainwater can be initiated by autoconversion of cloud

droplets to raindrops, melting of precipitating ice, or shedding

of excess water drops accreted by hail embryo and hail in the

wet growth regime.

Microphysical processes in the model are presented in

Appendix B. A complete description on microphysical para-

meterization can be found in Hong and Fan (1999) and Xiao

et al. (2004).

2.3. Silver iodide seeding

In the model, the possible mechanisms by which the silver

iodide can produce the ice phase include condensation

freezing/deposition nucleation and contact freezing nucleation.

188 B. Chen, H. Xiao / Atmospheric Research 96 (2010) 186207


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Contact nucleation mechanisms are limited to inertial impact

and Brownian collection. The seeding processes included in the

model are calculated as follows (Hsie et al.,1980).

(1) Collections due to cloud droplets and raindrops are

Sbc =XSt

= 4DSXSNcRc = 9:657 1017

XSNcT;

10

Sic =XSt

= XSNcR2c EcsVC = 3:142 10

16XSNc;

11

Sbr =XSt

= 4DSXSZ

0

1

2DrN0rexp rDr dDr

= 12DSXSN0r4r ;

12

Sir =XSt

= XSErsZ

0

4D

2r Vr Dr Nr Dr dDr

= 3:6914 1043 = 2XSN0rAvr1 =2

5:5r

;13

where Nc

is the number concentration of cloud droplets, N0r

is number of raindrops per unit diameter, r is the slope

parameter in rain distribution (cm1), Rc and Vc are the

radius and terminal velocity of a cloud droplet (assumed to

be 10 m and 1.0 cm s1, respectively), Ecs and Ers are the

collection efficiency of cloud droplets and raindrops for the

seeding agents (assumed to be 104 and 0.5 104,

respectively), and Ds is the diffusivity of the seeding agent,

given by Ds= kTB, B can be expressed as

B =1 + a Vd= RS

6RS; 14

where d, the mean free path, is assumed to be 0.1 m, a=

0.9, Rs, the radius of the seeding agent, is assumed to be0.1 m. k(=1.381023 J K1) is Boltzmann's constant.

(=1.81105 kg m1 s1) is the dynamic viscosity of air.

(2) The activated seeding agent as deposition nuclei under

saturation with respect to water is

SDV = msdNaD T

dt= w

A XSNa T

Az= Na 20 -C ; 5 V T b 20 -C

15

SDV = msNaD T = XSNa T = Na 20 -C ; Tz 20 -C 16

where ms is the mass of a seeding agent. NaD, the number of

the seeding agents active as deposition nuclei under thesupercooling T (=T0T, and T0=0 C) condition, iscalculated as

NaD T = XSNa T

Na 20 -C

= ms; 17

where Na(T) is the number of nuclei active at the super-

cooling T. The activation curve of AgI is taken in agreement

with Hsie et al. (1980)

Na T =exp 0:022 T 2 + 0:88 T 3:8

h i; 5 -C V T b 20 -C

160; Tz 20 -C

(

18where Na(T) is in unit of L1.

The interaction of the seeding agents with a cloud is

considered to follow the contact and deposition nucleation

processes, and only inertial impact and Brownian collection

are considered as possible mechanism for contact nucleation.

The processes included in the model are as follows.

(1) Contact freezing nucleation

NUcsi = Qc

Nac T

tNc = Sbc + Sic Na T

Na 20 -C QcN

1c m

1s ;

20

NUrse = QrNar T

tNr= Sbr + Sir

Na T

Na 20 -C QrN

1r m

1s ;

21

Where, NUcsi and NUrse are the rate of cloud water

transformation to cloud ice and the rate of rain water

Fig.1. Environmental characteristicsfrom 1330 MDT MilesCity sounding for 1

August 1981: (a) temperature(solid line)and dewpointtemperature(dashedline), and (b) wind components (m s-1) at the heights (km) indicated.



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transformation to graupel or frozen raindrops due to contact

nucleation, respectively. t is the timestep for numerical

integration. Nr is the number concentration of raindrops. Nac(T) and Nar(T) are the number concentrations of active ice

nuclei captured by cloud droplets and rain drops at a

supercoolingT, respectively.

(2) Deposition nucleation

This process is only considered under the condition of

saturation with respect to ice. The rate of deposition

nucleation is calculated as NUvsi

NUvsi = mi0Sdvm1s ; 22

where mi0 is the initial cloud ice mass formed by seeding

agent and assumed to be 1012 kg.

The major assumptions used in model related to AgI

seeding are given as follows:

(i) The size distribution is assumed to be monodispersed

for AgI particles, and the radius and mass are assumed

to be 0.1 m and 2.381017 kg, respectively.

(ii) Only one active ice nucleus is captured by one liquid

drop for contact freezing nucleation.

(iii) The terminal velocity of AgI particles is assumed small

enough to be ignored.

(iv) The collection rates of ice hydrometeors for AgI particle

droplets are not considered.

2.4. Model initiation

The model was initialized based on the rawinsonde

sounding shown in Fig. 1 taken from Miles City, Montana on

1 August 1981 during the Cooperative Convective Precipita-

tion Experiment (CCOPE) season. The sounding shows a small

water vapor supplies with mixing ratio of 12 g kg1 and a

cold cloud base with temperature of slightly more than 10 C.

