Impact of Cloud Microphysical Processes on the ... - Springer Link

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CHENG Rui, YU Rucong, FU Yunfei, et al.

Impact of Cloud Microphysical Processes on the Simulation of Typhoon Rananim near Shore. Part I: Cloud Structure and Precipitation Features∗ CHENG Rui1,2† (

), YU Rucong1 (

), FU Yunfei3 (

ì), and XU Youping ( 1,2

)

1 State Key Laboratory of Numerical Simulation for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, CAS, Beijing 100029 2 Graduate School of the Chinese Academy of Sciences, Beijing 100049 3 University of Science and Technology of China, Hefei 230026 (Received August 11, 2010)

ABSTRACT By using the Advanced Regional Eta-coordinate Model (AREM), the basic structure and cloud features of Typhoon Rananim are simulated and verified against observations. Five sets of experiments are designed to investigate the effects of the cloud microphysical processes on the model cloud structure and precipitation features. The importance of the ice-phase microphysics, the cooling effect related to microphysical characteristics change, and the influence of terminal velocity of graupel are examined. The results indicate that the cloud microphysical processes impact more on the cloud development and precipitation features of the typhoon than on its intensity and track. Big differences in the distribution pattern and content of hydrometeors, and types and amount of rainfall occur in the five experiments, resulting in different heating and cooling effects. The largest difference of 24-h rain rate reaches 52.5 mm h−1 . The results are summarized as follows: 1) when the cooling effect due to the evaporation of rain water is excluded, updrafts in the typhoon’s inner core are the strongest with the maximum vertical velocity of –19 Pa s−1 and rain water and graupel grow most dominantly with their mixing ratios increased by 1.8 and 2.5 g kg−1 , respectively, compared with the control experiment; 2) the melting of snow and graupel affects the growth of rain water mainly in the spiral rainbands, but much less significantly in the eyewall area; 3) the warm cloud microphysical process produces the smallest rainfall area and the largest percentage of convective precipitation (63.19%), while the largest rainfall area and the smallest percentage of convective precipitation (48.85%) are generated when the terminal velocity of graupel is weakened by half. Key words: typhoon structure, precipitation, cloud microphysical processes, AREM model Citation: Cheng Rui, Yu Rucong, Fu Yunfei, et al., 2011: Impact of cloud microphysical processes on the simulation of Typhoon Rananim near shore. Part I: Cloud structure and precipitation features. Acta Meteor. Sinica, 25(4), 441–455, doi: 10.1007/s13351-011-0405-0.

1. Introduction In recent 10 years, more and more attention has been paid to the influence of cloud microphysical processes on typhoon intensity, structure, and evolution (Liu et al., 1997; Wang, 2001; Franklin et al., 2005; Wu et al., 2006; Zhu and Zhang, 2006; Pattnaik and Krishnamurti, 2007). However, it is still not clear about how the cloud microphysical processes influences in detail the typhoon intensity and structure. The use of ∗ Supported

numerical simulations to study the influence of cloud microphysical processes on typhoons could assess the fundamental dynamics, track, intensity, cloud structure and precipitation of the tropical cyclone (TC). The simulated fundamental dynamical fields are not sensitive to the microphysical processes (Franklin et al., 2005). Zhu and Zhang (2006) pointed out if the modeled typhoon intensity was strong, the cloud microphysical processes would make a little influence on typhoon track, which is basically consistent with the

by the National Basic Research and Development (973) Program of China (2004CB418304). author: [email protected]. (Chinese version published in Vol. 67, No. 5, 764–776, 2009) c The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2011

