Open access peer-reviewed chapter

Thermodynamic Aspects of Precipitation Efficiency

By Xinyong Shen and Xiaofan Li

Submitted: November 4th 2010Reviewed: March 7th 2011Published: November 2nd 2011

DOI: 10.5772/19750

Downloaded: 2269

1. Introduction

Precipitation efficiency is one of important meteorological parameters and has been widely used in operational precipitation forecasts (e.g., Doswell et al., 1996). Precipitation efficiency has been defined as the ratio of precipitation rate to the sum of all precipitation sources from water vapor budget (e.g., Auer and Marwitz, 1968; Heymsfield and Schotz, 1985; Chong and Hauser, 1989; Dowell et al., 1996; Ferrier et al., 1996; Li et al., 2002; Sui et al., 2005) after Braham (1952) calculated precipitation efficiency with the inflow of water vapor into the storm through cloud base as the rainfall source more than half century ago. Sui et al. (2007) found that the estimate of precipitation efficiency with water vapor process data can be more than 100% or negative because some rainfall sources are excluded or some rainfall sinks are included. They defined precipitation efficiency through the inclusion of all rainfall sources and the exclusion of all rainfall sinks from surface rainfall budget derived by Gao et al. (2005), which fixed precipitation efficiency to the normal range of 0-100%.

In additional to water vapor processes, thermal processes also play important roles in the development of rainfall since precipitation is determined by environmental thermodynamic conditions via cloud microphysical processes. The water vapor convergence and heat divergence and its forced vapor condensation and depositions in the precipitation systems could be major sources for precipitation while these water vapor and cloud processes could give some feedback to the environment. Gao et al. (2005) derived a water vapor related surface rainfall budget through the combination of cloud budget with water vapor budget. Gao and Li (2010) derived a thermally related surface rainfall budget through the combination of cloud budget with heat budget. In this chapter, precipitation efficiency is defined from the thermally related surface rainfall budget (PEH) and is calculated using the data from the two-dimensional (2D) cloud-resolving model simulations of a pre-summer torrential rainfall event over southern China in June 2008 (Wang et al., 2010; Shen et al., 2011a, 2011b) and is compared with the precipitation efficiency defined from water vapor related surface rainfall budget (Sui et al., 2007) to study the efficiency in thermodynamic aspect of the pre-summer heavy rainfall system.

The impacts of ice clouds on the development of convective systems have been intensively studied through the analysis of cloud-resolving model simulations (e.g., Yoshizaki, 1986; Nicholls, 1987; Fovell and Ogura, 1988; Tao and Simpson, 1989; McCumber et al., 1991; Tao et al., 1991; Liu et al., 1997; Grabowski et al., 1999; Wu et al., 1999; Li et al., 1999; Grabowski and Moncrieff, 2001; Wu, 2002; Grabowski, 2003; Gao et al., 2006; Ping et al., 2007). Wang et al. (2010) studied microphysical and radiative effects of ice clouds on a pre-summer heavy rainfall event over southern China during 3-8 June 2008 through the analysis of sensitivity experiments and found that microphysical and radiative effects of ice clouds play equally important roles in the pre-summer heavy rainfall event. The total exclusion of ice microphysics decreased model domain mean surface rain rate primarily through the weakened convective rainfall caused by the exclusion of radiative effects of ice clouds in the onset phase and through the weakened stratiform rainfall caused by the exclusion of ice microphysical effects in the development and mature phases, whereas it increased the mean rain rate through the enhanced convective rainfall caused by the exclusion of ice microphysical effects in the decay phase. Thus, effects of ice clouds on precipitation efficiencies are examined through the analysis of the pre-summer heavy rainfall event in this chapter. Precipitation efficiency is defined in section 2. Pre-summer heavy rainfall event, model, and sensitivity experiments are described in section 3. The control experiment is discussed in section 4. Radiative and microphysical effects of ice clouds on precipitation efficiency and associated rainfall processes are respectively examined in sections 5 and 6. The conclusions are given in section 7.

2. Definitions of precipitation efficiency

The budgets for specific humidity (q v ), temperature (T), and cloud hydrometeor mixing ratio (q l ) in the 2D cloud resolving model used in this study can be written as

qvt=(u'qv')xu¯oqv'xw¯oqv'zw'q¯vz1ρ¯zρ¯w'qv'Sqvu¯oq¯vxw¯oq¯vzE1
Tt=x(u¯o+u')T'πu¯oθ¯oxπw¯oz(θ¯+θ')πw'θ¯zπρ¯z(ρ¯w'θ')+Qcncp+QRcpE2
qlt=(uql)x1ρ¯zρ¯wql+1ρ¯zρ¯(wTrqr+wTsqs+wTgqg)+SqvE3

where

Sqv=PCND+PDEP+PSDEP+PGDEPPREVPPMLTSPMLTGE4
Qcn=LvSqv+LfP18E5

θ is potential temperature; u and w are zonal and vertical components of wind, respectively; ρ is air density that is a function of height; cp is the specific heat of dry air at constant pressure; L v , L s , and L f are latent heat of vaporization, sublimation, and fusion at T o =0oC, respectively, L s =L v +L f ,; T oo =-35 oC; and cloud microphysical processes in (2) can be found in Gao and Li (2008). Q R is the radiative heating rate due to the convergence of net flux of solar and IR radiative fluxes. wTr, wTs, and wTg in (1c) are terminal velocities for raindrops, snow, and graupel, respectively; overbar denotes a model domain mean; prime is a perturbation from model domain mean; and superscript o is an imposed observed value. The comparison between (1) and (2) shows that the net condensation term (S qv ) links water vapor, heat, and cloud budgets.