The wind hodograph showed moderate wind shear in the

lowest 6 km (5103 s1) but little shear above the 6 km

level. Note that a strongly southeasterly relative inflow exists

in sub-cloud layer.

All simulations were integrated to 6600 s using a

horizontal grid spacing of 1 km over a 36-km 36-km domain

and a vertical grid spacing of 0.5 km over a 19-km depth.

Convection was initiated by a warm thermal bubble of 16 km

wide and 5 km deep, which was centered at 2.5 km above

ground level in a horizontally homogeneous environment.

The maximum thermal perturbation was 1.5 K in the center of

the bubble. A domain moving method that the grids translate

with the center of total hydrometeor mass is used to keep the

simulated storm within the computational domain.

3. Results

3.1. Simulation of the reference (unseeded) case

3.1.1. Temporal and spat ial patterns of dynamical and

microphysical structures

Thesimulated storm is strong andlong-lived.Fig. 2 shows the

time series of the maximum updraft velocity in the model

domain at each level during each 1-min interval for the 110 min

integration. The natural cloud systemconsists of two main cycles

during the period of integration. The first cycle (050 min) is

produced by a model perturbation, and the second-cycle clouds

(50110 min) are initiated by downdraft outflow induced by

precipitation in the boundary layer. Note that the second-cycle

cloud also consists of two cells. The cores of the updraft for both

cycles are at about 8 km altitude and 30 to40 C level. Andthe maximum values for each cycle are 424 6 m s1. That value

issimilar to the 47 m s1 derived by Doppler radar (Miller et al.,

1990).

The ice hydrometeors play a dominant role in the total

hydrometeor mass. The mass percentage of the hydrometeor,

including both non-precipitating (cloud water and cloud ice)and

precipitating (rain, snow, graupel, frozen drop and hail),

integrated over the entire domain with respect to time is

Fig. 2. Time vs. height sections of maximum updraft (m s1) and mean

temperature (C) in the cloud.

Fig. 3. Mass percentage of total condensate, integrated over the entire

simulation domain (353518.5 km3), for both non-precipitating hydro-

meteors (cloud water and cloud ice) and precipitating ones (rain, snow,graupel, frozen drops and hail).



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Fig. 4. Time vs.height sections of maximum content (g m

3) for(a) cloud water, (b)rain water, (c)graupel,(d) frozendrops and(e) hailin themodeldomain. Themean temperature in the cloud also presents.



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shown in Fig. 3. One can see that the liquid phase (cloud water

and rain water) is far less abundant than the ice phase (cloud ice,

snow, graupel, frozen drops and hail). Overall, the percentages of

the hydrometeor in the domain for the whole integration time

are 25% for graupel, 22% for snow, 16% for cloud ice, 5% for frozen

drop and 4% for hail, while 20% for cloud water and 8% for rain.

The ice hydrometeors account for 72% of the total hydrometeor

mass.

The supercooled liquid water (SLW) accounts for 56% of total

liquid water content. But only a small fraction of the SLW is rain

water. The liquid waterfields including the cloud water and rain

water are presented in Fig. 4a and b, respectively. Most of the

cloud water is above 4.0 km, at temperatures below 0 C.

Maximum cloud water content is 4.6 g m3 at 20 min, located at

~9 km. After 40 min, the maximum value of the supercooled

cloud water decreases to 3.8 g m3. Rain water exists primarily

below the melting level except for the early stage of cloud

development. In detail, nearly all rain water consists of super-

cooled water before 24min, whilewarm rain appears during 25

50min. After 50min, most rainis located below themelting level.

Basedon the time-averaged domain-integrated mass percentage

statistics, more than 95% of the SLW is the cloud water in the

simulation. The simulated maximum content of SLW atz=6km

is3.5gm3 after 50 min for the second-cycle clouds. That value

is similar to the 4 g m3 detected by the T-28 aircraft (Kubesh

et al., 1988).

The graupel, frozen drops and hail fields are presented in

Fig. 4ce. One can see that the frozen drops and hail have two

clear cycles corresponding to the updraft. Most of graupel, frozen

drops andhail are located above 4 km level, especially during the

first 22 min. After that time, some of these particles fall through

the 0 C level and melt into rain or reach to the ground. Peak

Fig. 5. Production of precipitating hydrometeors forthe simulatedstorm: (a)frozen drops,(b) graupel, (c)hail and(d) rain. Thecurves arethe resultof thevariousrates being summed over the entire domain and accumulated to the indicated time.



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values are 3.3 g m3 for graupel, 3.8 g m3 for frozen drops and

2.4 g m3 for hail, respectively.

In the simulation, the accumulated precipitationpartitioning

at the surface is roughly 90.3% for rain, 8.0% for hail, 1.4% for

graupel and 0.3% for frozen drops, so that the rain precipitation

significantly dominates the total precipitation at the surface.

Previous studies have shown that melting of graupel/hail and

shedding of rain during wet growth are the largest sources of the

rain production forthe HighPlainsstorm(e.g.,Kubesh et al.,1988;

Lin et al., 2005). In this study, we have similar results as well.

Detailed microphysical processes will be presented in the

following sections.

