Vol.16 No.4 JOURNAL OF TROPICAL METEOROLOGY December 2010

Article ID: 1006-8775(2010) 04-0390-12 A SENSITIVITY SIMULATION ABOUT CLOUD MICROPHYSICAL PROCESSES OF CHANCHU

1 1 1 2 LIN Wen-shi (林文实) , WU Jian-bin (吴剑斌) , LI Jiang-nan (李江南) , LIANG Xu-dong (梁旭东) , FANG 1 1 Xing-qin (方杏芹) , XU Sui-shan (徐穗珊)

(1. School of Environmental Sciences and Engineering, Sun Yat-sen University, Guangzhou 510275 ; 2. Typhoon Institute, China Meteorological Administration, Shanghai 200030 China)

Abstract: With the Reisner-2 bulk microphysical parameterization of the fifth-generation Pennsylvania State University–U.S. National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5), this paper investigates the microphysical sensitivities of . Four different microphysical sensitivity experiments were designed with an objective to evaluate their respective impacts in modulating intensity forecasts and microphysics budgets of the typhoon. The set of sensitivity experiments were conducted that comprised (a) a control experiment (CTL), (b) NEVPRW from which evaporation of rain water was suppressed, (c) NGP from which graupel was taken, and (d) NMLT from which melting of snow and graupel was removed. We studied the impacts of different cloud microphysical processes on the track, intensity and precipitation of the typhoon, as well as the kinematics, thermodynamics and vertical structural characteristics of hydrometeors in the inner core of the typhoon. Additionally, the budgets of the cloud microphysical processes in the fine domain were calculated to quantify the importance of each microphysical process for every sensitivity experiment. The primary results are as follows: (1) It is found that varying cloud microphysics parameters produce little sensitivity in typhoon track experiments. (2) The experiment of NGP produces the weakest storm, while the experiment of NMLT produces the strongest storm, and the experiment of NEVPRW also produces stronger storms than CTL. (3) Varying parameters of cloud microphysics have obvious impacts on the precipitation, kinematics, and thermodynamics of the typhoon and the vertical structural characteristics of hydrometeors in the typhoon’s inner core. (4) Most budgets of cloud microphysics in NMLT are larger than in CTL, while they are 20%–60% smaller in NEVPRW than in CTL. Key words: Typhoon Chanchu; cloud microphysics; simulation CLC number: P444 Document code: A doi: 10.3969/j.issn.1006-8775.2010.04.011

1 INTRODUCTION 10, 11] was developed approximately 20 years ago when coarser resolution models were in use, and it is not In recent years, it has become more and more known whether these observations are still popular to use explicit cloud microphysics in [3] [1-3] representative of tropical cyclones. Recently, Wang modeling tropical cyclones . In high-resolution conducted a series of sensitivity experiments with models, bulk microphysical parameterization (BMP) idealized initial conditions to study the effects of schemes play a particularly important role in the varying cloud microphysical processes on tropical model-produced quantitative precipitation forecast cyclones, and found that although neither the (QPF)[4]. Braun and Tao[2] modified the Goddard [5] intensification rate nor intensity of the typhoon is microphysical scheme in their simulations of sensitive to the parameterizations of cloud Hurricane Bob to have more accurate representation microphysics, the cloud structures and the peak and of conditions observed in tropical cyclones. [6] area coverage of precipitation in the simulated tropical McFarquhar and Black pointed out that the majority cyclone are quite sensitive to the details of the cloud of current microphysical schemes are based on microphysics parameterization in the model. microphysical observations obtained in midlatitudes[7, 8] [9, Sensitivity studies of numerically simulated tropical . The basis for most bulk parameterization schemes cyclone convection to ice-phase microphysical

