Researchers Simulate Monster EF5 Tornado 61
New submitter Orp writes: I am the member of a research team that created a supercell thunderstorm simulation that is getting a lot of attention. Presented at the 27th Annual Severe Local Storms Conference in Madison, Wisconsin, Leigh Orf's talk was produced entirely as high def video and put on YouTube shortly after the presentation. In the simulation, the storm's updraft is so strong that it essentially peels rain-cooled air near the surface upward and into the storm's updraft, which appears to play a key role in maintaining the tornado. The simulation was based upon the environment that produced the May 24, 2011 outbreak which included a long-track EF5 tornado near El Reno Oklahoma (not to be confused with the May 31, 2013 EF5 tornado that killed three storm researchers).
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This is totes cray cray!
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Technically, it is still moving... but in a virtual space
LOL (Score:1)
It's Saturday night, I've got one in my sleeping bag right now.
Amazing (Score:5, Interesting)
This is pretty amazing. I've heard the theory before that tornadoes are formed from that same baroclinic horizontal vortex tilting upwards, but the mechanism for that has never made a whole lot of sense to me. The idea of it getting pulled up into the actual mesocyclone itself and powering it so that the tornado can form makes a lot more sense. It also makes it a lot more clear what role the RFD has in tornadogenesis. And that parade of vortices, I'd never heard of that before.
Hopefully this will help the weather people start to see clues that a tornado is trying to form even before the hook starts to become obvious on radar.
Wow.. (Score:1)
Presentation as seen on YouTube well done (Score:3)
That was a very well done presentation even if it was so far over my head that I understood little but, "oooh, pretty".
The pacing was fast, confident, and even had the audience laughing at times. Congratulations.
Now I feel an evil urge to make a joke about how, since your model didn't properly account for "hydrometeor centrifigal whatzits" then it is therefore worthless and you, Mr Orf, like those climate researchers, are in it for the big bucks in grant money to fund your lavish Toyotas and suburban middle class homes.
Or something. I've likely failed at humour. But you've succeeded in your research, kudos.
Re:Presentation as seen on YouTube well done (Score:5, Informative)
There was a lot of jargon. Let's see if I can help.
baroclinic == energy created by movement of air because of differences in pressure.
forward flank downdraft == I believe the downdraft caused by the air cooled by the rain, also could be air forced down by the forward movement of the storm. Usually cool and wet.
rear flank downdraft == warm, dry air that hits the storm from the back and is forced downward.
mesocyclone == the rotating updraft in a supercell.
Basically, he's stating that the interface between the different types of air on the ground is creating a rolling tube of air, and the updraft of the storm is so powerful that it sucks that tube up - and the energy of the rotation helps to give the updraft an extra "kick"... which helps to power and maintain the rotation of a long-track tornado. That tube isn't the tornado itself, it just powers the updraft that spins the tornado.
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Thanks, that helped give the terms and overall presentation some context. Of course, most of it still went way above my head as one might expect, since this is for consumption by specialists in the field and not laypersons like us. Fascinating stuff even so.
All programmers and engineers should watch this video, and remember that when they talk to average people about computers in any sort of depth, this is how you sound to them as well.
Re:Presentation as seen on YouTube well done (Score:5, Interesting)
Learning how thunderstorms work is something I think everyone should do on a basic level - they are utterly fascinating. What is amazing to me is that these repeatable and frankly amazing structures are created out of nothing but air, water, and heat in varying combinations.
In layman's terms (and while I'm a layman I'm educated enough to be able to say that) a thunderstorm is simply caused by buoyant air rising. As it rises, the moisture inside of the air condenses, creating a cloud and releasing heat (latent heat). That warms the "parcel" of air more (another term he used frequently) and it rises faster.
Air that rises sucks up more air from below it because it creates a low pressure region. Eventually the air hits the top of the troposphere, which is stable (stable means that the air is warmer than the rising parcel and the rising air is no longer buoyant. Keep in mind that "warmer" is relative and can mean -60F.)
In conditions that cause a storm like this to form, vertical wind shear is important. In a pulse thunderstorm, the downdraft (the rain cooled air) gets in the way of the updraft and chokes it off. But the wind shear not only causes the updraft to rotate, it pushes the downdraft out of the way of the updraft, so nothing chokes it off. This is why when you look at a supercell, it is nearly always tilted to the direction of travel. (mesocyclone is another word for updraft in this case.)
Now that the storm is created, you have room for the other factors he was mentioning, such as the RFD, FFD, etc. Basically there is a certain combination of factors required to set the air at the ground to spinning. The interesting thing about this simulation was that the managed to find the sweet spot and get their simulation to create a long-tracked tornado. Much of his presentation was spent highlighting certain parcels of air and showing how they got ingested by either the meso or the tornadic circulation (which are related but not necessarily the same thing.)
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Very informative again, thanks. What's really fascinating is that, with the benefit of these high-level explanations (which the researchers take for granted that the other professionals well understand this already - they just hint at it here and there), you can really see that process in action using the various visualization techniques employed, such as the visualization of air flow from specific points in front of the structure, or the visualization of the positively versus negatively buoyant air. I ca
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Yes, it is quite fascinating. I started learning this stuff because as a child I was afraid of thunderstorms. Learning how they worked made them far more interesting than scary, though a loud thunderclap in the middle of the night can still freak me out.