3.1.2. Microphysical productions of precipitating hydrometeors

In order to better understand the precipitation micro-

physical processes and the effects of cloud seeding, Fig. 5

presents various sources of the precipitating hydrometeors

including frozen drops, graupel, hail and rain. Because no

snowfall exists on the ground in the simulation, the micro-

physical processes of snow are not presented herein.

The production terms for frozen drops are illustrated in

Fig. 5a. Note the largest sourceis accretion of cloud water (CLcf),

accounting for 47% of the total frozen drop production. Freezing

of raindrops (NUrf), contributing 17%, is the second largest

source. And accretion of rain (CLrf), is the third largest source,

averaging an 14% contribution. This three processes account for

78% of the total frozen drop production.

Similar as thesituationof thefrozen drops, thelargest source

for graupel is also accretionof cloudwater (CLcg),accounting for

60% of the total graupel production. The second largest isaccretion of snow (CLsg), contributing 18%. Deposition of water

vapor (VDvg) is the third largest source, contributing 10%.

Accretion of rain (CLrg) is significantly less than above three

sources, contributing only 4%, because less supercooled rain is

produced.

In the simulation, the number concentrations of frozen

drops are significantly smaller than that of graupel, maximum

values averaging 1530 m3 for frozen drops and 7300 m3 for

graupel. Thus, conversion of graupel is the largest source of the

hail formation, contributing 80%, while conversion of frozen

drops accounts foronly 20%. Theratio of graupelto frozendrops

in the hail embryos is similar to the observations (Rasmussen

and Heymsfeild, 1987). The maximum hail concentration isabout 5 m3, a little higher than the observed 3 m3 (Kubesh

et al., 1988), but both in the same order of magnitude. After

formation, growth by accretion of cloud water (CLch) rapidly

becomes a dominant mechanism for hail production (Fig. 5c).

The production terms for rain are shown in Fig. 5d. One can

see that warm microphysics including autoconversion of cloud

water (CNcr) and accretion of cloud water (CLcr) dominate the

first 30 min when the ice-phase hydrometeors are still at the

middle and upper levels. After that time, the rain source is

gradually dominated by the melting of graupel (MLgr), frozen

drops (MLfr) and hail (MLhr). Overall, the source percentage of

the rain production accounts 58% for melting of graupel,15% for

melting of frozen drops, 12% for melting of hail, while less than

1% for autoconversion of cloud water and 14% of accretion of

cloud water. Thus, themelting of graupel, frozendrop andhail is

the largest sources forrain,together accounting for about 85%of

the total rain production. The accretion of cloud water alsoplays

a very important role in the rain production.

In summary, the primary growth mechanism of the

precipitating hydrometeor in the simulated storm is accretion

of cloud water, while the primary generation mechanism of

the rain is melting of precipitating ice, especially, the melting

of graupel.

3.2. Cloud seeding simulation

3.2.1. Methods

In this subsection, four sensitivity tests have been carried

out firstly to investigate the effects of seeding at a different

release mode (instantaneous or continuous, one grid point or

several grid points), and with different amounts of the

seeding agent. Among these seeding scenarios, we further

select one that leads to significantly enhanced precipitation to

analyze the dynamical and microphysical effects of seeding.

All of the seeded cases are seeded in the region of thestrongest updraft when the model cloud top was passing the

10 C level at 10 min. The center point of the initial seedingagent is centered at 4 km level (~0 C). Except case A1 that is

limited to one grid point, all other three cases extend

Table 1

Seeding parameters and the differences in rain, hail and total precipitation

accumulated on the ground at 110 min with respect to the unseeded case.

Cases Seeding

time (min)

Xs0

(g/g)

Seeding

amounts(g)

TR

(kt)

TH

(kt)

TP

(kt)

Unseeded / / / 7968.0 706.6 8827.3

A1 10 1.1 1010 240 1450.7

(18%)

353.7

(50%)

1934.4

(22%)

A2 10 1.6 1011 245 1064.8(13%)

298.5(42%)

1509.6(17%)

A3 10 1.6 1010 2450 2003.3

(25%)

815.0

(115%)

3166.3

(36%)

A4 1015 1.7 1011 2540 1192.7

(15%)

603.1

(85%)

2021.5

(23%)

Here, Xs0 is maximum value of the initial seeding agent mixing ratio; TR, TH

and TP are rain, hail and total precipitation, respectively. The numbers in the

parentheses are the percentage increase compared to the unseeded cases.

Fig. 6. Thepercentageof seeding agent in thedomain versustime forcase A3.



9/22

vertically and horizontally over three grid points. Forcases A2,

A3 and A4, the initial distribution of the seeding agent, similar

to Hsie et al. (1980) but extended over three dimensions,

which forms a cubic block, has maximum values in the center

and decreases with distance outward. Cases A2 and A3 are

used to test the effects of seeding with different amounts of

agent. For cases A1, A2 and A3, the seeding agents are

assumed to be released instantaneously within 30 s. Case A4

is used to simulate the effects of continuous seeding for 300 s.

Some features of sensitivity tests are shown in Table 1.

3.2.2. General description of sensitivity tests

For all cases the accumulated precipitation at the surface

has been increased due to seeding. The greatest magnitude of

precipitation enhancement is about 36%, occurring in the case

A3 with a larger amount of seeding agent.

Case A2 is seeded at the same time and region as case A3, but

withone-tenth of theseeding agent amountwhich is used in case

A3. One can see that the total accumulated precipitation at the

surface shows a 17% increase compared to the unseeded case.

