• Grabowski, W. W., X. Wu, and M. W. Moncrieff, 1996: Cloud-resolving model of tropical cloud systems during Phase III of GATE. Part I: Two-dimensional experiments. J. Atmos. Sci., 53 , 36843709.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., . 1999: Cloud-resolving model of tropical cloud systems during Phase III of GATE. Part III: Effects of cloud microphysics. J. Atmos. Sci., 56 , 23842402.

    • Search Google Scholar
    • Export Citation
  • Krueger, S. K., Q. Fu, K. N. Liou, and H-N. S. Chin, 1995: Improvement of an ice-phase microphysics parameterization for use in numerical simulations of tropical convection. J. Appl. Meteor., 34 , 281287.

    • Search Google Scholar
    • Export Citation
  • Li, X., C-H. Sui, K-M. Lau, and M-D. Chou, 1999: Large-scale forcing and cloud–radiation interaction in the tropical deep convective regime. J. Atmos. Sci., 56 , 30283042.

    • Search Google Scholar
    • Export Citation
  • Lin, Y-L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22 , 10651092.

    • Search Google Scholar
    • Export Citation
  • Liu, Y., D-L. Zhang, and M. K. Yau, 1997: A multiscale numerical study of Hurricane Andrew (1992). Part I: Explicit simulation and verification. Mon. Wea. Rev., 125 , 30733093.

    • Search Google Scholar
    • Export Citation
  • Lord, S. J., H. E. Willoughby, and J. M. Piotrowicz, 1984: Role of a parameterized ice-phase microphysics in an axisymmetric, nonhydrostatic tropical cyclone model. J. Atmos. Sci., 41 , 28362848.

    • Search Google Scholar
    • Export Citation
  • Rutledge, S. A., and P. V. Hobbs, 1983: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. Part VIII: A model for the “seeder-feeder” process in warm-frontal rainbands. J. Atmos. Sci., 40 , 11851206.

    • Search Google Scholar
    • Export Citation
  • Rutledge, S. A., . 1984: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. Part XII: A diagnostic modeling study of precipitation development in narrow cold-frontal rainbands. J. Atmos. Sci., 41 , 29492972.

    • Search Google Scholar
    • Export Citation
  • Soong, S. T., and Y. Ogura, 1980: Response of tradewind cumuli to large-scale processes. J. Atmos. Sci., 37 , 20352050.

  • Soong, S. T., and W-K. Tao, 1980: Response of deep tropical cumulus clouds to mesoscale processes. J. Atmos. Sci., 37 , 20162034.

  • Sui, C-H., K-M. Lau, W-K. Tao, and J. Simpson, 1994: The tropical water and energy cycles in a cumulus ensemble model. Part I: Equilibrium climate. J. Atmos. Sci., 51 , 711728.

    • Search Google Scholar
    • Export Citation
  • Sui, C-H., Y. Takayabu, and D. Short, 1997: Diurnal variations in tropical oceanic cumulus ensemble during TOGA COARE. J. Atmos. Sci., 54 , 639655.

    • Search Google Scholar
    • Export Citation
  • Sui, C-H., X. Li, and K-M. Lau, 1998: Radiative–convective processes in simulated diurnal variations of tropical oceanic convection. J. Atmos. Sci., 55 , 23452359.

    • Search Google Scholar
    • Export Citation
  • Takayabu, Y. N., K-M. Lau, and C-H. Sui, 1996: Observation of a quasi-2-day wave during TOGA COARE. Mon. Wea. Rev., 124 , 18921913.

  • Tao, W-K., and J. Simpson, 1993: The Goddard Cumulus Ensemble model. Part I: Model description. Terr. Atmos. Oceanic Sci., 4 , 3572.

  • Tao, W-K., and M. McCumber, 1989: An ice-water saturation adjustment. Mon. Wea. Rev., 117 , 231235.

  • Weller, R. A., and S. P. Anderson, 1996: Surface meteorology and air–sea fluxes in the western equatorial Pacific warm pool during TOGA COARE. J. Climate, 9 , 19591990.

    • Search Google Scholar
    • Export Citation
  • Willoughby, H. E., H. L. Jin, S. J. Lord, and J. M. Piotrowicz, 1984: Hurricane structure and evolution as simulated by an axisymmetric, non-hydrostatic numerical model. J. Atmos. Sci., 41 , 11691186.

    • Search Google Scholar
    • Export Citation
  • Wu, X., W. W. Grabowski, and M. W. Moncrieff, 1998: Long-term evolution of cloud systems in TOGA COARE and their interactions with radiative and surface processes. Part I: Two-dimensional cloud-resolving model. J. Atmos. Sci., 55 , 26932714.

    • Search Google Scholar
    • Export Citation
  • Wu, X., W. D. Hall, W. W. Grabowski, M. W. Moncrieff, W. D. Collins, and J. T. Kiehl, 1999: Long-term evolution of cloud systems in TOGA COARE and their interactions with radiative and surface processes. Part II: Effects of ice microphysics on cloud–radiation interaction. J. Atmos. Sci., 56 , 31773195.

