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Geophysical Research Letters
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Supporting Information for
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It is all about timing: evaluation of aerosol effects on warm rain processes
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Guy Dagan1, Ilan Koren1* and Orit Altaratz1*
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Department of Earth and Planetary Sciences, The Weizmann Institute, Rehovot
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76100, Israel
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*Corresponding authors. E-mails:
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ilan.koren@weizmann.ac.il and orit.altaratz@weizmann.ac.il
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Content of this file
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Text S1:
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1.1 Technical details of the model.
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1.2 Theoretical initialization profiles.
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1.3 Aerosol size distributions.
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1.4 Field of droplet number concentration.
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1.5 Simulated times for cloud's maximal depth (Ttop) and maximum collected mass
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(Tcol).
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1.6 Rain efficiency.
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1.7 List of additional simulations
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Figures S1 to S5
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Introduction
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This supporting information file contains technical details of the model, description of
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the theoretical initialization profiles and aerosol size distributions. It also presents
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examples of field of droplet number concentration under different aerosol
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concentration and size distribution and the resulted simulated times for cloud's
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maximal depth (Ttop) and maximum collected mass (Tcol) and the rain efficiency of the
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simulated clouds.
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The last part of this file contains a list of additional simulations that were conducted
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for some of the initialization profiles, for the purpose of a more accurate
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determination of the optimum aerosol concentration (Nrain_op).
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1.1 Technical details of the model
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We used the Tel Aviv University axisymmetric nonhydrostatic cloud model (TAU-
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CM) with a detailed treatment of warm cloud microphysics [Reisin et al., 1996;
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Tzivion et al., 1994].
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The included warm microphysical processes were nucleation of CCN, condensation
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and
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microphysical processes were formulated and solved using a multimoment bin
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method [Tzivion et al., 1987]. The bin radius that corresponds to the smallest aerosol
evaporation,
collision–coalescence,
breakup,
and
sedimentation.
The
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was 0.004 µm and for the smallest droplet it was 1.56 µm. The aerosol and drop's
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spectrum is divided into 34 bins with mass doubling in each successive bin. This
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model was used before in many papers for studying clouds evolution, rain
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development, aerosol-cloud interactions e.g. [Altaratz et al., 2008a; Altaratz et al.,
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2008b; Dagan et al., 2015; Koren et al., 2014; Reisin et al., 1996; Teller and Levin,
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2006; Yin et al., 2000].
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The model resolution was set to 50 m in both the vertical and horizontal directions,
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with a time step of 1 s. Convection was initiated by a momentary warm perturbation
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near the bottom of the domain.
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1.2 Theoretical initialization profiles
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Figure S1 presents three of the initial profiles used to simulate warm convective
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clouds: T1 combined with RH1 (T1RH1), T2 with RH2 (T2RH2), and T3 with RH3
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(T3RH3). To better separate the influential factors, we ran the model with nine
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different sets of initial conditions based on idealized atmospheric profiles that
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describe a tropical moist environment [Garstang and Betts, 1974]. Each of the
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profiles included a well-mixed subcloud layer between 0 and ~1000 m, conditionally
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unstable cloud layer between 1000 and 4000 m (profile T1), 3000 m (T2), and 2000 m
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(T3), and an overlying inversion layer (2˚C increase over 50 m). We assigned three
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dew-point temperature profiles equivalent to 95% (profile RH1), 90% (RH2) and 80%
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(RH3) to each of the temperature profiles. The RH above the inversion layer was set
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to 30% in all profiles.
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Figure S1. Thermodynamic diagram presenting examples of three of the initial
atmospheric profiles: T1RH1 (black), T2RH2 (red), and T3RH3 (green). Solid lines
denote temperature profiles and dashed lines represent dew-point temperatures. In
total, we ran simulations for nine different initialization profiles.
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1.3 Aerosol size distributions
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Figure S2 presents five examples of the aerosol size distributions used in the
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simulations: 250, 1000 and 10,000 cm-3 for the no GCCN case, and 10,000 cm-3 for
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the background GCCN and addition of GCCN cases. The concentration of GCCN
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(>1µm) in the background aerosol size distribution was 0.066cm-3 (out of total of
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~295cm-3). In the cases of no GCCN the concentration was 0 for all aerosol
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concentration levels, for the case of background GCCN it was kept constant for all
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aerosol concentration and for the case of addition of GCCN it was increased in the
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same proportions as the total aerosol concentration (e.g. for aerosol concentration of
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1000cm-3 the GCCN concentration was 0.22cm-3).