Received date: 2010-01-11; revised date: 2010-08-30 Foundation item: National Science Foundation of China (40775066); Shanghai Typhoon Research Foundation (2008ST07) Biography: LIN Wen-shi, associate professor, Ph.D., mainly undertaking the research with numerical simulation. E-mail for corresponding author: [email protected] No.4 LIN Wen-shi (林文实), WU Jian-bin (吴剑斌) et al. 391 parameters showed that the model was sensitive to microphysical budget, which is different from the changes in the graupel fall speed parameters[12]. Zhu previous works. and Zhang[13] (hereafter referred to as ZZ) found that The remainder of this paper is organized as varying cloud microphysics processes produce little follows. Section 2 describes the setup of sensitivity sensitivity in hurricane track, except for very weak experiments. Section 3 describes the simulation and shallow storms, but they produce pronounced results and impacts of microphysics on the track, departures in hurricane intensity and inner-core intensity, precipitation, propagation speed, kinematics structures. McFarquhar et al.[14] indicated that the and thermodynamics, vertical structural characteristics, application of bulk ice microphysics in cloud models and the microphysical processes of simulated might be case specific, and microphysical sensitivity Typhoon Chanchu. The significance of the results is studies for other cloud systems may not apply to summarized in section 4. hurricanes. They conducted simulations of Hurricane Erin (2001) with MM5 to examine roles of 2 SYNOPTIC OVERVIEW OF TYPHOON microphysical, thermodynamic, and boundary-layer CHANCHU processes in hydrometeor distributions and in the structure and evolution of Erin and showed that the Typhoon Chanchu formed in the western Pacific simulated intensity of Erin is insensitive to the choice on 8 May, 2006 about 420 km west-southwest of Yap of microphysical parameterization schemes and Islands and acquired the name Chanchu at 1200 coefficients used to describe graupel fall velocities. (Coordinated Univeral Time, UTC hereafter) on 9 [15] Pattnaik and Krishnamurti (hereafter referred to as May. At 0600 UTC 10 May, it continued to intensify PK) studied the impacts of cloud microphysical into a tropical storm as it tracked predominantly processes on the track, intensity, precipitation, west-northwestward along the southern periphery of a propagation speed, kinematics and thermodynamics of subtropical ridge. It was upgraded to a typhoon at the typhoon, and vertical structural characteristics of 1800 UTC 10 May. Chanchu made its first in the hurricane inner core. Their major findings are that the on 11 May with sustained winds of the inter-conversion processes such as melting and around 33 m s-1. It then moved towards the northwest evaporation among hydrometeors and associated after coming ashore, passing to the southeast of feedback mechanisms significantly modulate the . Tracking mainly towards the west-northwest, intensity of the hurricane. Therefore, an evaluation of it traversed the central Philippines and re-strengthened the impact of cloud microphysics parameterization on despite the hindering effects of land. The storm structure and intensity in a emerged into the early on 13 May three-dimensional tropical cyclone model is required. where it slowly began to reorganize. Drifting Although these previous works have also westwards, Chanchu underwent demonstrated that varying cloud microphysics over the South China Sea on 14 May. It abruptly processes have impacts on the track, intensity, veered north at 0000 UTC 15 May. It remained a structure, surface precipitation, and the hydrometeor powerful system for the next two days while moving [3, 13, 14, 15] profiles of the tropical cyclone , the on a north to north-northeasterly trajectory through variations of the cloud microphysical processes affect the South China Sea towards the Chinese coast, away first the production and depletion of a specific from . The typhoon made landfall again hydrometeor category, the size distribution and fall near , province, late on 17 May speed of the hydrometeors, and then the release of with the maximum surface wind (MSW) estimated at latent heat, and ultimately the thermodynamics and 38 m s-1. Chanchu continued its northward movement dynamics fields and the strength of the storm. along the coastal areas of neighboring province Therefore, it is necessary to calculate the conversion during daytime on 18 May, bringing gale-force winds rate of a specific microphysical process in simulating and rainstorms to the regions and triggering flooding, tropical cyclones in order to assess the sensitivity of mountain torrents and landslides. It had weakened to a these simulations to variations in some of the tropical storm at 0000 UTC, 18 May. That evening, it underlying assumptions in the cloud microphysics entered the and became an scheme. In this paper the authors not only investigate . impacts of different cloud microphysics parameters on the track, intensity, structure, surface precipitation, 3 SETUP OF SENSITIVITY EXPERIMENTS and the hydrometeor profiles of the tropical cyclone, but also calculate the conversion rates of a specific The model to be used for the simulation is the microphysical process and compare among them in fifth-generation U.S. National Centers for different cloud microphysics parameters. The main Atmospheric Research (NCAR)/Penn State objective of this research is to investigate the role of non-hydrostatic primitive equation mesoscale model different uncertainties in the microphysical parameters. (MM5)[16, 17]. Several different options were employed, It modulates the typhoon intensity, especially

