Simulating a CaPe Convective Squall line using MM5 coupled with ...

Simulating a CaPe Convective Squall line using MM5 coupled with Spectral Bin Microphysics. Model Description and Preliminary results Barry H. Lynn1, Alexander P. Khain1, Jimy Dudhia2, Axel Seifert3, Daniel Rosenfeld1 and Andrei Pokrovsky1

Abstract Considerable investments in research efforts have been made to improve the accuracy of forecasting precipitation systems in cloud resolving, mesoscale atmospheric models. Yet, despite a significant improvement in model grid resolution and a decrease in initial condition uncertainty, the accurate prediction of precipitation amount and distribution still remains a difficult problem. The development of spectral (bin) microphysics (SBM), though, offers significant potential for improving the description of precipitation forming processes in atmospheric models. For these purposes, a new spectral (bin) microphysics (SBM) model, SBM Fast, has been developed, using an adaptation of the SBM package of the Hebrew University Cloud Model (HUCM) (Khain and Sednev, 1995, Khain et. al., 1996). The original SBM model is based on solving a system of equations for size 1

The Hebrew University of Jerusalem, Givat Ram, Jerusalem 2

National Center for Atmopheric Research, Boulder, CO. 3University

of Karlsruhe, Germany

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distribution functions for cloud condensational nuclei (CCN), water drops, three types of ice crystals (plates, columns, and dendrites) as well as snowflakes, graupel, and hail/frozen drops. Each size distribution is represented by 33 mass doubling categories (bins), so mass mk in the category k is determined as mk=2mk-1, where k=2,..,33. The size distribution for water drops includes all drop sizes from small cloud droplets to raindrops. So, in the SBM approach there is no need for separate cloud species and rain species (as it is assumed in bulk microphysics schemes). The minimum mass in the hydrometeor mass grids (except aerosols) corresponds to that of a 2 µm-radius droplet. The mass grids used for hydrometeors of all types are similar. This simplifies the calculation of interaction between hydrometeors of different bulk densities. Within each mass bin the species are assumed to be of uniform sized. The model microphysics is specifically designed to take into account the effect of atmospheric aerosols on cloud development and precipitation formation. The nucleation (droplet activation) procedure is based on the utilization of a separate distribution function for CCN, which eliminates the need for parameterization of the spectrum of newly nucleated droplets. The initial size distribution of CCN is calculated using concentration N of activated CCN (or concentration of nucleated droplets) given by an empirical dependence N (S), where S refers to saturation. The main features of the full microphysics are valid in the fast method, SBM Fast, but with one simplifying assumption. Following the

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method of Hall (1980) and Ovtchinnikov and Kogan (2000), it was assumed that small ice particles can be assigned to ice crystals, while large ice particles were assumed to be graupel, snowflakes (aggregates), or hail. Note, while Hall (1980) and Ovtchinnikov and Kogan (2000) assume that ice hydrodometeors are limited to crystals and graupel, we assume three types of crystals, and three types of large particles. Such separation of ice types along size distributions simplifies description of deposition/sublimation of ice particles. For instance, it is natural to assume that diffusion growth of branch-type crystals (dendrites) should lead to the formation of particles with characteristics close to snow (or aggregates), while diffusion growth of plates (that have density of 0.9 g/cm^3) leads to the formation of hail with the same density. Likewise, columns that grow through diffusion are assumed to become graupel. As a result, instead of six size distributions used in the full SBM scheme for description of ice particles, in the new approach only three size distribution functions are used, consisting of dendrites and snow, columns and graupel, plates and hail. The use of three size distribution functions does, of course, significantly reduce the time require to perform collisions and advection, when compared to the full SBM model. Thus, the SBM Fast model runs about twice as fast as the original SBM model (in MM5). The SBM Fast has been coupled with the three-dimensional mesoscale model, MM5, which allows SBM Fast to simulate microphysics within a realistic, time-varying mesoscale environment.

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The coupling of SBM to MM5 adapted code already in use in MM5. Advection of size distribution functions is conducted using the standard MM5 code written for advection of integral contents and other parameters. Advection of one size distribution corresponds to the advection of the 33 fields corresponding to each bin (actually advection of only non empty mass bins is conducted). The sedimentation velocity of each bin is also taken into account during the advection. Particles of different size and type (belonging to different mass bins) fall with different terminal velocities, and terminal velocities depend on height. Calculation of the sedimentation also uses the standard MM5 code. To account for the effect of clouds on vertical velocity and radiation, calculated size distributions were integrated over all bin of masses to obtain corresponding integral values for use in the non-hydrostatic equation (i.e, to include the drag term due to loading of hydrometeors) for vertical velocity and radiation equations. SBM Fast also calculates the effect of microphyics on temperature and moisture tendencies. Through these couplings, the SBM Fast affects MM5 dynamics, the radiation balance, and surface hydrology, in potentially different and important ways than obtained with bulk microphysics. The time and spatial scales of microscale cloud physical processes differ from those of mesoscale processes. Thus, one can not simply run the mesoscale model and pass the cloud environmental parameters to the microphysical model. Rather, the dynamical responses of the model must

