Warm Rain Precipitation Processes

Transcription

1 Warm Rain Precipitation Processes Cloud and Precipitation Systems November 16, 2005 Jonathan Wolfe 1. Introduction Warm and cold precipitation formation processes are fundamentally different in a variety of ways. Warm precipitation is primarily a result of coalescence where cold precipitation usually involves ice processes. It was believed for many years that precipitation could not form in the absence of ice nuclei; however, Battan, Fig. 5, [1953] demonstrated that precipitation was found in clouds that did not have temperatures below 0 C. Further, most of the precipitating tropical cumuli found in this study did not exhibit temperatures below the freezing point of water shown in Fig. 1. This led to modeling and studying the microphysics that initiate warm rain precipitation processes. 2. Coalescence To define coalescence, it is easier to describe the processes that lead to coalescence. Condensation is necessary for coalescence to occur within clouds, and can be explained through the Kohler curve. Essentially, a Cloud Condensation Nuclei (CCN), which may consist of small Aitken particles all the way up to large salt particles, becomes activated when it reaches thermodynamic equilibrium, i.e. it will begin to accumulate water (the top most portion of the Kohler curve). At this point the particle will begin to grow as long as the environment is sufficiently supersaturated (note the supersaturation required

2 for droplet growth is typically between.01 and 1%, maybe even smaller depending on the size of the drop). The process of a activating a CCN particle and its growth into a droplet takes less than a second. Condensation leads to the formation of water drops. As water drops become increasingly omnipresent, they have a greater chance of running into one another. This is when coalescence occurs; it is the joining or combination of two or more water drops to form one larger drop. Coalescence is more likely to occur when the initial distribution of the spectrum is skewed towards larger particles causing a greater likelihood of drops running into other drops. However, when a parcel ascends the size distribution becomes increasingly narrow due to the affect of the surface to volume ratio of diffusional growth. Through the growth equation (Eq. 1), a larger particle will grow at a smaller rate than a smaller particle given all other things equal dr dt 1 = r S 1, (Eq.1) D + L where r is the radius, S the supersaturation, D the diffusional growth term, and L the thermal conductivity term. This inhibits coalescence, meaning that something needs to overcome this for precipitation to occur which will be noted later in this paper. Coalescence tends to occur much more often when a large drop sweeps out smaller drops due to its larger mass thus its larger terminal velocity; it s more apt to run into smaller drops much like a large raindrop on a windshield. Beard [1976] studied the terminal velocities of particles at different pressures and temperatures to better quantify inputs into cloud microphysics equations. His study determined that particle size has a profound effect on the terminal velocity of the particle. For example, the difference between the terminal velocity of a 0.25 and 3 mm diameter drop varies nearly over an order of magnitude.

3 Additionally complicating the terminal velocities are the shape differences that occur when drops grow to sizes >2 mm as shown by Kasten, Fig. 2, [1968]. This demonstrates just how difficult quantizing terminal velocities of particles can be lending ambiguity to the derivation of model predicted times for precipitation growth. Although there is some ambiguity in the parameters that are inherent to precipitation growth, equations have been developed to model this growth and reduce uncertainties. Beard and Och s [1983] refined the collection efficiencies, a parameter in the growth by coalescence equation, of water drops that prior to their study were assumed to be rigid spheres. This enabled a more precise calculation of the drop growth rate. Berry and Reinhardt [1974] made use of the Stochastic collection equation, an equation which attempts to account for collision and creation of water drops to predict the spectrum growth or change with time. These equations do fairly well at modeling what actually happens in a cloud. 3. The Warm Rain Precipitation Paradox For years, it was not understood how precipitation could form on the order of minutes after a cloud formed rather than the hours or days to precipitation formation predicted by models. This puzzle was unmasked by focusing on the basis for rapid precipitation development which involved the interactions of particles on the cloud microphysical

4 level. The rapid development of precipitation is primarily a function of the size distribution; particularly the Ultra-Giant and Giant aerosols (hereafter referred to as UG and G) that are suspended within a cloud; these are the cause of the enhanced temporal response of precipitation formation. The sizes of UG and G particles are on the order of 1 to 10 µm. To put these numbers into perspective, the average cloud droplet is on the order of 10 µm, drizzle approximately 40-to-100 µm, and rain 100 µm-to-10mm. It is shown by a modeling study conducted by Berry and Reinhardt, Fig. 2, [1974] that a small contribution of UG and G to the drop size distribution dramatically affects the shape of the distribution (the width and tail); a direct consequence is cloud droplets growing quickly into precipitation. 4. Turbulence and Mixing Turbulence acts to mix a parcel as shown by Shaw, Fig. 1, [1998] resulting in local high concentrations of particles increasing their chances of coalescing. After coalescence, the particle size distribution broadens much like Fig. 2. shown above increasing the probability of a precipitation event. This may be the reason adiabatic models don t capture the proper spectrum.