Case A1 is seeded at the same time and with almost the

same seeding agent amount as case A2, but only at one grid

point. The results indicate a 22% increase in the accumulated

precipitation due to seeding. Compared with the increasebetween case A2 and the unseeded, case A1 is more effective,

Fig. 7. Accumulated precipitation on the ground as a function of time for the unseeded (solid lines) and seeded (dashed lines) runs: (a) rain, (b) frozen drops,

(c) graupel and (d) hail.

Fig. 8. Spatial distribution of total precipitation rate (mm/h) on the ground

for the unseeded (solid lines) and the seeded (dashed lines) runs, withcontour intervals of 10 mm/h starting at 10 mm/h.



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Fig. 9. The differences in the hydrometeor fields (units in g m3) for domain averaged between the seeded and unseeded runs: (a) cloud ice, (b) cloud water,

(c) snow, (d) graupel, (e) frozen drop, (f) hail and (g) rain.



11/22

which is due to the more effective transport of the seeding

agent into a favorable region.

For case A4 the seeding agent is released continuously for

5 min at the same region as case A3. The results indicate a 23%

increase in the surface accumulated precipitation in the

seeded case. The magnitude of precipitation augmentation is

smaller than thatof case A3 though with a little largeramount

of seeding agent in case A4.

These results demonstrate that the effectiveness of

seeding for precipitation enhancement is affected signifi-

cantly by the intensity of seeding.

Note that the seeding increase both rain and hail

precipitation in each seeded case. The percentage increases

of rain and hail are 1325% and 42115%, respectively. In next

section, we shall answer the question what particular

microphysical and dynamical mechanisms mostly to differ-

ences between seeded and unseeded cases.

3.2.3. Case A3: analysis of cloud seeding effects

Hereafter we select test A3 for our analysis because it

simulates the greatest increase in precipitation amount

compared to other sensitivity tests.

3.2.3.1. Sinks and conservation of seeding agent. Atthetime of

seeding, thecloud tophas already reached the14 C level.Theseeding region is located above 4 km, the level of the updraftcore with the maximum value of about 10 m s1. After being

injected into the domain, the seeding agent is advected into the

supercooled region very rapidly. Fig. 6 shows clearly that most

of the seeding agent has been activated by 16 min (within

6 min). Deposition nucleation or condensationfreezing

nucleation is the most important mechanism responsible for

cloud ice production by seeding, accounting for 99.5% of the

total sink of the seeding agent. The contact nuclei are captured

primarily by cloud droplets, mainly through the Brownian

motion mechanism, although this amounts to less than 0.5%.

However, the seeding agent does not work effectively in case

A3. At the end of simulation, the total consumption of seedingagent is 1624 g, accounting for 67% of the initial seeding

amounts. This indicates thatmorethan 30%of theseeding agent

is decreased due to strong advection through the boundaries,

because of less than 0.2% of seeding material remaining in the

domain after 110 min. A similar situation occurs in case A2 and

A4. Compared to other three cases, conservation of the seeding

agent in case A1 is very good. The total mass of seeding agent in

the domain is only decreased about 0.1%. Therefore, compared

with the increase in surface precipitation between case A2 and

the unseeded run, case A1 is more effective, although with the

same seeding amount. This result shows that the effectiveness

of seeding is affected significantly by the release place.

3.2.3.2. Surface precipitation comparison. The seeding even-

tually increases all kinds of precipitation amounts, although

the precipitation has decreased within scores of minutes

after seeding (Fig. 7). The seeded run at 110 min has

produced 9971.3 kt of rain, 67.1 kt of frozen drops, 433.7 kt

ofgraupel and1521.6 ktof hail, while7968.0 kt of rain,27.5 kt

of frozen drops, 125.1 kt of graupel and 706.6 kt of hail in the

unseeded run. The accumulated precipitation on the ground

in the seeded run are increased by 25% for rain, 143% for

frozen drops, 247% for graupel and 115% for hail. The

convection in the seeded case is much stronger than that in

the unseeded case, as indicated by twice the amount of total

ice-phase precipitation, especially hail precipitation in the

seeded compared to the unseeded case.

The distribution of total precipitation rates on the ground

in the unseeded and seeded runs is shown in Fig. 8. Although

two modes of precipitation are clearly seen in both two runs,

the precipitation area in the seeded run is larger than that in

the unseeded run, especially for intense precipitation. More-

over, the precipitation on the ground has been redistributed

due to seeding, and more tends to shift southwardly. The

seeding slows down the simulated storm motion.Note that the rain and graupel precipitation at the surface

are delayed about 1 min by seeding, this is due to compete

beneficially for the available supercooled water resulting in

decreasing of graupel fallout and melting.

3.2.3.3. Variations in the hydrometeorfields and microphysical

processes. The differences in hydrometeor content for domain

averaged betweenthe seeded and unseeded runs in the timevs.

height sections are shown in Fig. 9. And the differences in the

accumulated production terms for hydrometeor vs. time are

shown in Fig. 10. Herein, contrastively insignificant terms are

not included.