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Figure S2. Examples of initial aerosol size distributions. For the no-GCCN case,
three size distributions are presented: 250 (magenta), 1000 (red) and 10,000 (green)
cm-3. For the background (light blue) and addition of GCCN (dark blue) cases, the
10,000 cm-3 size distribution is presented.
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1.4 Field of droplet number concentration
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The figure below (fig.S3) presents a few examples of fields of droplets concentration
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after 50 minutes of simulation for four clouds simulated under the same initialization
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profile (T1RH2). The four clouds presented were simulated with aerosol conditions
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of: 1) 125 cm-3 and no GCCN, 2) 1000 cm-3 and no GCCN, 3) 1000 cm-3 and
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background GCCN and 4) 1000 cm-3 and addition of GCCN.
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Figure S3. examples of field of droplets concentration after 50 minute of simulation
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for four clouds simulated under initialization profile T1RH2. The four clouds
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presented were simulated with aerosol conditions of: 1) 125 cm-3 and no GCCN
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(upper left), 2) 1000 cm-3 and no GCCN (upper right), 3) 1000 cm-3 and background
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GCCN (lower left) and 4) 1000 cm-3 and addition of GCCN (lower right).
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The figure demonstrates the droplet number concentration increase in accordance with
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the aerosol loading. The impact of the GCCN is demonstrated through the comparison
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between the three 1000 cm-3 concentration clouds plots that show a gradual decrease
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in the droplet number concentration near cloud top with the addition of GCCN, driven
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by the faster initiation of the collision-coalescence.
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1.5 Simulated times for maximal cloud depth (Ttop) and maximum collected mass
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(Tcol)
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Figure S4 presents the simulated time until cloud maximal top (Ttop, red curves) and
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maximum collected mass (Tcol, blue curves). The time to maximal cloud depth was
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defined as the first time the cloud top reached a maximum height (the cloud top was
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defined by the height level of 0.01 g kg-1 liquid water content), and if the cloud had
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more than one top maximum, the first one was chosen. All clouds started developing
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at the same time (after 27 min of simulation). The solid lines represent the case of no
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GCCN while the dashed lines are for the case with addition of GCCN (the cases of
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background GCCN gave similar results to the latter and are not shown here).
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Figure S4. Time of cloud development to first maximal top (Ttop, red curves) and
time of the maximum collected mass (Tcol, blue curves) for each simulated cloud as
a function of the aerosol concentration used in the simulation. Each curve
represents 10 simulations conducted using the same atmospheric profile (a total of
9 different initialization profiles). T1 represents a profile with an inversion layer
located at 4 km, T2 at 3 km, and T3 at 2 km. RH1 represents a profile with 95% RH
in the cloudy layer, RH2 – 90%, and RH3 – 80%. The solid lines are for the case of
no GCCN while the dashed lines are for the case of addition of GCCN.
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1.6 Rain efficiency
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Figure S5 presents the rain efficiency for each simulation as a function of aerosol
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concentration per given T and RH profile. It represents 10 simulations (each having a
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different aerosol loading) per initialization profile (when the resolution of the aerosol
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concentration around Nrain_op was not detailed enough, more runs were conducted for a
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few more levels of aerosol loading, see details below). The rain efficiency curves
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show the same general behavior as the total rain yield curves (shown in Fig. 1 in the
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main text). Adding aerosols increased the rain efficiency to an optimum, later
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followed by a decline. Here as well, the aerosol concentration that yielded the
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maximum rain efficiency depended on cloud size and environmental conditions.
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Larger clouds with higher RH values in the cloudy layer needed higher aerosol
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concentrations to yield maximum rain efficiency. The presence of GCCN in the
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aerosol spectrum acted to maintain higher rain efficiency in the most polluted cases.
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For a given aerosol loading, as the cloud size decreased (moving vertically in Fig. S5)
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or the cloudy layer RH decreased (moving horizontally in Fig. S5), the rain efficiency
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decreased. This is due to the change in the effect of the entrainment process [Stirling
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and Stratton, 2012].
