Condensation rate-mass flux correlation: implications for supersaturation in shallow cumulus clouds

2021 ◽  
Author(s):  
Yefim Kogan
Keyword(s):  
Mass Flux ◽  
Condensation Rate ◽  
Cumulus Clouds ◽  
Shallow Cumulus

scholarly journals Investigating the Scale Adaptivity of a Size-Filtered Mass Flux Parameterization in the Gray Zone of Shallow Cumulus Convection

2018 ◽  
Vol 75 (4) ◽  
pp. 1195-1214 ◽  
Author(s):  
Maren Brast ◽  
Vera Schemann ◽  
Roel A. J. Neggers
Keyword(s):  
Mass Flux ◽  
Turbulent Transport ◽  
Eddy Diffusivity ◽  
Coarse Grained ◽  
Parameterization Scheme ◽  
Quasi Equilibrium ◽  
Gray Zone ◽  
Cumulus Clouds ◽  
Eddy Simulation ◽  
Shallow Cumulus

Abstract In this study, the scale adaptivity of a new parameterization scheme for shallow cumulus clouds in the gray zone is investigated. The eddy diffusivity/multiple mass flux [ED(MF)n] scheme is a bin-macrophysics scheme in which subgrid transport is formulated in terms of discretized size densities. While scale adaptivity in the ED component is achieved using a pragmatic blending approach, the MF component is filtered such that only the transport by plumes smaller than the grid size is maintained. For testing, ED(MF)n is implemented into a large-eddy simulation (LES) model, replacing the original subgrid scheme for turbulent transport. LES thus plays the role of a nonhydrostatic testing ground, which can be run at different resolutions to study the behavior of the parameterization scheme in the boundary layer gray zone. In this range, convective cumulus clouds are partially resolved. The authors find that for quasi-equilibrium marine subtropical conditions at high resolutions, the clouds and the turbulent transport are predominantly resolved by the LES. This partitioning changes toward coarser resolutions, with the representation of shallow cumulus clouds gradually becoming completely carried by the ED(MF)n. The way the partitioning changes with grid spacing matches the behavior diagnosed in coarse-grained LES fields, suggesting that some scale adaptivity is captured. Sensitivity studies show that the scale adaptivity of the ED closure is important and that the location of the gray zone is found to be moderately sensitive to some model constants.

scholarly journals Fluctuations in a quasi-stationary shallow cumulus cloud ensemble

2014 ◽  
Vol 1 (2) ◽  
pp. 1223-1282 ◽  
Author(s):  
M. Sakradzija ◽  
A. Seifert ◽  
T. Heus
Keyword(s):  
Weibull Distribution ◽  
Mass Flux ◽  
Flux Distribution ◽  
Deep Convection ◽  
Convective Cloud ◽  
Cloud Base ◽  
Cumulus Clouds ◽  
Large Eddy Simulation Model ◽  
Shallow Cumulus ◽  
Distribution Mode

Abstract. We propose an approach to stochastic parameterization of shallow cumulus clouds to represent the convective variability and its dependence on the model resolution. To collect the information about the individual cloud lifecycles and the cloud ensemble as a whole, we employ a Large-Eddy Simulation model (LES) and a cloud tracking algorithm, followed by conditional sampling of clouds at the cloud-base level. In the case of a shallow cumulus ensemble, the cloud-base mass flux distribution is bimodal due to the different shallow cloud subtypes. Each distribution mode can be approximated with a Weibull distribution, explaining the deviation from a single-parameter exponential shape through the diversity in cloud lifecycles. The exponential distribution of cloud mass flux previously suggested for deep convection parameterization is a special case of the Weibull distribution, which opens a way towards unification of the statistical convective ensemble formalism of shallow and deep cumulus clouds. Based on the empirical and theoretical findings, a stochastic model has been developed to simulate a shallow convective cloud ensemble. It is formulated as a compound random process, with the number of convective elements drawn from a Poisson distribution, and the cloud mass flux sampled from a mixed Weibull distribution. Convective memory is accounted for through the explicit cloud lifecycles, making the model formulation consistent with the choice of the Weibull cloud mass flux distribution function. The memory of individual shallow clouds is required to capture the correct convective variability. The resulting distribution of the subgrid convective states in the considered shallow cumulus case is scale-adaptive – the smaller the grid size, the broader the distribution.

scholarly journals Life Cycle of Numerically Simulated Shallow Cumulus Clouds. Part I: Transport

2005 ◽  
Vol 62 (5) ◽  
pp. 1269-1290 ◽  
Author(s):  
Ming Zhao ◽  
Philip H. Austin
Keyword(s):  
Life Cycle ◽  
Mass Flux ◽  
Conceptual Models ◽  
Life Cycles ◽  
Cumulus Clouds ◽  
Eddy Simulation ◽  
Shallow Cumulus ◽  
Mass Fluxes ◽  
Large Eddy ◽  
Negatively Buoyant

