Info

Figure 8. Frequency distributions of the MODIS-retrieved re for the cloud top minus re for the cloud base for the overcast warm (> 273 K) clouds. The raining (a) and nonraining (b) categories are determined based on the AMSR-E products.

of the total rainfall in the western equatorial Pacific Ocean. They suggest that this percentage may be underestimated due to certain inabilities of the microwave measurements. The amount of precipitation from warm clouds that reaches the ground on global scales is a key question in understanding the hydrological cycle.

4. Concluding Remarks

Clouds remain a major source of uncertainty in climate simulation and weather forecast models. Improvements in cloud modeling have been slow, in part because the conventional satellite observations cannot provide detailed information on cloud vertical structure for model evaluation and cloud vertical structure represents a critical gap in determining how clouds interact with the local and large-scale environments. Measuring the vertical profile of cloud properties is fundamentally important, and requires the combination of both active and passive instruments.

The active satellite remote sensing of CloudSat and CALIPSO has promised to provide the Earth science community with the first data product of satellite-measured profiles of cloud physical and optical properties. While the radar can measure the vertical profile of cloud hydrometer particle information, the lidar can detect thin cirrus layers that may not be detectable by the radar. Many of the scientific questions discussed above are expected to be addressed with the data acquired from a combination of the two active radar and lidar instruments. For example, concerning the cloud vertical distributions: What are the frequencies of occurrence of high-level, mid-level, and low-level clouds? What are the frequencies of occurrence of multilayer clouds? Are they composed of two layers or more layers of clouds? What are the percentages of single-layer and overlapped cirrus clouds? How different or similar are the cloud vertical structures observed by CloudSat and CALIPSO in comparisons with the MODIS-retrieved distributions of cloud top pressure? What are the vertical structures of cloud hydrometer phase and particle size, and can we use the droplet effective radius to enhance the detection of warm precipitating clouds? The CloudSat observations may help answer these questions.

While the two active satellite instruments observe the global cloud vertical structure, their observations are limited to a nadir-viewing direction that does not convey the horizontal observations of clouds. Fortunately, CloudSat and CALIPSO will be flying in constellation with the Aqua A-Train platform. The cloud vertical structure observed by CloudSat and CALIPSO can be combined with cloud and radiation quantities measured by the Aqua MODIS, AMSR-E, and CERES instruments. The MODIS provides a wealth of information on the horizontal distributions of cloud top height and cloud optical and microphysical properties. The AMSR-E provides the column-integrated total water path needed to constrain the CloudSat algorithm for retrieving the vertical profile of cloud optical and microphysical properties (Austin and Stephens, 2001).

The CERES incorporates the MODIS-retrieved cloud properties and provides enhanced top-of-atmosphere (TOA) radiative flux measurements (Wielicki et al., 1996; Loeb et al., 1999, 2000). Development of improved angular distribution models requires satellite measurements from different cloud scene-type identifications in obtaining accurate estimates of the radiative fluxes or albedos of the Earth-atmosphere system (Loeb et al., 1999, 2000; Chang et al., 2000a,b). Areas affected by optically thick clouds can have a net cloud radiative cooling of —100 W/m2 and the net global cloud radiative forcing is on the order of —15 W/m2 at the tropopause (Wielicki et al., 1995). The interaction of radiation with cloud properties, particularly cloud optical depth, has a significant impact on the climate, which is several times the impact of a CO2 doubling in the atmosphere. A small change in cloud optical properties can modify the amounts of radiative fluxes that enter and leave the Earth-atmosphere system and alter the climate.

To date, climate simulations agree that clouds have a strong influence on climate change, but we are far from knowing the magnitude, and even the sign, of this influence. It is indeed a daunting task in climate simulations on cloud feedback. However, progress toward an improved understanding of the cloud-climate feedback is beginning to emerge through the coordination of many world-class research programs in advancing the observations and models; for example, the efforts under the international Global Energy and Water Cycle Experiment (GEWEX) Cloud System Study (GCSS) Program and the Intergovernmental Panel on Climate Change (IPCC).