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Figure S5. Rain efficiency as a function of the aerosol concentration used in the
simulation. Each curve represents 10 simulations done for specific atmospheric
profiles (total of 9 different initialization profiles) and specific aerosol size
distribution. For each atmospheric profile, the three aerosol size distributions are
presented: no GCCN (blue), background GCCN (red) and addition of GCCN
(green). T1 represents a profile with an inversion layer located at 4 km, T2 at 3 km,
and T3 at 2 km. RH1 represents a profile with 95% RH in the cloudy layer, RH2 –
90%, and RH3 – 80%.
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1.7 List of additional simulations
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For cases in which the resolution of the aerosol concentration around Nrain_op was not
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sufficiently detailed, more runs were conducted for a few more levels of aerosol
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loading for a more accurate determination of the optimum (in addition to the 10
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simulations conducted for each of the initialization profiles). A list of these
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simulations is given here:
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
750 cm-3 was conducted.
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

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For profile T2RH1, an additional simulation with an aerosol concentration of
375 cm-3 was conducted.
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For profile T1RH3, an additional simulation with an aerosol concentration of
300 cm-3 was conducted.
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For profile T1RH2, an additional simulation with an aerosol concentration of

For profile T2RH2, an additional simulation with an aerosol concentration of
375 cm-3 was conducted.
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References
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Altaratz, O., I. Koren, and T. Reisin (2008a), Humidity impact on the aerosol effect in
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warm cumulus clouds, Geophysical Research Letters, 35(17).
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Altaratz, O., I. Koren, T. Reisin, A. Kostinski, G. Feingold, Z. Levin, and Y. Yin
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(2008b), Aerosols' influence on the interplay between condensation, evaporation and
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rain in warm cumulus cloud, Atmospheric Chemistry and Physics, 8(1), 15-24.
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Dagan, G., I. Koren, and O. Altaratz (2015), Competition between core and periphery-
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based processes in warm convective clouds–from invigoration to suppression,
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Atmospheric Chemistry and Physics, 15(5), 2749-2760.
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Garstang, M., and A. K. Betts (1974), A review of the tropical boundary layer and
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cumulus convection: Structure, parameterization, and modeling, Bulletin of the
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American Meteorological Society, 55(10), 1195-1205.
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Khain, A., T. V. Prabha, N. Benmoshe, G. Pandithurai, and M. Ovchinnikov (2013),
189
The mechanism of first raindrops formation in deep convective clouds, Journal of
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Geophysical Research: Atmospheres, 118(16), 9123-9140.
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Koren, I., G. Dagan, and O. Altaratz (2014), From aerosol-limited to invigoration of
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warm convective clouds, science, 344(6188), 1143-1146.
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Kostinski, A. B., and R. A. Shaw (2005), Fluctuations and luck in droplet growth by
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coalescence, Bulletin of the American Meteorological Society, 86(2), 235-244.
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Low, T. B., and R. List (1982), Collision, coalescence and breakup of raindrops. Part
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I: Experimentally established coalescence efficiencies and fragment size distributions
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in breakup, Journal of the Atmospheric Sciences, 39(7), 1591-1606.
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McTaggart-Cowan, J. D., and R. List (1975), Collision and breakup of water drops at
199
terminal velocity, Journal of the Atmospheric Sciences, 32(7), 1401-1411.
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Reisin, T., Z. Levin, and S. Tzivion (1996), Rain Production in Convective Clouds As
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Simulated in an Axisymmetric Model with Detailed Microphysics. Part I: Description
202
of the Model, Journal of the Atmospheric Sciences, 53(3), 497-519.
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Stirling, A., and R. Stratton (2012), Entrainment processes in the diurnal cycle of deep
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convection over land, Quarterly Journal of the Royal Meteorological Society,
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138(666), 1135-1149.
206
Teller, A., and Z. Levin (2006), The effects of aerosols on precipitation and
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dimensions of subtropical clouds: a sensitivity study using a numerical cloud model,
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Atmospheric Chemistry and Physics, 6, 67-80.
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Tzivion, S., G. Feingold, and Z. Levin (1987), An efficient numerical solution to the
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stochastic collection equation, Journal of the atmospheric sciences, 44(21), 3139-
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3149.
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Tzivion, S., T. Reisin, and Z. Levin (1994), Numerical simulation of hygroscopic
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seeding in a convective cloud, Journal of Applied Meteorology, 33(2), 252-267.
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Yin, Y., Z. Levin, T. G. Reisin, and S. Tzivion (2000), The effects of giant cloud
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condensation nuclei on the development of precipitation in convective clouds—a
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numerical study, Atmospheric research, 53(1), 91-116.
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