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392 Journal of Tropical Meteorology Vol.16 such as altering the types of grid spacing, the 16 May 2006 to 0000 UTC 17 May 2006. This time resolution, physical parameterization schemes, and duration covers the most intensive phase of Chanchu more importantly for this study, explicit cloud and it will help us to evaluate the impact of schemes. The model is set up with triply nested microphysical sensitivity on the intensification and domains (D01, D02, and D03) with horizontal grid microphysics budget during that period. The three spacing of 27, 9, and 3 km, respectively (Fig. 1). The other sensitivity experiments were carried out in all following physics options are used: the Reisner-2 the model domains by modifying different explicit moisture scheme[18], the medium-range microphysical parameters in the Reisner-2 explicit forecast (MRF) planetary boundary layer scheme[19], scheme as follows. NEVPRW: the evaporation of rain 5-layer simple soil model[20], NCAR Community water was not allowed, NGP: no graupel was allowed, Climate Model (CCM2) longwave and shortwave NMLT: the melting of snow and graupel schemes[21] and the surface energy budget for hydrometeors was suppressed. All these experiments calculating the ground temperature for all domains. In and respective modifications are illustrated in Table 1. addition, the Kain-Fritsch cumulus parameterization All experiments were carried out with identical initial scheme[22] is used for the two outermost domains boundary conditions and physics options except for (D01 and D02), whereas for the finest domain (D03) the changes made in the microphysical parameters of convection is assumed to be resolved reasonably well the Reisner-2 explicit cloud microphysical by the explicit microphysical parameterization scheme parameterization scheme[18]. and no cumulus parameterization scheme is used[23, Table 1. Design of the sensitivity experiments 24].

Experiment name Description of experiments

Control simulation with the CTL Reisner-2 bulk microphysical parameterization scheme As in CTL but the evaporation NEVPRW of rain water was not allowed

As in CTL but no graupel was NGP allowed As in CTL but no melting of NMLT snow and graupel hydrometeors were allowed.

Other sensitivity experiments, such as those without ice microphysics variables (NICE in ZZ) and with the addition of latent heat of fusion for phase Fig. 1. Nested domains for 27 km (D01), 9 km (D02), and 3 km changes occurring below the temperature of 0°C (D03) (NICE2 in ZZ), no melting of snow, graupel and cloud ice were allowed (NMLT3 in PK), doubled The initial and boundary conditions (atmospheric intercept parameter of snow (SNWI in PK), doubled variables, soil moisture and temperature) are intercept parameter of graupel (GRAI in PK), doubled interpolated from the U.S. National Center for fall speed of graupel (GFALL in PK), doubled fall Environmental Prediction (NCEP) final reanalysis speed of snow (SFALL in PK), and some sensitivity (FNL) data (http://dss.ucar.edu/datasets/ds083.2) experiments in Wang[3] and McFarquhar et al.[14], horizontally to the coarse model resolution (with a were not conducted. More comparison results of grid length of 27 km) and vertically to 34 sigma levels. sensitivity experiments can be seen in their papers. The datasets are at 6-hour intervals and with a horizontal grid resolution of 1°×1°. Surface and 4 RESULTS AND DISCUSSION rawinsondes conventional observation data were incorporated into the analysis using standard MM5 4.1 Track, intensity, and precipitation initialization procedure based on Cressman-type analysis technique[25]. A bogus vortex[26] was inserted Figure 2a shows a total of 60 hours of simulated for the coarsest domain. and observed track of the typhoon every 6 hours. In The control experiments were initialized in the general, the difference between the track of the four domains D01 and D02 at 1200 UTC 15 MAY 2006 experiments and the observation is small. The landfall and were integrated for 60 hours. The integrated location of NMLT was slightly westward than the period in the finest domain D03 was from 0000 UTC control run, but the landfall locations of NGP and

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No.4 LIN Wen-shi (林文实), WU Jian-bin (吴剑斌) et al. 393 NEVPRW were slightly eastward than the control run. agree with the observation within the first 48 hours. The track produced by the four experiments well