Acknowledgements

The author thanks Dr Jim Coakley, Jr of the Oregon State University and another, anonymous reviewer for their valuable comments and suggestions for improving the quality of this article.

[Received 31 December 2005; Revised 13 September 2007; Accepted 16 September 2007.]

References

Ackerman, S. A., K. I. Strabala, W. P. Menzel, R. A. Frey, C. C. Moeller, and L. E. Gumley, 1998: Discriminating clear-sky from clouds with MODIS. J. Geophys. Res., 103, 32141-32158.

Ackerman, T. P., and G. Stokes, 2003: The atmospheric radiation measurement program. Phys. Today, 56, 38-45.

Arkin, P. A., and P. Ardanuy, 1989: Estimating climatic-scale precipitation from space: a review. J. Climate, 2, 1229-1238.

Arking, A., 1991: The radiative effects of clouds and their impact on climate. Bull. Amer. Meteor. Soc., 72, 795-813.

Austin, R. T., and G. L. Stephens, 2001: Retrieval of stratus cloud microphysical parameters using millimeter-wave radar and visible optical depth in preparation for CloudSat. I. Algorithm formulation, J. Geophys. Res., 106, 2823328242.

Betts, A. K., and Harshvandhan, 1987: Thermodynamic constraint on the liquid water feedback in climate models. J. Geophys. Res., 92, 8483-8485.

Cess, R. D., and coauthors, 1990: Interpretation of climate feedback processes in 19 atmospheric general circulation models. J. Geophys. Res., 95, 16601-16615.

Chahine, M. T., 1974: Remote sounding of cloudy atmospheres. I. The single cloud layer. J. Atmos. Sci, 31, 233-243.

Chang, F.-L., and J. A. Coakley Jr, 1993: Estimating errors in fractional cloud cover obtained with infrared threshold methods. J. Geophys. Res., 98, 8825-8839.

Chang, F.-L., Z. Li, and A. P. Trishchenko, 2000a: The dependence of TOA reflectance aniso-tropy on cloud properties inferred from ScaRaB satellite data. J. Appl. Meteorol., 39, 24802493.

Chang, F.-L., Z. Li, and S. A. Ackerman, 2000b: Examining the relationship between cloud and radiation quantities derived from satellite observations and model calculations. J. Climate, 13, 3842-3859.

Chang, F.-L., and Z. Li, 2002: Estimating the vertical variation of cloud droplet effective radius using multispectral near-infrared satellite measurements. J. Geophys. Res., 107, AAC 7 1-12.

Chang, F.-L., and Z. Li, 2003: Retrieving vertical profiles of water-cloud droplet effective radius: algorithm modification and preliminary application. J. Geophys. Res., 108, AAC 3, 1-11.

Chang, F.-L., and Z. Li, 2005a: A new method for detection of cirrus overlapping water clouds and determination of their optical properties. J. Atmos. Sci., 62, 3993-4009.

Chang, F.-L., and Z. Li, 2005b: A near-global climatology of single-layer and overlapped clouds and their optical properties retrieved from Terra/MODIS data using a new algorithm. J. Climate, 18, 4752-4771.

Chang, F.-L., and J. A. Coakley Jr, 2007: Relationships between marine stratus cloud optical depth and temperature: inferences from AVHRR observations, J. Climate, 20, 2022-2036.

Chen, R., F.-L. Chang, Z. Li, R. Ferraro, and F. Weng, 2007: Impact of the vertical variation of cloud droplet size on the estimation of cloud liquid water path and rain detection. J. Atmos. Sci., 64, 3843-3853.

Chou, M.-D., M. J. Suarez, C.-H. Ho, M.-H. Yan, and K.-T. Lee, 1998: Parameterizations for cloud overlapping and shortwave single-scattering properties for use in general circulation and cloud ensemble models. J. Climate, 11, 202-214.

Chou, M.-D., K.-T. Lee, S.-C. Tsay, and Q. Fu, 1999: Parameterization for cloud longwave scattering for use in atmospheric models. J. Climate, 12, 159-169.

Coakley, Jr J. A., and F. P. Bretherton, 1982: Cloud cover from high-resolution scanner data: detecting and allowing for partially filled fields of view. J. Geophys. Res., 87, 4917-4932.

Coakley, Jr J. A., M. A. Friedman, and W. R. Tahnk, 2005: Retrieval of cloud properties for partly cloudy imager pixels. J. Atmos. Ocean. Tech., 22, 3-17.

Curry, J. A., C. D. Ardeel, and L. Tian, 1990: Liquid water content and precipitation characteristics of stratiform clouds as inferred from satellite microwave measurements. J. Geophys. Res., 95, 16659-16671.

Feigelson, E. M., 1978: Preliminary radiation model of a cloudy atmosphere. 1. Structure of clouds and solar radiation. Beitr. Phys. Atmos., 51, 203-229.

Greenwald, T. J., G. L. Stephens, T. H. Vonder Haar, and D. L. Jackson, 1993: A physical retrieval of cloud liquid water over the global oceans using Special Sensor Microwave/Imager (SSM/I) observations. J. Geophys. Res., 98, 18471-18488.

Grody, N. C., J. Zhao, R. Ferraro, F. Weng, and R. Boers, 2001: Determination of precipitable water and cloud liquid water over oceans from the NOAA-15 advanced microwave sounding unit. J. Geophys. Res., 106, 2943-2954.

Hansen, J. E., and L. D. Travis, 1974: Light scattering in planetary atmospheres. Space Sci. Rev., 16, 527-610.

Harshvardhan, B. A. Wielicki, and K. M. Ginger, 1994: The interpretation of remotely sensed cloud properties from a model parameterization perspective. J. Climate, 7, 1987-1998.

Jin, Y., W. B. Rossow, and D. P. Wylie, 1996: Comparison of the climatologies of high-level clouds from HIRS and ISCCP. J. Climate, 9, 2850-2879.

Jacobowitz, H., L. L. Stowe, G. Ohring, A. Heidinger, K. Knapp, and N. R. Nalli, 2003: The Advanced Very High Resolution Radiometer Pathfinder Atmosphere (PATMOS) climate dataset: a resource for climate research. Bull. Amer. Meteor. Soc., 84, 785-793.

King, M. D., and coauthors, 2003: Cloud and aerosol properties, precipitable water, and profiles of temperature and humidity from MODIS. IEEE Trans. Geosci. Remote Sens., 41, 442-458.

Liou, K.-N., 1986: Influence of cirrus clouds on weather and climate processes: a global perspective. Mon. Wea. Rev., 114, 1167-1199.

Liu, G., J. A. Curry, and R.-S. Sheu, 1995: Classification of clouds over the western equatorial Pacific Ocean using combined infrared and microwave satellite data. J. Geophys. Res., 100, 13811-13826.

Loeb, N. G., P. O'R. Hinton, and R. N. Green, 1999: Top-of-atosphere albedo estimation from angular distribution models: a comparison between two approaches. J. Geophys. Res., 104, 31, 255-260.

Loeb, N. G., F. Parol, J.-C. Buriez, and C. Van-bauce, 2000: Top-of-atmosphere albedo estimation from angular distribution models using scene identification from satellite cloud property retrievals. J. Climate, 13, 1269-1285.

Menzel, W. P., D. P. Wylie, and K. I. Strabala, 1992: Seasonal and diurnal changes in cirrus clouds as seen in four years of observations with the VAS. J. Appl. Meteor., 31, 370-385.

Menzel, W. P., B. A. Baum, K. I. Strabala, and R. A. Frey, 2002: Cloud top properties and cloud phase — Algorithm Theoretical Basis Document: ATBD-MOD-04. 61 pp. Available at http://m0dis-atm0s.gsfc.nasa.g0v/_ docs / atbd_mod04.pdf.

Miles, N. L., J. Verlinde, and E. E. Clothiaux, 2000: Cloud droplet size distributions in low-level stratiform clouds, J. Atmos. Sci., 57, 295-311.