    • Search Google Scholar
    • Export Citation
  • Xu, K-M., and D. A. Randall, 1996: Explicit simulation of cumulus ensembles with the GATE Phase III data: Comparison with observations. J. Atmos. Sci., 53 , 37103736.

    • Search Google Scholar
    • Export Citation
  • Zhang, M. H., and J. L. Lin, 1997: Constrained variational analysis of sounding data based on column-integrated budgets of mass, heat, moisture, and momentum: Approach and application to ARM measurements. J. Atmos. Sci., 54 , 15031524.

    • Search Google Scholar
    • Export Citation
  • View in gallery

    Time evolution of (a) vertical velocity (cm s−1), (b) zonal wind (m s−1), and (c) sea surface temperature (°C) taken from TOGA COARE for a 6-day period. Upward motion in (a) and westerly wind in (b) are shaded

  • View in gallery

    Time evolution and horizontal distribution of surface rain rate (mm h−1) within 400–700 km on 20 Dec 1992. The box between hours 6–20 (0600–2000 local time) is used to analyze the life cycle of convection in Figs. 3–7

  • View in gallery

    Time–height distributions of zonally averaged (a) vertical velocity (cm s−1), (b) zonal wind (m s−1), (c) specific humidity anomaly (g kg−1), and (d) temperature anomaly (°C) in hours 6–20 on 20 Dec 1992 in the box shown in Fig. 2. Shaded areas are (a) upward motion, (b) westerly wind, (c) positive specific humidity anomaly, and (d) positive temperature anomaly

  • View in gallery

    Time–height distributions of zonally averaged mixing ratios of (a) cloud water, (b) raindrops, (c) cloud ice, (d) snow, and (e) graupel (10−2 g kg−1) in hours 6–20 on 20 Dec 1992 in the box shown in Fig. 2. Values larger than 0.001 g kg−1 are shaded

  • View in gallery

    Cloud microphysics budgets averaged in (a) hours 6–11, (b) hours 12–18, and (c) hours 19–20 on 20 Dec 1992 in the box shown in Fig. 2. Units for cloud hydrometeors and conversions are mm and mm h−1, respectively

  • View in gallery

    Vertical profiles of mean vertical velocity (cm s−1) averaged in the left half (solid line) and the right half (dotted line) of the box in hours 13–15

  • View in gallery

    Cloud microphysics budgets averaged in (a) the left half and (b) the right half of the box in hours 13–15. Units for cloud hydrometeors and conversions are mm and mm h−1, respectively.>

  • View in gallery

    Time–height distributions of differences of (a) temperature (°C) and (b) specific humidity (g kg−1) of simulations with the simplified set of cloud microphysics equations (9) minus simulations with the original set (A1).>

  • View in gallery

    Time series of (a) surface rain rate (mm h−1), (b) convective rain rate, and (c) stratiform rain rate in simulations with the simplified set of cloud microphysics equations (solid lines) and simulations with the original set (dashed lines).>

  • View in gallery

    As in Fig. 9 except for the fractional covers of (a) nonraining stratiform clouds (%), (b) convective clouds, and (c) raining stratiform clouds

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 47 41 5
PDF Downloads 14 13 1

Dominant Cloud Microphysical Processes in a Tropical Oceanic Convective System: A 2D Cloud Resolving Modeling Study

View More View Less
  • 1 NOAA/NESDIS/ORA/Atmospheric Research and Applications Division, Camp Springs, Maryland
  • | 2 NASA Goddard Space Flight Center, Greenbelt, Maryland
© Get Permissions
Full access

Abstract

Dominant cloud microphysical processes associated with a tropical oceanic convective system are investigated based on a 2D cloud resolving simulation. The model is forced by the zonal-mean vertical velocity, zonal wind, sea surface temperature, and horizontal temperature and moisture advections measured and derived from the TOGA COARE period. The analysis of cloud microphysics budgets shows that cloud water forms due to vapor condensation, but most of the conversion of cloud water to precipitation occurs primarily through two mechanisms, depending on the temperature when they occur: through riming of cloud water onto precipitation ice (snow or graupel) at colder than 0°C and collection of cloud water by rain at warmer temperatures. Processes involving the liquid phase are dominant during the early stages of convection development. The collection process produces rain, and the riming process enhances ice clouds. Ice processes are more dominant during the later stages. The melting of precipitation ice and vapor deposition become important in producing rain and ice clouds, respectively.

Based on the analysis of dominant cloud microphysical processes, a simplified set of cloud microphysics parameterization schemes are proposed. Simulations with the simplified and original sets show similar thermodynamic evolution and cloud properties.