P18=PDEP+PSDEP+PGDEPPMLTSPMLTG+PSACW(TTo)+PSFW(TTo)+PGACW(TTo)+PIACR(TTo)+PGACR(TTo)+PSACR(TTo)+PGFR(TTo)PRACS(TTo)PSMLT(TTo)PGMLT(TTo)+PIHOM(TToo)PIMLT(TTo)+PIDW(TooTTo),E6

Following Gao et al. (2005) and Sui and Li (2005), the cloud budget (1c) and water vapor budget (1a) are mass integrated and their budgets can be, respectively, written as

PSQCM=QWVS=QWVOUT+QWVINE7
QWVT+QWVF+QWVE=QWVSE8

where

PS=Pr+Ps+PgE9
Pr=ρ¯wTrqr|z=0,E10
Ps=ρ¯wTsqs|z=0,E11
Pg=ρ¯wTgqg|z=0,E12
QCM=[ql]t[uqlx][wqlz]E13
QWVOUT=[PCND]+[PDEP]+[PSDEP]+[PGDEP]E14
QWVIN=[PREVP][PMLTG][PMLTS]E15
QWVT=[qv]tE16
QWVF=[u¯oq¯vx][w¯oq¯vz][(u'qv')x][u¯oqv'x][w¯oqv'z][w'q¯v'z]E17
QWVE=EsE18

Here, P S is precipitation rate, and in the tropics, P s =0 and P g =0, P S =P r ; E s is surface evaporation;[()]=zbztρ¯()dz, z t and z b are the heights of the top and bottom of the model atmosphere, respectively.

The heat budget (1b) is mass integrated and can be written as

SHT+SHF+SHS+SLHLF+SRAD=QWVSE19

where

SHT=cpLv[T]tE20
SHF=cpLv[x(u¯o+u')T'+πu¯oθ¯ox+πw¯oz(θ¯+θ')+πw'θ¯z]E21
SHS=cpLvHsE22
SLHLF=LfLvP18E23
SRAD=1LvQRE24

H s is surface sensible heat flux.

The equations (3), (4), and (6) indicate that the surface rain rate (P S ) is associated with favorable environmental water vapor and thermal conditions through cloud microphysical processes (Q WVOUT +Q WVIN ). Following Gao and Li (2010), the cloud budget (3) and water vapor budget (4) are combined by eliminating Q WVOUT +Q WVIN to derive water vapor related surface rainfall equation (P SWV ),

PSWV=QWVT+QWVF+QWVE+QCME25

In (8a), the surface rain rate (P SWV ) is associated with local atmospheric drying (Q WVT >0)/moistening (Q WVT <0), water vapor convergence (Q WVF >0)/divergence (Q WVF <0), surface evaporation (Q WVE ), and decrease of local hydrometeor concentration/hydrometeor convergence (Q CM >0) or increase of local hydrometeor concentration/hydrometeor divergence (Q CM <0). Similarly, the cloud budget (3) and heat budget (6) are combined by eliminating Q WVOUT +Q WVIN to derive thermally related surface rainfall equation (P SH ),

PSH=SHT+SHF+SHS+SLHLF+SRAD+QCM.E26

In (8b), the surface rain rate (P SH ) is related to local atmospheric warming (S HT >0)/cooling (S HT <0), heat divergence (S HF >0)/convergence (S HF <0), surface sensible heat (S HS ), latent heat due to ice-related processes (S LHLF ), radiative cooling (S RAD >0)/heating (S RAD <0), and decrease of local hydrometeor concentration/hydrometeor convergence (Q CM >0) or increase of local hydrometeor concentration/hydrometeor divergence (Q CM <0). P SWV = P SH = P S .

From (8), precipitation efficiencies can be respectively defined as

PEWV=PSi=14H(Qi)QiE27
PEH=PSi=15H(Si)Si+H(QCM)QCME28

where Q i =(Q WVT , Q WVF , Q WVE , Q CM ); S i =(S HT , S HF , S HS , S LHLF , S RAD ); H is the Heaviside function, H(F)=1 when F>0, and H(F)=0 when F 0. Large-scale heat precipitation efficiency (PEH) is first introduced in this study, whereas large-scale water vapor precipitation efficiency (PEWV) is exactly same to LSPE2 defined by Sui et al. (2007).