Minnis, P., and coauthors, 1995: Cloud Optical Property Retrieval (Subsystem 4.3). Clouds and the Earths Radiant Energy System (CERES) Algorithm Theoretical Basis Document, III: Cloud Analyses and Radiance Inversions (Subsystem 4), NASA RP 1376, Vol. 3, ed. CERES Science Team, pp. 135-176.

Minnis, P., D. P. Garber, D. F. Young, R. F. Arduini, and Y. Takano, 1998: Parameterization of reflectance and effective emittance for satellite remote sensing of cloud properties. J. Atmos. Sci., 55, 3313-3339.

Mitchell, J. F. B., 1993a: Simulation of climate. Renewable Energy, 3, 421-432.

Mitchell, J. F. B., 1993b: Simulation of climate change. Renewable Energy, 3, 433-445.

Platnick, S., and coauthors, 2003: The MODIS cloud products: algorithms and examples from Terra, IEEE Trans. Geosci. Remote Sens., 41, 459-473.

Rossow, W. B., and R. A. Schiffer, 1991: ISCCP cloud data products, Bull. Amer. Meteor. Soc., 72, 2-20.

Rossow, W. B., L. C. Garder, P. J. Lu, and A. Walker, 1991: International Satellite Cloud Climatology Project (ISCCP) documentation of cloud data. WMO/TD No. 266 (World Meteorological Organization, Geneva), 76 pp.

Rossow, W. B., and R. A. Schiffer, 1999: Advances in understanding clouds from ISCCP, Bull. Amer. Meteor. Soc., 80, 2261-2287.

Smith, W. L., and C. M. R. Platt, 1978: Comparison of satellite-deduced cloud heights with indications from radiosonde and ground-based laser measurements. J. Appl. Meteorol., 17, 1796-1802.

Somerville, R. C. J., and L. A. Remer, 1984: Cloud optical thickness feedbacks in the CO2 climate problem. J. Geophys. Res., 89, 96689673.

Stephens, G. L., and coauthors, 2002: The Cloudsat mission and the A-train: a new dimension of space-based observations of clouds and precipitation. Bull. Amer. Meteor. Soc., 83, 1771-1790.

Stephens, G. L., 2005: Cloud feedbacks in the climate system: a critical review. J. Climate, 18, 237-273.

Stowe, L. L., H. Jacobowitz, G. Ohring, K. R. Knapp, and N. R. Nalli, 2002: The Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmosphere (PATMOS) climate dataset: initial analyses and evaluations. J. Climate, 15, 1243-1260.

Tselioudis, G., W. B. Rossow, and D. Rind, 1992: Global patterns of cloud optical thickness variation with temperature. J. Climate, 5, 1484-1495.

Tselioudis, G., and W. B. Rossow, 1994: Global, multiyear variations of optical thickness with temperature in low and cirrus clouds. Geophys. Res. Lett., 21, 2211-2214.

Tselioudis, G., A. D. Del Genio, W. Kovari Jr, and M.-S. Yao, 1998: Temperature dependence of low cloud optical thickness in the GISS GCM: contributing mechanisms and climate implications. J. Climate, 11, 3268-3281.

Warren, S. G., C. J. Hahn, and J. London, 1985: Simultaneous occurrence of different cloud types, J. Climate Appl. Meteor., 24, 658-667.

Webb, M., C. Senior, S. Bony, and J.-J. Morcrette, 2001: Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate models, Climate Dynamics, 17, 905-922.

Webster, P. J., and G. L. Stephens, 1984: Cloud-radiation interaction and the climate problem. The Global Climate, ed. J. Houghton (Cambridge University Press, New York), pp. 63-78.

Wentz, F. J., 1997: A well-calibrated ocean algorithm for SSM/I. J. Geophys. Res., 102, 87038718.

Wentz, F. J., and T. Meissner, 1999: AMSR ocean algorithm, version 2. RSS Tech. Rep. 121599A, 66 pp. (Available from Remote Sensing Systems, 438 First Street, Suite 200, Santa Rosa, CA 95401, USA.)

