Utilization of spectral bin microphysics and bulk parameterization schemes to simulate the cloud structure and precipitation in a mesoscale rain event

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 112,, doi: /2007jd008475, 2007 Utilization of spectral bin microphysics and bulk parameterization schemes to simulate the cloud structure and precipitation in a mesoscale rain event B. Lynn 1 and A. Khain 1 Received 30 January 2007; revised 4 May 2007; accepted 10 July 2007; published 29 November [1] Sea breeze convection in Florida on 27 July 1991, accompanied by squall line formation, was simulated using MM5 with various microphysical schemes, including the Hebrew University spectral (bin) microphysics (SBM) and three recently developed bulk model parameterizations. The bulk schemes are the Seifert full two-moment scheme (FTMS), the Reisner-Thompson two-moment ice scheme (TMIS), and the Thompson two-moment ice scheme. The results were evaluated using observed rainfall and radar reflectivity, including radar derived contour frequency with altitude diagrams (CFAD). The SBM simulated quite well the time evolution of average and maximum rainfall amounts. A comparison of a CFAD derived from observations and CFADs derived from model calculated radar reflectivity suggests that the SBM simulates the three-dimensional structure of squall line convection and stratiform mixed phase cloud more realistically than the bulk parameterization schemes. However, the Thompson scheme shows a qualitative improvement over the other bulk parameterization schemes in the simulation of the three-dimensional structure of the squall line as indicated by comparison of its CFAD with the observed. All of the new bulk models simulate precipitation better than the earlier bulk parameterization schemes, but each still produces too much precipitation during too short periods of time and underestimates the area covered by stratiform clouds. Citation: Lynn, B., and A. Khain (2007), Utilization of spectral bin microphysics and bulk parameterization schemes to simulate the cloud structure and precipitation in a mesoscale rain event, J. Geophys. Res., 112,, doi: /2007jd Introduction [2] Colle and Mass [1996] and Bruintjes et al. [1994] documented marked improvements in the ability of highresolution mesoscale models to accurately simulate the evolution of the thermodynamic and dynamic fields over complex mountainous terrain. Yet, Colle et al. [1999] and Colle et al. [2000] noted significant deficiencies in the precipitation forecasts by the same models, associated with both synoptically forced and topographically enhanced precipitation. A field study called Improvement of Microphysical Parameterization through Observations Verification Experiment (IMPROVE) took place during November and December 2001 over the Oregon Cascades [Garvert et al., 2005a; Locatelli et al., 2005]. Its purpose was to gather detailed observations that could be used to improve bulk model parameterizations, used, for example, in the Mesoscale Modeling System Version 5 (MM5) [Dudhia, 1993; Grell et al., 1994]. Although the MM5 simulated the thermodynamic and dynamic forcing well, it experienced deficiencies in its simulation of in-cloud 1 Department of Atmospheric Sciences, Hebrew University of Jerusalem, Jerusalem, Israel. Copyright 2007 by the American Geophysical Union /07/2007JD microphysical processes using bulk parameterizations [Evans et al., 2005; Garvert et al., 2005b]. [3] An alternative to bulk microphysical parameterization is Spectral (bin) Microphysics (hereafter, SBM [Khain et al., 2004, 2005; Lynn et al., 2005a], which simulates the size spectra of liquid and ice particles. Recent upgrades in computer resources enabled the use of SBM in a threedimensional (3-D) mesoscale model at high horizontal grid resolution (ranging from 1 to 3 km). The SBM was used in MM5 to simulate a developing sea breeze in the Florida Panhandle [Lynn et al., 2005a, 2005b]. The SBM more realistically simulated hourly average and maximum precipitation amounts, and extent of cloud cover when compared to the results using the bulk schemes. The SBM also had overall smaller differences between calculated and observed horizontally planer radar reflectivity than the bulk models. SBM was recently also implemented into the Weather Research and Forecasting Model (WRF [Skamarock et al., 2001]) and used to simulate the sensitivity of orographic precipitation to aerosol concentration [Lynn et al., 2007]. [4] Kessler [1969] proposed the first bulk parameterization scheme. Since that time, much efforts have been dedicated to improving the skill of bulk parameterization schemes to simulate cloud processes and precipitation in models of different scales. Recent observational [e.g., Rosenfeld, 2000; Givati and Rosenfeld, 2004; Andreae et 1of13