1000 60 CTL NEVPRW NGP NMLT CTL NEVPRW NGP NMLT 980 50 NMLT

960 NGP 40 NEVPRW CTL CTL NGP 940 NEVPRW 30 NMLT 920 20 Minimum Pressure (hPa) Pressure Minimum (c) (b) Maximum Sureface Wind (m/s) 900 10 12Z15 00Z16 12Z16 00Z17 12Z17 00Z18 12Z15 00Z16 12Z16 00Z17 12Z17 00Z18 Forecast Dates Forecast Dates

Fig. 2. (a) 6-hourly tracks of Typhoon Chanchu from the best analysis and the model simulations; (b) the minimum sea-level pressure (hPa); (c) the maximum surface wind (m s-1) for all the model simulations.

Figures 2b and 2c show the minimum sea-level observed precipitation larger than 250 mm is pressure (MSLP) and the MSW for all the model distributed mainly on the right of the track, whereas simulations of Chanchu. The simulated results show the control-run precipitation larger than 250 mm is that NMLT produces the most intense storm with distributed mainly on the left of the track. However, minimum sea-level pressure 10 hPa smaller and the spatial rainfall distribution of larger than 100 mm maximum surface wind speed 5 m s-1 higher than the produced from the control run is fairly consistent with control run. The simulated intensity of NEVPRW is that of the observed. It is obvious that the weaker than NMLT and stronger than CTL, and its accumulative precipitation in NMLT (i.e., no melting intensity decreases slowly. NGP produces the weakest of snow, graupel and cloud ice) is the largest (with the storm with minimum sea-level pressure 5 hPa higher maximum value being 550 mm) and its range of the and maximum surface wind speed 5 m s-1 smaller than spatial distribution of accumulative precipitation the control run. larger than 300 mm is also the largest in all the Figures 3a–3e show the total 60-hour rain rate sensitivity experiments. Moreover, the range of the (mm h-1) from the observation of the Tropical Rainfall spatial distribution of accumulative precipitation in Measuring Mission (TRMM) rainfall product NEVPRW (i.e., no evaporation of rainwater) larger (3B42RT) and four simulations for Chanchu, than 200 mm is the smallest in all the sensitivity respectively. The observed rainfall is based on the experiments. TRMM 3B42RT algorithm[27]. We note that the magnitude of precipitation estimates in the control run was somewhat larger than that of the observation. The

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a d

b e

c Fig. 3. The 60 hour accumulative precipitation (mm) of Typhoon Chanchu from 1200 UTC 15 May 2006 to 0000 UTC 17 May 2006 for (a) observed TRMM 3B42RT, and (b) control (CTL) forecast, (c) the NEVPRW experiment, (d) the NGP experiment, and (e) the NMLT experiment. The simulated tracks of the four experiments are shown in block black lines, respectively.

4.2 Vertical structure The cross sections of horizontal wind speed (Figs. 4a–4d) show the wall structure for the four simulated storms. The maximum wind speeds in the -1 four experiments (>40 m s ) occur to the east of the typhoon centre, and the horizontal wind speed on the western side is smaller than that on the eastern side. The horizontal wind speed in NMLT is larger compared to the other sensitivity experiments and vertically extends up to the level of 150 hPa, well on the eastern side of the eye wall. In addition, the vertical distribution of horizontal wind speeds in NMLT is less tilted upward than those in the other experiments. This situation is ideal to the maintainance and intensification of the typhoon, as shown in Figs. 2b and 2c. The weakest storm produced by the NGP experiment has a small area of

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No.4 LIN Wen-shi (林文实), WU Jian-bin (吴剑斌) et al. 395 maximum winds (>40 m s-1).

