ترغب بنشر مسار تعليمي؟ اضغط هنا

Rain initiation time in turbulent warm clouds

39   0   0.0 ( 0 )
 نشر من قبل Misha Stepanov
 تاريخ النشر 2004
  مجال البحث فيزياء
والبحث باللغة English




اسأل ChatGPT حول البحث

We present a mean-field model that describes droplet growth due to condensation and collisions and droplet loss due to fallout. The model allows for an effective numerical simulation. We study how the rain initiation time depends on different parameters. We also present a simple model that allows one to estimate the rain initiation time for turbulent clouds with an inhomogeneous concentration of cloud condensation nuclei. In particular, we show that over-seeding even a part of a cloud by small hygroscopic nuclei one can substantially delay the onset of precipitation.


قيم البحث

اقرأ أيضاً

186 - Fabian Heitsch 2009
We present two sets of grid-based hydrodynamical simulations of high-velocity clouds (HVCs) traveling through the diffuse, hot Galactic halo. These HI clouds have been suggested to provide fuel for ongoing star formation in the Galactic disk. The fir st set of models is best described as a wind-tunnel experiment in which the HVC is exposed to a wind of constant density and velocity. In the second set of models we follow the trajectory of the HVC on its way through an isothermal hydrostatic halo towards the disk. Thus, we cover the two extremes of possible HVC trajectories. The resulting cloud morphologies exhibit a pronounced head-tail structure, with a leading dense cold core and a warm diffuse tail. Morphologies and velocity differences between head and tail are consistent with observations. For typical cloud velocities and halo densities, clouds with H{small{I}} masses $< 10^{4.5}$ M$_odot$ will lose their H{small{I}} content within 10 kpc or less. Their remnants may contribute to a population of warm ionized gas clouds in the hot coronal gas, and they may eventually be integrated in the warm ionized Galactic disk. Some of the (still over-dense, but now slow) material might recool, forming intermediate or low velocity clouds close to the Galactic disk. Given our simulation parameters and the limitation set by numerical resolution, we argue that the derived disruption distances are strong upper limits.
We employ the framework of the Koopman operator and dynamic mode decomposition to devise a computationally cheap and easily implementable method to detect transient dynamics and regime changes in time series. We argue that typically transient dynamic s experiences the full state space dimension with subsequent fast relaxation towards the attractor. In equilibrium, on the other hand, the dynamics evolves on a slower time scale on a lower dimensional attractor. The reconstruction error of a dynamic mode decomposition is used to monitor the inability of the time series to resolve the fast relaxation towards the attractor as well as the effective dimension of the dynamics. We illustrate our method by detecting transient dynamics in the Kuramoto-Sivashinsky equation. We further apply our method to atmospheric reanalysis data; our diagnostics detects the transition from a predominantly negative North Atlantic Oscillation (NAO) to a predominantly positive NAO around 1970, as well as the recently found regime change in the Southern Hemisphere atmospheric circulation around 1970.
The representation of clouds and associated processes of rain and snow formation remains one of the major uncertainties in climate and weather prediction models. In a companion paper (Part I), we systematically derived a two moment bulk cloud microph ysics model for collision and coalescence in warm rain based on the kinetic coalescence equation (KCE) and used stochastic approximations to close the higher order moment terms, and do so independently of the collision kernel. Conservation of mass and consistency of droplet number concentration of the evolving cloud properties were combined with numerical simulations to reduce the parametrization problem to three key parameters. Here, we constrain these three parameters based on the physics of collision and coalescence resulting in a region of validity. Furthermore, we theoretically validate the new bulk model by deriving a subset of the region of validity that contains stochastic parameters that skillfully reproduces an existing model based on an a priori droplet size distribution by Seifert and Beheng (2001). The stochastic bulk model is empirically validated against this model, and parameter values that faithfully reproduce detailed KCE results are identified. Furthermore, sensitivity tests indicate that the stochastically derived model can be used with a time step as large as 30 seconds without significantly compromising accuracy, which makes it very attractive to use in medium to long range weather prediction models. These results can be explored in the future to select parameters in the region of validity that are conditional on environmental conditions and the age of the cloud.
We propose a mathematical methodology to derive a stochastic parameterization of bulk warm cloud micro-physics properties. Unlike previous bulk parameterizations, the stochastic parameterization does not assume any particular droplet size distributio n, all parameters have physical meanings which are recoverable from data, and the resultant parameterization has the flexibility to utilize a variety of collision kernels. Our strategy is a new two-fold approach to modelling the kinetic collection equation. Partitioning the droplet spectrum into cloud and rain aggregates, we represent droplet densities as the sum of a mean and a random fluctuation. Moreover, we use a Taylor approximation for the collision kernel which allows the resulting parameterization to be independent of the collision kernel. To address the two-moment closure for bulk microphysical equations, we represent the higher (third) order terms as points in an Ornstein-Uhlenbeck-like stochastic process. These higher order terms are aggregate number concentration, and aggregate mixing ratio, fluctuations and product fluctuations on regions of a 2D space of pre-collision droplets. This 2D space is naturally partitioned into four domains, each associated with a collision process: cloud and rain self-collection, auto-conversion, and accretion. The stochastic processes in the solution to the kinetic collection equation are defined as the sum of a mean and the product of a standard deviation and a random fluctuation. An order of magnitude argument on the temporal fluctuations of the evolving cloud properties eliminates the terms containing a standard deviation and a random fluctuation from the mean evolution equations. The remaining terms form a coupled set of ODEs without any ad-hoc parameters or any assumed droplet size distributions and flexible enough to accept any collision kernel without further derivations.
We present results from moist convection in a mixture of pressurized sulfur hexa-flouride (liquid and vapor) and helium (gas) to model the wet and dry components of the earths atmosphere. To allow for homogeneous nucleation, we operate the experiment close to critical conditions. We report on the nucleation of microdroplets in the wake of large cold liquid drops falling through the supersaturated atmosphere and show that the homogeneous nucleation is caused by isobaric cooling of the saturated sulfur hexaflouride vapor. Our results carry over to atmospheric clouds: falling hail and cold rain drops may enhance the heterogeneous nucleation of microdroplets in their wake under supersaturated atmospheric conditions. We also observed that under appropriate conditions settling microdroplets form a rather stable horizontal cloud layer, which separates regions of super and sub critical saturation.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا