The physics of precipitation in cumulus clouds

1956 ◽  
Vol 35 (1) ◽  
pp. 126-138
Author(s):  
Roscoe R. Braham
Keyword(s):  
Cumulus Clouds

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 Differences between Nonprecipitating Tropical and Trade Wind Marine Shallow Cumuli

2016 ◽  
Vol 144 (2) ◽  
pp. 681-701 ◽  
Author(s):  
Virendra P. Ghate ◽  
Mark A. Miller ◽  
Ping Zhu
Keyword(s):  
Surface Fluxes ◽  
Cloud Base ◽  
Trade Wind ◽  
Cumulus Clouds ◽  
Wind Speeds ◽  
Lower Cloud ◽  
Observational Site ◽  
Velocity Scale ◽  
Atmospheric Radiation Measurement ◽  
The Mean

Abstract Marine nonprecipitating cumulus topped boundary layers (CTBLs) observed in a tropical and in a trade wind region are contrasted based on their cloud macrophysical, dynamical, and radiative structures. Data from the Atmospheric Radiation Measurement (ARM) observational site previously operating at Manus Island, Papua New Guinea, and data collected during the deployment of ARM Mobile Facility at the island of Graciosa, in the Azores, were used in this study. The tropical marine CTBLs were deeper, had higher surface fluxes and boundary layer radiative cooling, but lower wind speeds compared to their trade wind counterparts. The radiative velocity scale was 50%–70% of the surface convective velocity scale at both locations, highlighting the prominent role played by radiation in maintaining turbulence in marine CTBLs. Despite greater thicknesses, the chord lengths of tropical cumuli were on average lower than those of trade wind cumuli, and as a result of lower cloud cover, the hourly averaged (cloudy and clear) liquid water paths of tropical cumuli were lower than the trade wind cumuli. At both locations ~70% of the cloudy profiles were updrafts, while the average amount of updrafts near cloud base stronger than 1 m s−1 was ~22% in tropical cumuli and ~12% in the trade wind cumuli. The mean in-cloud radar reflectivity within updrafts and mean updraft velocity was higher in tropical cumuli than the trade wind cumuli. Despite stronger vertical velocities and a higher number of strong updrafts, due to lower cloud fraction, the updraft mass flux was lower in the tropical cumuli compared to the trade wind cumuli. The observations suggest that the tropical and trade wind marine cumulus clouds differ significantly in their macrophysical and dynamical structures.

Aircraft Observations of Transport and Diffusion in Cumulus Clouds

1986 ◽  
Vol 25 (12) ◽  
pp. 1959-1970 ◽  
Author(s):  
Jeffrey L. Stith ◽  
Don A. Griffith ◽  
R. Lynn Rose ◽  
John A. Flueck ◽  
James R. Miller ◽  
...  
Keyword(s):  
Cumulus Clouds ◽  
Aircraft Observations ◽  
Transport And Diffusion ◽  
And Diffusion

scholarly journals The Spread of Ice in Cumulus Clouds

1966 ◽  
Vol 23 (6) ◽  
pp. 753-756 ◽  
Author(s):  
Raymond Wexler ◽  
Ralph J. Donaldson
Keyword(s):  
Cumulus Clouds

Geometric Calibration of Digital Cameras for 3D Cumulus Cloud Measurements

2009 ◽  
Vol 26 (2) ◽  
pp. 200-214 ◽  
Author(s):  
Jiuxiang Hu ◽  
Anshuman Razdan ◽  
Joseph A. Zehnder
Keyword(s):  
Field Measurements ◽  
Geometric Error ◽  
Digital Cameras ◽  
Cumulus Clouds ◽  
Geometric Calibration ◽  
Earth Observing System ◽  
Stereo Photogrammetry ◽  
Camera Parameters ◽  
The Right ◽  
Coarse To Fine

Abstract A technique for calibrating digital cameras for stereo photogrammetry of cumulus clouds is presented. It has been applied to characterize the formation of summer thunderstorms observed during the Cumulus Photogrammetric, In Situ, and Doppler Observations (CuPIDO) project. Starting from gross measurements of locations, orientations of cameras, and landmark surveys, accurate locations and orientations of the cameras are obtained by minimizing a geometric error (GE). Once accurate camera parameters are obtained, 3D positions of cloud-feature points are computed by triangulation. The main contributions of this paper are as follows. First, it is proven that the GE has only one minimum in the neighborhood of the real parameters of a camera. In other words, searching the minimum of the GE enables the authors to find the right camera parameters even if there are significant differences between the initial measurements and their true values. Second, a new coarse-to-fine iterative algorithm is developed that minimizes the GE and finds the camera parameters. Numerical experiments show that the coarse-to-fine algorithm is efficient and effective. Third, a new landmark survey based on a geographic information system (GIS) rather than field measurements is presented. The GIS landmark survey is an effective and efficient way to obtain landmark world coordinates for camera calibrations in these experiments. Validation of this technique is achieved by the data collected by a NASA/Earth Observing System satellite and an instrumented aircraft. This paper builds on previous research and details the calibration and 3D reconstructions.

