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Center for Nonlinear Studies |
Spacecraft images of Jupiter show that the cloud features, which are associated with dynamical features such as vortices and zonal flows, are rapidly changing. We can reach a much better understanding of the dynamics of Jupiter's climate by studying the velocity fields of the dynamical features, rather than just their cloud patterns. Because we are interested in following changes in Jupiter's climate and weather over the past 30 years, we have to use data from different spacecraft with very different spatial and temporal resolutions. Traditional methods for measuring velocities on Jupiter involve manually tracking the motion of cloud features. These methods typically produce hundreds to a few thousand measurements. We find the number of measurements must be greater by at least a factor of 100 to accurately detect changes in the properties of dynamical features. Although automated methods are capable of producing hundreds of thousands of velocity measurements, previous automated methods have failed to measure velocities from images separated by more than 2 hours. This is a problem because the accuracy of a velocity measurement is inversely proportional to the separation time between images. Here, we show a new automated method that can find hundreds of thousands to millions of precise measurements using images with time separations on the order of 10 hours, resulting in about a factor of 5 improvement in accuracy.
Host: Mark Petersen, CCS-2