Harmonic Cloud Patterns on Jupiter

SciVee ◽  
2008 ◽  
Keyword(s):  
Cloud Patterns

DYNAMIC CLASSIFICATION OF MESOSCALE CLOUD PATTERNS

1979 ◽  
pp. 81-86 ◽  
Author(s):  
Gérard Szejwach ◽  
Michel Desbois
Keyword(s):  
Cloud Patterns

Zooming through scales by the billion

2019 ◽  
Author(s):  
Shaun Lovejoy
Keyword(s):  
The Internet ◽  
Greek Mythology ◽  
Complex Objects ◽  
Cumulus Clouds ◽  
Warm Up ◽  
Weather And Climate ◽  
Extreme Cold ◽  
Cloud Patterns ◽  
Molecular Nature ◽  
Molecular Substructure

“The climate is what you expect, the weather is what you get”: The climate is a kind of average weather. But is it really? Those of us who have thirty years or more of recall are likely aware of subtle but systematic changes between today’s weather and the weather of their youth. I remember Montreal winters with much more snow and with longer spells of extreme cold. Did it really change? If so, was it only Montreal that changed? Or did all of Quebec change? Or did the whole planet warm up? And which is the real climate? Todays’ experience or that of the past? The key to answering these questions is the notion of scale, both in time (du­ration) and in space (size). Spatial variability is probably easier to grasp because structures of different sizes can be visualized readily (Fig. 1.1). In a puff of cigarette smoke, one can casually observe tiny wisps, whirls, and eddies. Looking out the window, we may see fluffy cumulus clouds with bumps and wiggles kilometers across. With a quick browse on the Internet, we can find satellite images of cloud patterns literally the size of the planet. Such visual inspection confirms that structures exist over a range of 10 billion or so: from 10,000 km down to less than 1 mm. At 0.1 mm, the atmosphere is like molasses; friction takes over and any whirls are quickly smoothed out. But even at this scale, matter is still “smooth.” To discern its granular, molecular nature, we would have to zoom in 1,000 times more to reach submicron scales. For weather and climate, the millimetric “dissipation scale” is thus a natural place to stop zooming, and the fact that it is still much larger than molecular scales indicates that, at this scale, we can safely discuss atmos­pheric properties without worrying about its molecular substructure. Clouds are highly complex objects. How should we deal with such apparent chaos? According to Greek mythology, at first there was only chaos; cosmos emerged later.

scholarly journals Potential Impacts of Assimilating All-Sky Satellite Infrared Radiances on Convection-Permitting Analysis and Prediction of Tropical Convection

2020 ◽  
Vol 148 (8) ◽  
pp. 3203-3224
Author(s):  
Man-Yau Chan ◽  
Fuqing Zhang ◽  
Xingchao Chen ◽  
L. Ruby Leung
Keyword(s):  
Data Assimilation ◽  
Brightness Temperature ◽  
Large Scale ◽  
Spatial Scales ◽  
Tropical Convection ◽  
Tropical Oceans ◽  
Geostationary Meteorological Satellite ◽  
Infrared Radiances ◽  
Cloud Patterns ◽  
Dry Bias

Abstract Geostationary infrared satellite observations are spatially dense [>1/(20 km)2] and temporally frequent (>1 h−1). These suggest the possibility of using these observations to constrain subsynoptic features over data-sparse regions, such as tropical oceans. In this study, the potential impacts of assimilating water vapor channel brightness temperature (WV-BT) observations from the geostationary Meteorological Satellite 7 (Meteosat-7) on tropical convection analysis and prediction were systematically examined through a series of ensemble data assimilation experiments. WV-BT observations were assimilated hourly into convection-permitting ensembles using Penn State’s ensemble square root filter (EnSRF). Comparisons against the independently observed Meteosat-7 window channel brightness temperature (Window-BT) show that the assimilation of WV-BT generally improved the intensities and locations of large-scale cloud patterns at spatial scales larger than 100 km. However, comparisons against independent soundings indicate that the EnSRF analysis produced a much stronger dry bias than the no data assimilation experiment. This strong dry bias is associated with the use of the simulated WV-BT from the prior mean during the EnSRF analysis step. A stochastic variant of the ensemble Kalman filter (NoMeanSF) is proposed. The NoMeanSF algorithm was able to assimilate the WV-BT without causing such a strong dry bias and the quality of the analyses’ horizontal cloud pattern is similar to EnSRF’s analyses. Finally, deterministic forecasts initiated from the NoMeanSF analyses possess better horizontal cloud patterns above 500 km than those of the EnSRF. These results suggest that it might be better to assimilate all-sky WV-BT through the NoMeanSF algorithm than the EnSRF algorithm.

scholarly journals Cloud Patterns in Hurricane Daisy, 1958

Tellus ◽  
1961 ◽  
Vol 13 (1) ◽  
pp. 8-30 ◽  
Author(s):  
Joanne S. Malkus ◽  
Claude Ronne ◽  
Margaret Chaffe
Keyword(s):  
Cloud Patterns