Corresponding author address: Dr. Xiaofan Li, NOAA/NESDIS/ORA/ARAD, 5200 Auth Road, Room 601, Camp Springs, MD 20746. Email: Xiaofan.Li@noaa.gov

Abstract

Dominant cloud microphysical processes associated with a tropical oceanic convective system are investigated based on a 2D cloud resolving simulation. The model is forced by the zonal-mean vertical velocity, zonal wind, sea surface temperature, and horizontal temperature and moisture advections measured and derived from the TOGA COARE period. The analysis of cloud microphysics budgets shows that cloud water forms due to vapor condensation, but most of the conversion of cloud water to precipitation occurs primarily through two mechanisms, depending on the temperature when they occur: through riming of cloud water onto precipitation ice (snow or graupel) at colder than 0°C and collection of cloud water by rain at warmer temperatures. Processes involving the liquid phase are dominant during the early stages of convection development. The collection process produces rain, and the riming process enhances ice clouds. Ice processes are more dominant during the later stages. The melting of precipitation ice and vapor deposition become important in producing rain and ice clouds, respectively.

Based on the analysis of dominant cloud microphysical processes, a simplified set of cloud microphysics parameterization schemes are proposed. Simulations with the simplified and original sets show similar thermodynamic evolution and cloud properties.

Corresponding author address: Dr. Xiaofan Li, NOAA/NESDIS/ORA/ARAD, 5200 Auth Road, Room 601, Camp Springs, MD 20746. Email: Xiaofan.Li@noaa.gov

1. Introduction

Clouds play an important role in regulating tropical climate. The latent heat of clouds is a major source of energy that drives tropical climate. However, the latent heat is largely balanced by the vertical thermal advection so that their residual determines the vertical structures of temperature. Thus, accurate parameterization of cloud microphysical processes is of fundamental importance in numerical simulations of tropical climate.

The parameterization of ice processes are important in the numerical simulation of hurricanes. Willoughby et al. (1984) conducted an experiment with parameterized ice-phase cloud microphysics using an axisymmetric, nonhydrostatic tropical cyclone model, and argued that widespread cooling associated with melting of graupel in mesoscale downdrafts affects the formation of multiple convective rings and the development of hurricanes. Lord et al. (1984) performed further budget analysis that supported the hypothesis of Willoughby et al (1984). Liu et al. (1997) carried out a multiscale numerical study of Hurricane Andrew (1992) using an improved version of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research nonhydrostatic Mesoscale Model (MM5), and found that inclusion of the microphysics parameterization schemes in MM5 led to more realistic simulations of cloud structures of model hurricanes when compared to the observations.

The cloud resolving models that include cloud microphysics parameterization schemes have been demonstrated to well simulate atmospheric thermodynamical states in the Tropics during the GARP (Global Atmospheric Research Program) Atlantic Tropical Experiment (GATE; e.g., Xu and Randall 1996; Grabowski et al. 1996) and Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE; e.g., Wu et al. 1998; Li et al. 1999). In their 7-day cloud resolving simulations during TOGA COARE, Li et. al. (1999) used the improved schemes of the growth of cloud ice by the Bergeron process and the conversion of cloud ice to snow by Krueger et al. (1995) and found that the increase of the mixing ratio of cloud ice led to better simulations of atmospheric thermodynamical states and surface fluxes. Grabowski et al. (1999) in their 7-day cloud resolving simulations during Phase III of GATE found that cloud microphysics affects the temperature and moisture profiles in a way that retains relative humidity, responses of surface processes to cloud microphysics are important when the radiative tendencies are prescribed, and the temperature in the upper troposphere is modified by the effect of cloud microphysics on the anvil clouds when the radiation and clouds are fully interacted. In their 39-day cloud resolving simulations during TOGA COARE, Wu et al. (1999) showed that simulations of cloud radiative properties are improved by the modified ice microphysical parameterization schemes. They also found that the radiative flux, cloud radiative forcing, and albedo are sensitive to the effective radius of ice particles.

In this study, dominant cloud microphysical processes in a tropical oceanic convective system are analyzed based on a 2D cloud resolving simulation. In the next section, the model is briefly described. In section 3, the dominant microphysical processes in a life cycle of the convection—in particular, the responses of cloud microphysics to the vertical profile of upward motion—are examined. A simplified set of cloud microphysics parameterization schemes are proposed. The summary is given in section 4.

2. Model

The cloud resolving model was originally developed by Soong and Ogura (1980), Soong and Tao (1980), and Tao and Simpson (1993). The 2D version of the model used by Sui et al. (1998) and modified by Li et al. (1999) is used in this study. The governing equations with an anelastic approximation can be expressed as follows:
i1520-0493-130-10-2481-e1
Here, u and w are zonal, and vertical air wind components; θ and qυ are air potential temperature and specific humidity, respectively; C = (qc, qr, qi, qs, qg), qc, qr, qi, qs, and qg are the mixing ratios of cloud water (small cloud droplets), raindrops, cloud ice (small ice crystals), snow (density 0.1 g cm−3), and graupel (density 0.4 g cm−3), respectively; ρ is a mean air density, which is a function of height only; wTV is a terminal velocity which is zero for cloud water and ice; π = (p/po)κ, κ = R/cp, R is the gas constant, cp is the specific heat of dry air at constant pressure p, and po = 1000 mb; c, e, d, and s denote condensation, evaporation, deposition, and sublimation, respectively; Qcn = Lυ(ce) + Ls(ds) + Lf(fm) denotes the net latent heat release through phase changes among different cloud species, where f and m are fusion and melting, respectively; Lυ, Ls, and Lf are heat coefficients due to phase changes; QR is the radiative heating rate due to convergence of net flux of solar and infrared radiative fluxes; SC is the source and sink of various hydrometeor species described by (A1b)–(A1f) in the appendix (see Li et al. 1999); Ds are turbulent dissipation terms; overbar () denotes a zonal mean; subscript b denotes an initial value, which does not vary with time; superscript o denotes imposed observed variables in the model.

