[FRIAM] Simulating Cloud Dynamics at Interactive Rates

[Steve Smith]

and me, still excited about Ken Perlin's  "Perlin Noise" work applied to
*visually convincing* cloud dynamics circa 2007.

https://ieeexplore.ieee.org/document/5694143

I think this general topic/point is a good study on the
difference/utility of "visually compelling" models vs ones useful for
*prediction* or even *understanding*.   I suppose *explanatory* comes in
one level of abstraction beyond those two?   In the case of clouds,
"what kinds of physical processes can we intuit from the specific
structural features our visual system is prone to identify?"

Pirk's work in this domain is very compelling, not just the
clouds/plumes but his general work in the domain of "semantic
segmentation of images".    The residue from (or inertial trajectory
of)  Mapillary's acquisition by FaceBook is something I'm trying to
follow.   Does this blunt or diffract their work, or re-energize it, or
do the heavy-lifters from Mapillary go forward into new ventures in this
area?  

This all has implications for human visual perception... I haven't
checked in recently on the work being done by Gar Kenyon's group at LANL
(formerly PetaVision) but it seems likely they are converging.  

- Steve

On 10/8/20 11:09 PM, Stephen Guerin wrote:
> Nick, you should appreciate the video associated with the paper in
> this link. We are considering something like this to help interpolate
> fire plumes from our work with alertwildfire.org
> <http://alertwildfire.org> cameras.
>
> http://computationalsciences.org/publications/haedrich-2020-stormscapes.html
>
> Stormscapes: Simulating Cloud Dynamics in the Now
> Metadata
> T. Hädrich, M. Makowski, W. Pałubicki, D. T. Banuti, S. Pirk, and D.
> L. Michels.
> Stormscapes: Simulating Cloud Dynamics in the Now.
> ACM Transactions on Graphics (SIGGRAPH Asia 2020), Vol. 39, No. 6,
> Article 175.
>  
> Abstract
> The complex interplay of a number of physical and meteorological
> phenomena makes simulating clouds a challenging and open research
> problem. We explore a physically accurate model for simulating clouds
> and the dynamics of their transitions. We propose first-principle
> formulations for computing buoyancy and air pressure that allow us to
> simulate the variations of atmospheric density and varying temperature
> gradients. Our simulation allows us to model various cloud types, such
> as cumulus, stratus, and stratoscumulus, and their realistic
> formations caused by changes in the atmosphere. Moreover, we are able
> to simulate large-scale cloud super cells – clusters of cumulonimbus
> formations – that are commonly present during thunderstorms. To enable
> the efficient exploration of these stormscapes, we propose a
> lightweight set of high-level parameters that allow us to intuitively
> explore cloud formations and dynamics. Our method allows us to
> simulate cloud formations of up to about 20km×20km extents at
> interactive rates. We explore the capabilities of physically accurate
> and yet interactive cloud simulations by showing numerous examples and
> by coupling our model with atmosphere measurements of real-time
> weather services to simulate cloud formations in the now. Finally, we
> quantitatively assess our model with cloud fraction profiles, a common
> measure for comparing cloud types.
>
>
> _______________________________________________________________________
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