Title

An Examination Of The Local Dynamics Of Ice Giant Atmospheric Phenomena

Abstract

Meteorological phenomena are commonly studied by computational simulations combining computational fluid dynamics with other numerical models. One such phenomenon is geophysical vortices. Though hurricanes on Earth and the Great Red Spot on Jupiter are probably the most familiar examples of these features, much can also be gained by examining vortices on other planets. The Ice Giants, Uranus and Neptune, have sported multiple large vortex features, some of which have exhibited remarkable dynamics. For example, the Great Dark Spot, discovered by Voyager II in 1989, was tracked over eight months and traveled towards the equator by ten degrees in latitude. It also oscillated in shape over an eight-day period during the month of closest observation. In several cases, these dark spots were accompanied by bright companion clouds that were linked to the spot motions. Recent simulations have shown the clouds might have significant effects on the vortex dynamics, indicating a more complex system with possibly more commonalities with hurricanes than previously expected. This paper will examine the local behavior of vortex and cloud features, emphasizing the Uranian Dark Spot. High resolution simulations will study the effects these clouds have on the observed vortex motions, including attention to vertical influences. This continuing study of these features will enhance our understating of Ice Giant meteorology, including the prediction of future Ice Giant vortex feature movements, but may also provide insight into the dynamics of vortex features in other atmospheres including Earth. Copyright © 2012 by the authors.

Publication Date

12-1-2012

Publication Title

50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

Number of Pages

-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.2514/6.2012-561

Socpus ID

84873817262 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84873817262

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