Wielicki, B. A., and J. A. Coakley Jr, 1981: Cloud retrieval using infrared sounder data: error analysis. J. Appl. Meteorol., 20, 157-169.

Wielicki, B. A., and L. Parker, 1992: On the determination of cloud cover from satellite sensors: the effect of sensor spatial resolution. J. Geophys. Res., 97, 12799-12823.

Wielicki, B. A., R. D. Cess, M. D. King, D. A. Randall, and E. F. Harrison, 1995: Mission to Planet Earth: role of clouds and radiation in climate. Bull. Amer. Meteorol. Soc., 76, 2125-2153.

Wielicki, B. A., B. R. Barkstrom, E. F. Harrison, R. B. Lee, III, G. L. Smith, and J. E. Cooper, 1996: Cloud and the Earth radiant energy system (CERES): an earth observing system experiment. Bull. Amer. Meteorol. Soc., 77, 853-868.

Winker, D. M., J. Pelon, and M. P. McCormick, 2002: The CALIPSO mission: spaceborne lidar for observation of aerosols and clouds. SPIE Asia-Pacific Symposium on Remote Sensing of the Atmosphere, Environment and Space (23-27 Oct. 2002; Hangzhou, China).

Wylie, D. P., and W. P. Menzel, 1989: Two years of cloud cover statistics using VAS. J. Climate, 2, 380-392.

Yao, M.-S., and A. D. Del Genio, 1999: Effects of cloud parameterization on the simulation of climate changes in the GISS GCM. J. Climate, 12, 761-779.

Zhang, M. H., and coauthors, 2005: Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements, J. Geophys. Res., 110, D15S02, doi:10.1029/2004JD005021.

Recent Advances in Research on Micro- to Storm-Scale Ice Microphysical Processes in Clouds

Pao K. Wang, Hsinmu Lin, Hui-Chun Liu, Mihai Chiruta and Robert E. Schlesinger

Department of Atmospheric and Oceanic Sciences, University of Wisconsin, Madison, Wisconsin, USA [email protected]

In this paper, we summarize our recent research results from ice processes in the atmosphere from storm- to microscale. First, we tested the sensitivity of ice processes in a simulated US High Plains supercell storm using a 3-dimensional cloud model by specifying three different ice physics schemes; namely, the control run, the all-liquid run with normal latent heats, and the all-liquid run with latent heat of sublimation. We showed that the absence of ice processes would result in a substantially shorter lifespan of the storm. Furthermore, we showed that this impact is due to the microphysical properties of ice rather than the thermodynamics of the cloud due to latent heat release.

Secondly, we tested the sensitivity of ice crystal habits on the development of thin cirrus clouds using a 2-dimensional cirrus model with detailed microphysics. Four different ice crystal habits were studied: plates, columns, rosettes and spheres. The results show that the cirrus development is indeed greatly influenced by the habit of ice crystals in the cloud. The largest differences exist between cirrus consisting of rosettes and that consisting of spheres. The largest impact is in the long-wave heating rates where the peak heating rates between these two cases differ by more than 6 times. Other cloud properties are also significantly influenced by the different habits.

Finally, calculations of the ice crystal capacitance for three ice habits: rosettes, solid columns and hollow columns were performed using finite element techniques. The results show that the homomorphic solid and hollow columns of same dimensions have nearly the same capacitance, implying that the mass growth rates of the two are nearly the same but the hollow column will have greater linear growth rate due to its hollowness. The results for rosettes show that the capacitance of rosettes is a nonlinear function of the number of lobes, and hence previous assumptions that their capacitances can be approximated by spheres or prolate/oblate spheroids of the same diameter may result in substantial errors. The computed capacitances can be used in the calculations of crystal growth rates in ice clouds.

1. Introduction

Ice processes in clouds have great impacts on our atmosphere, but their significance was not appreciated by the atmospheric science community until late into the 20th century. The early studies of ice processes were limited to the basic physical phenomena, such as the nucleation phenomenon, the Bergeron-Findeisen process of light rain initiation, the formation of hail, and a few minor aspects. One of the main reasons is most likely that ice processes occur mostly in the upper troposphere, and few airborne platforms at the time could reach such altitudes to perform in situ observations. Remote sensing techniques such as radar and lidar have their own difficulties in observing ice processes.

