Numerical Simulation of Seeding Extra-Area Effects of Precipitation Using MM5
Zhen Zhao (1) and Heng-Chi Lei (2)
(1) Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, People's Republic of
China, E-mail: zhaozhen@mail.iap.ac.cn
(2) Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, People's Republic of China,
E-mail: leihc@mail.iap.ac.cn
1.
Introduction
2.1 Silver iodide seeding
The question of “extra-area” effects is prominent
among
those
questions
most
commonly
The simulation of silver iodide seeding that was
posed
included in the MM5 two-moment cloud scheme follows
regarding cloud seeding to increase precipitation; that
(Chen and Orville, 1977; Hsie et al., 1980). Brownian
is, whether seeding will affect the weather beyond the
(Sbc and Sbr,) and inertial impact (Sic and Sir) collection
targeted temporal or spatial range. A number of
rates (due to cloud droplets and raindrops, respectively)
researchers have suggested that extra-area effects
as well as deposition growth rate (Sdv) could be
may have occurred during intensive modification
calculated as follows.
1) Collection due to cloud droplets and raindrops
programs (Elliott et al., 1971; Meitín et al., 1984). Field
observations over the central Sierra Nevada reported
are
that the effects of cloud seeding with silver iodide
persisted for over 90-min after seeding and 100 km
S bc =
ΔX s
= −2πDs X s N c Dc
Δt
,
S ic =
ΔX s
π
= − D c2 X sU c E cs N c ,
Δt
4
downwind of the seedline (Deshler and Reynolds,
1990). The hypotheses that attempt to explain these
effects range from direct microphysical effects to
mesoscale dynamic effects; however, the cloud
seeding extra-area effects have not been investigated
using fully interacting mesoscale models yet. The
importance of mesoscale processes to the modification
of stratiform clouds is the most obvious because the
∞1
ΔX s
= −4πDs X s ∫ Dr nr dDr
0 2
Δt
,
N Γ(2 + α )
= −2πDs X s 0r 2+α r r
S br =
λr
clouds themselves are directly formed from mesoscale
meteorological processes.
∞π
ΔX s
Dr2U r ( D)nr dDr
= − X s E rs ∫
0
4
Δt
⎡ a Γ(3 + α )
π
= − X s E rs N 0r ⎢ 0 3+α r r
4
⎣ λr
S ir =
2. Model description
The three-dimensional non-hydrostatic mesoscale
model MM5 was used to test the extra-area effects of
stratiform
cloud
two-moment
seeding
microphysical
with
silver
iodide.
parameterization
A
for
+
a1Γ(4 + α r )
λ4r+α
r
+
a 2 Γ(5 + α r )
λ5r+α
r
+
a 3 Γ (6 + α r ) ⎤
⎥.
λ6r+α r
⎦
mixed-phase clouds has been established in MM5
where Xs is the mixing ratio of the seeding agent, Nc is
(Zhao et al., 2005). The scheme uses a gamma
the number concentration of cloud droplets. The
distribution law for rain and for all of the ice species,
raindrop size distribution is assumed to follow a gamma
which
distribution function of the form
predicts
mixing
ratios
and
number
concentrations of cloud droplets, raindrops, cloud ice,
snow, and graupel. A two-moment scheme with the
seeding agent field and its effect on clouds was
developed in MM5.
n r = N 0r D α r exp( −λ r D )
.
2) The activated seeding agent as deposition
nuclei when the air is saturated with respect to water is
dN aD (ΔT )
dt
,
∂[ X s N a (ΔT )]
=w
> 0, 5°C ≤ ΔT < 20°C
N a (20°C )∂Z
and number concentration Nx are the following:
N aD (ΔT )
Δt
,
N a (ΔT )
= Xs
, ΔT ≥ 20°C
N a (20°C )Δt
∂p∗ Nx
= −ADV( p∗ Nx ) + DIV( p∗ Nx ) + D(Nx ) + p∗ Nx0
∂t
.
∗ Nx
+ p { (Sbc + Sic + Sbr + Sir − Sdv )}
Xs
S dv = mx
∂p ∗ X s
= − ADV ( p ∗ X s ) + DIV ( p ∗ X s ) + D( X s )
,
∂t
∗
∗
+ p X s0 + p ( S bc + S ic + S br + S ir − S dv )
S dv = mx
where mx is the mass of an AgI particle, and
Here the source terms Xs0 and Nx0 represent the
Na(∆T) is the number of nuclei active at the
seeding agent added at the time of seeding. Thus, the
supercooling ∆T (∆T =273.15-T). The Na(∆T) is
added latent heat due to silver iodide seeding is
dθ
= Ls Pisv + Lf ( Pisc + Pssr ) .
dt
computed by (Blair et al., 1973)
⎧0, ΔT < 5°C
⎪ 3
2
⎪10 exp( −0.022 ΔT + 0.88ΔT − 3.8),
N a ( ΔT ) = ⎨ 0
⎪5 C ≤ ΔT < 20°C
⎪1.6 × 10 5 , ΔT ≥ 20°C
⎩
The equations for the mixing ratio of water vapor,
cloud water, rainwater, cloud ice, and snow and for the
number concentrations of cloud water, rainwater, cloud
ice, and snow are modified to include seeding effects.
The silver iodide particles released in the model
interact with cloud water, rain, and water vapor fields to
produce ice crystals and snow. The processes in the
Interactions with rainwater
and center of Shaanxi Province and moved across the
number concentration
center of Shanxi Province; this was associated with a
(designated by Nqqq) from rainwater to snow due to
mid-latitude low trough that developed mid-level
seeding are
stratiform cloud bands. The model simulation was
(designated by
Pssr = −qr (Sbr + Sir )
and
Between 1200 UTC 12 September and 1200 UTC
extended from the east of Gansu Province to the north
The transformation rate of the mixing ratio
Pqqq)
Results
3.1 Method of simulation
13 September 2002, light and moderate rainfall
model are described as follows.
