There is not a great correlation between the clouds governing a planetary vortex and its actual size and shape. To make detailed comparisons with theory and observations it is necessary to determine the atmospheric velocities of vortices and zonal flows. Velocities in the Jovian atmosphere have traditionally been determined by a manual method: identify a part of a cloud in one image, find the same part (if you can) in a second image taken at a different time; divide the vector displacement of the cloud part by the time between the images, and define the resulting vector to the velocity of the atmosphere at the mean location of the cloud part. Not only is this method subject to large errors, but also there is no convenient way to quantify the errors of those velocities. (Nor is there a way to easily quantify the errors in the spatial derivatives of those velocities, which are needed to compute the vorticity, potential vorticity, and horizontal divergence. Small errors in the velocities tend to make very large errors in their spatial derivatives.) In addition, for many Jovian vortices, only a few dozen velocity vectors can be manually extracted from the images. For the Great Red Spot, which is one of the largest and best-studied features of Jupiter, only a few thousand velocity vectors have been extracted manually. It may sound as if that is large number of vectors, but in fact it is small because it makes the average separation between the vectors large, ~600 km. That is too large a spacing between vectors to accurately differentiate them and obtain vorticity fields. To measure velocities in laboratory flows, automatic methods, such as Particle Image Velocimetry and Correlation Image Velocimetry, are used. These methods rely on finding correlations among 2 or more images of the flow. These methods are more accurate than manual methods, provide self-consistent ways of measuring the errors, and moreover, typically produced millions of velocity vectors from image pairs. Until recently automatic methods could not be used successfully to extract the atmospheric velocities of Jupiter or Saturn because they failed to find correlations between images that were separated in time by more than 50 minutes. With such a small separation in time (and consequently such a small displacement of the clouds), the velocities contained very large errors. In 2009, we developed a new automatic method called Advection Corrected Coherent Image Velocimetry (ACCIV) that finds correlations (and iteratively corrects errors in the correlations) among images separated in time by as much as 10 hours. With ACCIV, millions of velocity vectors with small errors can be extracted for Jupiter’s zonal flows, jet streams, and vortices. In turn, these velocity fields have allowed us to make detailed comparisons with numerical simulations and theory and have also allowed us to track how these features change in time.
Published Papers and Abstracts
“High S/N Keck and Gemini AO imaging of Uranus during 2012–2014: New cloud patterns, increasing activity, and improved wind measurements”, LA Sromovsky, I de Pater, PM Fry, HB Hammel, P Marcus, June 2015, Icarus, vol. 258 pp. 192–223, doi:10.1016/j.icarus.2015.05.029.
“A Benchmark for Cloud Tracking Wind Measurements”, KM Sayanagi, J Mitchell, AP Ingersoll, SP Ewald, PS Marcus, I de Pater, MH Wong, DS Choi, M Sussman, K Ogohara, T Imamura, T Kouyama, M Takagi, N Satoh, AD Del Genio, J Barbara, A Sanchez-Lavega, R Hueso, E García-Melendo, AA Simon-Miller, American Geophysical Union Fall Meeting Abstracts, vol. 1, pp. 1335, published December 2010.