Silver iodide seeding directly affects the production of cloudice. The amount of cloud icehas increased except for theearlier

times in the upper level of the seeded cloud (Fig. 9a). And

significant enhancement in cloud ice appears after 50 min. The

increase in cloud ice content is mainly due to the increase in

homogenous freezing of cloud water (HFZci) and depositional

growth(VDvi). In the simulation, the role ofHM multiplication

is less significant to the cloud ice generation because the

average droplet diameter in the clouds is only about 12 m,

which is typicalof continental droplet spectra, simulated herein

and observed by aircraft (Kubesh et al., 1988). That value is

smaller than that required in theHM mechanismwith droplet

diameter larger than 24 m (Pruppacher and Klett, 1997).

The seeded case has less cloud water content than theunseeded case within 10 min after seeding because the

Fig. 9 (continued).



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Fig. 10. The differences in accumulated production terms for hydrometeor vs. time between the seeded and unseeded runs: (a) cloud ice, (b) snow, (c) graupel,

(d) frozen drop, (e) hail and (f) rain.



13/22

increased ice crystals grow at the expense of the supercooled

cloud water (Fig.9b). After50 min, theamount ofcloud water at

lowleveland middlelevelhas significantlyincreased,especially

at7090 min; this is a direct result of enhanced supply of water

vapor by seeding. After 100 min, the cloud water content is

decreased slightly.

The snow content is decreased by seeding before 60 min

(Fig. 9c). This is primarily due to the loss of increasing in

accretion by graupel (CLsg, Fig. 10c). After that time, the snow

content has increased in theseeded run because of the increase

in cloud ice content, resulting in the increase in accretion and

conversion of cloud ice (CNis). However, the difference in snow

fields to produce surface precipitation is insignificant.

The differences between the seeded and unseeded runs in

graupel content and various production terms are presented in

Figs. 9d and 10c, respectively. Due to seeding, the number

concentration of graupel has increased significantly (Fig. 11a).

But in the early seeding stage, riming growth of the graupel issuppressed as a result of competition beneficially for the

available supercooled cloud water, resulting in decrease in the

graupel content in the mid-lower level. Furthermore, the

average graupel diameter is decreased. While the graupel

content in upper level is increased because of the increases in

depositional growth and accretion of cloud ice and snow. After

50 min,the increase in accretion of cloud water directly leads to

the increase in the content and diameter of graupel. Abundant

amounts of graupel are present in the lower regions at the later

times. This increased amount of graupel in the lower portion is

favorable for rain formation because melting of graupel is the

largest source of the rain production.

Although the number concentration of frozen drop has

increased in the upper portion of the seeded run due to the

increase in accretion of rain bycloud iceas a result of theincrease

in amount of the cloud ice, the frozen drop content has

significantly decreased for the almost entire domain in the

seeded run before 80 min (Fig. 9e). This is because the decreases

in probabilistic freezing of rain and accretion of cloud water

(Fig.10d). After 80 min, the increase in accretional growth of rain

and cloud water leads to the increase in the frozen drop content.

Both hail and rain content are less in the seeded run than in

the unseeded run before 50 min (Fig. 9fg). Due to seeding,

more graupel and frozen drops are formed. These seeding-

induced more numerous hail embryos compete beneficially for

the available supercooled cloud water which has already been

reduced by the generation and growth of additional cloud ice

producedby seedingat the earliertimes.As shown in Fig.11,the

relative increase in total number are greater than in total mass

of the graupel and frozen drops before 50 min, resulting in

reduced size of embryo particles and the formation of less hail

in the seeded run (Fig. 10e). Most of these numerous small

embryos remain in upper parts of the cloud by the strong

updraft and less fall through the 0 C level, resulting in reduced

amounts of rainwater due to thedecrease in melting of graupeland frozen drop (Fig. 10f).

After 50 min, thehail and rainwater contenthave increased

in theseededrun. The increase in thehail content is mainly due

to the increase in autoconversion of graupel and accretion of

cloud water byhail.As shown in Fig.9b, the cloud water content

Fig. 12. Time evolution of updraft and downdraft velocity maxima over themodel domain in the seeded and unseeded cases.

Fig. 11. Relative increase in domain totals of mass and number concentration

by seeding for the (a) graupel and (b) frozen drop.



14/22

is significantly increased after 50 min.The earlierformation and

increasedamountsof graupel in the seeded run can grow in the

plentiful supercooled cloud water region, resulting in the

formation of more hail. Subsequently, these small hailstones

grow in the plentiful supercooled cloud water region, resulting

in increased amounts of the hail. While the fallout and melting

of the enhanced amounts of graupel, hail and subsequentaccretion of cloud water result in the increase in rain content at

the later times. Farley et al. (2004a,b) reports similar results

that cloud seeding can increase both rain and hail if the clouds

process more supercooled cloud water.

In summary, the increased amounts of cloud water in the

seededcase at the later times enhancethe accretional growthof

cloud water by precipitating hydrometeors, that is, graupel,

frozen drop, hail and rain, andfi

nally lead to the increase insurface precipitation.

Fig.13. Surface horizontal divergence (solid) and convergence (dashed)field for the unseededcaseat (a) 44 min and (b) 50 min,and the seededcase at(c) 44 min

and (d) 50 min.