The experiment analyzed in this study is conducted using the model with lateral periodic boundaries. The model is forced by zonally uniform vertical velocity, zonal wind, and horizontal advections derived by Prof. M. Zhang, who used the 6-hourly TOGA COARE observations within the Intensive Flux Array (IFA) region (M. Zhang 1999, personal communication). The time evolution of vertical velocity by Zhang is similar to that calculated by Sui et al. (1997). Some amplitude differences are evident because the calculations by Zhang are constrained by the boundary conditions such as surface rain rate and fluxes that balance the water budget. The calculations are based on the constrained variational method on column-integrated budgets of mass, heat, moisture, and momentum proposed by Zhang and Lin (1997). Hourly sea surface temperature at the Improved Meteorological (IMET) surface mooring buoy (1.75°S, 156°E) (Weller and Anderson 1996) is also uniformly imposed in the model. The model is integrated from 0400 18 December to 0400 25 December 1992 (local time) [1800 UTC 17 December–1800 UTC 24 December 1992]. The horizontal domain is 768 km. A grid mesh of 1.5 km and a 12-sec step are used in model integrations. More discussion of the model is reported in Li et al. (1999).

3. Results

Figure 1 shows the time evolution of vertical distribution of the large-scale atmospheric vertical velocity, zonal wind, and sea surface temperature (SST) during 19–25 December 1992 that are imposed in the model. During this period, the upward motion is dominant, indicating strong convection. The diurnal and two-day signals are detected in Fig. 1a as indicated by Sui et al. (1997) and Takayabu et al. (1996) respectively. The large-scale westerly winds increase significantly in the lower and midtroposphere and reach their maximum of 12 m s−1 around 650 mb about 23 December 1992 (Fig. 1b). Except for the first day, SST shows weak diurnal variations with a slowly decreasing trend. As mentioned previously, the model is also forced by the observed horizontal advection of temperature and moisture (not shown), which has much smaller amplitudes than the vertical advection.

Figure 2 shows the time evolution of the horizontal distribution of surface rain rate within 400–700 km on 20 December 1992 when a strong, organized convection occurs. The rainbands propagate westward before hour 20 and they then start to propagate eastward. The change of moving direction of the rainbands results from intensification of lower-tropospheric westerly winds and weakening of midtropospheric easterly winds (Fig. 1b). To analyze dominant cloud microphysical processes associated with the development of convection, a life cycle of convection is chosen in a box that is along the propagation of the major rainband. Since the rainband propagates westward with a nearly constant rate of 3.3 m s−1, the box is a parallelogram in time–space frame. The box starts at hour 6 and ends at hour 20, and has the zonal length of 18 km. Figures 3–7 are plotted based on the information in the box.

The mean vertical velocity shows weak upward and downward motion within hour 6–11 on 20 December 1992 (Fig. 3a). Then, the upward motion intensifies and reaches the maximum of more than 100 cm s−1 around hour 14 and 500 mb. The upward motion weakens in hours 14–18 and the downward motion develops in the mid and lower troposphere in hours 18–20. The zonal wind shows the westerly winds below 600 mb and the easterly winds between 400 and 600 mb (Fig. 3b). The maximum positive specific humidity and temperature anomalies are associated with the maximum upward motion around hour 14 (Figs. 3c,d), indicating the enhanced moistening and heating in the deep convection. The upward propagation of the positive moisture and temperature anomalies is also evident.

The mixing ratios of the water clouds start to increase significantly at hour 8 when the mean upward motion occurs in the lower troposphere (Figs. 4a,b). The downward motion develops in the upper troposphere that suppresses the growth of the ice clouds in hours 8–11 (Figs. 4c–e). Thus, the mixing ratios of cloud water and raindrops are much larger than those of cloud ice, snow, and graupel. When the upward motion extends to the upper troposphere around hour 11, the mixing ratios of cloud ice, snow, and graupel start to increase, indicating the development of ice clouds. When the mean upward motion reaches its maximum at hour 14, all the cloud hydrometeors reach the maxima. The mixing ratios of the cloud hydrometeors decrease after hour 14 as the mean upward motion weakens and the downward motion develops. Thus, three phases are used for the analysis: onset phase (hours 6–11), mature phase (hours 12–18), and dissipating phase (hours 19–20). Cloud microphysics budgets analyzed in the following discussions are expressed in terms of vertically integrated quantities averaged in the different phases.