In the 1980s, however, it was increasingly recognized that the Earth-atmosphere system must be viewed as a whole, and that the components of this system act upon one another. A casual look at any zonal mean cross-section of the atmospheric temperature profile would easily reveal that the total volume of the global tropospheric air that is colder than 0°C is greater than the warmer one. This implies that the spatial probability of ice particle formation in atmospheric clouds is greater than that of liquid drop formation, although one needs to consider further the probability of supercooling to ascertain this point. In any case, atmospheric ice layers represent such a prominent part of the troposphere that it is only logical to think that it would have great influences on the behavior of our atmosphere. Thus, Ramanathan et al. (1983) pointed out that high cirrus clouds, which were traditionally considered as thin and tenuous, and hence were of minor importance to the atmosphere, could have great impact on the global climate process because of their strong interaction with solar and terrestrial radiations (see, for example, Liou 2002).

Ice processes exert their influences via the constituent ice particles in clouds. These ice particles may interact strongly with long and short wave radiations according to their size and shape, and impact the heat budget of the atmosphere. To understand such impacts accurately, we need to know the habit of these particles and their growth modes and rates. Alternatively, these particles may influence the development of the cloud by changing their dynamic environment via their drag on the cloudy air and evaporation-driven downdrafts. Of course, the influences mentioned above are not independent of each other, but intertwined in a complex way. Careful studies are necessary to unravel these influences and the roles ice particles play in them.

This article will summarize some recent studies (from 2000 to the present) performed by our research group on the micro- to storm-scale ice processes in clouds. We will first describe studies at the cloud scale (including both convective and cirrus clouds), followed by microscale studies.

2. The Role of Ice Processes in the

Lifespan of US High Plains

Thunderstorms

The majority of thunderstorm studies performed by meteorologists in recent decades are concerned with the dynamical and thermody-namic processes, and rarely about microphysical processes. Tao et al. (1991) performed numerical simulations of a subtropical squall line over the Taiwan Strait using a two-dimensional cloud model. In that study, they also performed a sensitivity study of ice microphysics and found that the storm starts to dissipate earlier without ice processes. Johnson et al. (1993) examined the effects of cloud microphysics on the overall dynamical behavior of thunderstorms using a three-dimensional nonhydrostatic cloud-resolving model equipped with explicit cloud microphysics (including both liquid and ice processes) to perform a sensitivity study of the effect of the ice process. They performed model simulations with and without the ice process. By comparing the two results, they found that the simulated storm with full ice physics lasts longer than the one without, and the different behavior can be attributed to the density of ice particles being lower than that of liquid hydro-meteors. Barth and Parsons (1996) studied the microphysical processes associated with intense frontal rainbands using a 2D cloud model, and found that the melting and sublimation of ice hydrometeors increase the intensity of the down-draft and cold pool, which strengthens the squall line convection. Similar conclusions were obtained by Phillips et al. (2007), who also used a 2D cloud model with a refined melting scheme to simulate the dynamics and precipitation production in maritime and continental storm clouds. Liu et al. (1997) also performed a study on two tropical squall lines using 2D cloud model simulation, and found that the addition of ice microphysics did produce more intense storms. Aside from these simulation studies, Heymsfield et al. (2005) performed a study of the homogeneous ice nucleation in subtropical and tropical convection based on observational data. The importance of many ice processes was deduced from the study.

In the above studies, Johnson et al. (1993) were the only team to use a 3D model, which is important for studying the severe convective storms, as the lack of the third dimension may impose severe restrictions on the dynamics and hence influence the interpretation of the results. However, that study did not definitely resolve whether the observed effects are completely due to microphysics or thermodynamics. The discussions to be summarized in this section are based on a follow-up study that removed the ambiguity.