† Corresponding

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ACTA METEOROLOGICA SINICA

results obtained by Pattnaik and Krishnamurti (2007). However, many studies show that the parameterization of cloud and precipitation can affect typhoon intensity. The warm cloud microphysical process produces a rapid intensification of the typhoon compared with the ice-phase microphysical process (Willoughby et al., 1984; Lord et al., 1984; Wang, 2002). If the evaporation of rain water and the melting of snow and graupel are excluded, the typhoon will be intensified faster and its intensity will be increased significantly (Wang, 2002; Zhu and Zhang, 2006; Pattnaik and Krishnamurti, 2007). But if the graupel is excluded, the typhoon intensity will be slightly reduced (Zhu and Zhang, 2006). The cloud structure of the typhoon is very sensitive to the cloud microphysical parameterization (Wang, 2002). Different cloud microphysics schemes produce quite different distributions and contents of hydrometeors. If the melting of snow and graupel is excluded, the graupel will reach the ground as precipitant and the rain water will be confined to the narrow eyewall region. Zhu and Zhang (2006) suggested that if the evaporation of cloud water and rain water was excluded, the eyewall would become wider. Weaker spiral rainbands showed up in the warm cloud experiment and the experiments without considering the evaporation of rain water and the melting of snow and graupel. Franklin et al. (2005) found that if the terminal velocity of graupel was increased, its content in the middle and upper troposphere would be reduced notably, and graupel particles would be mostly restricted at the inner core. Brown and Swann (1997) assessed some key cloud microphysical parameters in a three-dimensional cloud model and found that the model surface precipitation was sensitive not only to the average size and the terminal velocity of graupel, but also to its capture rate of snow and cloud ice. McCumber et al. (1991) reported that about 13% of the total tropical convective precipitation was affected by the ice-phase cloud microphysics parameterization. Wang (2002) believed that the warm cloud microphysics parameterization produced higher precipitation rate but smaller rainfall area, which was similar to the results obtained by Mc-

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Cumber et al. (1991). Zhu and Zhang (2006) pointed out that if the melting of cloud ice, snow, and graupel was excluded, the precipitation amount would increase significantly. Franklin et al. (2005) found that the graupel was the most important hydrometeor in both the convective and stratiform precipitation regions. Besides, greater terminal velocity of graupel would result in higher rain rate at the inner core and a clearer structure of stratiform rainbands. Although the above studies are physically consistent, there are still two major problems concerning the simulation of typhoons by using explicit cloud microphysical parameterizations. First, there are still different opinions about the influence of cloud microphysical processes on the thermodynamic structure and strength of typhoons. For example, there was a difference between the analysis presented by Lord et al. (1984) and Wang (2002) about the effect of ice-phase and warm cloud microphysical processes on the final intensity of typhoon. There were also differences between the descriptions by Wang (2002) and Zhu and Zhang (2006) about the impact of cloud microphysical processes on the typhoon strength, as well as the vertical structure of latent heating. Meanwhile, the discussion by Lord et al. (1984) on the effects of the terminal velocity of graupel on typhoon development and the important role of ice-phase particles was different from the experimental analysis by Franklin et al. (2005). These disagreements indicate that the cloud microphysics itself contains great uncertainty. Second, all the cloud microphysical parameterization schemes in the current typhoon simulations are based on the observation and theory of mesoscale convective systems during the 1970s–1980s. Hence, a large number of experiments should be carried out to demonstrate whether these schemes can be applied to the TC model. Lou et al. (2004) made a detailed comparison assessment on the cloud microphysical parameterization schemes in MM5 (Mesoscale Model version 5) through the numerical experimental analysis of Hurricane Andrew (1992). They pointed out that the explicit microphysical parameterization scheme in current numerical models might not describe some cloud microphysical processes reasonably. For instance, we

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often controlled the autoconversion of hydrometeors using threshold values, and calculated the number concentration of rain water, snow, and graupel diagnostically. Therefore, accurate analysis and quantitative assessment on the impact of cloud microphysical parameterizations on the structure and intensity of typhoons are still necessary not only for the typhoon simulation but also for the optimization of cloud microphysical parameterization schemes. This paper will try to quantitatively reveal the influence of the detailed cloud microphysical processes on the intensity and structure of Typhoon Rananim through numerical sensitivity experiments. Moreover, we will discuss how to improve the cloud microphysical parameterization so as to better simulate the typhoon structure and strength. We will show the impact of the cloud microphysical processes on cloud structure and precipitation features in this paper, and on typhoon intensity and track in the companion paper (part II of this study). 2. Experimental design In this study, five sensitivity experiments (see Table 1) are designed to examine the effects of cloud microphysical parameterization details on the typhoon intensity, cloud structure, and precipitation features using the Advanced Regional Eta-coordinate Model (AREM; Yu, 1989, 1994a, b, c, 1995; Yu and Xu, 2004). These experiments mainly focus on the impacts of ice-phase microphysical processes, downdraft and its cooling as well as graupel particles. The model setup is the same for these sensitivity experiments except for the cloud microphysical processes. The grid distance of 5 km is used to explicitly resolve the inner core and spiral rainbands of the typhoon. The cloud microphysical parameterization scheme by Wang (2002) is chosen to explicitly represent the cloud and precipitation processes. The non-local planetary boundary layer (PBL) parameterization scheme (Holtslag and Borille, 1993) is adopted to calculate the vertical turbulent mixing, and the Zeng scheme (Zeng et al., 1998) is chosen to calculate the surface turbulent fluxes. The Rankin vortex is incorporated to the