3. Pre-summer rainfall case, model, and experiments

The pre-summer rainy season is the major rainy season over southern China, in which the rainfall starts in early April and reaches its peak in June (Ding, 1994). Although the rainfall is a major water resource in annual water budget, the torrential rainfall could occur during the pre-summer rainfall season and can lead to tremendous property damage and fatalities. In 1998, for instance, the torrential rainfall resulted in over 30 billion USD in damage and over 100 fatalities. Thus, many observational analyses and numerical modeling have been contributed to understanding of physical processes responsible for the development of pre-summer torrential rainfall (e.g., Krishnamurti et al., 1976; Tao and Ding, 1981; Wang and Li, 1982; Ding and Murakami, 1994; Simmonds et al., 1999). Recently, Wang et al. (2010) and Shen et al. (2011a, 2011b) conducted a series of sensitivity experiments of the pre-summer torrential rainfall occurred in the early June 2008 using 2D cloud-resolving model and studied effects of vertical wind shear, radiation, and ice clouds on the development of torrential rainfall. They found that these effects on torrential rainfall are stronger during the decay phase than during the onset and mature phases. During the decay phase of convection on 7 June 2008, the increase in model domain mean surface rain rate resulting from the exclusion of vertical wind shear is associated with the slowdown in the decrease of perturbation kinetic energy due to the exclusion of barotropic conversion from mean kinetic energy to perturbation kinetic energy. The increase in domain-mean rain rate resulting from the exclusion of cloud radiative effects is related to the enhancement of condensation and associated latent heat release as a result of strengthened radiative cooling. The increase in the mean surface rain rate is mainly associated with the increase in convective rainfall, which is in turn related to the local atmospheric change from moistening to drying. The increase in mean rain rate caused by the exclusion of ice clouds results from the increases in the mean net condensation and mean latent heat release caused by the strengthened mean radiative cooling associated with the removal of radiative effects of ice clouds. The increase in mean rain rate caused by the removal of radiative effects of water clouds corresponds to the increase in the mean net condensation.

The pre-summer torrential rainfall event studied by Wang et al. (2010) and Shen et al. (2011a, 2011b) will be revisited to examine the thermodynamic aspects of precipitation efficiency and effects of ice clouds on precipitation efficiency. The cloud-resolving model (Soong and Ogura, 1980; Soong and Tao, 1980; Tao and Simpson, 1993) used in modeling the pre-summer torrential rainfall event in Wang et al. (2010) is the 2D version of the model (Sui et al., 1994, 1998) that was modified by Li et al. (1999). The model is forced by imposed large-scale vertical velocity and zonal wind and horizontal temperature and water vapor advections, which produces reasonable simulation through the adjustment of the mean thermodynamic stability distribution by vertical advection (Li et al., 1999). The modifications by Li et al. (1999) include: (1) the radius of ice crystal is increased from 50μm (Hsie et al., 1980) to 100μm (Krueger et al., 1995) in the calculation of growth of snow by the deposition and riming of cloud water, which yields a significant increase in cloud ice; (2) the mass of a natural ice nucleus is replaced by an average mass of an ice nucleus in the calculation of the growth of ice clouds due to the position of cloud water; (3) the specified cloud single scattering albedo and asymmetry factor are replaced by those varied with cloud and environmental thermodynamic conditions. Detailed descriptions of the model can be found in Gao and Li (2008). Briefly, the model includes prognostic equations for potential temperature and specific humidity, prognostic equations for hydrometeor mixing ratios of cloud water, raindrops, cloud ice, snow, and graupel, and perturbation equations for zonal wind and vertical velocity. The model uses the cloud microphysical parameterization schemes (Lin et al., 1983; Rutledge and Hobbs, 1983, 1984; Tao et al., 1989; Krueger et al., 1995) and solar and thermal infrared radiation parameterization schemes (Chou et al., 1991, 1998; Chou and Suarez, 1994). The model uses cyclic lateral boundaries, and a horizontal domain of 768 km with 33 vertical levels, and its horizontal and temporal resolutions are 1.5 km and 12 s, respectively.

The data from Global Data Assimilation System (GDAS) developed by the National Centers for Environmental Prediction (NCEP), National Oceanic and Atmospheric Administration (NOAA), USA are used to calculate the forcing data for the model over a longitudinally oriented rectangular area of 108-116oE, 21-22oN over coastal areas along southern Guangdong and Guangxi Provinces and the surrounding northern South China Sea. The horizontal and temporal resolutions for NCEP/GDAS products are 1ox1o and 6 hourly, respectively. The model is imposed by large-scale vertical velocity, zonal wind (Fig. 1), and horizontal temperature and water vapor advections (not shown) averaged over 108-116oE, 21-22oN. The model is integrated from 0200 Local Standard Time (LST) 3 June to 0200 LST 8 June 2008 during the pre-summer heavy rainfall. The surface temperature and specific humidity from NCEP/GDAS averaged over the model domain are uniformly imposed on each model grid to calculate surface sensible heat flux and evaporation flux. The 6-hourly zonally-uniform large-scale forcing data are linearly interpolated into 12-s data, which are uniformly imposed zonally over model domain at each time step. The imposed large-scale vertical velocity shows the gradual increase of upward motions from 3 June to 6 June. The maximum upward motion of 18 cm s-1 occurred around 9 km in the late morning of 6 June. The upward motions decreased dramatically on 7 June. The lower-tropospheric westerly winds of 4 - 12 m s-1 were maintained during the rainfall event.