1)
3
Na (ΔT)
P ρ
mx−1ρ , N ssr = ssr
Na (20°C)Nr
ms0
performed during the same period with a three-domain
.
interactive nested grid domain. The computational
domains consisted of a 30-km grid with a mesh size of
2) Interactions with cloud water
109×112 (D01), a 10-km grid with a mesh size of
The rate at which cloud water transforms to cloud
94×112 (D02), and a 3.3-km grid with a mesh size of
ice due to contact nucleation can be written as
133×109 (D03). The three grids had 33 full-sigma
Na (ΔT)
P ρ
Pisc = −qc (Sbc + Sic )
mx−1ρ , N isc = isc
Na (20°C)Nc
mc
levels in the vertical. Grid-scale precipitation was
.
3) Deposition nucleation
determined using the mixed phase scheme for D01, the
Reisner-2 scheme for D02 and the two-moment
scheme for D03.
This process is only considered under conditions
The simulation of the release of the seeding
of saturation with respect to water. The rate (Pisv, Nisv) is
material was performed for the finest grid only. In the
Pisv = N aD (ΔT )a1m
a2
x
, N isv
P ρ
= isv .
mi0
2.2 Silver iodide conservation equations
The equations for the silver iodide mixing ratio Xs
seeding simulation, the horizontal domain of seeding in
the D03 was 8×8 grid points centered on the grid point
at Yan’an station (36.6°N, 109.5°E). The AgI was
released continuously for 30-min at the -5 to -10°C level
at 720 min into the model run. Seeding rate was set to
0.2 g s-1 in this case.
the supercooled liquid water has been depleted to 0.3 g
kg-1 in the seeded downdraft region where a dissipation
Distribution and transport of silver iodide
zone is obvious. The decreased supercooled cloud
The simulation of AgI seeding was carried out in
water regions have expanded from the seeded levels to
the cloud supercooled layer. The updrafts were very
the upper levels of cloud. The cloud water content
3.2
-1
weak with vertical motions of approximately 10 cm s .
starts to decrease rapidly at about 810 min (60 min
Figure 1 shows the distribution of seeding agent at 750
after seeding), and after 180 min the depletion ability
min and 810 min. The AgI drifted with the westerly
becomes weak. A large amount of cloud ice of more
upper-level wind and was transferred to a downdraft
than 300 L-1 increases in the region of supercooled
area while moving downward at about 20 m s-1. At 750
water. The expanding of the cloud ice region
-7
min (Fig. 1a), a maximum AgI mixing ratio of 1.2×10 g
corresponds to reductions in the supercooled cloud
kg-1 was the value imposed at the center of the seeding
water region. Cloud ice, via the Bergeron process and
domain. As time progresses, the AgI mixing ratio
contact freezing, acts to diminish the liquid water in the
decreased as the seeding agent moved. Most of the
seeded model cloud. The values of the snow mixing
AgI particles acted as deposition nuclei in the model.
ratio increase to 0.2 g kg-1 in the seeded case. The
The contact nuclei were captured primarily by cloud
more cloud ice that is formed, the more snow that is
droplets,
produced via ice conversion. Rain water increases by
mainly
through
the
Brownian
motion
mechanism, although this amounts to less than 5%.
more than 0.1 g kg-1 in conjunction with the increment
regions of snow, which indicates that extra snow
melting has enhanced rain formation.
Figure 1 Horizontal cross sections of AgI mixing ratio (10-7 g
kg-1) and vectors of horizontal winds at 500 hPa (top). Vertical
-7
-1
Figure 2 Vertical cross sections of the differences of (a)
cross sections of AgI mixing ratio (10 g kg ) and
cloud water mixing ratio (g kg-1), (b) ice number concentration
temperature (dashed lines) along 36.8°N (bottom).
(L-1), (c) snow mixing ratio (g kg-1), and (d) rain water mixing
ratio (g kg-1) with and without seeding and temperature
3.3
Evolution
of
cloud
and
precipitation
processes in seeded and unseeded clouds
(dashed lines) along 36.8°N.
The output of the microphysical processes that
The effect of cloud seeding on microphysical
describe the interaction of the seeding agent with the
quantities was quite significant by 0200 UTC 13
cloud yields the following results. Deposition nucleation
September 2002. Figure 2 shows the differences in
is the most efficient mechanism for additional cloud ice
cloud hydrometer parameters between the seeded and
formation. The increase in the snow melting rate due to
unseeded cloud. Two hours after the start of seeding,
seeding contributes mainly to rain enhancement.
Figure 3 shows the distributions of unseeded
Deposition
nucleation
was
the
most
efficient
precipitation and enhanced precipitation after seeding
mechanism for additional cloud ice formation. The
at the surface for a period of three hours. The value is
model results indicated that seeding could cause
between 0.1 mm and 0.7 mm, which corresponds to a
extra-area effects that may increase precipitation by
5%-25% precipitation enhancement over the unseeded
5%-25% in the area downwind of the target location
case,
extending to distances varying from 80 km to 250 km.
and
the
figure
shows
that
considerable
redistribution has occurred. An increase in precipitation
occurs downwind of the seeding area with distances
Acknowledgements
varying from 80 km to 250 km. The simulated seeding
extra-area effects are caused by downwind transport of
This research was supported by the National
silver iodide from the seeding source to an area well
Natural Science Foundation of China (Grant No.
removed from the intended area of effect.
40805056) and the Knowledge Innovation Program of
the Chinese Academy of Sciences (Grant No.
KZCX2-EW-203)
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4 Conclusions
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