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modified background to build the intial conditions of the typhoon. The coarser meshes with a resolution of 15 km provide the 5-km meshes with time-dependent lateral boundary conditions. The NCEP (National Center for Environmental Prediction) daily SST data (0.5◦ ×0.5◦ ) is chosen to form the underlying surface conditions of the ocean. The model is initialized at 0000 UTC 11 August 2004 and terminated at 1200 UTC 12 August for 36 h, which covers the strengthening and maintaining of Typhoon Rananim near its landfall. The experiment WRM only retains cloud water and rain water as well as associated microphysical processes. The experiment NEVP excludes the cooling effect due to the evaporation of rain water and therefore noticeably reduces the downdraft intensity. The experiment NMLT excludes the melting of snow and graupel, which weakens the downdrafts as well. The terminal velocity of graupel is reduced by half in the experiment HLF− AG in order to find out how the terminal velocity impacts on the development of the typhoon. It should be noted that Ye et al. (2007) performed a numerical analysis on the mechanism of the offing reinforcement of Typhoon Rananim based on the AREM, with the experimental scheme disigned slightly different from that in this paper. As a first step, we need to confirm that the AREM can reproduce the observed characteristics of Rananim so as to establish a convincing database for comparison with the sensitivity experiments. The model results can be verified against various observations including typhoon reports from operational centers, mesoscale reanalysis, hourly satellite retrieved precipitation, vertical profiles of cloud water and cloud ice derived from the TRMM observation, TRMM brightness temperature data, and Doppler radar reflectivity products. We will only present the verification of the fundamental structure and hydrometeor features of the typhoon herein. It is known that parameterization of the cloud microphysical processes is of considerable uncertainty. The processes involve complex conversion mechanisms among different hydrometeors and strong nonlinearity in the calculation, which might be more prominent in the explicit simulation of a typhoon. Consequently, it

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Table 1. Description of the sensitivity experiments Experiment CTRL

Description Control experiment Horizontal grid space about 5 km Model domain consisting of 301×601×32 grid points Simulation area covering 16◦ –36◦ N, 115◦ –135◦ E, and from surface to 10 hPa Time step of 10 s Wang’s cloud microphysical parameterization scheme, non-local planetary boundary layer scheme, Zeng’s surface flux calculation scheme, simplified surface radiation computation scheme (Ghan et al., 1982) Initialized with incorporation of Rankin vortex into 1◦ ×1◦ NCEP analysis Lateral boundary condition specified with coarser model results 0.5◦ ×0.5◦ daily SST as the constant underlying ocean surface condition

WRM

As the CTRL, but only the warm cloud microphysics considered

NEVP

As the CTRL, but the cooling effect due to the evaporation of rain water excluded

NMLT

As the CTRL, but the melting of snow and graupel excluded

HLF− AG

As the CTRL, but the terminal velocity of graupel reduced by half

is difficult for us to establish the cause and effect relationships among those processes. Minor adjustments of cloud microphysical parameters are not actually effective, so significant modifications (e.g., to exclude the ice-phase process or to neglect the melting of snow and graupel) are done to ensure a response. This approach is often adopted by researchers in their sensitivity experimental analysis of cloud microphysical processes and interesting results are obtained (Franklin et al., 2005). Similarly, we design the warm cloud microphysical sensitivity experiment to demonstrate the effect of the ice-phase process. Also, the impacts of the melting of snow and graupel, and the falling feature of graupel particles on the typhoon simulation are examined through exclusion of snow and graupel melting and reduction of the graupel terminal velocity, respectively. 3. Simulation and verification of Rananim