2 al., 2004; Jirak and Cotton, 2006] and numerical [e.g., Khain et al., 2005; Lynn et al., 2005a, 2005b; Van den Heever et al., 2006; Wang, 2005] findings also indicate significant aerosol effects on cloud microphysical structure and precipitation, including hail. Hence recent improvements include both an increase in the number of hydrometeors types (up to five to include hail) and the number of moments of size distribution treated in the schemes. [5] As a result of such improvements, some bulk parameterization schemes have become quite comprehensive and contain ten times more variables than the first generation microphysical schemes. It should be noted that implementation of two moments into bulk parameterization scheme does not lead automatically to an increase in their ability to reproduce microphysical processes more accurately. These schemes face new problems as compared with one-moment schemes. For instance, different settling velocities for mass and concentration may lead to the result that in some grid points there is only hydrometeor mass, but no concentration, while in other grid points there might be concentration but no content (or unrealistically small hydrometeor content). As a result, two-moment schemes include additional assumptions and intuitive bounding values imposed on their masses, concentrations, etc. It is beyond the framework of the paper to present a detailed description of problems that bulk microphysical schemes still face. These include the problem of droplet nucleation (including in-cloud nucleation), which requires an aerosol budget and the calculation of drop nucleation based on the aerosol budget and super saturation. There are also difficulties in describing processes highly depending on particle size (e.g., freezing, collisions, sedimentation). Each of the processes affects the tails of particle distributions (e.g., freezing should lead to a cutoff of the tail of raindrops and create a corresponding tail in the hail distribution) in real clouds and require, in principle, knowledge of higher moments. [6] At the same time SBM schemes still use about 10 times more variables as compared to the new bulk schemes because a mass grid containing several tens of bins describes each size distribution. As a result, SBM still requires times more computer time than the stateof-the-art bulk parameterizations. The computer time requirements in SBM case are partially compensated by the necessity to often perform intensive tuning of bulk schemes parameters to adjust simulation results to observations. The SBM, which is based on the first principles, does not need such tuning, but just the utilization of sounding and aerosol data as input. [7] The differences in SBM and bulk parameterizations reflect the differences in original purposes of these approaches: while bulk parameterization schemes aimed at improving the representation of convection in highresolution mesoscale models (correspondingly, cloud properties were prescribed by choice of the size distributions shape), SBM schemes have been developed for the investigation of cloud processes and precipitation formation, so that particle size distributions are the main model output. Thus, when simulating one or another atmospheric phenomenon, a scientist has to choose between more detailed SBM and less time-consuming bulk parameterization schemes. In order to make such a decision, a comparison between results obtained using these schemes is required. [8] In this paper, we compare MM5-SBM to more recently developed bulk microphysical schemes. The first bulk scheme is a fully two-moment bulk scheme (hereafter, FTMS) detailed by Seifert and Beheng [2005a, 2005b], and the second is the two-moment ice (particle) scheme of Reisner et al. [1998] and Thompson et al. [2004]. This scheme is referred to as the TMIS. The third scheme is based on the Reisner-Thompson scheme, but has been substantially modified. At the moment, it is still only a two-moment scheme with regard to ice particles (although a second moment for cloud water will soon be added; G. Thompson, personal communication, 2007). It is referred to as the Thompson scheme [Thompson et al., 2006]. The FTMS is being developed for the German Forecast model. The Thompson scheme has been implemented in MM5 and WRF and is being used in the Rapid Update Cycle forecast model to produce aviation forecasts for the Continental United States. [9] In this study, the comparison is between simulated versus observed rainfall data for the same rain event accompanied by a squall line formation described by Lynn et al. [2005b], and horizontal cross sections of radar observations with calculated radar cross sections from the model simulations. We also compare a calculated contour frequency with altitude diagram from the squall line (CFAD [Yuter and Houze, 1995]) with those calculated from the radar results obtained from SBM and the bulk parameterizations. Fourth, we contrast vertical cross sections of simulated cloud masses from the SBM and bulk schemes. These comparisons allow us to evaluate how the models simulated the vertical structure of precipitation within the squall line in the presence of both convective and stratiform clouds. 