Search

Precipitation forms in warm clouds via collision-coalescence. This process is difficult to observe directly in situ and its implementation in numerical models is uncertain. We use aircraft observations of the drop-size distribution (DSD) near marine stratocumulus tops to estimate collision-coalescence rates. Marine stratocumulus is a useful system to study collisional growth because it is initiated near the cloud top and the clouds evolve slowly enough to obtain statistically useful data from aircraft. We compare rate constants estimated from observations with reference rate constants derived from a collision-coalescence box model, the result of which is termed the enhancement factor (EF). We evaluate two hydrodynamic collision-coalescence kernels, one quiescent and one including the effects of small-scale turbulence. Due to sampling volume limitations, DSDs must be averaged over length-scales much greater than those relevant to the underlying physics, such that we also examine the role of averaging length-scale with respect to process representation. Averaging length-scales of 1.5 and 30 km are used, corresponding roughly to the horizontal grid lengths of cloud-resolving models and high-resolution climate models, respectively. EF values range from 0.1 to 40, with the greatest EFs associated with small mode diameter cases and a generally decreasing trend with drop size. For any given drop size or averaging length-scale, there is about an order of magnitude variability in EFs. These results suggest that spatial variability on length-scales smaller than 1.5 km prevents accurate retrieval of rate constants from large-scale average DSDs. Large-scale models must therefore account for small-scale variability to represent cloud microphysical processes accurately. The turbulent kernel reduces EFs for all drop sizes, but can only account for at most half of the calculated EFs in marine stratocumulus. [ABSTRACT FROM AUTHOR]

Abstract: For a long time, classical ensemble forecasts have concentrated on simulating the uncertainties of the observations by variations of the initial and boundary conditions. To supplement this with inherent contributions from the forecast model itself, stochastic physics has been proposed as an appropriate approach to improve the forecast quality and to increase the ensemble spread of underdispersive ensembles. Concentrating on cloud processes and their temporal development we here introduce a set of stochastic parameters into existing deterministic physical parameterization schemes as for the trigger function, closure assumption and turbulent mixing in the convection scheme, for the initial ice concentration and turbulent collection kernel in the grid scale precipitation/microphysics scheme and for the subgrid scale cloud cover in the radiation scheme. Considering the stochastic parameters to be subject to unresolved turbulent fluctuations, we derive the corresponding stochastic processes from a Langevin equation based on an approach of direct numeric simulation. This allows for the inclusion of a turbulent autocorrelation time calculated from the eddy dissipation rate and the turbulent kinetic energy of the hosting model. Ensemble simulations of two meteorologicaly different real cases are compared to deterministic control simulations and observations of 1h precipitation sums. The turbulent autocorrelation time shows pronounced spatial variability correlated with frontal dynamics and convective activity. Maps of spatial precipitation patterns and continuous ranked probability score (CRPS) improvements show a better forecast quality of the ensemble mean including stochastic physics. Depending on the meteorological situation, the ensemble spread and the CRPS improvement both exhibit a spin up time and reach a saturation. For air-mass convection/postfrontal showers the spread and the CRPS improvement are higher than for frontal precipitation. The stochastic parameterization of subgrid scale cloud cover is found to degrade the forecast quality of the ensemble. This is explained by a too homogeneous spatial distribution of the added variability. Consequently, this part of the stochastic parameterization has been excluded from the ensemble simulations and is left in its operational deterministic version. [Copyright &y& Elsevier]

Abstract: Convection can cause severe precipitation events and is thus of major interest for quantitative precipitation forecasts. Due to its small horizontal scale convection usually has to be parameterized in numerical weather prediction (NWP) models as a subgrid scale phenomenon. Classical mass flux convection schemes assume grid box sizes much larger than the scale of the convective circulation. Thus, the convective mass transport is closed in the local grid column and no net mass transport occurs on the grid scale. In contemporary NWP models with grid sizes of a few kilometers, where convection is already partially resolved, the classical approach leads to a conceptual problem. This can be overcome by a hybrid mass flux convection scheme (HYMACS), in which only the small scale convective updrafts and downdrafts are parameterized, whereas the treatment of the larger scale environmental subsidence is left to the grid scale equations. Different to the classical schemes, HYMACS produces a net convective mass flux exerting pressure gradient forces to the grid scale model. We discuss real cases of different meteorological situations simulated with the operational weather forecast model COSMO of the German Meteorological Service as the hosting model of HYMACS. Precipitation and cloud top pressures simulated with HYMACS are compared with results from classical convection schemes and observational data from the DWD station network and the SEVIRI (MSG) satellite instrument. [Copyright &y& Elsevier]

Abstract: The purpose of this paper is to present a rigorous derivation of a model equation system for the mass and energy open precipitating atmosphere. The derivation is based on the treatment of the atmosphere in terms of three open thermodynamic subsystems consisting of cloudy air, rain water and snow, respectively. Each of the three subsystems moves with a different velocity as caused by the different fall velocities of rain and snow in comparison to the cloudy air. A basic assumption of the model consists in the neglect of the inertia terms in the momentum equation of the falling hydrometeors. It will be shown that this simplification yields additional source terms in the budget equations of the total momentum and the total kinetic energy of the system. However, the prognostic equations for the atmospheric wind field and the corresponding kinetic energy equation remain free of these source terms. The model equations strictly conserve the total mass and energy of the system. It is concluded that the combination of the barycentric velocity of the cloudy air and the total density of the system is the best choice to describe the time evolution of the atmospheric wind field. [Copyright &y& Elsevier]