Cloud Patterns in the Trades Have Four Interpretable Dimensions

“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.

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Harmonic Cloud Patterns on Jupiter

SciVee ◽

10.4016/5281.01 ◽

2008 ◽

Keyword(s):

Cloud Patterns

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

Monthly Weather Review ◽

10.1175/mwr-d-19-0343.1 ◽

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.

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Cloud Patterns in Hurricane Daisy, 1958

Tellus ◽

10.3402/tellusa.v13i1.9439 ◽

1961 ◽

Vol 13 (1) ◽

pp. 8-30 ◽

Cited By ~ 22

Author(s):

Joanne S. Malkus ◽

Claude Ronne ◽

Margaret Chaffe

Keyword(s):

Cloud Patterns

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

Bulletin of the American Meteorological Society ◽

10.1175/1520-0477-41.6.291 ◽

1960 ◽

Vol 41 (6) ◽

pp. 291-297 ◽

Cited By ~ 3

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.

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

Monthly Weather Review ◽

10.1175/mwr-d-14-00406.1 ◽

2015 ◽

Vol 144 (1) ◽

pp. 139-148 ◽

Cited By ~ 4

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.

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