2. Model Descriptions 2.1. Spectral Bin Microphysics Model [10] The coupling of SBM to MM5 uses the original microphysical package contained within the Hebrew University Cloud Model (HUCM [Khain and Sednev, 1996; Khain et al., 2004]). Equations of size distribution functions are solved for water drops, cloud condensation nuclei, ice crystals, aggregates, graupel and hail/frozen drops. Each is defined the same as in the works by Berry and Reinhardt [1974] and Bott [1998], and is represented by 33 mass doubling categories (bins), where mass m k in the category k is determined as m k =2m k-1, where k = 2,..., 33. The minimum mass in the hydrometeor mass grids (except aerosols) corresponds to that of a 2 mm radius droplet. The mass grids used for hydrometeors of all types are identical. This simplifies calculations concerning interactions between hydrometeors of different bulk densities. [11] The model also calculates size distribution of dry aerosol particles (AP). The AP size distribution also contains 33 mass bins with the maximum size of AP of 2 mm. The spectrum of AP changes when some fraction of aerosols is activated and becomes cloud droplets. Growth/ evaporation of drops and deposition/sublimation of ice particles are described by simultaneous solving the equation for diffusion growth and the equation for super saturation 2of13

3 Figure 1. Model geometry used in simulations. with respect to water and ice. The model also includes ice particle nucleation [Meyers et al., 1992; Takahashi et al., 1991], freezing [Vali, 1975, 1994; Pruppacher, 1995], melting, and secondary ice generation [Hallett and Mossop, 1974]. An efficient and precise method for solving the stochastic kinetic equation for droplet collisions [Bott, 1998] was extended to a system of stochastic kinetic equations that are used to calculate water-water, water-ice, and ice-ice collisions. To take into account the effect of turbulence on drop-drop collision, the collision rate between droplets in convective clouds was increased following Pinsky and Khain [2002]. Changes in the droplet size distribution (DSD) due to breakup are represented as described by Low and List [1982] and Seifert et al. [2005]. [12] The microphysical processes are calculated with time steps smaller than the characteristic times of corresponding processes, for instance, the dynamical time step. The shape of DSD changes with time. At low temperatures, the processes of freezing and riming affect the shape of the DSD making it (and distributions of ice hydrometeors) different from the standard gamma function used in even higher order schemes [e.g., Milbrandt and Yau, 2005]. The Figure 2. Radar observations from WSI data for 27 July of13

4 Figure 3. Mirror east to west image of radar reflectivity form squall line moving toward the left at 2130 UTC [after Halverson et al., 1996]. advection of size distribution functions uses the standard MM5 code written for advection of integral contents and other parameters. Note that the advection of one size distribution corresponds to the advection of the 33 fields corresponding to each bin. Regarding sedimentation, particles of different mass and type fall with different terminal velocities, and the height dependence of terminal velocities is also taken into account. All integral parameters (moments of size distributions) such as concentrations, mass contents, radar reflectivity, etc. are calculated using the size distributions Reisner-Thompson Bulk-Parameterization Scheme [13] The Reisner-Thompson scheme (or TMIS) modified the well-known Reisner2 scheme. In TMIS, ice nucleation by deposition and condensation freezing is described on the basis of results of Cooper [1986] rather than on the Fletcher [1962] curve as in Reisner2. Cooper s curve generally gives fewer nucleated particles than Fletcher s at low temperatures, nearly the same number of ice particles at approximately 20 C, and more ice at higher temperatures. In the Reisner2 scheme, ice was allowed to initiate as soon as ice saturation was attained for all temperatures less than 0 C. In TMIS, ice forms at air temperature below 5 C in cases of super saturation with respect to ice greater than 5%. However, as in Reisner2 scheme, ice continues to be formed by freezing of cloud droplets (T < 5 