Typhoon

3.1 General structure We implant the Rankine vortex into the NCEP analysis-based background to build the typhoon initial condition. The comparison between AREM initial fields and Japan high resolution reanalysis (JRA) shows that both of them have the same TC center location and velocity distribution, as well as similar central sea level pressure (SLP) and the maximum wind

speed at the eyewall (figure omitted). At 0000 UTC 12 August, the AREM simulated typhoon center location and velocity structure are in a good agreement with the JRA data (Fig. 1). However, the AREM simulated SLP is weaker but the maximum wind speed is stronger than the results from JRA. For the landfall of Rananim at 36 h (figure omitted), both the model and JRA well capture the landfall location although they give much weaker intensities than the reality. For instance, the AREM simulated central SLP is more than 30 hPa weaker than the observation, and the JRAbased SLP is over 20 hPa weaker than the reality. 3.2 Cloud water Figure 2 shows a comparison of cloud water profiles between the model and TRMM satellite retrieval. The vertical profiles are averaged within the circumference where hydrometeor extremes appear (about 100 km away from the TC center). Additionally, the profiles are normalized using rain water maxima so that the vertical pattern of the hydrometeor can be shown more clearly. It is indicated from Fig. 2 that the model cloud water has the same vertical distribution as the TRMM retrieval. Both of them have a cloud water peak near 4.5 km. Thus, we can see an increase of cloud water with height below about 4.5 km and a decrease with height above this level. Generally, the model has produced better results at lower levels than at upper levels.

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Fig. 1. Comparison of sea level pressure (solid lines; hPa), 850-hPa velocity (shaded; m s−1 ), and wind vectors between (a) JRA and (b) the AREM simulation at 0000 UTC 12 August 2004.

Fig. 2. Comparison of normalized cloud water profile at

Fig. 3. As in Fig. 2, but for cloud ice profile.

the eyewall between the observation (OBS) and the AREM simulation (SIM) at 0800 UTC 12 August 2004.

3.3 Cloud ice Figure 3 gives a comparison of cloud ice profiles between the model and TRMM satellite retrieval. The vertical profiles are averaged and normalized in the same way as we do for the cloud water. It is seen that the simulated cloud ice exhibits a similar vertical pattern to the TRMM retrieval with a cloud ice peak near 12 km. The cloud ice concentration increases with height from 6 to 12 km, but decreases with height above 12 km. The above results demonstrate that the simulated

typhoon structure and cloud features by AREM are fairly consistent with those of the reanalysis and observation. Therefore, the AREM simulation can provide reliable results for the cloud microphysical sensitivity analysis. 4. Impact of the cloud microphysical processes on the cloud structure 4.1

Hydrometeor mixing ratio and vertical velocity

It is noted that high reflectivities ( 35 dBZ) in the eyewall in all experiments display a nearly symmetrical distribution at 24 h. Thus, the cloud struc-

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ture of typhoon can be described using radius-height cross-sections of azimuthal mean fields. In the control experiment (Fig. 4), noticeable updrafts appear in the eyewall with the maximum value of –8 Pa s−1 near 700 hPa and they extend vertically to about 100 hPa. The downdrafts are dominant in the eye area near the tropopause and in the outer spiral rainbands in the lower and middle troposphere (near 4 km). But the downdraft intensity is very weak with the peak value of 0.25 Pa s−1 . Furthermore, various kinds of hydrometeors are similar in the radius-height distribution and are mainly present near the eyewall. The cloud water and rain water are mostly found below the freezing level while other ice-phase particles are mostly seen above it. The cloud water is closely related to the ascending motion and exhibits a dual-center pattern with one center at 550 hPa and the other at 750 hPa (the peak value being 0.45 g kg−1 ). The rain water is distributed at levels lower than the cloud water and farther away from the typhoon center with the peak

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value of 2.0 g kg−1 at 800 hPa. The cloud ice is located at higher levels (near 200 hPa) than other hydrometeors. Snow particles are seen close to the cloud ice with the smallest content, but the graupel shows the largest content and reaches its peak of 3.1 g kg−1 roughly near 375 hPa. When the ice-phase microphysical process is excluded in the experiment WRM, the descending motion outside the eyewall is confined to the narrow region while the ascending motion in the inner core is intensified, and the updraft maximum is 4 Pa s−1 stronger than that in CTRL (Fig. 5). Furthermore, the downdrafts near the eye area disappear while the slight descending motion is present in the PBL about 100 km away from the eyewall. The cloud water content shows two peaks vertically with one at 650 hPa and the other at 250 hPa, and its maximum content approximates that in CTRL. In addition, the peak content of rain water is 0.2 g kg−1 larger than that in CTRL and rain water extends vertically to nearly

Fig. 4. The radius-height cross-sections of azimuthally averaged hydrometeor concentrations (g kg−1 ) and vertical velocity (Pa s−1 ) at 24 h for experiment CTRL. (a) Vertical velocity, (b) cloud water, (c) rain water, (d) cloud ice, (e) snow, and (f) graupel. The contour numbers represent the values of each element, and the bold solid line denotes the freezing level.

