
The Tropical Warm Pool International Cloud Experiment (TWPICE) was conducted in January–February 2006 in Darwin under different atmospheric conditions during the northern Australian monsoon season. At the start of the experiment, the region experienced an active monsoon period. During 23–24 January, a strong mesoscale convective system passed through the domain, which was followed by a relatively suppressed monsoon period. A little rain occurred during 3–5 February, which was followed by a monsoon break period to the end of the field campaign. Full details of the meteorological conditions can be found in May et al. (2008). This study focused on the active monsoon period of 20–25 January and the suppressed monsoon period from 28 January to 2 February. The field campaign attempted to describe the development of tropical convective cloud and the associated cirrus cloud. Detailed observations of the cloud properties and the impact of the cloud on the local environment have been reported in previous studies (e.g., May et al., 2008; Wapler et al., 2010; Xie et al., 2010).

GRAPES is a new generation of numerical weather prediction system, suitable for both global and regional numerical weather prediction, which has been developed by the China Meteorological Administration since 2000 (Chen et al., 2008; Xue and Chen, 2008; Ma et al., 2018). The GRAPES_SCM has been constructed for evaluating and developing the physics parameterizations of GRAPES. The GRAPES_SCM is adhered to the full model, which guarantees that the physical process parameterizations in both the full atmospheric model and the SCM are unified. By introducing new modules and options in the “namelist” in the GRAPES code, the SCM run is realized with a feasible treatment of observational data input as well as different configurations of physical processes. A diagram of the GRAPES_SCM integral is shown in Fig. 1. A detailed flow chart and the equations of the GRAPES_SCM can be seen in Yang and Shen (2011). Wang et al. (2013) used the GRAPES_SCM to evaluate cumulus parameterization schemes and to examine a new modified scheme, and they concluded that the modified scheme was more reasonable than the previous version. Li et al. (2018) adjusted the Liuma scheme using the GRAPES_SCM. Their results showed that the revised Liuma scheme had improved performance in simulating icephased hydrometeors, and that the simulated surface precipitation rate was much closer to the observations.
Studies have shown that SCM solutions are sensitive to initial conditions because of the nonlinear nature of model physics (Cripe, 1998; Hack and Pedretti, 2000). Therefore, an ensemble methodology is necessary to represent the solution of a single model. In accordance with Davies (2009), this study ran key ensemble members (5th, 25th, 50th, 75th，and 95th) to obtain the best estimate simulation. The results presented in this paper are the means of those simulations. As also mentioned by Davies (2009), the simulations were initialized with observed temperature and moisture profiles from 19 January 2006. Temperature and moisture have only horizontal advective tendencies. The vertical terms are calculated by the model. There was no nudging of the temperature and moisture fields, and the simulation comprised a continuous simulation (no reinitialization) of the entire TWPICE period.
In this study, all settings and parameterization schemes other than the microphysical scheme were kept the same. The simulation center was located at Port Darwin (12.425°S, 130.891°E). The model had a fixed timeinvariant sea surface temperature of 29°C and the interactive surface fluxes were calculated in the model. The time step of the simulation was 10 s (note that substepping was used for fast processes such as sedimentation in the microphysics scheme). The model was run with the SAS cumulus scheme (Arakawa and Schubert, 1974), RRTMG scheme (Mlawer et al., 1997) for both longwave and shortwave radiation, and the slab scheme for the boundary layer (Blackadar, 1978).

The Liuma microphysics scheme is a twomoment mixedphase scheme developed from the convective and stratus cloud model of Hu and Yan (1986) and Hu and He (1987). This scheme predicts the mass mixing ratios of cloud water, rain water, cloud ice, snow, and graupel, as well as the number concentrations of rain drops, cloud ice, snow, and graupel. The Liuma microphysics scheme is used for weather forecasting and scientific research (e.g., Liu et al., 2003; Zhang and Liu, 2006; Shi et al., 2015; Ma et al., 2018). Li et al. (2018, 2019) have tested the performance of the Liuma microphysics scheme in various models and they have improved its mixedphased microphysical processes.