2.1. The cloud model and the CCOPE supercell

The tool utilized for the present study is the Wisconsin Dynamical/Microphysical Model (WISCDYMM). The WISCDYMM predicts the three wind components — turbulent kinetic energy, potential temperature, pressure deviation — and mixing ratios for water vapor, cloud water, cloud ice, rain, graupel/hail and snow. It adapts the quasi-compressible, nonhy-drostatic primitive equation system of Anderson et al. (1985), rearranging the mass continuity equation to predict the pressure deviation much as in the fully compressible 3D cloud model of Klemp and Wilhelmson (1978), but allows time steps approximately three times larger by assigning acoustic waves a reduced pseudosound speed roughly twice the maximum anticipated wind speed. As in Klemp and Wilhelmson, subgrid transports are parametrized via 1.5-order "K-theory" to predict turbulent kinetic energy, from which a time- and space-dependent eddy coefficient is diagnosed for momentum, and set 35% larger for the heat and moisture predic-tands (Straka, 1989).

As elaborated by Straka (1989), a version of the WISCDYMM called the HPM (Hail Para-metrization Model) features a bulk microphysics parametrization that entails water vapor and five hydrometeor types: cloud water, cloud ice, rain, graupel/hail and snow, with 37 individual transfer rates (source/sink terms). Adapted largely from Lin et al. (1983) and Cotton et al. (1982, 1986), this package treats all hydrome-teors as spheres except for cloud ice, which is treated as small hexagonal plates. Cloud water and cloud ice are assumed monodisperse, with zero fallspeed relative to the air. All three precipitation classes have inverse-exponential size distributions, with temperature-dependent intercepts for snow and graupel/hail, while the intraspectral variation of particle fallspeed versus diameter for each is assumed to satisfy a power law.

If necessary, the WISCDYMM can also be run in the HCM (Hail Category Model) mode, in which the evolution of hailstones can be tracked by studying the growth of hail sizes in a number (e.g. 25) of size bins. The HCM has been tested successfully by Straka (1989).

The WISCDYMM is also programmed to activate one or more of the following three iterative microphysical adjustments (Straka, 1989) where and if needed:

A saturation adjustment is performed to: (a) condense cloud water (or depose cloud ice) to eliminate supersaturation, releasing latent heat of evaporation (or sublimation), or (b) evaporate cloud water (or sublimate cloud ice) in sub-saturated air until either saturation is reached or the cloud water (or cloud ice) is exhausted, absorbing latent heat instead. Cloud water is adjusted first and cloud ice second, incrementing the water vapor and temperature to suit. In-cloud saturation mixing ratios are weighted between their values with respect to water and ice in proportion to the relative amounts of cloud water and cloud ice respectively. Where no cloud is present, saturation is taken w.r.t. water or ice if the temperature is above or below 0°C respectively. More than three iterations are rarely needed. Prior to the saturation adjustment, any cloud ice at temperatures above 0°C is melted, and any cloud water at temperatures below —4°C is frozen, respectively absorbing or releasing latent heat of fusion.

After partial update of the moisture fields by advection and turbulent mixing, the decrement in each hydrometeor field due to the net sink (the sum of the individual microphysical sink terms) is compared to its available supply, defined as its partially updated mixing ratio plus the increment due to its net source (the sum of the individual microphysical source terms). If the net sink of a hydrometeor class exceeds its available supply, it is prorated downward along with each of its components so as to not exceed 25% of the available supply. The procedure is iterative because prorating down the sinks of one class also reduces the corresponding source terms for one or more other classes, but more than two iterations are rarely needed.

The storm chosen for the simulation in this study is a supercell that passed through the center of the Cooperative Convective Precipitation Experiment (CCOPE) observational network in southeastern Montana on 2 August 1981. The storm and its environment were intensively observed for more than 5

h by a combination of 7 Doppler radars, 7 research aircraft, 6 rawinsonde stations and 123 surface recording stations as it moved east-southeastward across the CCOPE network. Again, the observational history of this storm has been reported previously (Miller et al., 1988; Wade, 1982), and readers are referred to these sources for further details. The storm has also been successfully simulated using the WISCDYMM, and the general dynamical and microphysical behaviors were reported by Johnson et al. (1993, 1995). The current study uses the simulated CCOPE storm that was initialized by the same conditions as in Johnson et al. (1993, 1995) and with the same resolution, i.e. 1 x 1 x 0.5km3.