Figure 1.

Temporal and vertical distribution of (a) vertical velocity (cm s-1) and (b) zonal wind (m s-1) from 0200 LST 3 June – 0200 LST 8 June 2008. The data are averaged in a rectangular box of 108-116oE, 21-22oN from NCEP/GDAS data. Ascending motion in (a) and westerly wind in (b) are shaded.

In the control experiment (C), the model is integrated with the initial vertical profiles of temperature and specific humidity from NCEP/GDAS at 0200 LST 3 June 2008. The model is integrated with the initial conditions and constant large-scale forcing at 0200 LST 3 June for 6 hours during the model spin-up period and the 6-hour model data are not used for analysis. The comparison in surface rain rate between the simulation and rain gauge observation averaged from 17 stations over southern Guangdong and Guangxi reveals a fair agreement with a gradual increase from 3-6 June and a rapid decrease from 6-7 June (Fig. 2). Their RMS difference (0.97 mm h-1) is significantly smaller than the standard derivations of simulated (1.22 mm h-1) and observed (1.26 mm h-1) rain rates. The differences in surface rain rate between the simulation and observation can reach 2 mm h-1, as seen in the previous studies (e.g., Li et al., 1999; Xu et al., 2007; Wang et al., 2009). The differences may partially be from the comparison of small hourly local sampling of rain gauge observations over 35% of model domain over land and no rain gauge observations over 65% of model domain over ocean with large model domain averages of model data in the control experiment with imposed 6-hourly large-scale forcing. The convection may be affected by land-ocean contrast and orography; these effects are included in the large-scale forcing imposed in the model.

Figure 2.

Time series of surface rain rates (mm h-1) simulated in the control experiment (solid) and from rain gauge observation (dash).

Figure 3.

Time series of model domain means of (a) P SWV (dark solid), Q WVT (light solid), Q WVF (short dash), Q WVE (dot), Q CM (dot dash), and (b) P SH (dark solid), S HT (light solid), S HF (short dash), S HS (dot), S LHLF (long short dash), S RAD (long dash), and Q CM (dot dash) in C. Unit is mm h-1.

To investigate effects of ice clouds on precipitation efficiency, two sensitivity experiments are examined in this study. Experiment CNIR is identical to C except that the mixing ratios of ice hydrometeor are set to zero in the calculation of radiation. Experiment CNIR is compared with C to study radiative effects of ice clouds on rainfall responses to the large-scale forcing. Experiment CNIM is identical to C except in CNIM ice clouds (the ice hydrometeor mixing ratio and associated microphysical processes) are excluded. The comparison between CNIM and CNIR reveals impacts of the removal of microphysical efficient of ice clouds on rainfall responses to the large-scale forcing in the absence of radiative effects of ice clouds. The hourly model simulation data are used in the following discussions of this study.

4. The control experiment: C

Model domain mean surface rain rate starts on 3 June 2008 with the magnitude of about 1 mm h-1 (Fig. 3), which corresponds to the weak upward motions with a maximum of 2 cm s-1 at 6-8 km (Fig. 1a). The rain rate increases to 2 mm h-1 as the upward motions increase up to over 6 cm s-1 on 4 June. When the upward motions weaken in the evening of 4 June and a weak downward motion occurs near the surface, the mean rainfall vanishes. As upward motions pick their strengths on 5 June, the mean rain rate intensifies (over 2 mm h-1). The mean rainfall reaches its peak on 6 June (over 4 mm h-1) as the upward motions have a maximum of over 20 cm s-1. The upward motions rapidly weaken on 7 June, which leads to the significant reduction in the mean rainfall. Thus, four days (4, 5, 6, and 7 June) are defined as the onset, development, mature, and decay phases of the rainfall event, respectively. During 3-6 June, the mean rainfall is mainly associated with the mean water vapor convergence (Q WVF >0) in water vapor related surface rainfall budget and the mean heat divergence (S HF >0) in thermally related surface rainfall budget. Local atmospheric drying (Q WVT >0) and moistening (Q WVT <0) occur while the mean local atmospheric cooling (S HT <0) prevails. The mean hydrometeor loss/convergence (Q CM ) has small hourly fluctuations. The mean radiative heating during the daytime and mean radiative cooling during the nighttime have the much smaller magnitudes than the mean heat divergence and the mean local heat change do in thermally related surface rainfall budget. On 7 June, the mean water vapor related surface rainfall budget shows that the rainfall is associated with local atmospheric drying while water vapor divergence prevails. The mean thermally related surface rainfall budget reveals that the rainfall is related to heat divergence while the heat divergence cools local atmosphere.