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100 hPa. Cloud water is largely generated in the upper troposphere, and is inevitably accompanied by strong condensational latent heating. Then, the warm air parcels are advected into the eye by the upper divergent inflow and thereby the air density is reduced there, which is unfavorable for the development of the dynamically forced downdrafts in the eye region. Meanwhile, the cloud water is transported outwards by the centrifugal radial outflow in the middle and upper troposphere so as to form the anvil or stratiform cloud outside the eyewall.

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The results from experiment NEVP are shown in Fig. 6. It is seen that the ascending motion in the inner core reaches –19 Pa s−1 , which is the largest among all the experiments when the cooling effect due to the evaporation of rain water is excluded. Forced by the strong updrafts, the apparent compensation downdrafts occur in the eye area with the peak value of 2 Pa s−1 at 200 hPa. The downdrafts outside the eyewall have a larger intensity than those in CTRL, which is different from the analysis of Wang (2002). However, the intensity of the descending motion is still very

Fig. 5. As in upper panels in Fig. 4, but for experiment WRM.

Fig. 6. As in Fig. 4, but for experiment NEVP.

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weak, and its peak value of 0.5 Pa s−1 is present either near the freezing level or inside the PBL, resulting in little cooling at lower levels. Compared with CTRL, all kinds of hydrometeors are greatly increased near the eyewall and their peak values are present at higher levels, among which the rain water and graupel are most typical with an increase of 1.8 and 2.5 g kg−1 , respectively, in the mixing ratio. Additionally, the high concentration of graupel and strong updrafts in the inner core indicate that the riming and depositional growth occur in the eyewall. Moreover, the cloud water content has two peaks above and below the freezing level with larger intensity below. The cloud water and rain water, accompanied by updrafts, are present closer to the eye, showing a more compact eyewall in the middle and lower troposphere. When the melting of snow and graupel is excluded in experiment NMLT, the vertical velocity shows a dual-center pattern (not so typical for other experiments) with one at 750 hPa and the other at 450 hPa (Fig. 7), which reflects the effect of condensational latent heating at the lower levels and deposition at the middle levels, respectively. The downdrafts in the outer rainbands are stronger than in CTRL,

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which mainly result from the vertical advective cooling driven by the fallout of graupel. However, the cold invasion into the eyewall in the PBL is factually weaker than that in CTRL (Figs. 12a, d) since the melting of snow and graupel and the cooling effect due to the evaporation after their melting are not considered. In addition, various hydrometeors have larger contents than those in CTRL. Furthermore, the graupel particles can reach the sea surface as precipitants when the melting of graupel is excluded. The peak of eyewall updrafts in experiment HLF− AG is as large as that in CTRL, but they are confined to a much smaller region when the terminal velocity of graupel is reduced by half (Fig. 8). Nevertheless, the range of downdrafts outside the eyewall is enlarged and its strength is markedly increased (twice as large as in CTRL) as well. Moreover, we can see more cloud water and snow but less rain water and cloud ice, and an unnoticeable variation in graupel. The graupel is closer to the snow in radial distribution since the fallout effect of graupel is decreased when its terminal velocity is halved. All the hydrometeors appear farther away from the center than those in CTRL, so the typhoon eye is relatively large. It can be easily

Fig. 7. As in Fig. 4, but for experiment NMLT.

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Fig. 8. As in Fig. 4, but for experiment HLF− AG.

seen that the above analyses are closely related to the fact that the typhoon in HLF− AG is weaker. We show profiles of the radially averaged mixing ratios of hydrometeors in the eyewall and the spiral rainbands (Figs. 9 and 10) to better illustrate the effect of cloud microphysical processes on the cloud structure of the typhoon. Generally, the rain water and graupel have larger intensity in the eyewall in all experiments (rain water being the largest in WRM). The larger intensity of these two hydrometeors and the higher level where their maximum mixing ratios appear are favorable for the presence of the stronger typhoon. Specifically, in Figs. 9 and 10, NEVP gives the graupel maximum of 4.5 g kg−1 at 300 hPa and the rain water maximum of 2.5 g kg−1 at 700 hPa; CTRL has the graupel peak of 1.5 g kg−1 at 425 hPa and the rain water peak of 1.25 g kg−1 at 850 hPa; HLF− AG shows the graupel maximum of 1.0 g kg−1 at 450 hPa and the rain water maximum of 0.75 g kg−1 at 900 hPa. Additionally, the graupel content is the largest but the snow content is the smallest in NEVP; whereas in HLF− AG, the graupel content is the small-