In the onset phase (Fig. 5a), the mixing ratios of water clouds (Sqc + Sqr = 0.57 + 0.74 mm) are much larger than those of ice clouds (Sqi + Sqs + Sqg = 0.001 + 0.04 + 0.12 mm), indicating the dominance of water clouds. The condensation of supersaturated vapor (PCND: 4.77 mm h−1) is a dominant source for growth of clouds. The collection of cloud water by raindrops (PRACW: 3.79 mm h−1) is a major process for development of rain. The accretion of cloud water by graupel [PGACW(T < To): 0.26 mm h−1] and by snow [PSACW(T < To): 0.21 mm h−1] is an important process for development of precipitating ice clouds in the early stage.

In the mature phase (Fig. 5b), the mixing ratios of water clouds (Sqc + Sqr = 1.1 + 3.6 mm) and ice clouds (Sqi + Sqs + Sqg = 0.4 + 0.5 + 2.8 mm) have the same orders of magnitudes. The melting of graupel [PGMLT(T > To): 7.6 mm h−1] and the PRACW (11.7 mm h−1) are equally important for the production of rain. The deposition of supersaturated vapor (PDEP: 3 mm h−1), PGACW(T < To)(4.9 mm h−1) PSACW(T < To)(1.1 mm h−1) are equally important for strong development of ice clouds, although PDEP is about one-sixth of the PCND.

In the dissipating phase (Fig. 5c), the cloud microphysics budget is similar to that in the mature phase. The melting of graupel [PGMLT(T > To): 3.2 mm h−1] becomes a dominant process to produce rain. The PDEP (1.2 mm h−1) is about a half of the PCND (2.7 mm h−1).

To further examine how the surface rainfall and associated cloud microphysical processes respond to different vertical profiles of vertical velocity, the vertical velocity is averaged in the left half and right half of the box in hours 13–15 when the upward motion reaches the maximum. The maximum upward motion averaged in the left half has the maxima of 100 cm s−1 between 500 and 750 mb and around 200 mb, whereas the maximum upward motion averaged in the right half has the maximum of 200 cm s−1 around 400 mb (Fig. 6). As seen in Fig. 7, the surface rain rate in the left half of the box (41.7 mm h−1) is twice as large as that in the right half (19.5 mm h−1). This is due to the fact that the rate of the mixing ratio of raindrop in the left half of the box (36.2 mm h−1) is twice as large as that in the right half (15.3 mm h−1). Fig. 7 shows that the collection of cloud water by rain and the melting of graupel are the major processes in producing rain, and the difference of the collection rates between the two halves of the box is much larger than the difference of the melting rates. Thus, the collection processes determine growth rate of rain. The cloud water forms due to the vapor condensation. The rates of the vapor condensation in the two halves of the box are about the same. The conversion from cloud water to precipitation occurs primarily through riming of cloud water onto precipitation ice (snow or graupel) above the melting level and collection of cloud water by rain below, depending on the temperature when they occur. The maximum upward motion between 500 and 750 mb enhances the collection process and causes large surface rain rate in the left half of the box, whereas the maximum upward motion around 400 mb enhances the riming process and causes a small surface rain rate in the right half.

The PRACW and PGACW(T < To) respond to the profile of upward motion differently. Following Rutledge and Hobbs (1983, 1984), PRACW and PGACW(T < To) can be respectively expressed by
i1520-0493-130-10-2481-e7
Here ERC(=1) is the rain/cloud water collection efficiency; EGC(=1) is the graupel/cloud water collection efficiency; N0R(=8 × 106 m−4) is the intercept value in raindrop size distribution; N0G(=4 × 106 m−4) is the intercept value in graupel size distribution; a0 = −0.267 m s−1, a1 = 5.15 × 103 s−1, a2 = −1.0225 × 106 m−1 s−1, a3 = 7.55 × 107 m−2 s−1, which are the coefficients in polynomial fall speed relation for rain; Γ is the gamma function; λR[=(πρLN0R/ρqr)1/4] is the slope of raindrop size distribution; ρL(=103 kg m−3) is the density of raindrops; λG[=(πρGN0G/ρqg)1/4] is the slope of graupel size distribution; ρG(=400 kg m−3) is the density of graupel; a(=19.3 m1−b s−1) is the constant in fall speed relation for graupel; b(=0.37) is the fall speed exponent for graupel. Since the second term of PRACW is much larger than the other terms (not shown), the PRACW and PGACW(T < To) are approximately proportional to the covariances between mixing ratios of cloud water and raindrops and between mixing ratios of cloud water and graupel respectively. In the right half of the box, the large mixing ratio of graupel associated with the maximum upper-tropospheric upward motion enhances the riming of cloud water by graupel, and suppresses the collection of cloud water by rain. In the left half of the box, the large mixing ratio of raindrops associated with the maximum mid- and lower-tropospheric upward motion enhances the collection of cloud water by rain, and suppresses the riming of cloud water by graupel.
Table 1 shows minor cloud microphysical terms that are not displayed in Fig. 5. The three terms PRAUT, PSMLT(T > To) and PMLTG(T > To) have magnitudes larger than 0.01 mm h−1 and the other terms have magnitudes smaller than 0.01 mm h−1. If the terms with the magnitudes smaller than 0.01 mm h−1 are neglected, the equations (A1a)–(A1f) are simplified by
i1520-0493-130-10-2481-e9a