C) and by a secondary ice production method [Hallett and Mossop, 1974]. Whereas the Reisner2 scheme uses a mass-dependent intercept parameter for snow number concentration, TMIS used a temperature-dependent intercept for snow number concentration. [14] Additional modifications from Reisner2 include the representation of warm-rain processes. In place of the Kessler autoconversion used in Reisner2, TMIS uses a Berry and Reinhardt [1974] parameterization similar to Walko et al. [1995]. This allows more explicit treatment of the cloud droplet number concentration when converting cloud water into rain. Figure 4. Accumulated rainfall obtained from Portable Automated Mesonet (PAM) sites (pluses) and NCDC observing stations (diamonds) on 27 July 1991 between 2100 and 2400 UTC. The analyses were produced using a Barnes interpolation [from Lynn et al., 2001]. Figure 5. Reflectivity CFAD from 27 July 1991, 2320 UT. 4of13

5 Figure 6. Radar observations from WSI data for 27 July 1991 at 2300 UT and calculations using different microphysical schemes. [15] A special intercept parameter is introduced into the new scheme allowing one to shift the droplet spectrum distribution to larger or smaller sizes, which controls the fall velocities of raindrops depending on raindrop mass. In TMIS (as in Reisner2), the effects of aerosols are taken into account indirectly by prescribing different cloud droplet concentrations Thompson Bulk Parameterization Scheme [16] The Thompson scheme [Thompson et al., 2006] was adopted from the Reisner-Thompson and is designed to mimic processes currently only described using SBM type schemes. For example, the treatment of hydrometeor collection of two species that results in a third is obtained by summing both the collected and collector species to get the new mass. Additionally, the probability of certain volume of drops freezing at specified temperatures is precomputed and stored in a lookup table. Larger raindrops freeze into graupel whereas the smaller cloud droplets freeze into cloud ice. The fraction of ice mass with particle diameters greater than 125 microns is immediately transferred to the snow category. The new TMIS scheme also utilizes a look-up table with 100 size bins of rain and snow. In the case of rain collecting snow (and its inverse), the scheme scans the snow size bins in the table and if the mass of the water drop exceeds the mass of the snow particle, it is assumed that the two particles join as one thus freezing the drop into graupel. If, on the other hand, the water drops mass is less than the snow particle mass, the snow simply accretes the water drop, thus increasing the snow mass and decreasing the rain mass Fully Two-Moment Microphysical Scheme (FTMS) [17] The FTMS predicts the mixing ratio and number concentration of most liquid and ice species (cloud water, rain, cloud ice, snow and graupel). By predicting the number concentration, assumptions about the intercept parameter of the size distribution are no longer necessary. The scheme uses the autoconversion scheme of Seifert and Beheng [2001], tuned to the results of a spectral bin model. FTMS uses different number and mass-weighted fall speeds for particle sedimentation. The mean mass contents and 5of13

6 Figure 7. Observed and simulated averaged and maximum rain rate on 27 July 1991 from 51 stations (or closest model grid elements to these stations). mean concentrations sediment with different velocities. In contrast to the TMIS, the FTMS uses Meyers et al. s [1992] scheme for heterogeneous nucleation, i.e., the number of ice nucleated as a function of temperature and ice supersaturation. With this scheme a reasonable agreement was achieved in a comparison with a spectral bin microphysics model [Seifert et al., 2006]. [18] In FTMS, it is assumed that cloud condensation nuclei (CCN) are represented by an infinitely large source of aerosols, and cloud droplet concentration is limited by the value of super saturation only. In all bulk schemes discussed here, the growth of droplet mass is calculated assuming that super saturation arising after advection and ice growth is removed immediately to create cloud droplets. 