Modeling of rainfall enhancement by seeding tropical

Modeling of rainfall enhancement by seeding tropical convective clouds
1
V Vlado Spiridonov and Mladjen Curic
1
2
Republic Hydrometeorological Institute of Macedonia, Skupi bb 1000 Skopje
2
Institute of Meteorology, University of Belgrade, Belgrade, Serbia
1. Introduction
where
Convective clouds are characterized by strong
updrafts, enhanced transport of heat and moisture in
the upper layers and rainfall processes which are
activated in very limited time interval and space and
with a variable intensities. The cloud seeding for rain
enhancment of such clouds is a big challenge and
common practice in many areas over the world for
more than 50 years. There is a strong scientific
evidence and assessment on the status of weather
modification from literature (see List et al, (1993),
Cotton and Pielke (2007), Rosenfeld and Woodley,
(1989, 1993); Silverman (2001), Levin and Cotton
(2008). Over the years, there have been a number of
projects which showed a more or less confidence for
a rain augumentation by applying static glaciogenic
seeding of cumulus convective clouds (e.g. Isaac et
al. (1982), Dennis, 1980; Mason, 1982; Silverman,
1986;
Cotton, 1986 and many others) A
comprehensive analysis by Kessler et al. (2007, in
Hebrew) and a subsequent summary by Sharon et al.
(2008) concluded that seeding enhanced the amount
of precipitation by about 30% in storms that produced
less that 5 mm per day in Israel. In recent years
models become a powerfull tools in weather
modification research. Well-designed scientific
experiments that include extensive measurements
and model simulations are needed to determine
whether artificial seeding can modify cloud structures
and the effects of the seeding on precipitation
enhancement Farley et al (1994); Bruintjes, (1999),
Reisin et al., 1996; Cotton et al. (2006), Curic et al.
(2007) and many others.
the sink or source term of mixing ratio and
2. Model framework
A three-dimensional non-hydrostatic 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
tropical convective clouds. The model is a set of
conservation equations for momentum, energy and
mass (air and water contents). An additional
conservation equation is considered here:
∂U j
∂X S ∂X S U j
+
− XS
= S XS + E X S
∂t
∂x j
∂x j
(1)
X S is the mixing ratio of AgI particles, S XS is
E XS is the
subgrid-scale contribution. The activation of AgI is
parameterized by the three nucleation mechanisms
based on Hsie (1980) and Kopp (1988) which are
deposition (including sorption) nucleation, contact
freezing nucleation – Brownian collection and inertial
due to cloud droplets and raindrop. These
impact
are the sink terms of X S which can be calculated as:
1.) Contact
collection,
freezing
nucleation-Brownian
S BC , and inertial impact due to
cloud drops, S IC ,
S BC = −4 πD S R C X S N C
(2)
2
S IC = − πR C X S N C VC E CS ;
2.) Contact
collection,
freezing
(3)
nucleation-Brownian
S BR , and inertial impact due to
raindrops, S IR ,
S BR = −2π DS X S N OR λR
−2
S IR = −2.54E RS ρ -0.375 X S q R
(4)
0.875
(5)
3.) Deposition nucleation due to water vapor at
ice supersaturation
 m dNaD ( ∆T) when 5°C≤∆T<20°C
dt
 s

SDN= 
m N ( ∆T) when ∆T ≥20°C
 s aD


where DS is a diameter of AgI particles, NC and
(6)
VC
the concentration and terminal velocity of cloud
droplet, RC the cloud droplet radius, NOR parameter of
the raindrop size distribution, λR the slope parameter
of rain, ECS and ERS are the collection efficiency of
cloud water and rain water collecting AgI particles
respectively, ρ the air density, NOR the rainwater
mixing ratio, ∆T supercooling and NaD is the number
of AgI particles active as a deposition nuclei at a
supercooling ∆T, ms the mass of the AgI particle.
These are the sink terms of XS, while the initial mixing
ratio XS0 of agent homogeneously distributed in the
seeding zone at the seeding moment is the source
term of XS. A more detail information about the model
physics and agent nucleatin mechanisms could be
found in Telenta and Aleksic. (1986), Spiridonov and
Curic (2006).
experiments has been performed by seeding tropical
convective clouds around the supercooled water
region in cloud developing stage in updraft portion of
the cloud where maximum cloud modification effects
are expected. A proper seeding parameters in sense
of optimum dose rate into the proper location and
appropriate time is crucial to obtain the optimum
effects.