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be made to match the output from SBM, while SBM must use information derived from the mesoscale model calculations to calculate the microphysics. Thus, SBM requires the grid-scale variables of wind, temperature, pressure, and moisture at the beginning of the time step, but also intermediate values of temperature and moisture that result from the effect of advective processes on the mesoscale variables. An example of results from the current work are shown below, from a simulation of a convective, mesoscale precipitating system that developed over Florida 27th July 1991, accompanied by a squall line formation, with extensive stratiform cloud. The MM5 SBM Fast was simulated with both a maritime and continental initial aerosol concentration to demonstrate a range of sensitivity of model results to initial aerosol concentration. The model results from these two simulations were compared to observations, against themselves, and against results from MM5 with bulk parameterizations. Sensitivity tests show the importance/utility of rain-drop breakup, the effect of in-cloud turbulence on the collision of drop-drop and drop-ice (Khain et. al., 2001) -- two physical schemes added recently to SBM. Figure 1 and Table 1 show that including SBM Fast in MM5 leads to a significant improvement in the reproduction of mean and maximum rain rates, and accumulated rain, regardless of initial aerosol concentration. Yet, the most realistic simulation used the maritime initial aerosol concentration, as MM5 SBM Fast in a contintental aerosol environment produced rain cells with

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characteristics typical of continental clouds (i.e, with a larger maximum than observed). Additional analysis of MM5 SBM Fast results, not shown, shows improved spatial distribution of rainfall, and radar reflectivity when compared to results from model simulations with bulk parameterization. As noted, the SBM Fast includes a budget for aerosols which means that the initial drop spectrum at any location can change because of scavenging or injection of aerosol sources during the course of the model simulation. The aerosol concentration can affect the size spectrum of droplets that develop in different locations. In fact, in the simulation with SBM Fast and a initial continental aerosol concentration, moist convection affected a 70% decrease in the aerosol concentration. Thus, the model can be used to better understand the microphysical cold processes that depend very much on the initial and evolving drop size spectrum that can lead to extreme hail and lightning events, severe, wintertime icing and snow, including changes in the storm’s energy budget that affect its evolution. The ability to accurately forecast, with lead times of 12-24 h, the location and timing, of clouds, cloud-clusters, MCCs, and squall lines, etc, is an important step forward in providing accurate forecasts to both ground transportation, aviation interests, and the general public. The use of this type of model, however, requires very powerful computers, limiting the potential applicability of the MM5 SBM Fast at this time to research past weather events or to create data sets to be used to improve bulk parameterization, where possible.

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References Hall, W.D., 1980: A detailed microphysical model within a twodimensional dynamic framework: model description and preliminary results., J. Atmos. Sci., 37, 2486-2507. Khain, A. P. and I. Sednev, 1995: Simulation of hydrometeor size spectra evolution by water-water, ice-water, and ice-ice interactions. Atmos. Res., 36, 729744

Table 1: Accumulated rainfall rates from simulation described above. Accu Obs SBM FastM SBM FastC Reisner2 GFCC Schultz

cm 0.88 1.44 1.36 1.63 2.65 1.94

Khain, A. P., I. Sednev, V. Khvorostyanov, 1996: Simulation of coastal circulation in the Eastern Mediterranean using a spectral microphysics cloud ensemble model. J. Climate.: 9, 3298–3316. Khain, A., M. Pinsky, M. Shapiro, A. Pokrovsky, 2001: Collision rate of small graupel and water drops. J. Atmos. Sci. 58, 2571–2595. Ovtchinnikov, M. and. Kogan, Y. L, 2000: An Investigation of Ice Production Mechanisms in Small Cumuliform Clouds Using a 3D Model with explicit microphysics. Part I: Model description, J. Atmos Sci.: Vol. 57, No. 18, pp. 3004– 3020. Figure 1: Average and maximum rainfall from simulations with MM5 using SBM Fast with an initial maritime (M) or continental (C) aerosol concentration. Also shown are results using MM5 with Reisner2, GSFC, or Schultz. The date was 27 July 1991, during the CaPE experiment, Florida.

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