The cloud microphysical schemes can be computationally expensive in numerical models because of the small time steps required for numerical stability. This is particular true, for example, when calculating the vertical flux convergence of precipitation at low levels where the model levels can be quite thin. About 30%–40% of CPU time is used to compute the original set of cloud microphysics Eqs. (A1a)–(A1f) in cloud resolving simulations. The better simulation of precipitation and cloud properties by mesoscale numerical models requires a set of cloud microphysics parameterization schemes, but the operational prediction cannot afford the computation time. A simplified version of cloud microphysics equations is needed to contain dominant cloud microphysical processes. Thus, an additional experiment is carried out with the simplified set of cloud microphysics Eqs. (9a)–(9f), and is compared to the experiment with the original set of cloud microphysics Eqs. (A1a)–(A1f) in terms of the profiles of temperature and moisture, surface rain rates, and fractional cloud covers. The maximum difference of temperature between the two experiments is about 0.5°C (Fig. 8a), and the maximum difference of specific humidity is about 0.3 g kg−1 (Fig. 8b). The differences are mainly due to the phase shift of convection (see Fig. 9). Thus, the differences are insignificant. Surface rain rates in the two experiments show similar time evolution in general (Fig. 9a). The difference of the surface rain rates is mainly due to the difference of convective rain rate (Fig. 9). [Calculations of convective and stratiform rain rates and fractional covers of convective, raining and nonraining stratiform clouds are referred to in Sui et al. (1994).] Fractional covers for convective, raining and nonraining stratiform clouds in the two experiments also show similar time evolution (Fig. 10). However, the fractional cover for nonraining stratiform clouds simulated with the simplified set of cloud microphysics equations is larger than that simulated with the original set. The fractional covers for convective and raining stratiform clouds simulated with the simplified set of cloud microphysics equations are smaller than those simulated with the original set. Thus, the cloud model with the simplified set of cloud microphysics equations simulate clouds and thermodynamics well compared to the simulation with the original set.

4. Summary

Dominant cloud microphysical processes associated with a tropical oceanic convective system are investigated based on a 2D cloud resolving simulation. The cloud resolving model with a periodic horizontal boundary is forced by zonally uniform vertical velocity, zonal wind, horizontal temperature and moisture advections, and sea surface temperature measured and derived from TOGA COARE. The analysis of cloud microphysics budgets shows that vapor condensation causes growth of cloud water, the collection of cloud water by raindrops produces rain, and riming of cloud water enhances ice precipitation in the early stage of development of convection. In the later stages, the melting of graupel becomes a dominant process to produce rain. Vapor deposition and the riming process are equally important in the development of ice clouds.

The vertical profile of upward motion affects the production of rain and surface rain rates through the collection of cloud water by raindrop and riming of cloud water by graupel. The upward motion in the mid and lower troposphere causes a large collection rate of cloud water by raindrops that leads to the large growth rate of raindrops and surface rain rates, whereas the upward motion in the upper troposphere produces large riming rate of cloud water by graupel that results in the large growth rate of graupel. Thus, surface rain rates respond to the upward motion in the mid and lower troposphere more than to the the upward motion in the upper troposphere.

Based on the analysis of a cloud microphysics budget, a simplified set of cloud microphysics equations is proposed. The experiment with the simplified set of cloud microphysics equations is conducted and compared to the experiment with the original set of cloud microphysics equations. Both experiments show similar time evolution and magnitudes of temperature and moisture profiles, surface rain rate and its components (convective and stratiform rain rates), and fractional covers of convective, raining and nonraining stratiform clouds. This suggests that the original set of cloud microphysics equations be replaced by the simplified set in simulations of tropical oceanic convection.

Acknowledgments

This research is supported by the TRMM project of NASA Earth Science Enterprise. Authors thank Prof. M. Zhang at the State University of New York at Stony Brook for allowing us to use his TOGA COARE forcing data, and two anonymous reviewers for their constructive comments. X. Li thanks Mrs. Frances C. Holt, Chief of Atmospheric Research and Applications Division, NOAA/NESDIS/ORA, for her support of completion of this work.

REFERENCES

  • Grabowski, W. W., X. Wu, and M. W. Moncrieff, 1996: Cloud-resolving model of tropical cloud systems during Phase III of GATE. Part I: Two-dimensional experiments. J. Atmos. Sci., 53 , 36843709.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., . 1999: Cloud-resolving model of tropical cloud systems during Phase III of GATE. Part III: Effects of cloud microphysics. J. Atmos. Sci., 56 , 23842402.

    • Search Google Scholar
    • Export Citation
  • Krueger, S. K., Q. Fu, K. N. Liou, and H-N. S. Chin, 1995: Improvement of an ice-phase microphysics parameterization for use in numerical simulations of tropical convection. J. Appl. Meteor., 34 , 281287.