15/22

3.2.3.4. Dynamical effects of seeding on clouds. The seeding

has a significant effect on the cloud dynamics. Fig. 12 shows the

timeevolution of maximumvertical velocities fortheseeded and

unseeded runs. The difference in the maximum vertical

velocities between the seeded and unseeded runs is not

significant until 44 min. The average increases in maximum

updraft and downdraft caused by seeding are about 0.5 m s1

and2.3ms1, respectively. At certain time, the increase is great.

Forexample,the maximumupdraft anddowndraftareincreased

by 11.3 m s1 at 86 min and 15.7 m s1 at 89 min, respectively.

Exclusive of the early peak from the initial perturbation, the

updraft maximain the seededrunis 51 m s1 versus45ms1 in

the unseeded run, while the downdraft maxima in the seeded is

32ms1 versus20ms1 in the unseeded run.Fig.12 alsoshows

thatthe seeding leads to more rapid formation and development

of the convection and thereby hastens the cloud system

evolution. After 100 min, the convection in the seeded case is

much weaker than that in the unseeded case. The results

indicate that the life time of the cloud system is shortened by

seeding. Subsequently, the duration of precipitation has been

shortened, which canbe drawn fromthevariations of maximum

precipitation rate vs. time (figure omitted).

In this case, the loading effect of increasing precipitation

mass caused by seeding is very crucial to the secondary-cloud

growth. Compared to this, the effect of release of latent heat of

fusion on cloud dynamics is negligible, due to thelow efficiency

of freezing via accretion of liquid water by ice particles and

through the Bergeron effectat middle-low level. That is why the

maximum updraft velocity has not increased significantly

within about 30 min after seeding.

The seeded cloud has more precipitation mass than the

unseeded cloud. Subsequent unloading of the enhanced

precipitation mass causes more intense downdrafts and

interactions with the environment, which strengthens conver-

gence in the boundary layer and simulates more active

secondary convection. Fig. 13a and b shows that the seeded

cloud has much stronger divergence at the surface than the

unseeded cloud at 44 min. Note that two main regions

coexisting (denoted by D1 and D2) in both unseeded and

seeded cases, caused by the precipitation-induced downdrafts

(Fig. 14a and c). Under environment of strongly southeasterly

relative inflow at low level, the enhancement of the downdraft

by seeding increases the convergence at its gust front, as

illustrated in Fig. 13b and d. The enhanced convergence

simulates more active secondary cloud growth. Compared to

the unseeded case, the new cell C2 in the seeded case has

stronger updraft and produces more precipitating hydrometer,mainly in the form of rain water. Moreover, as illustrated in

Fig.13d, this secondarycloud gives riseto squall-line formation.

Whereas the secondary cloud formed in the unseeded case, is

much weaker and does not develop into a squall line through

the whole life time, still being the form of several isolated cells

(denoted by C1, C2andC3). Theresults indicate that the seeding

increases cell merging and cause the cloud to grow larger.

The seeding increases the inflow and causes more water

vapor into the cloud.Fig.15 shows the difference in water vapor

mass transported upward through a given altitude in an

updraft, which is computed from awqvdA, and a is airdensity, w is updraft magnitude and A is area. One can see that

the total water vapor mass have increased between 50 and

100 min due to seeding, particularly in the mid-low level.

Furthermore, significant increase is present in the lowest 4 km.

Two major flux cores formed at 64 min with a peak of

2.7106 kg s1, and 92 min with a peak of 4.3106 kg s1,

respectively, both centered at about 2.5 km. Fig. 15 also shows

that the water vapor mass entering the base of the cloud has

increased in the seeded case. The augmentedwater vapor mass,

together with stronger updraft, causes more condensation and

leads to increased cloud water contents at the later times.

The second-cycle cloud is the dominant precipitation

producer of the simulation, contributes 78% of the total surface

precipitation for the unseeded case, and 87% for the seeded

case. Although the cell C2 in both cases caused by thefirst-cycle

precipitation-induced downdrafts, the early formation of

strong convection in the seeded case leads to the earlier

formation of precipitating particles. These precipitating parti-

cles subsequently grow in the abundant cloud water available

viarimingand accretion, resulting in more precipitationmass in

the seeded cloud and greater developemt of the cloud, as

illustrated in Fig. 16. And the earlier formation and increased

amounts of graupel particles in the seeded case grow in the

plentiful supercooled water region resulting in the formation of

more hail in the seeded run.

4. Discussion

The results obtained here support the hypotheses that

glaciogenic seeding of convective clouds would enhance the

vertical air motion in clouds and thereby vertically process

more water vapor influx through the clouds, resulting in the

surface precipitation enhancement. Theseresults are consistent

with the fundamental concept of dynamic cloud seeding.