est but the snow content is the largest, suggesting that the variation in typhoon strength is closely related to these two particles. When the melting of snow and graupel is excluded, snow has a small change but graupel is intensified at lower levels and rain water is increased significantly. As a result, the autoconversion between cloud water and rain water at lower levels is quite dominant and the melting of snow and graupel in the eyewall may not be the major factor in generating rain water. Figure 10 shows profiles of hydrometeors in the outer spiral rainbands. The mixing ratios of hydrometeors outside the inner core are much smaller than those in the eyewall. The cloud ice and graupel have larger contents in all the experiments except WRM and they may be the dominant particles in the structural variation of cloud in the outer rainbands. The cloud water in WRM shows two peaks with one at 900 hPa and the other at 150 hPa while the rain water peak is fairly small. The rain water in NMLT nearly reaches null since the melting of graupel and snow is excluded. Therefore, the melting of graupel and snow

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Fig. 9. Profiles of the mixing ratios of various hydrometeors (g kg−1 ) in the eyewall (radially averaged from the distance of 0.5◦ –1.0◦ from the eye) at 24 h for (a) CTRL, (b) WRM, (c) NEVP, (d) NMLT, and (e) HLF− AG. The parameters qc , qr , qi , qs , and qg represent the mixing ratios of cloud water, rain water, cloud ice, snow, and graupel, respectively.

is one of the important mechanisms for the rain water growth in the spiral rainbands, which is similar to the assumption that large stratiform precipitation may result from the melting of ice-phase hydrometeors in the extratropics. In addition, the graupel is increased below the freezing level rather than above it. All the hydrometeors except cloud water have higher concentrations in NEVP than in CTRL. The graupel in HLF− AG has a larger intensity than that in CTRL, and its snow content is the largest among all experiments. 4.2 Thermodynamic structure of cloud From the radius-height cross-sections of pseudoequivalent potential temperature at 24 h (Fig. 11), the col pattern of the thermodynamic field is present within the radius of the maximum wind speed (RMW) in all experiments. It can be described that vertically,

the eyewall higher temperatures are present in the upper troposphere and the PBL, but lower temperatures show in the middle troposphere; whereas radially, the eyewall temperature is higher than that in the eye and the spiral rainbands. Large radial gradients of temperature occur (with the largest in NEVP) in the “saddle” of θse , and the “saddle” appears close to the eyewall edge. Moreover, the torrential rainfall region with reflectivity over 40 dBZ is superposed by the moist neutral stratification and the RMW in all experiments except for HLF− AG. This reveals that the moist neutral stratification is favorable for the increase of updraft strength and precipitation rate, which is similar to the heavy rainfall process (Xu et al., 1997). We can also see that air parcels first experience the unstable environment, then the neutral background, and finally the stable condition during their ascending. Additionally, NEVP shows a thicker unstable layer with weaker

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Fig. 10. Profiles of the mixing ratios of various hydrometeors (g kg−1 ) in the spiral rainband (radially averaged from the distance of 2.5◦ –3.0◦ from the eye) at 24 h for (a) CTRL, (b) WRM, (c) NEVP, (d) NMLT, and (e) HLF− AG. Indications of line patterns are the same as in Fig. 9.

unstability while other experiments have thinner unstable layers but larger unstability. 5. Impact of the cloud microphysical processes on typhoon precipitation 5.1 General precipitation features Some of the above structural characteristics of cloud can also be seen from the model radar reflectivity. Figure 12 shows that the highest reflectivity in all experiments appears in the eyewall. The warm cloud parameterization shows small precipitation in the outer rainbands. NEVP has the smallest free-echo eye and strongest eyewall reflectivity with its peak value of 50 dBZ near 800 hPa. Furthermore, a large quantity of ice-phase particles at the middle and upper levels can bring about the cooling effect after

they fall through the melting level, which then result in the stratiform precipitation and associated downdrafts (see Figs. 6a, c). In addition, HLF− AG shows a fairly clear structure of stratiform precipitation. Its radar reflectivity at lower levels is weaker than that in CTRL and its high reflectivities in the eye area suggest that the weak precipitation associated with graupel is detrained from the eyewall (Wang, 2002). 5.2 Vertical profiles of precipitation From Fig. 13, it is seen that the stronger typhoon has the larger precipitation rate, and its rain rate center is closer to the typhoon eye (so with the smaller eye size) while using the mixed ice-phase cloud microphysical processes. The precipitation rate is the largest in NEVP (about 80 mm h−1 ) but the smallest in HLF− AG (about 27.5 mm h−1 ) with the