Figure 1 shows the sounding used as the initial conditions for starting the simulation.

eeope-08021981-add Atmosphere sounding

Figure 1. The 1746 MDT Knowlton, Montana sounding on 2 August 1981. The solid curve is for temperature and dashed curve for dew point. The portion of dew point curve above 300 hPa, which was missing in the original sounding, is constructed using an average August 1999 HALOE water vapor profile over 40—60N.

eeope-08021981-add Atmosphere sounding

Figure 1. The 1746 MDT Knowlton, Montana sounding on 2 August 1981. The solid curve is for temperature and dashed curve for dew point. The portion of dew point curve above 300 hPa, which was missing in the original sounding, is constructed using an average August 1999 HALOE water vapor profile over 40—60N.

This is the same as that used by Johnson et al. (1993, 1995) and is a typical case for producing US High Plains supercells. In the present study, we performed three different runs:

(a) Full Physics Run (FPR): This is a simulation with the complete set of cloud microphysics, including both liquid and ice physics.

(b) Normal Liquid Run (NLR): This is a simulation in which the ice physics is suppressed. The only condensed phase is liquid and the latent heat released during the phase change is the latent heat of evaporation.

(c) Enhanced Liquid Run (ELR): As in the case of the NLR, this is a simulation in which the ice physics is suppressed and the only condensed phase is liquid. However, the latent heat released during the phase change is the latent heat of sublimation. This is to say that we artificially enhance the latent heat released in order to test the sensitivity of it on the lifespan of the supercell under consideration.

2.2. Results and discussions

The results of both the FPR and the NLR were reported by Johnson et al. (1993), and the main conclusion was that the suppression of ice processes in this supercell would shorten its lifespan dramatically. The reason for this phenomenon was thought to be the density difference between ice and liquid hydrometeors in the cloud. In the case of the FPR, the most abundant hydrome-teors in the upper tropospheric level are the snowflakes. The lighter density of the flakes allows them to distribute in a much wider volume, and the eventual descending branch of the storm circulation occurs at much larger distances downstream from the ascending inflow of the unstable air. Thus, the descending and ascending branches of the storm circulation do not interfere destructively, but instead enhance each other, producing a long-lasting supercell. In contrast, the NLR results show that the higher density of liquid hydrometeors, consisting largely of raindrops, makes them fall very close to the main ascending branch of the storm circulation, effectively cutting off the inflow of the unstable air necessary for the life of the storm. As a result, the NLR storm starts to dissipate only after 1.5 h into the simulation.

While the above explanation points to the density difference of hydrometeors as the main reason for the quick dissipation of the pure liquid NLR storm, there remains the possibility that the smaller latent heat release in this storm as compared to that in the FPR storm could also contribute to its shorter lifespan. This is the motivation for performing the ELR storm simulation, as mentioned previously. By artificially enhancing the latent heat release during condensation, we hope to eliminate the above-mentioned ambiguity.

Figure 2. The rendered contour surfaces of hydro-meteor mixing ratio 0.1 gm-3 of the three simulated storms as viewed from south for t = 20 to 120 min. The vertical axis range is 0—20 km and the horizontal range is 0—55 km.

Figure 2 shows a comparison of the development history of the three simulated storms from t = 20 to 120 min. It demonstrates that the ELR storm is the one that dissipates the earliest, and hence the enhanced latent heat obviously does not help prolong the lifespan of this storm; rather, it does the opposite. This establishes which the long life of the FPR storm is indeed due to the microphysical property of ice particles (their lighter densities) which influences the internal dynamics of the storm. It appears that the additional heat energy prompts the formation of a large amount of liquid hydrometeors faster than the NLR case, and hence cuts off the inflow of unstable air sooner, resulting in the quicker dissipation of the ELR storm.

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