5 The cumulus dynamics within a cloud are complex. Much more abstract than the simplified single updraft that is usually assumed. Rather these clouds have a pulsating updraft with many fluctuations occurring on a relatively small scale. One of which is the penetrative downdraft shown to occur in Paluch, Fig. 4, [1979]. This figure demonstrates that θ q is preserved linearly throughout the cloud meaning that air from cloud top is mixing with air from the cloud base thus exemplifying that penetrative downdrafts exist and are a major contributor to cloud mixing.

6 Liquid Water Content (LWC) is another term that can change the magnitude of the distribution as it is fuel for clouds to persist. This is illustrated in Cotton, Fig. 1, [1975] where three different LWC s are used for the same initial concentration. It is clear that from Fig.1 the higher the LWC the faster the conversion to a larger drop size distribution due to the availability of water.

7 . An adiabatic LWC profile is conducive to cloud growth (this is common in marine stratocumulus), whereas the usual trend for convective clouds is for the LWC to diminish at the edges of the penetrating updraft (illustrated in Cotton, Fig. 11, [1977]) as the parcel rises which is caused due to mixing with the dryer environmentally entrained air; however, the majority of LWC is present within the updraft.

8 To keep the air at saturation, i.e. maintain a cloud, the dryer air needs to reach the saturation vapor pressure. To do this it evaporates some of the LWC, which takes energy, cooling the air becoming negatively buoyant resulting in more mixing such as that described by Paluch [1979]. This is depicted by the rapid positive and negative vertical velocities within a cloud profile obtained by Macpherson and Issac, Fig. 5, [1977].

9 5. Modeling The key to modeling is to accurately determine the initial spectrum distribution, width, and magnitude as well as representing the mixing that occurs within a cloud. A brief summary of a few of the terms or equations used in models is described hereafter. The Stochastic collection equation is an attempt to model how fast the size distribution changes. The equation predicts a statistical likelihood that collisions will occur, in other words how much coalescence is occurring. Twomey s equation determines the

10 maximum supersaturation, which through the Kohler curve allows one to determine the number of CCN activated. Kessler s parameterized approach (the auto conversion of cloud) reduces the complexity of models by emphasizing dynamics, but strives to preserve the microphysics. Hall [1980] used a combination of dynamical and microphysics to assess the warm rain process. Some major terms he included were buoyancy, loading, friction, continuity equation for water vapor and heat, and, ventilation, which encompassed condensation nuclei at cloud base, growth by condensation, advection, coalescence, and breakup. His results were promising as he recreated near-actual observations as can be seen in Fig. 14; however he mentions that there still are processes that need resolving such as solute and curvature terms, diffusional growth, amongst others. There are numerous models, each of them having their own faults. Some even predicted results opposite of what were observed. Their main difficulties lie within how to best account for mixing. Better resolving and understanding the microphysical interactions and dynamics of these clouds hopefully will improve model results. 6. Conclusion In conclusion, a narrow size distribution spectrum is not conducive to producing precipitation whereas a broad spectrum has larger terminal velocities resulting in more

11 collisions, more coalescence, and hence forming precipitation quickly. Turbulence and mixing act to broaden the spectrum, where parcel ascent tends to narrow the spectrum, particularly it should be noted that spectrum broadness is critically related to the environmental supersaturation. Further, a higher LWC content (the closer to adiabatic) allows the precipitation to thrive. Given these results it can be seen why warm rain processes can result in precipitation on the time scale that they do, and why models have a hard time capturing the essence of this complicated process. 7. References: Battan, L. J., 1953: Observations on the Formation and Spread of Precipitation in Convective Clouds. Journal of the Atmospheric Sciences: Vol. 10, No. 5, pp Beard, K.V., 1976: Terminal Velocity and Shape of Cloud and Precipitation Drops Aloft. Journal of the Atmospheric Sciences: Vol. 33, No. 5, pp Kasten, Fig. 2, [1968]. Berry,E. and Reinhardt, R., 1974: An Analysis of Cloud Drop Growth by Collection: Part I. Double Distributions. Journal of the Atmospheric Sciences: Vol. 31, No. 7, pp Cotton, W., Pielke, R., and Gannon, P. 1976: Numerical Experiments on the Influence of the Mesoscale Circulation on the Cumulus Scale. Journal of the Atmospheric Sciences: Vol. 33, No. 2, pp Hall, W., 1980: A Detailed Microphysical Model Within a Two-Dimensional Dynamic Framework: Model Description and Preliminary Results. Journal of the Atmospheric Sciences: Vol. 37, No. 11, pp MacPherson, J., and Isaac,G., 1977: Turbulent Characteristics of Some Canadian Cumulus Clouds. Journal of Applied Meteorology: Vol. 16, No. 1, pp Paluch,I., 1979: The Entrainment Mechanism in Colorado Cumuli. Journal of the Atmospheric Sciences: Vol. 36, No. 12, pp Shaw, R., Reade, W., Collins, L., and Verlinde, J., 1998: Preferential Concentration of Cloud Droplets by Turbulence: Effects on the Early Evolution of Cumulus Cloud Droplet Spectra. Journal of the Atmospheric Sciences: Vol. 55, No. 11, pp