    • Search Google Scholar
    • Export Citation
  • Li, X., C-H. Sui, K-M. Lau, and M-D. Chou, 1999: Large-scale forcing and cloud–radiation interaction in the tropical deep convective regime. J. Atmos. Sci., 56 , 30283042.

    • Search Google Scholar
    • Export Citation
  • Lin, Y-L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22 , 10651092.

    • Search Google Scholar
    • Export Citation
  • Liu, Y., D-L. Zhang, and M. K. Yau, 1997: A multiscale numerical study of Hurricane Andrew (1992). Part I: Explicit simulation and verification. Mon. Wea. Rev., 125 , 30733093.

    • Search Google Scholar
    • Export Citation
  • Lord, S. J., H. E. Willoughby, and J. M. Piotrowicz, 1984: Role of a parameterized ice-phase microphysics in an axisymmetric, nonhydrostatic tropical cyclone model. J. Atmos. Sci., 41 , 28362848.

    • Search Google Scholar
    • Export Citation
  • Rutledge, S. A., and P. V. Hobbs, 1983: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. Part VIII: A model for the “seeder-feeder” process in warm-frontal rainbands. J. Atmos. Sci., 40 , 11851206.

    • Search Google Scholar
    • Export Citation
  • Rutledge, S. A., . 1984: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. Part XII: A diagnostic modeling study of precipitation development in narrow cold-frontal rainbands. J. Atmos. Sci., 41 , 29492972.

    • Search Google Scholar
    • Export Citation
  • Soong, S. T., and Y. Ogura, 1980: Response of tradewind cumuli to large-scale processes. J. Atmos. Sci., 37 , 20352050.

  • Soong, S. T., and W-K. Tao, 1980: Response of deep tropical cumulus clouds to mesoscale processes. J. Atmos. Sci., 37 , 20162034.

  • Sui, C-H., K-M. Lau, W-K. Tao, and J. Simpson, 1994: The tropical water and energy cycles in a cumulus ensemble model. Part I: Equilibrium climate. J. Atmos. Sci., 51 , 711728.

    • Search Google Scholar
    • Export Citation
  • Sui, C-H., Y. Takayabu, and D. Short, 1997: Diurnal variations in tropical oceanic cumulus ensemble during TOGA COARE. J. Atmos. Sci., 54 , 639655.

    • Search Google Scholar
    • Export Citation
  • Sui, C-H., X. Li, and K-M. Lau, 1998: Radiative–convective processes in simulated diurnal variations of tropical oceanic convection. J. Atmos. Sci., 55 , 23452359.

    • Search Google Scholar
    • Export Citation
  • Takayabu, Y. N., K-M. Lau, and C-H. Sui, 1996: Observation of a quasi-2-day wave during TOGA COARE. Mon. Wea. Rev., 124 , 18921913.

  • Tao, W-K., and J. Simpson, 1993: The Goddard Cumulus Ensemble model. Part I: Model description. Terr. Atmos. Oceanic Sci., 4 , 3572.

  • Tao, W-K., and M. McCumber, 1989: An ice-water saturation adjustment. Mon. Wea. Rev., 117 , 231235.

  • Weller, R. A., and S. P. Anderson, 1996: Surface meteorology and air–sea fluxes in the western equatorial Pacific warm pool during TOGA COARE. J. Climate, 9 , 19591990.

    • Search Google Scholar
    • Export Citation
  • Willoughby, H. E., H. L. Jin, S. J. Lord, and J. M. Piotrowicz, 1984: Hurricane structure and evolution as simulated by an axisymmetric, non-hydrostatic numerical model. J. Atmos. Sci., 41 , 11691186.

    • Search Google Scholar
    • Export Citation
  • Wu, X., W. W. Grabowski, and M. W. Moncrieff, 1998: Long-term evolution of cloud systems in TOGA COARE and their interactions with radiative and surface processes. Part I: Two-dimensional cloud-resolving model. J. Atmos. Sci., 55 , 26932714.

    • Search Google Scholar
    • Export Citation
  • Wu, X., W. D. Hall, W. W. Grabowski, M. W. Moncrieff, W. D. Collins, and J. T. Kiehl, 1999: Long-term evolution of cloud systems in TOGA COARE and their interactions with radiative and surface processes. Part II: Effects of ice microphysics on cloud–radiation interaction. J. Atmos. Sci., 56 , 31773195.

    • Search Google Scholar
    • Export Citation
  • Xu, K-M., and D. A. Randall, 1996: Explicit simulation of cumulus ensembles with the GATE Phase III data: Comparison with observations. J. Atmos. Sci., 53 , 37103736.

    • Search Google Scholar
    • Export Citation
  • Zhang, M. H., and J. L. Lin, 1997: Constrained variational analysis of sounding data based on column-integrated budgets of mass, heat, moisture, and momentum: Approach and application to ARM measurements. J. Atmos. Sci., 54 , 15031524.