There is nothing like being underneath a supercell as it approaches, and the sirens are going off, and the lightning is crashing all around you. The fact that it's nothing but a giant cloud does nothing to dampen the experience.
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I've been a weather spotter for years and one of my dream jobs has always been as a storm chaser. I thought this video was incredible and the explanations that I found in the comments really helped me understand some of the things that went over my head. Many thanks to all who contributed to the discussion....y'all rock!
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I never understood how people could be afraid of thunderstorms until I moved to the midwest for a brief time and saw (and heard) a *real* thunderstorm. There's really no comparison between those storms and anything else I've seen. The midwest has some crazy weather, including the occasional green sky during a storm, which I happen to witness once while driving home from work. On that same drive I saw a lighting bolt hit right on the side of the road, practically blinding me, and scaring the bejezus out o
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I grew up in Ohio and live in Oregon now. The storms here are wimpy. I still miss the storms of the midwest.
Only to some degree, though. I don't miss the tornado warnings and pitch black sky.
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It's called the F5 -
From what I can gather, somewhere along the line they had to "enhance" the F ratings to get more f4's and ef 5's.
The F scale rates a tornado from F0 all the way to F5 with a F5 tornado having the fastest wind speeds and causing the most damage.
An F0 Tornado
* Have wind speeds between 40-72 mph
* Causes light damage.
* Branches breaks off of trees and pushes over smaller trees.
An F1 Tornado
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"It's called the F5 - From what I can gather, somewhere along the line they had to "enhance" the F ratings to get more f4's and ef 5's."
Not quite. From Wikipedia:
It was revised to reflect better examinations of tornado damage surveys, so as to align wind speeds more closely with associated storm damage. Better standardizing and elucidating what was previously subjective and ambiguous, it also adds more types of structures and vegetation, expands degrees of damage, and better accounts for variables such as d
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Somehow I don't think the scientists bothered to add trailer parks to their simulation.
Oddly, the question at the end *did* sort of cover that question, as trailer parks would count as ground friction.
Some questions for submitter (Score:2)
What computing system was used to render the video, and is there a paper available which describes some of the math behind the simulation?
Some technical info for slashdotters (Score:5, Interesting)
I wanted to give some info on the technical aspect of getting this to work that might be appreciated by slashdotters.
You can read about the Blue Waters hardware profile here [illinois.edu]. Our simulation "only" utilized 20,000 of the approximately 700,000 processing cores on the machine. Blue Waters, like all major supercomputers, runs a Linux kernel tuned for HPC.
The cloud model, CM1 [ucar.edu], is a hybrid MPI/OpenMP model. Blue Waters has 16 cores (or 32 depending on how you look at it) per node. We have 16 MPI processes going and each MPI rank can access two OpenMP threads. Our decomposition is nothing special, and it works well enough at the scales we are running at.
The simulation produced on the order of 100 TB of raw data. It is easy to produce a lot of data with these simulations - data is saved as 3D floating point arrays and only compresses roughly 2:1 in aggregate form (some types of data compress better than others). I/O is a significant bottleneck for these types of simulations when you save data very frequently, which is necessary for these detailed simulations, and I've spent years working on getting I/O to work sufficiently well so that this kind of simulation and visualization was possible.
The CM1 model is written in Fortran 90/95. The code I wrote to get all the I/O and visualization stuff to work is a combination of C, C++, and Python. The model's raw output format is HDF5, and files are scattered about in a logical way, and I've written a set of tools to interface with the data in a way that greatly simplifies things through an API that accesses the data at a low level but does not require the user to do anything but request data bounded by Cartesian coordinates.
I would have to say the biggest challenge wasn't technical (and the technical challenges are significant), but was physical: Getting a storm to produce one of these types of tornadoes. They are very rare in nature, and this behavior is mirrored in the numerical world. We hope to model more of these so we can draw more general conclusions; a single simulation is compelling, but with sensitivity studies etc. you can really start to do some neat things.
We are now working on publishing the work, which seems to have "passed the sniff test" at the Severe Local Storms conference. It's exciting, and we look forward to really teasing apart some of these interesting processes that show up in the visualizations.
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At the resolution I'm running at: decades, and this would require improvements in algorithms / hardware utilization, as well as finding a way to build and utilize machines on the exascale. Going from petascale to exascale is going to require methods/topologies that don't exist yet, I think (not just GPUs for example). And exascale power requirements are a real problem with existing hardware.
At "convection resolving" simulations (on the order of 1 km resolution - not 30 meters!) where a tornado-like vortex m
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Not Orp, but I would guess that this isn't possible because the entire storm acts as a system. Not only that, but the environment around it as well. That's why the box he was using was 120kmx120x20.