Fig. 4. East-west cross section of horizontal wind speed (m s-1) through the center of Typhoon Chanchu averaged between 0600 UTC 16 May and 1200 UTC 16 May, 2006 (3-hourly forecast intervals) for (a) CTL, (b) NEVPRW, (c) NGP, (d) NMLT

Figures 5a–5d show the averaged east-west cross magnitude of these updrafts varies in a range between sections for vertical velocity in the unit of m s-1. The 0.2 and 1.2 m s-1. We also note that at all vertical negative values of the vertical velocity or the levels, the intense updrafts are confined to a very downdraft region are shaded in the figures. It is narrow strip just about 10 km or less in width. evident that the intense storms had strong updrafts on Furthermore, there are downdrafts in the eye for the both sides of the eye wall, but the peak updrafts four experiments. occurred in the western segment of the eye wall. The

Fig. 5 Same as Fig. 3, but for vertical velocity (m s-1). Negative values are shaded. Contour interval is 0.4 m s-1.

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Figures 6a–6d show the 6-hour averaged (i.e., CTL, NEVPRW, NGP). The difference in east-west cross sections of temperature deviations in magnitude in the warm core temperature deviations K, for a control run, as well as for different between NMLT and CTL was around 2 K, whereas microphysical sensitivity experiments. We noted that between NGP and CTL was about -0.5 K. As far substantial variations in thermodynamic as the spatial distribution of heating is concerned, all characteristics of warm inner core structures due to simulated storms exhibited a broad strip of warming modifications made in the microphysical parameters. (>2 K) around the eye wall. These results are similar The magnitude of inner core warming in the NMLT to those explained in PK. was quite large compared to that of other experiments

Fig. 6. Same as Fig. 3, but for temperature anomaly. The contour interval is 0.5 K.

The 6-hour average cross sections of equivalent level in NMLT, there was a substantial accumulation potential temperature (θe) in K are described in Fig. of a greater graupel production and parts of graupel 7a–7d, respectively. There are high values of θe (>369 fall out on the ground. The graupel mixing ratio of the K) at the surfaces in the east-west cross sections for NEVPRW is smaller than the control run and the NGP the four experiments. There were higher values of θe experiment has no graupel at all. The rainwater (372–375 K) below the height of the 800-hPa layer in mixing ratio of the NMLT experiment is smaller than NMLT and NEVPRW compared to CTL. the control run because there is no conversion of graupel to rainwater in the NMLT experiment. The 4.3 Distribution of microphysical hydrometeors rainwater mixing ratio of the NEVPRW experiment is the smallest. Figures 8a–8d show the east-west cross sections Figures 9a–9d display the 6-hour averaged of 6-hour averaged distribution of graupel (contours) east-west cross sections of cloud ice (shaded), snow and rain water mixing ratio (shaded) in the unit of g -1 (solid contours) and cloud water mixing ratio (dotted kg . The results show that hydrometeors are around dash contours) in the unit of g kg-1. In all these the eye wall. It is apparent that in NMLT the graupel experiments we noted that cloud ice (shaded) was mixing ratio is quite large in the eye wall compared to mostly concentrated between the levels of 300 and the other experiments and vertically extends well up 150 hPa. The cloud ice mixing ratio of the NGP to the level of 150 hPa and down to below the zero experiment is the smallest (0.02 g kg-1) in all the asthermal level in the eye wall. Due to the lack of experiments (Fig. 9c). Similar to the distribution of melting of snow and graupel below the zero asthermal

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No.4 LIN Wen-shi (林文实), WU Jian-bin (吴剑斌) et al. 397 graupel (Fig. 8d), the snow mixing ratio of the NMLT the other experiments, there is a very large snow experiment is quite large in the eye wall compared to mixing ratio of the NGP experiment because there are the other experiments and vertically extends well up no graupel processes in it. The difference of the cloud to the level of 150 hPa and down to below the zero water distribution is not very large. However, there is asthermal level in the eye wall (Fig. 9d). Compared to some cloud water in the inner eye cores.

Fig. 7. Same as Fig. 3, but for equivalent potential temperature. The contour interval is 3 K.

Fig. 8. Same as Fig. 3, but for graupel and rainwater mixing ratio. The contour interval is 0.3 g kg-1.

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Fig. 9. Same as Fig. 3, but for cloud ice (shaded), snow (solid contours) and cloud water mixing ratio. The contour interval is 0.3 g kg-1.