The static glaciogenic seeding concept has been
applied to supercooled cumulonimbus clouds and
tested in a tropical region. The lack of rain
enhancement in Israel was partly due to the fact that
much of the seeding material did not reach the proper
heights in the clouds at the right time for it to be
effective (see Levin et al, 1997). Often, seeding
material that is dispersed from ground generators
placed upwind of the target area is used exclusively
or as a supplement to airborne seeding. The aim is to
disperse the glaciogenic material into as many clouds
as possible in the area upwind of the designated
target area. We perform a series of numerical
experiments on tropical convective cloud seeding by
agent AgI using a cloud model. The model
simulations are initiated based on seven different
upper air soundings for Bangkok, Thailand from 5 to
11 June, 2008. Environmental conditions for all
selected cases represent a tropical convective clouds
typical for a monsoon period. They are mainly
characterized by large moisture content, temperature
increase and wind shear and veering in PBL layers.
Numerical experiments are configured on domain
which covers a region 61 km × 61 km x 20 km in x, y
and z directions respectively, with 1 km x 1 km x 0.5
km grid intervals. The seeding agent AgI is
introducing in a supercooled cloud zone with
reflectivity greater than 25 dBz, between -8 °C and 12 °C isotherms. It is assumed that agent material is
instantaneously realized by rocket or airplane in the
region of strong updraft. Table 1 lists the initial
parametes used as the optimum seeding criteria of a
seven different tropical convective clouds .
Wm ax (m /s)
2.1 Experimental setup
20
18
16
14
12
10
8
6
4
2
0
W (unseeded)
W (seeded)
0
5 10 15 20 25 30 35 40 45 50 55 60
Simulation time (min)
Fig. 1 Time evolution of the simulate maximum updraft
(m/s). Unseeded case (dash red curve), Seeded case (blue
curve)
The time evolution of the maximum updrafts of
unseeded and seeded tropical convective cloud on 5
June, 2008 is shown in Fig. 1. It is seen that the AgI
seeding can significantly increase the vertical
velocity. Both cases has clearly illustrate that updrafts
remain with the same values until 30 min of the
simulation time. The great difference is found for
about 35 min as the result of agent seeding and
downdraft outflow induced by falling hydrometeors in
the PBL.
AgI
Tropical case of convective storm 5 June, 2008
20
15 min
0.12
15
0.11
0.10
10
0.09
0.08
5
0.07
0.06
5
10
15
20
25
30
35
40
45
50
55
60
0.05
0.04
0.03
0.02
0.01
20
15
Non-seeded case
60 min
(dBz)
10
5
Tab. 1 Seeding experiments and initial parameters
45
5
10
15
20
25
30
35
40
45
50
55
60
Seeding
placement
x,z (km,
km)
29, 7
30, 7
24,6.5
28,7
26,7
28,7
30,6.5
Fig. 2. Cloud seeding of a tropical convective cloud on 5
June, 2008.
Our main task was to model the rainfall enhancement
by tropical convective cloud seeding. The effects
cloud dynamics and microphysics interactions
affected by seeding of AgI are examined. A set of
Fig. 2 shows an example of a tropical convective
cloud seding experiment. Cloud seeding was
conducted in the frontal updraft part of the cloud in 15
min of the simulation time on the horizontal distance
29 km in the cloud model domain at 7 km height a.s.l.
where the ambient temperature is between -12 and -
Tropical
convective
cloud
Date
05/06/08
06/06/08
07/06/08
08/06/08
09/06/08
10/06/08
11/06/08
AgI
dose
(g/m)
1
0.4
15
0.2
2.0
0.6
15
AgI
Concentration
(g/m3)
-2
1.3x10
5.1x10-3
8.8 x10-3
2.5 x10-3
2.5 x10-2
7.6 x10-3
1.9 x10-2
Initial
seeding
time
(min)
15
25
20
20
15
20
20
3. Results
35
20
15
Seeded case
60 min
25
10
15
5
5
10
15
20
25
30
35
40
45
50
55
60
5
2
8 °C isotherms (see upper panel). The radar
reflectivity zone is greater than 25 dBz. Initial seeding
amount was 1 (g/m).
Case study on 05 June, 2008
70
Rad. ref. (dBz)
65
14
HA1
QLCI
10
QLCW
8
RA1
6
SN1
55
45
40
30
HA1
4
Nonseed.
Seeded
50
35
10 15 20 25 30 35 40 45 50 55 60
QLCI
2
QLCW
0
RA1
0
5
10 15 20 25 30 35 40 45 50 55 60
SN1
Simulation time (min)
Fig. 3 Time evolution of hydrometeor mixing ratios (g/kg)
for unseeded (dash curves) and seeded run (solid curves)
The effect of cloud seeding is evident analyzing the
vertical cross section of the radar reflectivity of
seeded relative to the unseeded cloud in 60 min of
the simulation time as well comparing the time
evolution of reflectivity echoes during simulation time
(Fig. 4). Seeded case shows vertical expansion of the
zone with a radar reflectivity between 15 and 25 dBz
mostly containing snow and ice particles and more
narrow reflectivity field of 35 dBz. Results from Fig. 5
illustrate that AgI seeding of such tropical convective
clouds, has evident effect on the rainfall
enhancement at the ground compared with the
unseeded process. Our experiments indicate that the
total accumulated rainfall at the ground in seeded
runs has a larger values relative to unseeded runs. A
cumulative rainfall increase from 6 to 35 % is found in
all seeded cases and well agree with weathe
rmodification findings from the published literature.