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Fig. 11. The radius-height cross-sections of pseudo-equivalent potential temperature (◦ C) at 24 h for (a) CTRL, (b) WRM, (c) NEVP, (d) NMLT, and (e) HLF− AG. The bold dashed lines denote the distribution of radar reflectivity of 40 dBZ and the bold solid lines for the RMW. The shaded areas represent the distribution of higher θse . 100

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5 1 2 3 4 51 Distance from typhoon center (degree)

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Fig. 12. The radius-height cross-sections of the simulated radar reflectivity (dBZ) at 24 h for (a) CTRL, (b) WRM, (c) NEVP, (d) NMLT, and (e) HLF− AG.

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Fig. 13. Profiles of the azimuthally averaged rain rate (mm h−1 ) at 24 h in various experiments.

peak value in CTRL of about 40 mm h−1 . Furthermore, HLF− AG shows two peaks in the precipitation rate outside a radius of 300 km, suggesting the existence of the active outer spiral rainbands. We can also see that the CTRL precipitation rate is considerably overestimated, as verified against the observation. However, the simulated rain rate is closer to the reality when the terminal velocity of graupel is reduced by half. 5.3 Classification of precipitation To better describe the effect of cloud microphysical processes on typhoon precipitation, the simulated rainfall field is classified based on the assumption that each grid point is categorized as convective/nonconvective if its rain rate is greater/less than 25 mm h−1 . We employ the method of Franklin et al. (2005) to partition the precipitation field but the threshold for convective rainfall rate is increased from 20 to 25 mm h−1 , mainly because the model precipita-

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tion rate is considerably larger than the reality. Furthermore, the convective precipitation is mostly embedded in the nonconvective precipitation region (figure omitted) and most of convective rainfall occurs in the eyewall area; meanwhile, the nonconvective precipitation may also be surrounded by the convective precipitation. We can also see that when the typhoon is stronger (e.g., in NEVP), the eyewall shows stronger convection and a weaker convectional system is generated in the outer spiral rainbands (being basically nonconvective rainfall there). However, the convection in the eyewall has the similar intensity to that in the outer spiral rainbands if the typhoon is weaker. Table 2 shows surface rainfall statistics for the typhoon simulation. It can be seen that WRM has the smallest precipitation area (same as McCumber et al., 1991) while HLF− AG shows the largest one. The rainfall region in CTRL is larger than that in NEVP, but smaller than that in NMLT. Furthermore, the stronger the typhoon is, the larger the average precipitation rate is. For instance, the strongest typhoon by NEVP produces the rain rate of 15.05 mm h−1 and the weakest TC by HLF− AG yields that of 10.87 mm h−1 . NEVP shows both the largest nonconvective and convective precipitation rates of 6.741 and 59.02 mm h−1 , respectively, and its convective rainfall rate is generally larger than those of other experiments. NMLT, however, shows both the smallest nonconvective and convective rainfall rates of 5.591 and 45.65 mm h−1 , respectively. Additionally, WRM has the smallest percentage of both nonconvective rain (36.81%) and nonconvective rain area (82.62%). Nevertheless, HLF− AG gives the largest percentage of