scholarly journals Observational estimates of detrainment and entrainment in non-precipitating shallow cumulus

2016 ◽  
Vol 16 (1) ◽  
pp. 21-33 ◽  
Author(s):  
M. S. Norgren ◽  
J. D. Small ◽  
H. H. Jonsson ◽  
P. Y. Chuang
Keyword(s):  
Mixed Layer ◽  
Vertical Transport ◽  
New Method ◽  
Atmospheric Composition ◽  
Cumulus Clouds ◽  
Neutral Buoyancy ◽  
Mass Fractions ◽  
Static Energy ◽  
Almost All ◽  
Entrained Air

Abstract. Vertical transport associated with cumulus clouds is important to the redistribution of gases, particles, and energy, with subsequent consequences for many aspects of the climate system. Previous studies have suggested that detrainment from clouds can be comparable to the updraft mass flux, and thus represents an important contribution to vertical transport. In this study, we describe a new method to deduce the amounts of gross detrainment and entrainment experienced by non-precipitating cumulus clouds using aircraft observations. The method utilizes equations for three conserved variables: cloud mass, total water, and moist static energy. Optimizing these three equations leads to estimates of the mass fractions of adiabatic mixed-layer air, entrained air and detrained air that the sampled cloud has experienced. The method is applied to six flights of the CIRPAS Twin Otter during the Gulf of Mexico Atmospheric Composition and Climate Study (GoMACCS) which took place in the Houston, Texas region during the summer of 2006 during which 176 small, non-precipitating cumuli were sampled. Using our novel method, we find that, on average, these clouds were comprised of 30 to 70 % mixed-layer air, with entrained air comprising most of the remainder. The mass fraction of detrained air was usually very small, less than 2 %, although values larger than 10 % were found in 15 % of clouds. Entrained and detrained air mass fractions both increased with altitude, consistent with some previous observational studies. The largest detrainment events were almost all associated with air that was at their level of neutral buoyancy, which has been hypothesized in previous modeling studies. This new method could be readily used with data from other previous aircraft campaigns to expand our understanding of detrainment for a variety of cloud systems.

scholarly journals High-resolution (375 m) cloud microstructure as seen from the NPP/VIIRS satellite imager

2014 ◽  
Vol 14 (5) ◽  
pp. 2479-2496 ◽  
Author(s):  
D. Rosenfeld ◽  
G. Liu ◽  
X. Yu ◽  
Y. Zhu ◽  
J. Dai ◽  
...  
Keyword(s):  
High Resolution ◽  
Forest Fires ◽  
Infrared Imaging ◽  
Cloud Formation ◽  
Cumulus Clouds ◽  
Marine Stratocumulus ◽  
Initial Stage ◽  
Microphysical Parameters ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. VIIRS (Visible Infrared Imaging Radiometer Suite), onboard the Suomi NPP (National Polar-orbiting Partnership) satellite, has an improved resolution of 750 m with respect to the 1000 m of the Moderate Resolution Imaging Spectroradiometer for the channels that allow retrieving cloud microphysical parameters such as cloud drop effective radius (re). VIIRS also has an imager with five channels of double resolution of 375 m, which was not designed for retrieving cloud products. A methodology for a high-resolution retrieval of re and microphysical presentation of the cloud field based on the VIIRS imager was developed and evaluated with respect to MODIS in this study. The tripled microphysical resolution with respect to MODIS allows obtaining new insights for cloud–aerosol interactions, especially at the smallest cloud scales, because the VIIRS imager can resolve the small convective elements that are sub-pixel for MODIS cloud products. Examples are given for new insights into ship tracks in marine stratocumulus, pollution tracks from point and diffused sources in stratocumulus and cumulus clouds over land, deep tropical convection in pristine air mass over ocean and land, tropical clouds that develop in smoke from forest fires and in heavy pollution haze over densely populated regions in southeastern Asia, and for pyro-cumulonimbus clouds. It is found that the VIIRS imager provides more robust physical interpretation and refined information for cloud and aerosol microphysics as compared to MODIS, especially in the initial stage of cloud formation. VIIRS is found to identify significantly more fully cloudy pixels when small boundary layer convective elements are present. This, in turn, allows for a better quantification of cloud–aerosol interactions and impacts on precipitation-forming processes.

A simplified model for the formation, movement, and dissipation of fair weather cumulus clouds

1965 ◽  
Vol 2 (1) ◽  
pp. 178-185
Author(s):  
Martin Fox
Keyword(s):  
Stochastic Model ◽  
Cloud Cover ◽  
Temporal Evolution ◽  
Cooling Effect ◽  
Simplified Model ◽  
Fair Weather ◽  
Cumulus Clouds ◽  
Solar Heat

Certain patches of ground are favorable to the formation of fair weather cumulus clouds. These are patches which reflect rather than absorb solar heat. As the air above these patches is warmed, it will rise and, if the humidity is sufficient, the cooling effect of higher elevation will cause formation of a cloud of the type known as the fair weather cumulus. This fact is used in the present paper to develop a stochastic model for the temporal evolution of the contribution to cloud cover by fair weather cumuli.

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
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