First of all, you have the updraft and the downdraft interacting with each other - and these two parts are actually the critical parts of a storm - and they're the *whole* storm. But the storm wouldn't be powerful without external factors influencing and driving it. A high CAPE (convective available potential
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Trying to make a model that only focuses on the critical parts of a storm would be like trying to make a model of a car that only focused on one piston.
Good insight in all your comments. Actually, tornado modeling has a long history. Think of those vortex chambers you see at some museums etc... pioneers like Neil Ward [wikipedia.org] studied tornadoes in this manner. Now, think of a numerical model of one of those chambers. This type of work has been done by folks like David Lewellen at West Virginia University.
What makes this new simulation special is that we've simulated the entire storm and allowed "things to evolve naturally" (for a very judicious interpretation of wh
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Thank you. Not bad for a sysadmin. :) I've always had an interest in how storms work, though it's not nearly as involved as yours, obviously.
I think there's a difference between showing how a tornado works when it's already on the ground (with which you could make models that accurately represent the tornado itself - the "most critical" parts of the storm) and how a tornado forms and is maintained by the larger structure of the storm. For the latter, you need a model of the scale that you created.
I actua
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Good insight in all your comments.
Adding the car analogy and having the guy miss it---priceless.
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I got about halfway through the video before the kids interrupted me (and it). So let me just ask:
Did your model take into account the energy gathering and discharge that would show a multi-amp, million-volt DC discharge? Because the energy implications of that are going to be enormous to the model.
Did it also have a mechanism that generated the lightning discharges of the storm? Because again, the lightning discharges are going to affect the electrical energy available to help / hinder the tornado.
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Interestingly, while those discharges are powerful, they don't really release energy in a form that a thunderstorm can actually use. The amount of energy being released just by condensation alone dwarfs the electrical output of a thunderstorm by a large margin. I think of the electrical discharges as just a byproduct of the storm, in much the same way as exhaust is the byproduct of the mechanism that makes a car move.
If I'm wrong, I'd most certainly like to know, because the mechanisms by which that kind
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I got about halfway through the video before the kids interrupted me (and it). So let me just ask:
Did your model take into account the energy gathering and discharge that would show a multi-amp, million-volt DC discharge? Because the energy implications of that are going to be enormous to the model.
Did it also have a mechanism that generated the lightning discharges of the storm? Because again, the lightning discharges are going to affect the electrical energy available to help / hinder the tornado.
No lightning in the model. I know of some research on modeling lightning in supercells, but I'm pretty sure they are one-way models; the lightning occurs based upon what we know about inductive and non inductive charge mechanisms, and flashes can happen - but they do not feed back into the storm. I don't think they probably feed back appreciably into real storms. Even though a lot of energy is released with lightning, so much more is released due to latent heating (phase changes between solid/liquid/gas) th
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okay, but does your model show a 1-A plus though the tornado? because that is known to exist, and the electrical power is same order of magnitude of the wind power, so it's bound to be a significant effect.
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1) What grid type does your simulation code use? If it's regular grid, have you considered switching to something more adaptive like AMR or unstructured grids?
2) Since I/O is your main bottleneck, have you considered further decimating your output and visualizing in situ to fill in the gap? I suspect your visual analysis is too co
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As a visualization guy, it always makes me happy to see such a good use of visualization. Thanks for providing some extra technical details here! A couple of questions, though:
1) What grid type does your simulation code use? If it's regular grid, have you considered switching to something more adaptive like AMR or unstructured grids?
2) Since I/O is your main bottleneck, have you considered further decimating your output and visualizing in situ to fill in the gap? I suspect your visual analysis is too complicated for current in situ techniques to cover everything you want to do, but I'd like to hear your thoughts on it.
It's isotropic (delta x = delta y = delta z) for most of the storm, and then uses an analytical stretch function to determine the mesh outside of that region. I used the stretch technique of Wilhelmson and Chen (1982; Journal of the Atmospheric Sciences).
AMR has its benefits but adds a lot of complexity, and I tend to wish to go towards less complex, not more. I am more interested in these new heaxongal grids (see: NCAR's MPAS [github.io]) which have very nice properties. I predict MPAS will be the Next Big Thing and w
Ground Friction (Score:2)
Not yet incorporated into the model if I understood the talk correctly.
This will be interesting to see when the ground is modeled. At some point, ground features (hils, valleys,etc.) may affect the growth and trajectory of a tornado. And it would be interesting to see if such models can provide a damage risk profile with respect to these features.
So I'll know where not to park my mobile home.
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My guess would be that for the most part ground friction really doesn't affect the growth of a tornado, or even the trajectory. I think what it *would* do is affect the intensity and damage of wind over the bottom 25 feet or so. I'd be interested in seeing what happens, though, when a tornado occurs over a cliff or a large hill.
I'm guessing they'll continue to run simulations and five years later we'll learn something else interesting and groundbreaking.
Model the Atmospheric Vortex Engine (Score:2)
You guys should contact Peter Thiel's Breakout Laboratories that funded a just-completed study of a physical model of a tornado with the potential of generating electricity -- baseload electricity at that -- from ambient heat.
Here are the most recent photographs and short video of that scale model [vortexengine.ca] which, at full scale, would be called an Atmospheric Vortex Engine.