Abstract This paper is the first in a two-part series in which the life cycles of numerically simulated shallow cumulus clouds are systematically examined. The life cycle data for six clouds with a range of cloud-top heights are isolated from an equilibrium trade cumulus field generated by a large-eddy simulation (LES) with a uniform resolution of 25 m. A passive subcloud tracer is used to partition the cloud life cycle transport into saturated and unsaturated components; the tracer shows that on average cumulus convection occurs in a region with time-integrated volume roughly 2 to 3 times that of the liquid-water-containing volume. All six clouds exhibit qualitatively similar vertical mass flux profiles with net downward mass transport at upper levels and net upward mass flux at lower levels. This downward mass flux comes primarily from the unsaturated cloud-mixed convective region during the dissipation stage and is evaporatively driven. Unsaturated negatively buoyant cloud mixtures dominate the buoyancy and mass fluxes in the upper portion of all clouds while saturated positively buoyant cloud mixtures dominate the fluxes at lower levels. Small and large clouds have distinct vertical profiles of heating/cooling and drying/moistening, with small clouds cooling and moistening throughout their depth, while larger clouds cool and moisten at upper levels and heat and dry at lower levels. The simulation results are compared to the predictions of conceptual models commonly used in shallow cumulus parameterizations.

scholarly journals Mass-Flux Budgets of Shallow Cumulus Clouds

2003 ◽  
Vol 60 (1) ◽  
pp. 137-151 ◽  
Author(s):  
Stephan R. de Roode ◽  
Christopher S. Bretherton
Keyword(s):  
Mass Flux ◽  
Cumulus Clouds ◽  
Shallow Cumulus

scholarly journals Fluctuations in a quasi-stationary shallow cumulus cloud ensemble

2015 ◽  
Vol 22 (1) ◽  
pp. 65-85 ◽  
Author(s):  
M. Sakradzija ◽  
A. Seifert ◽  
T. Heus
Keyword(s):  
Weibull Distribution ◽  
Exponential Distribution ◽  
Mass Flux ◽  
Flux Distribution ◽  
Deep Convection ◽  
Convective Cloud ◽  
Cloud Base ◽  
Cumulus Clouds ◽  
Shallow Cumulus ◽  
Distribution Shape

Abstract. We propose an approach to stochastic parameterisation of shallow cumulus clouds to represent the convective variability and its dependence on the model resolution. To collect information about the individual cloud lifecycles and the cloud ensemble as a whole, we employ a large eddy simulation (LES) model and a cloud tracking algorithm, followed by conditional sampling of clouds at the cloud-base level. In the case of a shallow cumulus ensemble, the cloud-base mass flux distribution is bimodal, due to the different shallow cloud subtypes, active and passive clouds. Each distribution mode can be approximated using a Weibull distribution, which is a generalisation of exponential distribution by accounting for the change in distribution shape due to the diversity of cloud lifecycles. The exponential distribution of cloud mass flux previously suggested for deep convection parameterisation is a special case of the Weibull distribution, which opens a way towards unification of the statistical convective ensemble formalism of shallow and deep cumulus clouds. Based on the empirical and theoretical findings, a stochastic model has been developed to simulate a shallow convective cloud ensemble. It is formulated as a compound random process, with the number of convective elements drawn from a Poisson distribution, and the cloud mass flux sampled from a mixed Weibull distribution. Convective memory is accounted for through the explicit cloud lifecycles, making the model formulation consistent with the choice of the Weibull cloud mass flux distribution function. The memory of individual shallow clouds is required to capture the correct convective variability. The resulting distribution of the subgrid convective states in the considered shallow cumulus case is scale-adaptive – the smaller the grid size, the broader the distribution.

scholarly journals A Cloud Microphysics Parameterization for Shallow Cumulus Clouds Based on Lagrangian Cloud Model Simulations

2018 ◽  
Vol 75 (11) ◽  
pp. 4031-4047 ◽  
Author(s):  
Yign Noh ◽  
Donggun Oh ◽  
Fabian Hoffmann ◽  
Siegfried Raasch
Keyword(s):  
Cloud Model ◽  
Cloud Microphysics ◽  
Heaviside Function ◽  
Mixing Ratio ◽  
Cumulus Clouds ◽  
Cloud Droplets ◽  
Shallow Cumulus ◽  
Initial Stage ◽  
Accretion Rates ◽  
The Moment