“Variability of Jupiter’s zonal winds on multiple timescales”, Xylar S Asay-Davis, Michael H Wong, Imke de Pater, Philip S Marcus, EGU General Assembly 2010, vol. 12, pp. 7369, May 2010. — PDF
“Changes in Jupiter’s Great Red Spot (1979–2006) and Oval BA (2000–2006)”, Sushil Shetty, Philip S. Marcus, Icarus, vol. 210, pp. 182-201, July 2010, doi: 10.1016/j.icarus.2010.06.026. — PDF
“Erratum to ‘Changes in Jupiter’s Great Red Spot (1979–2006) and Oval BA (2000–2006)’ ”, Sushil Shetty, Philip S Marcus, Icarus, vol. 217, pp. 432-432, January 2012, doi: 10.1016/j.icarus.2011.10.017. — PDF
“Changes in Jupiter’s zonal velocity between 1979 and 2008”, Xylar S. Asay-Davis, Philip S. Marcus, Michael H. Wong, Imke de Pater, Icarus, vol. 2, issue 2, pp. 1215-1232, November 2010, doi: 10.1016/j.icarus.2010.11.018. — PDF
“Jupiter’s Shrinking Great Red Spot and Steady Oval BA: Velocity Measurements with the Advected Corrected Correlation Image Velocimetry Automated Cloud Tracking Method”, Xyler S. Asay-Davis, Philip S. Marcus, Mike H. Wong, Imke de Pater, Icarus, vol. 203, pp. 164-188, published May 2009, doi: 10.1016/j.icarus.2009.05.001. — PDF
“Velocity Fields of Jovian Dynamical Features using the Advection Corrected Correlation Image Velocimetry Method”, Xylar Asay-Davis, Philip Marcus, Michael H Wong, Imke de Pater, APS Division of Fluid Dynamics Meeting Abstracts, vol. 53, November 2008. — PDF
“Extraction of Velocity Fields from Telescope Image Pairs of Jupiter’s Great Red Spot, New Red Oval, and Zonal Jet Streams”, Xylar Asay-Davis, Sushil Shetty, Philip Marcus, APS Division of Fluid Dynamics Meeting Abstracts, vol. 51, November 2006. — PDF
“Extraction of Velocity Fields from HST Image Pairs of Jupiter’s Great Red Spot, New Red Oval, and Zonal Jet Streams”, Xylar Asay-Davis, S Shetty, PS Marcus, I de Pater, S Lockwood, M Wong, C Go, September 2006, Bulletin of the American Astronomical Society, vol. 38, pp. 496.
Relevant Research:
(2021) Evolution of the Horizontal Winds in Jupiter's Great Red Spot From One Jovian Year of HST/WFC3 Maps, Geophysical Research Letters 48(18), p. e2021GL093982, pdf, doi:10.1029/2021GL093982
(2015) High S/N Keck and Gemini AO imaging of Uranus during 2012–2014: New cloud patterns, increasing activity, and improved wind measurements, Icarus 258(1), p. 192-223, url, doi:10.1016/j.icarus.2015.05.029
(2012) Erratum to " Changes in Jupiter's Great Red Spot (1979-2006) and Oval BA (2000-2006)" [Icarus 210 (2010) 182-201], Icarus 217(1), p. 432, pdf, doi:10.1016/j.icarus.2011.10.017
(2011) Changes in Jupiter’s zonal velocity between 1979 and 2008, Icarus 211(2), p. 1215-1232, pdf, doi:10.1016/j.icarus.2010.11.018
(2010) Changes in Jupiter’s great red spot (1979–2006) and oval BA (2000–2006), Icarus 210(1), p. 182-201, pdf
(2009) Jupiter’s shrinking Great Red Spot and steady Oval BA: Velocity measurements with the ‘Advection Corrected Correlation Image Velocimetry’ automated cloud-tracking method, Icarus 203(1), p. 164-188, url, doi:10.1016/j.icarus.2009.05.001
Abstracts
(2020) Changes in the Velocity Field of Jupiter's Great Red Spot on Short and Long Timescales, AAS/Division for Planetary Sciences Meeting Abstracts 52, p. 100-101, url
(2010) A benchmark for cloud tracking wind measurements, AGU Fall Meeting Abstracts 2010, p. P11A--1335
(2010) Variability of Jupiter's zonal winds on multiple timescales, EGU General Assembly Conference Abstracts, p. 7369, pdf
(2008) Velocity Fields of Jovian Dynamical Features using the Advection Corrected Correlation Image Velocimetry Method, APS Division of Fluid Dynamics Meeting Abstracts 61, p. AV-002, pdf
(2006) Modeling and Data Assimilation of the Velocity of Jupiter's Great Red Spot and Red Oval, APS Division of Fluid Dynamics Meeting Abstracts 59, p. FG-007, pdf
(2006) Extraction of Velocity Fields from Telescope Image Pairs of Jupiter's Great Red Spot, New Red Oval, and Zonal Jet Streams, APS Division of Fluid Dynamics Meeting Abstracts 59, p. FG-006, pdf
(2006) Extraction of Velocity Fields from HST Image Pairs of Jupiter's Great Red Spot, New Red Oval, and Zonal Jet Streams, AAS/Division for Planetary Sciences Meeting Abstracts 38, p. 4-11, pdf