The calculation of precipitation efficiency using model domain mean model simulation data shows that PEWV generally is higher than PWH (Fig. 4a) because the rainfall source from the mean water vapor convergence in water vapor related surface rainfall budget is weaker than the rainfall source from the mean heat divergence in thermally related surface rainfall budget (Fig. 3). This suggests that the precipitation system is more efficient in the consumption of rainfall source from water vapor than in the consumption of the rainfall source from heat. The root-mean-squared (RMS) difference between PEWV and PEH is 24.4%. Both PEWV and PEH generally increase as surface rain rate increases (Fig. 5a). This indicates that the precipitation system generally is more efficient for high surface rain rates than for low surface rain rates. The ranges of PEWV and PEH are smaller when surface rain rate is higher than 3 mm h-1 (70-100%) than when surface rain rate is lower than 3 mm h-1 (0-100%).

Figure 4.

Time series of PEWV (solid) and PEH (dash) calculated using (a) model domain mean data and data from (b) convective and (c) raining stratiform regions in C. Unit is %.

Figure 5.

PEWV versus P S (cross) and PEH versus P S (open circle) calculated using (a) model domain mean data and data from (b) convective and (c) raining stratiform regions in C. Units are % for PEWV and PEH and mm h-1 for P S .

Figure 6.

Time series of contributions of model domain mean of (a) P SWV (dark solid), Q WVT (light solid), Q WVF (short dash), Q WVE (dot), Q CM (dot dash), and (b) P SH (dark solid), S HT (light solid), S HF (short dash), S HS (dot), S LHLF (long short dash), S RAD (long dash), and Q CM (dot dash) from convective regions in C. Unit is mm h-1.

Model domain mean surface rainfall consists of convective and stratiform rainfall. Convective rainfall differs from stratiform rainfall in four ways. First, convective rain rates are higher than stratiform rain rates. Second, convective rainfall is associated with stronger horizontal reflectivity gradients than stratiform rainfall. Third, upward motions associated with convective rainfall are much stronger than those associated with stratiform rainfall. Fourth, the accretion of cloud water by raindrops via collisions in strong updraft cores and the vapor deposition on ice particles are primary microphysical processes that are responsible for the development of convective and stratiform rainfall,respectively (Houghton 1968). The convective-stratiform rainfall partitioning scheme used in this study is developed by Tao and Simpson (1993) and modified by Sui et al. (1994). This scheme partitions each vertical column containing clouds in 2-D x-z framework into convective or stratiform based on the following criterion. Model grid point is identified as convective if it has a rain rate twice as large as the average taken over the surrounding four grid points, the one grid point on either side of this grid point, and any grid point with a rain rate of 20 mm h-1 or more. All non-convective cloudy points are considered as stratiform. In addition, grid points over stratiform regions are further checked and identified as convective when following conditions are met. Over raining stratiform regions, cloud water below the melting level is greater than 0.5 g kg-1 or the maximum updraft above 600 hPa exceeds 5 m s-1, or over non-raining stratiform regions, cloud water mixing ratio of 0.025 g kg-1 or more exists or the maximum updraft exceeds 5 m s-1 below the melting level.

Figure 7.

Time series of contributions of model domain mean of (a) P SWV (dark solid), Q WVT (light solid), Q WVF (short dash), Q WVE (dot), Q CM (dot dash), and (b) P SH (dark solid), S HT (light solid), S HF (short dash), S HS (dot), S LHLF (long short dash), S RAD (long dash), and Q CM (dot dash) from raining stratiform regions in C. Unit is mm h-1.

Convective rain rate (Fig. 6) is much higher than stratiform rain rate (Fig. 7) and mainly accounts for model domain mean surface rain rate (Fig. 3). Over convective regions, rainfall is associated with water vapor convergence in water vapor related surface rainfall budget (Fig. 6a) and heat divergence in thermally related surface rainfall budget (Fig. 6b). Q CM is negative over convective regions whereas Q CM is positive over raining stratiform regions (Fig. 8), which indicates the transport of hydrometeor concentration from convective regions (Q CM <0) to raining stratiform regions (Q CM >0). The hydrometeor transport is associated with the local atmospheric drying (Q WVT >0) over convective regions because Q WVT and Q CM have similar magnitudes but opposite signs. The water vapor convergence, local atmospheric drying, and heat divergence are the rainfall sources whereas the local atmospheric cooling and the transport of hydrometeor concentration from convective regions to raining stratiform regions are the rainfall sinks. PEWV generally is higher than PEH (Fig. 4b) because the rainfall source from water vapor convergence in water vapor related surface rainfall budget is weaker than the rainfall source from heat divergence in thermally related surface rainfall budget (Fig. 6). PEWV is lower than PEH on 4 and 7 June because the rainfall source from water vapor convergence is stronger than the rainfall source from heat divergence. The RMS difference between PEWV and PEH is 12.5%. Both PWEV and PEH increase as convective rainfall intensifies (Fig. 5b). Precipitation efficiency ranges from 60% to 90% as convective rain rate is higher than 2 mm h-1, whereas it ranges from 0 to 100% as convective rain rate is lower than 2 mm h-1.