Abstract Cloud microphysics parameterizations for shallow cumulus clouds are analyzed based on Lagrangian cloud model (LCM) data, focusing on autoconversion and accretion. The autoconversion and accretion rates, A and C, respectively, are calculated directly by capturing the moment of the conversion of individual Lagrangian droplets from cloud droplets to raindrops, and it results in the reproduction of the formulas of A and C for the first time. Comparison with various parameterizations reveals the closest agreement with Tripoli and Cotton, such as and , where and are the mixing ratio and the number concentration of cloud droplets, is the mixing ratio of raindrops, is the threshold volume radius, and H is the Heaviside function. Furthermore, it is found that increases linearly with the dissipation rate and the standard deviation of radius and that decreases rapidly with while disappearing at > 3.5 μm. The LCM also reveals that and increase with time during the period of autoconversion, which helps to suppress the early precipitation by reducing A with smaller and larger in the initial stage. Finally, is found to be affected by the accumulated collisional growth, which determines the drop size distribution.

scholarly journals Mystery of ice multiplication in warm-based precipitating shallow cumulus clouds

2010 ◽  
Vol 37 (10) ◽  
pp. n/a-n/a ◽  
Author(s):  
Jiming Sun ◽  
Parisa A. Ariya ◽  
Henry G. Leighton ◽  
M. K. Yau
Keyword(s):  
Cumulus Clouds ◽  
Shallow Cumulus ◽  
Ice Multiplication

scholarly journals Transport and chemical transformations influenced by shallow cumulus over land

2005 ◽  
Vol 5 (5) ◽  
pp. 8811-8849
Author(s):  
J. Vilà-Guerau de Arellano ◽  
S.-W. Kim ◽  
M. C. Barth ◽  
E. G. Patton
Keyword(s):  
Boundary Layer ◽  
Mass Flux ◽  
Cloud Layer ◽  
Cloud Formation ◽  
Cloud Base ◽  
Chemical Transformations ◽  
Mixing Ratios ◽  
Shallow Cumulus ◽  
Photolysis Rates ◽  
Good Agreement

Abstract. The distribution and evolution of reactive species in a boundary layer characterized by the presence of shallow cumulus over land is studied by means of two large-eddy simulation models: the NCAR and WUR codes. The study focuses on two physical processes that can influence the chemistry: the enhancement of the vertical transport by the buoyant convection associated with cloud formation and the perturbation of the photolysis rates below, in and above the clouds. It is shown that the dilution of the reactant mixing ratio caused by the deepening of the atmospheric boundary layer is an important process and that it can decrease reactant mixing ratios by 10 to 50 percent compared to very similar conditions but with no cloud formation. Additionally, clouds transport chemical species to higher elevations in the boundary layer compared to the case with no clouds which influences the reactant mixing ratios of the nocturnal residual layers following the collapse of the daytime boundary layer. Estimates of the rate of reactant transport based on the calculation of the integrated flux divergence range from to −0.2 ppb hr−1 to −1 ppb hr−1, indicating a net loss of sub-cloud layer air transported into the cloud layer. A comparison of this flux to a parameterized mass flux shows good agreement in mid-cloud, but at cloud base the parameterization underestimates the mass flux. Scattering of radiation by cloud drops perturbs photolysis rates. It is found that these perturbed photolysis rates substantially (10–40%) affect mixing ratios locally (spatially and temporally), but have little effect on mixing ratios averaged over space and time. We find that the ultraviolet radiance perturbation becomes more important for chemical transformations that react with a similar order time scale as the turbulent transport in clouds. Finally, the detailed intercomparison of the LES results shows very good agreement between the two codes when considering the evolution of the reactant mean, flux and (co-)variance vertical profiles.

scholarly journals Automated tracking of shallow cumulus clouds in large domain, long duration Large Eddy Simulations

2013 ◽  
Vol 6 (2) ◽  
pp. 2287-2323 ◽  
Author(s):  
T. Heus ◽  
A. Seifert
Keyword(s):  
Large Data ◽  
Current Method ◽  
Large Eddy Simulations ◽  
Large Data Sets ◽  
Data Sets ◽  
Cumulus Clouds ◽  
3 Dimensional ◽  
Shallow Cumulus ◽  
Long Duration ◽  
Large Eddy

Abstract. This paper presents a method for feature tracking of fields of shallow cumulus convection in Large Eddy Simulations (LES) by connecting the projected cloud cover in space and time, and by accounting for splitting and merging of cloud objects. Existing methods tend to be either imprecise or, when using the full 3 dimensional spatial field, prohibitively expensive for large data sets. Compared to those 3-D methods, the current method reduces the memory footprint by up to a factor 100, while retaining most of the precision by correcting for splitting and merging events between different clouds. The precision of the algorithm is further enhanced by taking the vertical extent of the cloud into account. Furthermore, rain and subcloud thermals are also tracked, and links between clouds, their rain, and their subcloud thermals are made. The method compares well with results from the literature. Resolution and domain dependencies are also discussed. For the current simulations, the cloud size distribution converges for clouds larger than an effective resolution of 6Δx, and smaller than about 20% of the horizontal domains size.

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