Looks like a we're limited to the South Pole for PJ19...

Jupiter - PJ19-4

Jupiter - PJ19-5

Jupiter - PJ19-7

Jupiter - PJ19-5 - Map Projected

Full Version: Juno Perijove 19

Looks like a we're limited to the South Pole for PJ19...

Jupiter - PJ19-4

Jupiter - PJ19-5

Jupiter - PJ19-7

Jupiter - PJ19-5 - Map Projected

Jupiter - PJ19-4

Jupiter - PJ19-5

Jupiter - PJ19-7

Jupiter - PJ19-5 - Map Projected

Yes, the spacecraft was turned "sideways" for PJ19 in order for the Microwave Radiometer (MWR) to sweep out a bigger swath of longitude than the nearly "pencil beam" it does on its usual orbits. Both JunoCam and the near-infrared instrument JIRAM were turned off for safety issues, i.e. avoiding pointing into the sun and, for JIRAM, overheating. But they were turned on again once the MWR completed its observations and the spacecraft was again turned back into its normal orientation. The was the first time this approach was used, and there are currently none planned for the remainder of the mission - although that could always change once the full information this mode returned is fully evaluated. -Glenn Orton, Juno science team member

Thank you. Seems like a good reason to not use JunoCam. I hope some good science comes out of it.

Here's an overview of Perijove 19:

Perijove 19 - Overview

Here's an overview of Perijove 19:

Perijove 19 - Overview

This is image PJ19_1. First an approximately true color/contrast image and then a version with increased contrast that has also been processed to show color differences more clearly:

Click to view attachmentClick to view attachment

As John Rogers mentions at the Juno website, three small vortices can be seen in the currently whitened South Temperate Belt in the new JunoCam images. Below is a heavily processed map-projected image that shows these ovals:

Click to view attachment

Click to view attachmentClick to view attachment

As John Rogers mentions at the Juno website, three small vortices can be seen in the currently whitened South Temperate Belt in the new JunoCam images. Below is a heavily processed map-projected image that shows these ovals:

Click to view attachment

Gerald, your "weather forecast" GIF on missionjuno is very cool. A few questions, because I'm a curious cat...

Did you develop your own fluid dynamic simulation or are you using some other CFD software?

What is the simulation time step?

What are the grid dimensions?

Did you develop your own fluid dynamic simulation or are you using some other CFD software?

What is the simulation time step?

What are the grid dimensions?

Thanks, Brian!

I'm developing my own fluid dynamical simulation software from scratch, but stuff is non-trivial, so I'm serving myself of the shoulders of giants from a methodological point of view. Like in many other cases, you can really understand things only, if you implement them yourself. Better if you even develop the required methods yourself, and go through all the flaws and glitches you can make. The time step in the excerpt I've submitted to missionjuno is 1 hour, but the full version is calculated with fixed steps of 6 minutes. In order to keep the file size within reasonable limits, I've taken just each 10th image, and reduced it by a factor of 2. The method is grid-free on an inviscid flat 2D Biot-Savart approach with a stream-function-derived 2D vorticity field of the south polar region of PJ19 as initial condition of an initial value problem. Algorithmic complexity is O(tnē), with n the number of 4th order "Gauss-mollified" "vortons" and t the number of time-steps. I'm using single-step multi-stage explicit Runge-Kutta methods with fixed time-steps of order 4 or 5 for numerical integration of the ODEs. Dormand-Prince methods are useful to test for the quality of the convergence of the integration.

I'm working on a 2-spherical version in order to get a little more realistic. Simulating on the precise IAU Jupiter 2-spheroid might be algorithmically expensive, and difficult to implement, because of the according geodesic problem. I'm exploring how far I can go within reasonable computational and implementation costs. Even the 2-sphere simulation is much slower(but still O(tnē)) than the flat euclidean 2D plane.

There is much more to tell, maybe within a presentation on one of the conferences this year, provided I'll get an abstract submitted in time.

Finite volume methods on a grid appear less suitable in a very turbulent regime. They tend to get numerically unstable for large Reynolds numbers unless very short time steps are applied (see von Neumann criteria).

Some simulations of turbulence favor VIC (vortex in cell) methods. I've still to decide, whether I'll test this family of methods, too. But those hybrid methods are more difficult to understand, and I'm not yet quite sure, whether I'd been able to modify the required Poisson solver for non-euclidean geometry within short time, if necessary. So, my first choice is the above approach, which I already understand sufficiently well to modify it according to my requirements.

If you are interested in a good introductory paper, I'd recommend Cottet & al "Vortex Methods, Theory and Practice".

I'm developing my own fluid dynamical simulation software from scratch, but stuff is non-trivial, so I'm serving myself of the shoulders of giants from a methodological point of view. Like in many other cases, you can really understand things only, if you implement them yourself. Better if you even develop the required methods yourself, and go through all the flaws and glitches you can make. The time step in the excerpt I've submitted to missionjuno is 1 hour, but the full version is calculated with fixed steps of 6 minutes. In order to keep the file size within reasonable limits, I've taken just each 10th image, and reduced it by a factor of 2. The method is grid-free on an inviscid flat 2D Biot-Savart approach with a stream-function-derived 2D vorticity field of the south polar region of PJ19 as initial condition of an initial value problem. Algorithmic complexity is O(tnē), with n the number of 4th order "Gauss-mollified" "vortons" and t the number of time-steps. I'm using single-step multi-stage explicit Runge-Kutta methods with fixed time-steps of order 4 or 5 for numerical integration of the ODEs. Dormand-Prince methods are useful to test for the quality of the convergence of the integration.

I'm working on a 2-spherical version in order to get a little more realistic. Simulating on the precise IAU Jupiter 2-spheroid might be algorithmically expensive, and difficult to implement, because of the according geodesic problem. I'm exploring how far I can go within reasonable computational and implementation costs. Even the 2-sphere simulation is much slower(but still O(tnē)) than the flat euclidean 2D plane.

There is much more to tell, maybe within a presentation on one of the conferences this year, provided I'll get an abstract submitted in time.

Finite volume methods on a grid appear less suitable in a very turbulent regime. They tend to get numerically unstable for large Reynolds numbers unless very short time steps are applied (see von Neumann criteria).

Some simulations of turbulence favor VIC (vortex in cell) methods. I've still to decide, whether I'll test this family of methods, too. But those hybrid methods are more difficult to understand, and I'm not yet quite sure, whether I'd been able to modify the required Poisson solver for non-euclidean geometry within short time, if necessary. So, my first choice is the above approach, which I already understand sufficiently well to modify it according to my requirements.

If you are interested in a good introductory paper, I'd recommend Cottet & al "Vortex Methods, Theory and Practice".

A more complete answer is online now on the EGU2020 website, including an explanation video at the end of the abstract:

Fluid Dynamical 2D Simulations of Jupiter's South Polar Region Based On JunoCam Image Data

Thanks to excellent suggestions of my co-authors Candy and Glenn, and of John Rogers, I hope that the video is of an acceptable quality in the meanwhile. Obviously, further work is TBD.

I first tried to work with a more recent PJ. But PJ19 seems still to be the best JunoCam image sequence to analyse south polar dynamics.

You might imagine, why I didn't find much time for discussions the recent few months

Fluid Dynamical 2D Simulations of Jupiter's South Polar Region Based On JunoCam Image Data

Thanks to excellent suggestions of my co-authors Candy and Glenn, and of John Rogers, I hope that the video is of an acceptable quality in the meanwhile. Obviously, further work is TBD.

I first tried to work with a more recent PJ. But PJ19 seems still to be the best JunoCam image sequence to analyse south polar dynamics.

You might imagine, why I didn't find much time for discussions the recent few months

This is a "lo-fi" version of our main content. To view the full version with more information, formatting and images, please click here.

Invision Power Board © 2001-2020 Invision Power Services, Inc.