Over raining stratiform regions, rainfall is primarily associated with the transport of hydrometeor concentration from convective regions to raining stratiform regions because water vapor divergence dries local atmosphere on 3 and 7 June and water vapor convergence moistens local atmosphere on 4-6 June and heat divergence cools local atmosphere (Fig. 7). PWH is generally higher than PEWV on 3, 4, and 7 June whereas it is generally lower than PEWV on 5-6 June (Fig. 4c). The RMS difference between PEWV and PEH is 23.8%, which largely accounts for the RMS difference in the model domain mean calculations. The ranges of precipitation efficiencies for the surface rain rate of lower than 1 mm h-1 (0-100%) are larger than those for the surface rain rate of higher than 1 mm h-1 (45-100%).

5. Radiative effects of ice clouds: CNIR versus C

The calculations of model domain mean simulation data show that PEWV is insensitive to radiative effects of ice clouds on 4 June, whereas the exclusion of radiative effects of ice clouds decreases PEH (Table 1). The removal of radiative effects of ice clouds increases PEWV, but it barely affects PEH on 5 June. The elimination of radiative effects of ice clouds decreases PEWV and PEH on 6 June. The exclusion of radiative effects of ice clouds increases PEWV but it decreases PEH on 7 June. On 4 June, the water vapor realted surface rainfall budgets reveal that all rainfall processes contribute to rain rate in C and CNIR (Table 2), which leads to 100% of PEWV in the two experiments. The thermally related surface rainfall budgets show that rainfall is associated with heat divergence and radiative cooling in the two experiments (Table 3). Thus, local atmospheric cooling (S HT <0) makes PEH less than 100% in the two experiments.

(a)Model domain meanConvective regionsRaining stratiform regions
CCNIRCNIMCCNIRCNIMCCNIRCNIM
4 June100.0100.091.577.868.883.082.366.758.3
5 June92.098.481.071.570.380.6100.090.8100.0
6 June99.896.391.381.980.587.3100.082.2100.0
7 June62.864.660.455.067.277.228.136.851.5
(b)Model domain meanConvective regionsRaining stratiform regions
CCNIRCNIMCCNIRCNIMCCNIRCNIM
4 June77.072.068.573.564.177.065.267.547.0
5 June63.363.857.052.151.556.547.347.134.4
6 June71.969.368.770.669.477.649.143.734.3
7 June36.735.134.248.649.249.433.428.229.1

Table 1.

(a) PEWV and (b) PEH calculated data averaged daily and over model domain, convective regions, and raining stratiform regions in C, CNIR, and CNIM. Unit is %.

Model domain meanConvective regionsRaining stratiform regions
CCNIRCNIMCCNIRCNIMCCNIRCNIM
4
June
PSWV1.161.070.920.860.730.800.300.340.13
QWVT0.220.190.160.520.460.31-0.06-0.17-0.09
QWVF0.870.860.840.570.600.640.090.180.13
QWVE0.010.010.010.000.000.000.000.000.00
QCM0.050.01-0.09-0.24-0.33-0.160.280.320.09
5
June
PSWV1.541.591.270.920.990.930.620.590.35
QWVT-0.13-0.03-0.170.100.350.210.00-0.060.10
QWVF1.591.531.551.171.060.930.200.190.13
QWVE0.030.040.020.000.010.000.010.020.00
QCM0.050.04-0.13-0.37-0.42-0.220.410.440.12
6 JunePSWV2.952.842.692.212.212.360.740.640.32
QWVT0.340.410.310.770.920.870.03-0.140.17
QWVF2.572.512.621.921.801.830.230.380.03
QWVE0.040.040.020.010.010.000.010.020.00
QCM-0.01-0.11-0.26-0.49-0.53-0.340.470.380.12
7 JunePSWV0.540.510.460.290.330.320.250.190.15
QWVT0.640.590.67-0.090.080.080.650.300.19
QWVF-0.32-0.28-0.230.520.390.32-0.63-0.32-0.14
QWVE0.160.170.100.010.010.000.020.040.01
QCM0.050.03-0.08-0.15-0.16-0.090.200.170.09

Table 2.

Water vapor related surface rainfall budget (P SWV , Q WVT , Q WVF , Q WVE , and Q CM ) averaged daily and over model domain, convective regions, and raining stratiform regions in C, CNIR, and CNIM. Unit is mm h-1.

The removal of radiative effects of ice clouds decreases PEH from C to CNIR through the enhanced local atmospheric cooling associated with the enhanced radiative cooling because of similar heat divergence in the two experiments. On 5 June, the elimination of radiative effects of ice clouds increases PEWV through the reduced local atmospheric moistening (Q WVT <0) associated with the decreased water vapor convergence (Q WVF >0). The two experiments have similar PEH because of similar heat related rainfall processes. On 6 June, the exclusion of radiative effects of ice clouds decreases PEWV and PEH from C to CNIR through the enhanced hydrometeor gain (Q CM >0) because water vapor and heat processes have similar contributions to rainfall (Q WVT +Q WVF +Q WVE S HT +S HF +S HS +S LHLF +S RAD ) in the two experiments. On 7 June, the removal of radiative effects of ice clouds increases PEWV through the decreased water vapor divergence, whereas it decreases PEH through the enhanced local atmospheric cooling associated with the enhanced radiative cooling.

Over convective regions, the exclusion of radiative effects of ice clouds decreases PEWV and PEH through the intensified transport of hydrometeor concentration from convective regions to raining stratiform regions (Q CM <0) because water vapor and heat processes have similar contributions to rainfall in the two experiments on 4 June. Similar magnitudes of rainfall sources associated with water vapor, heat, and cloud processes lead to similar PEWV and PEH on 5 and 6 June. On 7 June, the exclusion of radiative effects of ice clouds increases PEWV through the local atmospheric change from moistening in C to drying in CNIR associated with the decrease in water vapor convergence while the two experiments have similar transport rates of hydrometeor concentration from convective regions to raining stratiform regions. The two experiments have similar PEH because of similar thermal processes. Note that radiative cooling is negligibly small over convective regions.

Over raining stratiform regions, the removal of radiative effects of ice clouds decreases PEWV through the enhanced local atmospheric moistening on 4 June. Because the increase in rainfall from C to CNIR is similar to the increase in rainfall source that is from the heat divergence and the transport of hydrometeor concentration from convective regions to raining stratiform regions, the increase in stratiform rainfall from C to CNIR leads to the increase in PEH. On 5 June, the elimination of radiative effects of ice clouds decreases PEWV because the local water vapor is barely changed in C and the local atmospheric moistening occurs in CNIR. The two experiments have similar PEH due to similar rainfall sources from thermal and cloud processes. On 6 June, the exclusion of radiative effects of ice clouds leads to the decreases in PEWV through the local atmospheric change from drying in C to moistening in CNIR and reduces PEH through the enhanced local atmospheric cooling. On 7 June, the removal of radiative effects of ice clouds increases PEWV through the slowdown in water vapor divergence. Because the decrease in rainfall is similar to the decrease in the rainfall source from heat divergence and transport of hydrometeor concentration from convective regions to raining stratiform regions as a result of similar local atmospheric cooling rate in the two experiments, the decrease in stratiform rainfall from C to CNIR leads to the decrease in PEH.

6. Microphysical effects of ice clouds: CNIM versus CNIR

The calculations of model domain mean simulation data show the decreases in PEWV and PEH from CNIR to CNIM during the life span of pre-summer heavy rainfall event (Figs. 9a-10a). On 4 June, the exclusion of microphysical effects of ice clouds decreases PEWV and PEH through the hydrometeor change from loss in CNIR to gain in CNIM and the weakened local atmospheric cooling (Figs. 11a-17a). On 5 June, the decrease in PEWV is associated with the intensification in local atmospheric moistening and the hydrometeor change from loss in CNIR to gain in CNIM. The reduction in PEH is related to the hydrometeor change from loss in CNIR to gain in CNIM. On 6 June, the decrease in PEWV corresponds to the strengthened hydrometeor gain. PEH is barely changed in the two experiments because of similar rates of rainfall source from thermal processes. On 7 June, the decreases in PEWV and PEH result from the hydrometeor change from loss in CNIR to gain in CNIM although water vapor divergence and local atmospheric cooling are weakened.

Over convective regions, PEWV and PEH are increased from CNIR to CNIM during the life span of pre-summer heavy rainfall event (Figs. 9 b -10b). The exclusion of microphysical effects of ice clouds increases PEWV and PEH through the weakened transport of hydrometeor concentration from convective regions to raining stratiform regions during 4-6 June (Figs. 11b-17b). The decrease in local atmospheric cooling also contributes to the increases in PEWV and PEH on 6 June. On 7 June, the removal of microphysical effects of ice clouds increases PEWV through the weakened transport of hydrometeor concentration from convective regions to raining stratiform regions, whereas it barely changes PEH because of the offset between the weakened transport of hydrometeor concentration from convective regions to raining stratiform regions and the enhanced local atmospheric cooling (Figs. 14b and 15b).

Model domain meanConvective regionsRaining stratiform regions
CCNIRCNIMCCNIRCNIMCCNIRCNIM
4
June
PSH1.161.070.920.860.730.800.300.340.13
SHT-0.34-0.41-0.33-0.05-0.06-0.08-0.16-0.16-0.14
SHF1.301.281.241.151.111.020.150.140.18
SHS-0.01-0.01-0.010.010.000.000.000.000.00
SLHLF0.020.000.00-0.01-0.020.000.030.020.00
SRAD0.130.180.120.010.020.010.000.020.00
QCM0.050.01-0.09-0.24-0.33-0.160.280.320.09
5
June
PSH1.541.591.270.920.990.930.620.590.35
SHT-0.88-0.88-0.82-0.46-0.48-0.49-0.68-0.66-0.65
SHF2.302.282.141.761.911.630.880.740.89
SHS-0.01-0.02-0.010.000.010.00-0.01-0.010.00
SLHLF0.010.010.00-0.01-0.030.000.030.040.00
SRAD0.070.160.090.000.010.01-0.010.030.00
QCM0.050.04-0.13-0.37-0.42-0.220.410.440.12
6 JunePSH2.952.842.692.212.212.360.740.640.32
SHT-1.15-1.14-0.97-0.40-0.40-0.34-0.76-0.81-0.62
SHF4.003.923.813.103.153.020.981.000.82
SHS0.00-0.010.010.020.010.01-0.01-0.010.00
SLHLF0.030.030.00-0.03-0.040.000.060.060.00
SRAD0.060.160.100.000.010.010.000.020.01
QCM-0.01-0.11-0.26-0.49-0.53-0.340.470.380.12
7 JunePSH0.540.510.460.290.330.320.250.190.15
SHT-0.90-0.92-0.81-0.15-0.17-0.23-0.48-0.47-0.36
SHF1.301.261.290.590.660.640.510.470.42
SHS-0.03-0.03-0.010.000.000.00-0.01-0.010.00
SLHLF0.020.010.000.000.000.000.010.010.01
SRAD0.090.150.060.000.000.000.000.010.00
QCM0.050.03-0.08-0.15-0.16-0.090.200.170.09