3. Design of Experiments 3.1. Model Setup [19] Lynn et al. [2005b] described in detail the setup of the simulations produced here. The observed wind field on the day simulated had a trajectory from the Gulf of Mexico, suggesting that the initial aerosol concentration was more likely maritime than continental. Therefore the results from each model were produced using maritime settings. For SBM, the initial size distribution of CCN was calculated using a well-known expression: N CCN = N 0 S k, where S is the super saturation in % (maritime: N 0 = 150 cm 3, k = 0.462) using the approach described by Khain et al. [2000]. Using a semiempirical dependence, the aerosol size distribution can be calculated as: dn CCN dr N = N 0 ks k 1dr N ds, where r N is the radius of dry aerosol particles. The derivative dr N ds was determined using the Kohler theory. Aerosol particles are assumed to be soluble and consist of NaCl. The approach allows the calculation of size distributions under any other chemical composition of aerosols. It should be mentioned that all size distributions should be quite similar because of very weak dependence of dr N ds on the chemical composition of aerosols [Khain et al., 2000]. [20] The maximum size of dry aerosol particles was assumed equal to 2 mm. The largest dry CCN produce nucleated droplets with radius of about 8 mm. At t > 0 the size distribution of aerosols changes because of drop nucleation and advection. The nucleation of specific aero- Table 1. Accumulated Rainfall Observed and Obtained in Using Different Microphysical Schemes Accumulated Rainfall, cm [Lynn et al., 2005b] Accumulated Rainfall, cm (This Study) Observed Microphysical scheme Reisner GSFC 2.65 Schultz 1.94 Warm 1.34 SBM 1.43 SBM 0.83 Reisner-Thompson 1.28 Thompson 1.38 FTMS 1.52 Figure UT. CFAD calculated with SBM radar reflectivity at 6of13

7 Figure 9. CFADs calculated from bulk models reflectivity for 27 July 1991, 2330 UT. sols within the aerosol spectrum depends on the super saturation, determining the critical aerosol particle size. Aerosols with sizes exceeding the critical one are activated to droplets. Corresponding bins in the aerosol size distribution become empty. Taking into account that the convection simulated takes place within a wet environment, most raindrops reach the surface without full evaporation. Therefore we neglect the release of aerosols back to the atmosphere by full drop evaporation. [21] In TMIS and Thompson, there is no aerosol concentration per se. Rather, one sets the number of cloud droplets. In the simulations, this parameter was set to 100 cm 3, which corresponded roughly to the cloud droplet concentrations simulated with SBM near cloud base (not shown). [22] In the FTMS scheme, the equation N CCN = N 0 S k is used to calculate the concentration of CCN (i.e., nucleated droplets), where N o is a time-independent coefficient. The parameters set for a maritime environment were N o = 150 cm 3 and k = The values of N o also gave similar initial ranges of cloud number concentrations near cloud base as obtained with SBM (not shown). [23] The MM5 model grids consisted of a coarse domain with 9 km grid spacing and a nested domain with 3 km spacing centered over Florida (Figure 1). To save computational time, Lynn et al. [2005b] implemented SBM on an inner core of grid elements surrounded by an outer band that used bulk microphysics. In the present study the SBM microphysics was implemented on all grid points within the nested domain. The lateral boundary conditions were updated using bulk hydrometeor values as described by Lynn et al. [2005a]. The time period 0000 UT 27 July to 0100 UT 28 July has been simulated at 9-km-resolution grid and the output from the 9 km domain was used to nest down to the 3 km grid for the time period 1000 UT 27 July to 0100 UT 28 July. The time step was 27 s for the 9 km grid and 9 s for the 3 km grid Observed Data [24] Halverson et al. [1996], Lynn et al. [2001], Baker et al. [2001], and Lynn et al. [2005a] each discuss in various detail the convective development that occurred on 27 July Here, we give a very brief description, and introduce an additional figure for later comparison with model simulation results. [25] The initial atmospheric conditions across the Florida Peninsula on 27 July 1991 had a relatively small initial Convective Available Potential Energy (CAPE) of 740 J kg 1. Lynn et al. [1998] showed that an average sounding derived from observations in the CaPE network had a relatively low lifting condensation level pressure of 1010 mbar (with a surface pressure of 1018 mbar), a level of free convection of 839 mbar, and a high equilibrium level of 190 mbar. Thus, upon heating and further moistening of the planetary boundary layer, the initial sounding was conducive to the development of deep moist convection. Moreover, the atmospheric wind profile was characterized by a low-level wind shear, with a maximum speed of 5.0 m s 1 from the west. The westerly wind then decreased in the middle troposphere. In the upper troposphere the wind was easterly with a maximum absolute value of about 3.7 m s 1. This wind profile is conducive to squall line formation. As discussed by Halverson et al. [1996], a weak short wave trough also crossed the Peninsula, leading to enhanced convection on this day. Initial cloud base was at about 500 m and the freezing level at about 5.1 km. 7of13