Cloud Patterns as Seen from Altitudes of 250 to 850 Miles — Preliminary Results

1960 ◽  
Vol 41 (6) ◽  
pp. 291-297 ◽  
Author(s):  
John H. Conover ◽  
James C. Sadler
Keyword(s):  
Large Scale ◽  
Vertical Motion ◽  
Time Lapse ◽  
Squall Line ◽  
Low Level ◽  
The Earth ◽  
Stationary Front ◽  
Ballistic Missiles ◽  
High Level ◽  
Cloud Patterns

Time-lapse films of the earth from high-flying ballistic missiles have provided the meteorologist with the first synoptic detailed coverage of cloud patterns over large areas. Analysis of the film obtained on 24 August 1959 shows the cloud patterns over an area corresponding to one-twentieth of the earth's total surface. Comparison of the rectified cloud positions with, the high- and low-level synoptic charts shows large-scale cloud patterns directly associated with high-level vortices and troughs as well as patterns associated with a quasi-stationary front and the intertropical convergence zone. Details suggesting low-level vortices, frontal waves, and a squall line appear, but they cannot be verified due to sparse surface observations. Other details, such as the effects of large and small islands, coastlines and rivers upon the pattern of vertical motion are indicated by the clouds.

scholarly journals Atmospheric Kármán Vortex Shedding from Jeju Island, East China Sea: A Numerical Study*

2015 ◽  
Vol 144 (1) ◽  
pp. 139-148 ◽  
Author(s):  
Junshi Ito ◽  
Hiroshi Niino
Keyword(s):  
Vortex Shedding ◽  
Bluff Body ◽  
Numerical Study ◽  
Jeju Island ◽  
Cloud Formation ◽  
Surface Drag ◽  
Convective Boundary ◽  
Sensitivity Experiment ◽  
Karman Vortex ◽  
Cloud Patterns

Abstract A mesoscale atmospheric numerical model is used to simulate two cases of Kármán vortex shedding in the lee of Jeju Island, South Korea, in the winter of 2013. Observed cloud patterns associated with the Kármán vortex shedding are successfully reproduced. When the winter monsoon flows out from the Eurasian continent, a convective mixed layer develops through the supply of heat and moisture from the relatively warm Yellow Sea and encounters Jeju Island and dynamical conditions favorable for the formation of lee vortices are realized. Vortices that form behind the island induce updrafts to trigger cloud formation at the top of the convective boundary layer. A sensitivity experiment in which surface drag on the island is eliminated demonstrates that the formation mechanism of the atmospheric Kármán vortex shedding is different from that behind a bluff body in classical fluid mechanics.

scholarly journals The role of advection in the formation of vortex cloud patterns

Tellus ◽  
1962 ◽  
Vol 14 (4) ◽  
pp. 409-421 ◽  
Author(s):  
JOHN A. LEESE
Keyword(s):  
Cloud Patterns

Cloud regimes and associated MJO variability over the Maritime Continent in austral summer

2020 ◽  
Author(s):  
Zijie Zhao ◽  
Claire Vincent ◽  
Todd Lane
Keyword(s):  
Rainfall Variability ◽  
Austral Summer ◽  
Maritime Continent ◽  
Convective Clouds ◽  
Scale Modelling ◽  
A New Technique ◽  
Climate Scale ◽  
High Level ◽  
Cloud Patterns ◽  
Cloud Regimes

<p>In this study, a new technique to determine distinct cloud regimes and their variation in space and time is proposed, evaluated, and applied to two satellite products over the Maritime Continent (MC). Compared to previous methods, the method presented here allows different types of cloud to co-exist in the same grid at the same time, giving rise to physically explainable and spatially continuous patterns in cloud regimes. Similar results generated by ISCCP – H and Himawari 8 data suggests that the method is robust. The 4 cloud regimes determined using this method are associated with shallow, mid-level, deep convective and high level clouds respectively. The analysis shows that he MJO–induced variation in total cloud fraction is dominated by day-time high–level clouds, while the diurnal MJO variability is mostly demonstrated by low–level cumulus. Spatial and temporal rainfall variability over the MC during austral summer is dominated by high–level clouds, while most local signatures and land–sea differences are attributed to deep convective clouds. Using an artificial neural network, the cloud patterns over the MC can be classified into nine categories, largely dominated by the MJO-phase. Active MJO activity is shown by systematic propagation around the cloud categories, with one category associated with the inactive MJO phase. The inhomogenous propagation of the MJO can partially be revealed in the generated patterns, which can be physically explained by the enhanced/suppressed convection over the Indian Ocean. This work has implications for understanding the MJO-scale variation in precipitation and diabatic heating associated with different cloud regimes, and its representation in mesoscale and climate scale modelling systems.</p>

Sign in / Sign up

Export Citation Format

Share Document