Table 2. The surface precipitation statistics for the typhoon simulation at 24 h Experiment CTRL WRM NEVP NMLT HLF− AG Number of grid points with rainfall 5529 4925 5357 5634 6222 10.95 12.58 15.05 11.07 10.87 Average rainfall rate (mm h−1 ) 5.782 5.604 6.741 5.591 6.286 Average nonconvective rainfall rate (mm h−1 ) 48.28 45.72 59.02 45.65 45.95 Average convective rainfall rate (mm h−1 ) Percentage of nonconvective rainfall (%) 46.40 36.81 37.68 43.58 51.15 Percentage of convective rainfall (%) 53.60 63.19 62.32 56.42 48.85 Percentage of nonconvective rainfall area (%) 87.85 82.62 84.11 86.32 88.44 Percentage of convective rainfall area (%) 12.15 17.38 15.89 13.68 11.56 Note: Average rainfall rate is the total precipitation amount divided by the number of grid points with rainfall; average nonconvective rainfall rate is the total nonconvective precipitation amount divided by the number of grid points with nonconvective rainfall; percentage of the nonconvective rainfall is the ratio of nonconvective precipitation to total rainfall; percentage of nonconvective rainfall area is the ratio of the number of grid points with nonconvective precipitation to the number of all grid points with rainfall.

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both nonconvective rain (51.15%) and nonconvective rain area (88.44%). As a result, the warm cloud microphysical scheme shows the smaller total rain area but the larger percentage of convective rainfall. When the terminal velocity of graupel is reduced by half, a great quantity of graupel can be advected outwards into the spiral rainbands, resulting in a larger share of the nonconvective precipitation. 6. Summary The impact of the cloud microphysical processes on the cloud structure and precipitation features of simulated Typhoon Rananim is studied through the sensitivity experimental analysis. We focus on the influences of cloud microphysical details such as the ice-phase cloud microphysical process, the cooling effect due to the evaporation of rain water, the melting of snow and graupel, and the terminal velocity of graupel as well. The rain water and graupel have larger contents than other hydrometeors in the eyewall for all experiments (rain water being the largest in WRM). It is shown that if these two particles are larger in content and their maximum mixing ratios appear at the higher level, a stronger typhoon will be produced. Consequently, the rain water and graupel may be the major influential particles for the intensity change of the typhoon. Moreover, the melting of graupel and snow is one of the important mechanisms for the rain water growth in the spiral rainbands although this seems not a dominant factor for the generation of rain water in the eyewall. From the sensitivity experiments, we can see that the stronger typhoon produces much larger convection in the eyewall than in the spiral rainbands, whereas the weaker typhoon shows the equivalent convection strength in both the eyewall and the outer rainbands. In addition, WRM has the smallest precipitation area but the highest percentage of convective rainfall (63.19%) while HLF− AG produces the largest precipitation area and the highest percentage of nonconvective precipitation (51.15%). The generation and maintenance of downdrafts in the typhoon eye area can be forced dynamically

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and affected by the cloud and precipitation processes. When the cooling effect due to the evaporation of rain water is excluded at lower levels, the unstability at all levels is increased and strong eyewall updrafts can force the formation of intense compensation downdrafts in the eye area. However, the condensational heating in the upper troposphere in WRM can reduce and even offset downdrafts when the ice-phase microphysical process is not considered. Franklin et al. (2005) pointed out that the major difference between the model precipitation and the observation was the lack of large regions of stratiform rainfall. Fortunately, a larger stratiform region is obtained in this paper by reducing the terminal velocity of graupel. In addition, the precipitation rate is closer to the reality than that in the control experiment when the terminal velocity is halved. Does this indicate that the terminal velocity of the hydrometeor is overestimated in the Wang’s cloud microphysical parameterization? We still need a large number of case studies to verify this hypothesis. Acknowledgments. The authors are grateful to Prof. Zhou Xiaoping at IAP/CAS, Prof. Yuqing Wang at Hawaii University, Prof. Xu Huanbin at Beijing Institute of Applied Meteorology, Prof. Zhou Tianjun at LASG/IAP/CAS, and Prof. Guosheng Liu at FSU for their helpful suggestions and comments in the typhoon simulation and manuscript writing. We thank Mrs. Sun Jiao at Beijing Institute of Applied Meteorology for English polishing. REFERENCES Brown, P. R. A., and H. A. Swann, 1997: Evaluation of key microphysical parameters in three-dimensional cloud-model simulations using aircraft and multiparameter radar data. Quart. J. Roy. Meteor. Soc., 123, 2245–2275. Franklin, Charmaine N., Greg J. Holland, and T. Peter, 2005: Sensitivity of tropical cyclone rainbands to ice-phase microphysics. Mon. Wea. Rev., 133, 2473–2493. Ghan, S. J., J. W. Lingaas, M. E. Schlesinger, R. L. Mobley, and W. L. Gates, 1982: A documentation of the OSU two-level atmospheric general circulation model. Report No. 25, Climate Research Institute,

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