8 Figure 10. Simulated mixing ratios for water (QC), rain (QR), ice crystals (QI), graupel (QG), snow (QS) and hail (frozen drops) (QH) in SBM run at 2230 UT in SBM. At this stage the leading cell experienced the period of new intensification. However, it does not reach the intensity of the mature cell. [26] The prevailing westerly winds triggered summertime convection typical to this type of day [Blanchard and Lopez, 1985]. Figure 2 shows the radar reflectivity field derived from WSI observed data from 1500 to 2430 UTC. The sea breeze convection developed first along the west coast, and then along the east coast. The convection developing along the west coast can be seen quite easily at 1600 UTC, but the convection along the east coast did not develop until after 1800 UTC (not shown). The west coast sea breeze penetrated further inland than the east coast sea breeze, as the prevailing west winds enhanced the eastward movement of the west coast front, while hindering the westward movement of the east coast front. At 2100 UTC (1600 LST), cold outflow from the west coast sea breeze front collided with the east coast sea breeze front. A welldeveloped squall line resulted and moved to near the east coast by 2200 UTC. This squall line extended from northwest of Cape Canaveral south to near Lake Okeechobee. At 2300 UTC, a broad stratiform rain region had expanded to the rear of the, now, dissipating squall line. The large zone of stratiform clouds forming from squall line anvil with radar reflectivities dbz is clearly seen in Figure 3 taken from Halverson et al. [1996]. [27] Spotty rainfall was observed from 1800 to 2100 UTC, mostly on the western half of the Peninsula (not shown). Figure 4 shows rainfall distribution from 2100 to 2400 UTC, and it shows two large maxima in rainfall on the eastern side of the Peninsula. There is also a large area of light stratiform precipitation surrounding, but mostly to the rear of these rainfall maxima. [28] The precipitation data from portable automated mesonet sites (PAM) were recorded at 30 min after the top of each hour, while the National Weather Service (NWS) stations recorded at approximately the top of each hour (there were 51 stations in total). Rather than associating the PAM rainfall data (e.g., at 1630 LT) with the previous hour s NWS record as by Lynn et al. [2005b], the PAM accumulated data was divided in two equal halves, with the first half associated with the previous NWS record (at 1600 LT) and the latter half with the next record (1700 LT). As a result, the observed rain varied more smoothly in time than previously shown by Lynn et al. [2005b]. These observations are discussed below. [29] The model results were analyzed using a contour frequency with altitude diagram (CFADs) described by Yuter and Houze [1995]. The ordinate of the plots is height and the abscissa is the value of the parameter whose 8of13

9 Figure 11. Simulated mixing ratios for water (QC), rain (QR), ice crystals (QI), graupel (QG), snow (QS) and vertical velocities in FTMS run at 2315 UT. distribution is being plotted. Contours of the frequency of the parameter value are then plotted. The contours in the CFADs are the percentage of points per unit variable per kilometer (or over the model grid level). The CFAD characterize the general structure of the fields investigated. The CFADs calculated from observed radar reflectivity (Figure 5) was compared to model calculated CFADs (from radar reflectivity calculated from simulated hydrometeor masses) at 2315 UTC, when the squall line approached the east coast of Florida and contained both convective and stratiform clouds (as indicated by radar). The area of the model data used in the analysis for CFADs encompasses the developing squall line in the vicinity and west of Cape Canaveral (from 81.5 to 80.5 W and from 28 to 29 N). This area roughly corresponds to the area of observed threedimensional radar observations. [30] In SBM, radar reflectivity was calculated according to its definition using the explicit size distributions of liquid drops and ice particles [Khain and Sednev, 1996; Lynn et al., 2005a]. CFADs were calculated for radar reflectivity falling within 5 dbz histogram bin widths and compared with derived CFADs from the observational data set. Radar CFADs were also calculated for the bulk simulation results. The radar calculation for the FTMS and TMIS is described by Seifert and Beheng [2005b]. The radar code used for the Thompson scheme will soon be released with the latest WRF code and it takes into account assumed size distributions of water and ice particles (G. Thompson, personal communication, 2007). 