The initial version of the Liuma microphysics scheme (hereafter, Liuma_init) contains both homogeneous freezing and the three modes of heterogeneous nucleation (deposition nucleation, condensation freezing, and immersion freezing), following Hu and Yan (1986) and Reisner et al. (1998). A detailed calculation flowchart of the microphysical processes can be seen in Li et al. (2018). In this study, the equations of the number concentration production rate of homogeneous freezing and immersion freezing are described as a unified form based on the rain–graupel conversion from Reisner et al. (1998), as shown below:
$$ {N_{\rm fci}} = \left\{ {\begin{split} & 0.1 \exp [0.66( T)  1] \rho q_{\rm c}, \; 0 > T >  40 {\text {\rm °C}} \\ & {1.0 \times 10^6 / {\Delta}t, \; T \leqslant  40 {\text {\rm °C}}} \end{split} } \right., $$ (1) where N_{fci} (s^{−1}) is the number concentration production rate, T (°C) is temperature, ρ (kg m^{−3}) is air density, q_{c} (kg kg^{−1}) is the cloud water mixing ratio, and
${\Delta}t $ (s) is the time step. The function shows that immersion freezing works between 0 and −40°C and that homogeneous freezing leads to all cloud water being converted into ice at temperatures below −40°C.Different parameterizations of deposition nucleation and condensation freezing were used to investigate how deep convective cloud and the associated cirrus are affected by heterogeneous nucleation. In recent research, the most widely adopted measurementbased deposition nucleation and condensation freezing parameterizations are from Meyers et al. (1992), DeMott et al. (2003), and Phillips et al. (2008); hereafter, denoted as Liuma_r1, Liuma_r2, and Liuma_r3, respectively. The equations of the number concentration production rate of the Liuma_init, Liuma_r1, Liuma_r2, and Liuma_r3 deposition nucleation and condensation freezing parameterizations are shown below:
$$ \!\!{N_{\rm f0}} = \left\{ {\begin{array}{*{20}{c}} {0.01 \exp \left[{0.6 \times \left({  T} \right)} \right] {{\left({\dfrac{{{q_{\rm v}}  {q_{\rm vsi}}}}{{{q_{\rm vs}}  {q_{\rm vsi}}}}} \right)}^{4.5}}, \;\;\; T >  26.5 {\text {\rm °C}}}\\ {0.01 \exp \left({0.6 \times 27.5} \right) {{\left({\dfrac{{{q_{\rm v}}  {q_{\rm vsi}}}}{{{q_{\rm vs}}  {q_{\rm vsi}}}}} \right)}^{4.5}},\;\;\; T \leqslant  26.5 {\text {\rm °C}}} \end{array}} \right., \quad\quad\quad\quad\quad\quad $$ (2) $$ {N_{\rm f1}} = 1000 \exp \left({\min \left({57,\left({12.96 \left({\frac{{{q_{\rm v}}}}{{{q_{\rm vsi}}}}  1} \right)  0.639} \right)} \right)} \right), \;\;\; T \leqslant  5 {\text {\rm °C},} \quad\quad\quad\quad\quad\!\! $$ (3) $$ \!\!\!\!\!\!\!\!\!{N_{\rm f2}} = 1000 \times 0.00446684 \exp \left({0.3108 \left({  \max \left({  40,T} \right)} \right)} \right), \quad\quad\quad\quad\quad\quad\quad\;\;\; $$ (4) $$ \; {N_{\rm f3}} = \left\{ {\begin{array}{*{20}{c}} {1000 \exp \left({\min \left({57,\left({12.96 \left({\dfrac{{{q_{\rm v}}}}{{{q_{\rm vsi}}}}  1} \right)  0.639} \right)} \right)} \right), \;\;\;  30 < T \leqslant  5{\text {\rm °C}}}\\ {1000 \exp {{\left({\min \left({57,\left({12.96 \left({\dfrac{{{q_{\rm v}}}}{{{q_{\rm vsi}}}}  1.1} \right)} \right)} \right)} \right)}^{0.3}}, \;\;\; T \leqslant  30 \;{\rm or} \;T >  5{\text {\rm °C}}} \end{array}} \right., $$ (5) where q_{v}, q_{vs}, and q_{vsi} (kg kg^{−1}) represent the water vapor mixing ratio of the environment and the saturation water vapor pressure over water and ice, respectively.