4.4 Microphysical budgets CTL experiment, all the cloud microphysics processes in the Reisner-2 scheme were maintained. In the It is important to calculate the microphysical NEVPRW, the evaporation of rainwater in budget for the assessment of the impact of sub-saturated regions was excluded from the control microphysical processes on . Colle and simulation (revp=0 mm h-1). In the NGP experiment, Zeng[28] indicated that a comprehensive study is the prognostic equation for graupel was removed from needed to quantify the microphysical budget for a the control experiment, i.e., all the cloud microphysics bulk BMP in order to determine how a change to a processes about graupel were removed. In the NMLT BMP impacts the other microphysical processes. In experiment, the melting of snow and graupel to form order to determine which microphysical process rainwater as they fall through the zero isothermal contributes the most to the production and depletion layer is neglected (gmlt=0 mm h-1, and smlt=0 mm of a specific hydrometeor category, the vertically h-1). There is fallout of rainwater, snow, and graupel integrated hydrometeor conversion rate Pq is used in the NMLT experiment because the melting of snow for a model microphysical budget. It is calculated by and graupel through the zero isothermal layer is * neglected. The fallout of total hydrometeors of the Pq = ∑ p ()i, j ×Pq (i, j, k )× Δσ ()k i, j,k NMLT experiment is the largest -1 where p* is the pressure difference between the (rprc+sprc+gprc=5.93 mm h ) and that of the NEVPRW is the smallest (3.56 mm h-1). surface and model top, is the conversion Pq (i, j, k ) In Fig. 10, the condensation rate is 3–5 times rate of a specific microphysical process averaged for larger than the evaporation rate although the two adjacent sigma levels, and Δσ is the sigma evaporation rate is larger than other rates of level difference. hydrometeor conversion. The condensation rate in -1 In this study, the conversion rate of the vertically NMLT (cond=8.78 mm h ) is the largest, and that in -1 integrated hydrometeor for each term in the NEVPRW (cond=4.37 mm h ) is about 50% smaller prognostic equations of each hydrometeor species in than that for the other three experiments. The -1 Reisner-2 scheme[28, 29] was output once every 1 hour. evaporation rate in NMLT (evap=1.81 mm h ) is Its unit is mm h-1. Figure 10 shows the vertically slightly smaller than in CTL and NGP, and that in -1 integrated cloud microphysics budgets for all the NEVPRW (evap=1.38 mm h ) is the smallest in the simulations in the domain D03 from 0600 UTC to four experiments. 1200 UTC 15 May. According to our design, in the

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a

CTL W.V. id 3 ep .9 =0 =1 .14 ap 2 v .4 id e 8 is sn d= ub =0 on =0 .00 c .02

ihfz=0.00 iiacw=0.00 ifzc=0.00 C.W. C.I. g d 0 imlt=0.00 e .1 g p 0 s = = g m u 0 s iac . n w lt b 1 =0 g = 5 ic .01 e 0 0 = .0 2 .0 gg 0 6 0 0 a sdep=1.09 . . cw 0 0 = =0 3 = ls s .9 i p s 5 c s a gs a I 7 c ac s 9 w w . =0 0 1 = .2 0 = 0 3 . rgacw=0.17 rgacw=0.17 rsacw=0.01 racw=5.20 ccnr=0.05 p .2 0 v 9 i= Isplg=0.00 e c Icng=0.01 graci=0.00 r ra

ssub=0.39 ssub=0.39 mltev=0.00 s giacr=0.05 gsacr=0.83 gacr=7.53 gfr=0.00

Rain ra Graupel cs= gmlt=1.42 gacrm=0.25 gacwm=0.00 0.8 1 s sm sac lt= r=0 0.1 .06 .3 1 s=0 4 s rac ia g cr= .11 0.0 g=0 1 Snow scn rprc=5.65

b NEVPRW W.V. i 8 de .3 p= =1 0.1 ap 7 7 v .3 i e 4 is ds d= ub n= on =0 0.0 c .02 0

ihfz=0.00 iiacw=0.00 ifzc=0.00 C.W. C.I. g d 0 imlt=0.00 e .1 g p 0 s = g m u 0 = ia . s cw lt b 1 n =0 g = 0 c .0 e 0 i 0 4 . 0 = 0 6 . gg 0 6 0 a sdep=0.69 . 0 cw 0 . = =0 4 0 ls s .6 i= p s 4 c s a gs a I c ac s w w =0 0 0 = .12 0 = 0 . rgacw=0.08 rgacw=0.08 rsacw=0.00 racw=2.15 ccnr=0.03 p .1 0 v 9 i= Isplg=0.00 e c Icng=0.00 graci=0.00 r ra