4. Results [31] Figure 6 presents horizontal cross sections of radar reflectivity at 2300 UTC according to observations and calculated from the results of the different microphysical schemes. Although the position of the majority of the SBM radar reflectivity echoes has advanced further eastward than observed, the SBM reproduced a radar reflectivity field pattern that appears to be closer to the observed than the bulk parameterization schemes. The bulk parameterization schemes tend to produce relatively isolated convective line segments, and underestimate the area covered by stratiform clouds. The versions of the new bulk parameterization schemes tested here do not produce cloud structure more realistically than the best of the bulk schemes tested earlier, i.e., Reisner 2 (Figure 6)). [32] Figure 7 shows observed and simulated average and maximum rainfall rates on 27 July The observed average values were calculated using the data of 51 stations, while the maximum hourly rates represent the maximum rainfall per hour from any of the 51 stations. In the comparison with model results, the grid points nearest to 9of13

10 Figure 12. Same as in Figure 10 but in TMIS run at 2230 UT. each of the station locations were used. The SBM simulated better average and maximum hourly precipitation rates than the FTMS, TMIS and Thompson bulk schemes. The bulk schemes generally produced too much average rain per hour with also maximum amounts larger than observed, although the precipitation results obtained with the new schemes are closer to the observations than the old (i.e., Reisner2 (Figure 7)). [33] Table 1 presents the accumulated rain in all simulations presented previously by Lynn et al. [2005b] and in this study. One can see that SBM in the present study gives precipitation closer to observations than in the work by Lynn et al. [2005b], which can be attributed to utilization of SBM over the entire area of the 3km domain as compared to the use of an inner core on the 3 km domain with SBM in the work by Lynn et al. [2005b]. The precipitation amounts produced by the bulk parameterizations tested in this study are closer to observed than those obtained using previous bulk parameterization schemes tested by Lynn et al. [2005b]. [34] A comparison of radar derived contour frequency with altitude diagram (CFAD; Figure 5) with height versus SBM model calculated CFAD (Figure 8) suggests that the SBM much better reproduced the vertical structure of both liquid and ice precipitation content than the FTMS and the TMIS (Figure 9). At 2315 UTC, the SBM correctly simulated the percentage of grid elements in the CFAD with radar reflectivity centered close to 30 dbz, although it simulated a higher percentage of clouds with smaller dbz than observed. It simulated the magnitude of the percentages in the CFAD and change in the radar reflectivity associated with the maximum in the CFAD above the freezing level quite well. The CFADS for the TMIS indicates that it produced too much liquid and ice content, while the FTMS produced too little liquid combined with too much ice content. The results from FTMS and TMIS were quite similar to those from Reisner2 (Figure 9). In comparison, the Thompson scheme (Figure 9), like SBM, was also able to simulate the percentage of grid points with 30 dbz, and at least to some extent, the decrease in radar reflectivity with height associated with the highest percentage of grid points. [35] Figure 10 shows a vertical cross section of cloud, rain, ice, snow, graupel, hail hydrometeor masses for SBM. In Figure 10 and Figures 11, 12, 13 (below), we chose the most illustrative cross section from a set of cross sections between 2200 and 2315 UT. At 2230 UT, the SBM produced a multiconvective cloud system within the squall line, with a large area of trailing stratiform precipitation. The structure of the cloud hydrometeor mass is quite consistent with the structure of radar reflectivity shown in Figures 3 and 5, including the extent of stratiform cloud, although the leading convective cell (located near 400 km) is less developed at this time than shown in Figure 5. Vertical velocities in the SBM simulation reached their maximum of 2 4 m/s at the edge of the squall line (not 10 of 13