Our results confirm the significant role of the retention of the

seeding-increased graupel mass in the cloud on the cloud

dynamics and precipitation. In the simulation, the graupel plays

a dominant role in theproduction ofrain andhail.Autoconversion

andmelting of large graupel particles arethe dominantsources of

hail and rain, respectively. Although the domain-integrated

graupel mass is increased due to seeding, the size of graupel

particles has reduced within 40 min after seeding as a result of

competition for the available supercooled water, deduced from

Fig. 11. These smaller graupel particles stay in upper level of the

cloud longer by strong updraft, resulting in retention of the

graupel. Note that if the cloud is short-lived, these particles haveno sufficient time for continuing growth and further falling

through the melting level; as a result, the precipitation would be

decreased, just as shownin Fig.7. Theresult affirmsthefindingsof

Cooper and Lawson (1984) that the short-livedconvective clouds

in the high plains did have no seeding potential for increased

precipitation. For clouds in this study, with a lifetime more than

110 min long, the seeding-increased amounts of small-sized ice

particles allow the particles to reside in the supercooled region

longer and achieve greater size resulting in the increased graupel

mass (Fig. 9d). Although other precipitating ice mass has

Fig. 14. Comparison of total precipitating hydrometeor (not including cloud water and cloud ice)content (shaded, unit in g m3), vertical velocity (contour, in m s1)

and stream lines inXZ(west-east) vertical cross-sections in the unseeded case at (a) 44 min and (b) 50 min, and the seeded case at (c) 44 min and (d) 50 min. The

shaded denotes the total water content greater than 1 g m3.



16/22



17/22

decreasedin theseededrun within 40 minafter seeding, the total

precipitation mass has increased as a resultof significant increase

in graupel. The eventual unloading of the increased precipitation

mass enhances the downdraft and promotes the second-cycle

clouds development rapidly, leading to stronger updraft and

more low-level water vapor entering the cloud.

The results show that the convective cloud with a cold

base but a strong updraft also has dynamic seeding potential.

Rosenfeld and Woodley (1993) suggest that their conceptual

model applies to warm-based convective clouds in which the

coalescence process is active to produce raindrops in the

supercooled region. But in the high plains region, convective

clouds are deficient in supercooled raindrops; the supercooled

liquid water consists of cloud water. Therefore, the seeding

cannot rapidly produce the bulk of the fusion heat release in the

seeded region; as a result, the cloud dynamics response to the

seedingis very slow. As shown in Fig.12, it is clear that maximum

vertical velocities did not significantly change until 30 min after

seeding. Although a small amount of raindrops exist in the

supercooled region at the early times, their contribution to the

seeding-induced change in cloud dynamics is negligible. It is the

retention of the increased precipitation mass high in the cloud

that eventually causes the cloud to produce significant dynami-

cal effects, even though it takes a long time.

It should be noted that this study has examined only the

effects of cloud seeding on one convective cloud with cold base in

the high plains. In future works we are going to investigate cloud

seeding effects on the development of convective clouds with

warm base and abundant supercooled rain water. In addition, the

hail precipitation at the surface has increased due to seeding in

the simulation. The schemes which produce positive effects for

rain enhancement and hail suppression by cloud seeding need

further study. Notwithstanding its limitation, this study can

clearly indicate that the dynamic seeding concept is a feasibletechnology for increasing precipitation for water resources.

5. Conclusions

In this study we investigate the effects of silver iodide

seeding on a High Plains long-lived convective storm by using

a three-dimensional nonhydrostatic cloud model. From the

results obtained herein one can conclude the following.

1) The simulated cloud system consists of two cloud cycles

during the period of integration. The first cloud cycle isproduced by a model initial perturbation, and the second-

cycle clouds are caused by interactions of the downdraft

outflow induced by falling precipitation from the first cycle

cloud in the boundary layer and the southeasterly relative

inflow at low level. The second-cycle clouds are the

dominant precipitation producer of the simulation, con-

tributes about 80% of the total surface precipitation.

2) The results of the numerical simulation further confirm that

the cold microphysical processes dominate the hydrome-

teor production in the High Plains hailstorms. The simulated

clouds have slightly supercooled rain but abundant super-

cooled cloud water. The main growth mechanisms for

precipitating ice particle are accretion of cloud water. Thedominant rain sources are the melting of graupel and

accretion of cloud water.

3) The cloud seeding results in substantial increases in

accumulated precipitation at the surface in all seeded

cases (by 2030%). The results show that the effectiveness

of cloud seeding for augment precipitation is closely related

to the intensity of the seeding.

4) The seeding increases both rain and hail precipitation. The

most important contribution to theincrease in hail is due to

conversion of graupel to hail and accretion of cloud water by

hail. Increase of graupel melting andsubsequentaccretionof

cloud water by rain contribute mostly to rain enhancement.

5) Numerical simulation herein support the hypothesis ofdynamic seeding that glaciogenic seeding of convective

clouds would enhance the vertical air motion in clouds and

thereby vertically process more water vapor influx through

the clouds, resulting in the precipitation enhancement. The

results also affirm that unloading of the seeding-increased

precipitation mass, mainly in the form of graupel, and

interactions with favorable environment in the boundary

layer did play very important role in the dynamic seeding

conceptual model.

6) This study indicates that the convective cloud with a cold

base and a strong updraft also has dynamic seeding

potential. But the cloud lifetime must be long enough to

remain the seeding-increased ice particles in the super-cooled water region longer and achieve precipitating size.

Otherwise, the dynamical effect of cloud seeding could not

be accomplished.

Acknowledgments

We thank Dr. Yan Yin and Dr. Zhaoxia Hu for their helpful

discussions. Thanks also due to the reviewers for their critical

and constructive comments. This work was partially supported

by the National Natural Science Foundation of China (Grant No.

40775005, 40675005, 40875080 and 40830958) and Ministryof Scienceand Technology of China (Grant No. 2006BAC12B07).

Fig. 15. Time vs. height section of the difference in areally integrated vertical

water vaporfl

ux (106

kg s1

) between the seeded and unseeded cases. Thethick solid line represents the mean cloud-base height for the seeded case,

which is made using a cloud water content threshold of 1025 g m3.