ssub=0.41 ssub=0.41 mltev=0.01 s giacr=0.04 gsacr=0.63 gacr=4.08 gfr=0.00

Rain ra Graupel cs= gmlt=0.88 gacrm=0.15 gacwm=0.00 0.5 0 s sm sa lt= cr= 0. 03 0.1 07 =0. 9 acs si gr ac 06 r=0 =0. .01 cng rprc=3.56 Snow s

Fig. 10 Simulated cloud microphysics budgets averaged between 0600 and 1200 UTC 16 May, 2006 for the domain D03 for (a) CTL and (b) NEVPRW. Their unit is mm h-1. Microphysical processes greater than 1 mm are in bold. Panels for NGP and NMLT are omitted.

Collection of rain by graupel is the main process vertical structural characteristics of hydrometeors in of graupel growth. It is found that collection of rain the inner core. Most budgets of cloud microphysics in by graupel in NMLT (gacr=45.38 mm h-1) is much NMLT are larger than in CTL, while most budgets of larger than that for the other three experiments cloud microphysics in NEVPRW are about 20%–60% because there is higher graupel mixing ratio below the smaller than in CTL. This study indicates that graupel zero asthermal level in NMLT. So is the collection of hydrometeor, cloud processes of melting of snow and rain by snow in NMLT (ssacr=38.92 mm h-1, graupel and evaporation of rain water cannot be gsacr=57.92 mm h-1). The microphysical processes of neglected. However, a number of uncertainties may graupel in NGP are equal to 0 because there is no still remain within the explicit microphysical schemes graupel in it. Collection of rain by snow in NGP is due to the lack of observations needed to attune these larger (ssacr=1.67 mm h-1) than in CTL (ssacr=0.34 sophisticated explicit moisture parameterization mm h-1) and NEVPRW (ssacr=0.19 mm h-1) because schemes. Future model simulations with finer there is a very large snow mixing ratio in NGP (Fig. resolutions, bin-resolved or multi-moment 7c). Collection of snow by rain in NGP (racs=2.50 microphysics should also help acquire a better mm h-1) is also larger than in CTL (racs=0.81 mm h-1) understanding of typhoons. and NEVPRW (racs=0.50 mm h-1). Accretion of cloud water by rainwater in NGP Acknowledgement: The authors would like express their (racw=5.32 mm h-1) is larger than in CTL (racw=5.20 appreciation to the Network and Information Technology mm h-1), and that in NEVPRW (racw=2.15 mm h-1) is Center at Sun Yat-sen University for providing computer resources needed in the research. the smallest. Evaporation of rainwater in NGP (revp=2.05 mm h-1) is the largest in the four experiments, whereas there is no evaporation of REFERENCES: rainwater in NEVPRW. Snow deposition in NMLT (sdep=1.34 mm h-1) is the largest and that in [1] LIU Y B, ZHANG D L, YAU M K. A multiscale numerical -1 study of Hurricane Andrew (1992). Part I: Explicit simulation NEVPRW (sdep=0.69 mm h ) is the smallest. and verification [J]. Mon. Wea. Rev., 1997, 125: 3 073-3 093. Other microphysical processes of cloud ice, such [2] BRAUN S A, TAO W K. Sensitivity of high-resolution as the riming of cloud ice, ice multiplication process, simulations of Hurricane Bob (1991) to planetary boundary initiation of cloud ice, melting of cloud ice, and layer parameterizations [J]. Mon. Wea. Rev., 2000, 128: 3 hetero/homogeneous freezing of cloud droplets, are 941-3 961. very small and can be neglected. [3] WANG Y. An explicit simulation of tropical cyclones with a triply nested movable mesh primitive equation model-TCM3. 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Citation: LIN Wen-shi, WU Jian-bin, LI Jiang-nan et al. A sensitivity simulation about cloud microphysical processes of typhoon Chanchu. J. Trop. Meteor., 2010, 16(4): 390-401.

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