Mission Overview

Jupiter Great Red Spot Velocity Fields from HST/WFC3 (GRS-WFC3)


Primary Investigator: Michael H. Wong

Contributing Program PIs: Amy A. Simon, Imke de Pater, Glenn Schneider

HLSP Authors: Michael H. Wong

Released: 15 April 2021

Updated: 15 April 2021

Primary Reference(s): Coming soon...

DOI: 10.17909/t9-jfs3-p240

Citations: Coming soon...

Read Me

Source Data:

Animations showing the Great Red  Spot over 10 hours.
This is approximately what it would look like if the Great Red Spot were continuously illuminated and viewed for 10 hours. HST/WFC3 data were used to measure the wind flow in and around Jupiter's Great Red Spot. This wind field was used to interpolate how the cloud features changed over one Jupiter day (about 10 hours). In reality, the Great Red Spot would have rotated from day to night to day over this time period, so this movie would be impossible to record directly. The data come from September 2020 Jupiter observations from the Outer Planet Atmospheres Legacy (OPAL) program (Simon et al. 2015).  NOTE: The original video is available on YouTube.


High precision velocity fields of Jupiter's Great Red Spot (GRS) have been created by correlating sequences of WFC3/UVIS images spanning typical sequence durations of 10 hours. Velocities were measured using the Advection Corrected Correlation Image Velocimetry (ACCIV) software, which was described in Asay-Davis et al. (2009) and is available at GitHub.

This archive node is an early release containing High-Level Science Products (HLSPs) based on observations near 2019-June-26, taken as part of the OPAL program (GO-15502). The velocity field data are used in a new paper led by Marzia Parisi (JPL).

A paper led by Mike Wong (UC Berkeley) is in preparation, entitled "Evolution of the Horizontal Winds in Jupiter’s Great Red Spot from over a Decade of HST/WFC3 Maps." This paper will describe the evolution of the GRS velocity field, and the other epochs of data will be released when the paper is submitted.

Data Products

Three products are provided for each velocity field in the form of .tar.gz files. Each tar file contains groups of additional data files. The tar file naming convention is as follows:



  • <timestamp> = The fractional year, example, "2019.48".
  • <dataset> = The label of the dataset, example, "grs19opal-d12".
  • <filter> = The name of the HST filter used, example, "f631n".
  • <bundle> = The type of data files in this bundle, one of "inputs", "output-analysis", or "output-data".

Data file types:

_inputs.tar.gz These collections of text and HDF5-format binary data are required as input to the ACCIV velocity retrieval code. ACCIV uses an iterative approach, and parameter files included in this bundle describe all parameter values needed to control the successive iterations of the retrieval. Binary grid files are included to specify the geometry of the data, and the cylindrically-projected HST data, after application of limb darkening corrections, are included as numbered frames. A full description of the parameters and the format of the HDF5 input data can be found at the ACCIV GitHub repository.

These bundles contain human-readable plots and maps (in PDF format), along with text files containing some tabular data used to generate the plots plus other quantities measured from the velocity fields. A full description of each plot/text file will be provided with the upcoming publication.


These bundles contain two binary files in HDF5 format. These data files describe the velocity field retrieved by the ACCIV process.  These are the files most likely to be directly useful for further analysis. The ACCIV GitHub repository and primary reference (Asay-Davis et al. 2009) should be consulted to understand how these data fiiles are derived.


Briefly, the included data files are:

outGridVelocity.h5 - Velocities in the x- and y-directions are interpolated onto a regular grid in latitude and longitude.


outScatteredVelocity.h5 - Individual velocity vectors are given, prior to gridding.

Data Access


Normal Text = This product is expected but not public yet.

Link Text = Click to download products.

UT Date

(Series Midpoint)

Fractional Year Dataset Label High Level Science Products
2009-09-22 16:37 2009.73 grs09 input | output-data | output-analysis
2012-09-20 15:42 2012.72 grs12 input | output-data | output-analysis
2015-01-19 13:53 2015.05 grs15p4 input | output-data | output-analysis
2015-01-19 13:53 2015.05 grs15p5 input | output-data | output-analysis
2016-02-09 16:03 2016.11 grs16opal_d12 input | output-data | output-analysis
2016-02-09 16:03 2016.11 grs16opal_d21 input | output-data | output-analysis
2016-12-11 19:42 2016.94 grs16pj03 input | output-data | output-analysis
2017-02-01 22:50 2017.08 grs17pj04_d12 input | output-data | output-analysis
2017-02-01 22:50 2017.08 grs17pj04_d21 input | output-data | output-analysis
2017-02-01 22:50 2017.08 grs17pj04_long input | output-data | output-analysis
2017-04-03 08:08 2017.25 grs17opal_long input | output-data | output-analysis
2018-04-17 07:49 2018.29 grs18opal_d12 input | output-data | output-analysis
2018-04-17 07:49 2018.29 grs18opal_d21 input | output-data | output-analysis
2019-04-09 18:44 2019.27 grs19pj19 input | output-data | output-analysis
2019-06-26 12:46 2019.48 grs19opal_d12 input | output-data | output-analysis
2019-06-26 12:46 2019.48 grs19opal_d23 input | output-data | output-analysis
2020-09-20 08:15 2020.72 grs20 input | output-data | output-analysis



Please remember to cite the appropriate paper(s) below and the DOI if you use these data in a published work. 

Note: These HLSP data products are licensed for use under CC BY 4.0.