Table 3.

Thermally related surface rainfall budget (P SWV , S HT , S HF , S HS , S RAD , and Q CM ) averaged daily and over model domain, convective regions, and raining stratiform regions in C, CNIR, and CNIM. Unit is mm h-1.

Over raining stratiform regions, the elimination of microphysical effects of ice clouds decreases PEWV through the weakened local atmospheric moistening and reduces PEH through the weakened local atmospheric cooling on 4 June. During 5-6 June, the exclusion of microphysical effects of ice clouds increases PEWV because all rainfall processes favors rainfall in CNIM but the local atmospheric moistening reduces rainfall in CNIR. Although the removal of microphysical effects of ice clouds barely impacts local atmospheric cooling on 5 June and it decreases local atmospheric cooling on 6 June, the decreases in stratiform rainfall are associated with the slowdown in transport of hydrometeor concentration from convective regions to raining stratiform regions. As a result, the decreases in stratiform rainfall lead to the decreases in PEH from CNIR to CNIM. On 7 June, the elimination of microphysical effects of ice clouds increases PEWV through the weakened water vapor divergence and increases PEH through the weakened local atmospheric cooling.

7. Conclusions

Precipitation efficiency can be well defined through diagnostic surface rainfall budgets. From thermally related surface rainfall budget, precipitation efficiency associated with heat processes (PEH) is first defined in this study as the ratio of surface rain rate and the rainfall source from heat and cloud budgets. Precipitation efficiency associated with water vapor processes (PEWV) was defined by Sui et al. (2007) as the ratio of surface rain rate to the rainfall source from water vapor and cloud budgets. In this study, both precipitation efficiencies and their responses to effects of ice clouds are investigated through an analysis of sensitivity cloud-resolving modeling data of a pre-summer heavy rainfall event over southern China during June 2008. The major results include:

  1. The calculations of model domain mean simulation data show that PEH is lower than PEWV because heat divergence contributes more to surface rainfall than water vapor convergence does. Precipitation efficiencies are lower during the decay phase than during the development of rainfall. PEH is generally lower than PEWV over convective regions, whereas it is generally higher than PEWV over raining stratiform regions. Precipitation efficiencies increase as surface rain rate increases.

  2. PEWV has different responses to radiative effects of ice clouds during the different stages of the rainfall event. The exclusion of Microphysical effects of ice clouds generally decreases PEWV in the calculations of model domain mean simulation data, whereas it generally increases PEWV over raining regions.

  3. The exclusion of radiative effects of ice clouds generally decreases PEH. The removal of microphysical effects of ice clouds generally decreases PEH except that it increases PEH over convective regions.

  4. Effects of ice clouds on precipitation efficiencies can be explained by the analysis of surface rainfall budgets. The changes in PEWV are mainly associated with the changes in local atmospheric moistening and transport of hydrometeor concentration from convective regions to raining stratiform regions during the life span of pre-summer heavy rainfall event and the change in water vapor divergence on 7 June. The changes in PEH are mainly related to the changes in local atmospheric cooling and radiative cooling and transport of hydrometeor concentration from convective regions to raining stratiform regions during the life span of pre-summer heavy rainfall event.

Acknowledgments

The authors thank W.-K. Tao at NASA/GSFC for his cloud resolving model, and Dr. N. Sun at I. M. Systems Group, Inc. for technical assistance to access NCEP/GDAS data. This study is supported by the National Key Basic Research and Development Project of China under Grant No. 2011CB403405, the National Natural Science Foundation of China under Grant No. 41075039, the Chinese Special Scientific Research Project for Public Interest under Grant No. GYHY200806009, and the Qinglan Project of Jiangsu Province of China under Grant No. 2009.

© 2011 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Xinyong Shen and Xiaofan Li (November 2nd 2011). Thermodynamic Aspects of Precipitation Efficiency, Thermodynamics - Interaction Studies - Solids, Liquids and Gases, Juan Carlos Moreno-Pirajan, IntechOpen, DOI: 10.5772/19750. Available from:

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