18/22

Fig.

16.

As

inFig

.14

,exceptfo

rtheunsee

de

dcaseat

(a)56m

in,

(b)66m

in,

(c)76m

inan

d(d)86m

in,

an

dthesee

de

dca

seat

(e)56m

in,

(f)66m

in,

(g)76m

inan

d(h

)86m

in.



19/22

Fig.

16

(continued)

.



20/22

Appendix A. Source and sink terms

The microphysical source/sink terms of the continuity equations of the proposed scheme are listed below.

The source and sink terms for the mass mixing ratios are

Sqv = QVDrv + QVDhv QVDvc QVDvi QVDvs QVDvg QNUvi QNUvai A1

Sqc = QVDvc QCNcr QCLcr QHFZci QCLci QCLcs QCLcg QCLcf QCLch QNUcai A2

Sqr =QCNcr + QCLcr + QMLir + QMLsr + QMLgr + QMLfr + QMLhr QCLri QCLrs

QCLrg QCLrf QCLrh QNUrg QNUrf QNUrag QNUraf QVDrv

A3

Sqi =QNUvi + QHFZci + QVDvi + QIMsi + QIMgi + QIMfi + QNUcai + QCLci QCNis QCNig

QCLii QCLis QCLig QCLif QCLih QCLri QMLir

A4

Sqs =QCNis + QCLii + QVDvs + QCLcs + QCLis QCNsg QCLsg QCLsf QCLsh QCLrs

QIMsi QMLsr

A5

Sqg =QCNig + QCNsg + QNUrg + QNUrag + QVDvg + QCLcg + QCLrg + QCLig + QCLsg

+ QCLrig + QCLrsg QCNgh QMLgr QIMgi

A6

Sqf =QNUrf + QNUraf + QVDvf + QCLcf + QCLrf + QCLif + QCLsf + QCLrif + QCLrsf

QCNfh QMLfr QIMfi A7

Sqh = QCNgh + QCNfh + QCLch + QCLrh + QCLih + QCLsh QVDhv QMLhr A8

The source and sink terms for the total number concentrations are

SNr =NCNcr + NMLir + NMLsr + NMLgr + NMLfr + NMLhr + NSHhr + NSHfr + NSHgr

NVDrv NCLrg NCLrf NCLrh NCLri NCLrs NNUrg NNUrfNNUrag NNUraf

8>>>>>>>>>>>>:

A15

where Lv, Ls and Lf are latent heat of condensation, sublimation and fusion, respectively.

Appendix B. Definition of acronyms for microphysical processes

Acronym Process

VDvc Condensation/Evaporation to/from cloud water

VDrv Evaporation of rain

CNcr Autoconversion of cloud water to rain

CLcr Accretion of cloud water by rain

NUvi Nucleation of cloud ice

(continued on next page)



21/22

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(continued)

Acronym Process

IMsi Secondary production of cloud ice from snow

IMgi Secondary production of cloud ice from graupel

IMfi Secondary production of cloud ice from frozen drop

HFZci Homogeneous freezing of cloud water to cloud ice

VDvi Vapor Deposition/Sublimation to/from cloud ice

VDvs Vapor Deposition/Sublimation to/from snow

VDvg Vapor Deposition/Sublimation to/from graupelVDvf Vapor Deposition/Sublimation to/from frozen drop

VDhv Vapor sublimation from hail

MLir Melting of cloud ice to rain water

MLsr Melting of snow to rain water

MLgr Melting of graupel to rain water

MLfr Melting of frozen drop to rain water

MLhr Melting of hail to rain water

CLci Accretion of cloud water by cloud ice

CLcs Accretion of cloud water by snow

CLcg Accretion of cloud water by graupel

CLcf Accretion of cloud water by frozen drop

CLch Accretion of cloud water by hail

NUrg Probabilistic freezing of rain to form graupel

NUrf Probabilistic freezing of rain to form frozen drop

CLri(g,f) Accretion of rain by cloud ice to form graupel or frozen drop

CLrh Accretion of rain by hailCLrf Accretion of rain by frozen drop

CLrg Accretion of rain by graupel

CLrs(g,f) Accretion of rain by snow to form graupel or frozen drop

CLii Accretion of cloud ice by cloud ice to snow

CLis Accretion of cloud ice by snow

CLig Accretion of cloud ice by graupel

CLif Accretion of cloud ice by frozen drop

CLih Accretion of cloud ice by hail

CLss Aggregation of snow

CLsg Accretion of snow by graupel

CLsf Accretion of snow by frozen drop

CLsh Accretion of snow by hail

SHgr Rain water shed from graupel

SHfr Rain water shed from frozen drop

SHhr Rain water shed from hail

CNis Autoconversion of cloud ice to snowCNig Autoconversion of cloud ice to graupel

CNsg Autoconversion of snow to graupel

CNgh Autoconversion of graupel to hail

CNfh Autoconversion of frozen drop to hail

NUvai Vapor deposition to cloud ice on AgI particles

NUcai Cloud water transformation to cloud ice due to contact freezing with AgI particles

NUrag(f) Rain water transformation to graupel (frozen drop) due to contact freezing with AgI particles

Appendix B. (continued)

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