The saturation water vapor mixing ratio over water and ice can also be represented as a fractional function of saturation water vapor (e_{s} and e_{si}) and pressure:
$$ {q_{\rm vs}} = \frac{{0.622 {e_{\rm s}}}}{{p  0.378 {e_{\rm s}}}},\quad\quad\quad\quad\quad $$ (6) $$ {q_{\rm vsi}} = \frac{{0.622 {e_{\rm si}}}}{{p  0.378 {e_{\rm si}}}}, \;\;\; T \leqslant 0{\text{°C}}, $$ (7) whereas saturation water vapor is related only to temperature:
$$ {e_{\rm s}} = \left\{ \begin{array}{l} 1000 \times 0.6112 \times \exp \left({\dfrac{{17.27 \times T}}{{T + 237.29}}} \right), \;\;\; T > 0 {\text{°C}}\\ 1000 \times0.6112 \times \exp \left({\dfrac{{17.67 \times T}}{{T + 243.5}}} \right), \;\;\; T \leqslant 0 {\text{°C}} \end{array} \right., $$ (8) $$ {\rm e_{si}} = 1000 \times 0.6112 \times \exp \left({\frac{{21.87 \times T}}{{T + 265.49}}} \right), \;\;\; T \leqslant 0{\text{°C}} . \quad\quad\quad\!\!\!\! $$ (9) The number concentration production rate of deposition nucleation/condensation freezing parameterizations under different water vapor mixing ratios is shown in Fig. 2. In general, the number concentration production rate increases significantly with decreasing temperature, but it increases smoothly with increases of pressure and water vapor mixing ratio. This is because, as already shown in Eqs. (1)–(8), the number concentration production rate is an exponential function of temperature and a fractional function of both pressure and water vapor mixing ratio. Thus, the number concentration production rate is much more sensitive to temperature than to pressure and water vapor mixing ratio. When the temperature is above −20°C, hardly any ice is formed under all parameterizations. The results from Liuma_init show that under a low water vapor mixing ratio (i.e., 0.2 g kg^{−1}), the number concentration production rate increases from 1.0 kg^{−1} s^{−1} at around −40°C to more than 1 × 10^{10} kg^{−1} s^{−1} at around −60°C, as shown in Fig. 2a. Under a high water vapor mixing ratio (i.e., 1.0 g kg^{−1}), such an increase occurs from around −20 to −40°C, as shown in Fig. 2i. The results from Liuma_r1–Liuma_r3 also show similar tendency. It should be noted that the number concentration production rate from Liuma_r2 does not change when the temperature is below −40°C (as shown in Figs. 2c, g, k) because of the limitation expressed in Eq. (3).
Figure 2. Number concentration production rate (kg^{−1} s^{−1}) of deposition nucleation/condensation freezing parameterizations. Left to right columns represent results of (a, e, i) Liuma_init, (b, f, j) Liuma_r1, (c, g, k) Liuma_r2, and (d, h, l) Liuma_r3, respectively; top to bottom panels represent environmental q_{v} of 0.2, 0.5, and 1.0 g kg^{−1}, respectively. Note the color bar is the logarithmic value of the production rate.
It is also shown that different deposition nucleation/condensation freezing parameterizations have considerable effect on the number concentration production rate. Under a low water vapor mixing ratio, the number concentration production rate from Liuma_r1 increases sharply from around −40 to −50°C, and it can be as high as 1.0 × 10^{20} kg^{−1} s^{−1} at −50°C, as shown in Fig. 2b. This is much higher than the result from Liuma_init at the same temperature. The number concentration production rate from Liuma_r3 also increases sharply from around −40 to −50°C, and it can be as high as 1.0 × 10^{10} kg^{−1} s^{−1} at −50°C, as shown in Fig. 2d. Under a high water vapor mixing ratio, the number concentration production rate has the same tendency as under a low water vapor mixing ratio. The number concentration production rate from Liuma_r2 is relatively low in comparison with the other parameterizations, and it retains the highest value of frozen number concentration (1.0 × 10^{5} kg^{−1} s^{−1}) when the temperature is below −40°C.
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Li, Z., Q. J. Liu, Z. S. Ma, et al., 2019: Simulation study of cloud properties affected by heterogeneous nucleation using the GRAPES_SCM during the TWPICE campaign. J. Meteor. Res., 33(4), 734–746, doi: 10.1007/s1335101982031 
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Manuscript History
Received: 14 January 2019
Final form: 05 May 2019
Published online: 26 August 2019