    • Search Google Scholar
    • Export Citation

APPENDIX

Microphysical Processes Parameterized in the Cloud Resolving Model

Microphysics parameterizations in the cloud resolving model used in this study are based on the schemes proposed by Rutledge and Hobbs (1983, 1984; hereafter RH83 and RH84), Lin et al. (1983, LFO), Tao et al. (1989, TSM), and Krueger et al. (1995, KFLC), respectively. Corresponding equations are as follows:
i1520-0493-130-10-2481-ea1a
where
i1520-0493-130-10-2481-ea2a
T0 = 0°C, and T00 = −35°C. The microphysical processes in the terms of the right-hand side of (A1) and corresponding schemes are described in Table A1.

Fig. 1.
Fig. 1.

Time evolution of (a) vertical velocity (cm s−1), (b) zonal wind (m s−1), and (c) sea surface temperature (°C) taken from TOGA COARE for a 6-day period. Upward motion in (a) and westerly wind in (b) are shaded

Citation: Monthly Weather Review 130, 10; 10.1175/1520-0493(2002)130<2481:DCMPIA>2.0.CO;2

Fig. 2.
Fig. 2.

Time evolution and horizontal distribution of surface rain rate (mm h−1) within 400–700 km on 20 Dec 1992. The box between hours 6–20 (0600–2000 local time) is used to analyze the life cycle of convection in Figs. 3–7

Citation: Monthly Weather Review 130, 10; 10.1175/1520-0493(2002)130<2481:DCMPIA>2.0.CO;2

Fig. 3.
Fig. 3.

Time–height distributions of zonally averaged (a) vertical velocity (cm s−1), (b) zonal wind (m s−1), (c) specific humidity anomaly (g kg−1), and (d) temperature anomaly (°C) in hours 6–20 on 20 Dec 1992 in the box shown in Fig. 2. Shaded areas are (a) upward motion, (b) westerly wind, (c) positive specific humidity anomaly, and (d) positive temperature anomaly

Citation: Monthly Weather Review 130, 10; 10.1175/1520-0493(2002)130<2481:DCMPIA>2.0.CO;2

Fig. 4.
Fig. 4.

Time–height distributions of zonally averaged mixing ratios of (a) cloud water, (b) raindrops, (c) cloud ice, (d) snow, and (e) graupel (10−2 g kg−1) in hours 6–20 on 20 Dec 1992 in the box shown in Fig. 2. Values larger than 0.001 g kg−1 are shaded

Citation: Monthly Weather Review 130, 10; 10.1175/1520-0493(2002)130<2481:DCMPIA>2.0.CO;2

Fig. 5.
Fig. 5.

Cloud microphysics budgets averaged in (a) hours 6–11, (b) hours 12–18, and (c) hours 19–20 on 20 Dec 1992 in the box shown in Fig. 2. Units for cloud hydrometeors and conversions are mm and mm h−1, respectively

Citation: Monthly Weather Review 130, 10; 10.1175/1520-0493(2002)130<2481:DCMPIA>2.0.CO;2

Fig. 6.
Fig. 6.

Vertical profiles of mean vertical velocity (cm s−1) averaged in the left half (solid line) and the right half (dotted line) of the box in hours 13–15

Citation: Monthly Weather Review 130, 10; 10.1175/1520-0493(2002)130<2481:DCMPIA>2.0.CO;2

Fig. 7.
Fig. 7.

Cloud microphysics budgets averaged in (a) the left half and (b) the right half of the box in hours 13–15. Units for cloud hydrometeors and conversions are mm and mm h−1, respectively.>

Citation: Monthly Weather Review 130, 10; 10.1175/1520-0493(2002)130<2481:DCMPIA>2.0.CO;2

Fig. 8.
Fig. 8.

Time–height distributions of differences of (a) temperature (°C) and (b) specific humidity (g kg−1) of simulations with the simplified set of cloud microphysics equations (9) minus simulations with the original set (A1).>

Citation: Monthly Weather Review 130, 10; 10.1175/1520-0493(2002)130<2481:DCMPIA>2.0.CO;2

Fig. 9.
Fig. 9.

Time series of (a) surface rain rate (mm h−1), (b) convective rain rate, and (c) stratiform rain rate in simulations with the simplified set of cloud microphysics equations (solid lines) and simulations with the original set (dashed lines).>

Citation: Monthly Weather Review 130, 10; 10.1175/1520-0493(2002)130<2481:DCMPIA>2.0.CO;2

Fig. 10.
Fig. 10.

As in Fig. 9 except for the fractional covers of (a) nonraining stratiform clouds (%), (b) convective clouds, and (c) raining stratiform clouds

Citation: Monthly Weather Review 130, 10; 10.1175/1520-0493(2002)130<2481:DCMPIA>2.0.CO;2

Table 1. 

Magnitudes of minor cloud microphysical terms that are not shown in Fig. 5. Unit is mm h−1

Table 1. 

Table A1. List of microphysical processes and their parameterization schemes in appendix

i1520-0493-130-10-2481-ta01
Save