11 Figure 13. Same as in Figure 10 but in the run with the Thompson scheme at 2230 UT. shown). Because W was not large and CCN concentration was low, which is typical of maritime clouds, warm rain formed rapidly and fell down. The remaining droplets ascend and give rise to ice particles formation, mainly snow (the lack of significant cloud water content above the freezing level hinders transformation of cloud ice into graupel and hail by riming). At the same time, the amount of supercooled water at low temperatures in the upper troposphere is negligible. This feature seems to be consistent with the observations of maritime convection. [36] The FTMS (at 2315 UT) and TMIS (at 2230 UT) bulk parameterizations tested in the present study, as well as the bulk parameterizations tested by Lynn et al. [2005b], produced significant amount of supercooled droplets and even supercooled raindrops at high levels (Figures 11 and 12). This feature is typical rather for continental clouds with high droplet concentration and very strong updrafts (Figures 11 and 12), which was especially the case in the FTMS. As a result of overestimation of supercooled cloud water, the FTMS (and to lesser extent the TMIS) most likely overestimated the rate of riming and, as a result, of graupel mass. Such a result would be consistent with he overestimation of the number grid points with large radar reflectivity above the freezing level as it is seen in Figure 8 for the FTMS and TMIS. Thus the results indicate that the bulk parameterizations produce at the same time strong convection and a large amount of supercooled water in the upper atmosphere (the feature typical of continental clouds) and very rapid rain production (typical for maritime convection). In comparison, the Thompson scheme (at 2230 UT; Figure 13) produced weaker updrafts (more consistent with maritime clouds) and less super cooled water and less frozen mass than the other bulk schemes. With the exception of the lack of apparent stratiform cloud, the results obtained with the Thompson scheme are more consistent with the SBM than with the other bulk schemes. 5. Summary and Conclusions [37] This paper compares simulated rainfall from MM5 simulations with different microphysical schemes, implemented within the last couple of years. Spectral (bin) microphysics still simulates the time variation of hourly and maximum rainfall more realistically than a full twomoment scheme, an adaptation of the Reisner2 (TMIS) microphysical scheme, and the Thompson scheme. The paper also compares a calculated contour frequency with altitude diagram from observed radar reflectivity in the squall line with model-calculated values. The results demonstrate that the SBM simulates well the three-dimensional structure of convection and stratiform developing, including mixed phase and ice processes. The CFAD dia- 11 of 13

12 grams imply that the full two-moment scheme and the TMIS bulk schemes did not simulate the three-dimensional field of convective and stratiform precipitating hydrometeors realistically. A comparison of the CFAD diagram obtained from the Thompson scheme, however, suggests a qualitative improvement in the simulation of both liquid and ice processes in this new scheme. [38] There was noted that the bulk schemes produced intense precipitation during a short time with an underestimation of the role of stratiform clouds, even when the scheme parameters were tuned to get surface rainfall amounts as close as possible to observations. We suggest that this behavior (typical of all bulk parameterization schemes) may result from the assumption that all hydrometeors fall with averaged fall velocities as soon as it exceeds the cloud updraft. This approach increases the rain rate at the expense of small hydrometeors that are forced to fall out, and decreases stratiform cloudiness, because of elimination of small hydrometeors from the volume because they fell down together with larger ones. [39] Note that the results obtained using TMIS and FTMS have been obtained after intensive tuning of the bulk parameterization parameters to make the results as close to observations as possible. The SBM results were obtained without any tuning of parameters. We see in this fact a significant advantage of the SBM for research of cloud processes and precipitation formation. [40] Another advantage is that SBM calculates explicitly the number of collisions between different ice particles, as well as sizes and fall velocities of colliding particles. Thus the SBM can be effectively applied to numerical studies of cloud electrification since the SBM is able to simulate both the charging process of cloud particles, as well as spatial charge separation. Another advantage of the SBM is its ability to simulate narrow size distributions of different hydrometeors like large hail. [41] Note in conclusion that both the SBM used in this study and bulk parameterization allow and require further developments. For instance, we plan to implement a detailed description of melting process as described by Khain et al. [2004] and Phillips et al. [2007a] into the SBM. At the same time Philips et al. [2007b] implemented recently an improved calculation of super saturation and droplet concentration into a two-moment bulk parameterization. [42] Acknowledgments. The authors are grateful to G. Thompson and A. 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