Non-seed.
08
08
11
.0
6
.2
0
08
10
.0
6
.2
0
08
.2
0
09
.0
6
08
.0
6
.2
0
08
07
.0
6
.2
0
.2
0
.2
0
.0
6
.0
6
06
05
08
Seeding
08
Accum ulated rainfall (m m )
100
90
80
70
60
50
40
30
20
10
0
Date
Fig. 6 Comparison of the accumulated rainfall at the
2
ground in (kg/m ) between unseeded and seeded clouds
When considering a time history of the accumulated
rainfall at the ground, seeded case shows an early
formation of rainfall and greater difference after 35
min of the simulation time compared to unseeded
case. Six 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 10C level at
10min, and produce significant effects.
Sim ulation tim e (min)
Fig. 4 Radar reflectivity history of unseeded and seeded
tropical convective cloud on 5 June, 2008
The cloud seeding results in substantial increases in
accumulated precipitation at the surface in all seeded
cases (by 6% to 35%) (Fig.7).
100
Acc. rainfall at the ground
(kg/m**2)
Mixing ratio (g/kg)
12
60
90
80
70
60
50
Non-seed.
40
Seeded
30
20
10
0
20
25
30
35
40
45
50
55
60
Simulation time (min)
Fig. 5 Time evolution of the modeled accumulated
rainfall at the ground
All of cases are seeded in the region of the strongest
updraft when the model cloud top was passing the
10C level at 10min, and produce significant effects.
The cloud seeding results in substantial increases in
accumulated precipitation at the surface in all seeded
cases (by 6% to 35%) (Fig.7). There is no
accumulated hailfalll at the ground in all simulated
tropical cases. Increase of graupel melting and
subsequent accretion of cloud water by rain
contribute mostly to rain enhancement. The seeding
enhances the unloading effect of precipitation mass
mainly in the form of graupel, leads to a stronger
downdraft outflow and enhanced convergence in the
boundary layer, further causes the secondary clouds
to form earlier and grow larger. The enhanced updraft
increases the inflow and 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 a tropical convective clouds.
The simulation not only supports the hypothesis of
statical seeding, but also demonstrates that the
convective cloud with a cold base but a long lifetime
has dynamic seeding potential as well.
3
4. Concluding remarks
The results show that a positive effect of rain
enhancement can be obtained when seeding in
suitable parts of the tropical convective clouds. A
greater benefit in the rain enhancement effect can be
gotten when seeding in the early developing stage of
clouds. The launching elevation of rockets has a
great influence on the effect. The above results
obtained are expected to help improving the seeding
efficiencies in practice. The study shows that the
precipitation in the experiment area is dominated by
cold cloud precipitation processes and the ice crystal
is the main source of grauple, which is produced by
the auto-conversion from ice crystal to grauple and
then grows through collecting ice crystals. AgIseeding should be done before the activation of most
ice nuclei start,s o as to enhance rainfall through
increasing ice crystals content,and deleting supercooled cloud water. Otherwise, there will be large
amount ice crystals grew in natural clouds, artificial
nuclei would be useless. The cloud water amount is
reduced and the rain water, ice crystal, graupel and
snow amounts are increased after the artificial AgI
seeding. The model results show that the cold
microphysical processes dominate the hydrometeor
production in the simulated storms. 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%. The intercomparison shows differences in
rainfall efficiency attributed to differences in the
interaction of cloud dynamics and microphysics and
precipitation flux processes.
40
35,8
R a in fa ll in cre a se (% )
35
30
25
25,2
24,5
19,96
20
15,83
13,29
15
10
6,15
5
0
05.06.08 06.06.08
07.06.08 08.06.08 09.06.08 10.06.08 11.06.08
Fig. 6 A cumulative rainfall increase in (%) in all topical
convective cloud seeding experiments
However a more comprehensive study with statistical
evaluations are essential to give confidence in the
outcome of the operations. Concerning seeding
effects, this study shows the potential to augment
precipitation by AgI seeding of tropical convective
clouds.
Acknowledgements
Authors would like kindly to acknowledge to Thai
Meteorological Department for provision of initial data and
rainfall data for convective case experiments.
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