- Welcome, everyone, to Wednesday Nite @ the Lab.
I'm Tom Zinnen.
I work here at the UW-Madison Biotechnology Center.
I also work UW-Extension Cooperative Extension,
and on behalf of those folks and our other co-organizers,
Wisconsin Public Television, the Wisconsin Alumni Association,
and the UW-Madison Science Alliance,
thanks again for coming to Wednesday Nite @ the Lab.
We do this every Wednesday night, 50 times a year.
Tonight we get to hear about tornadoes.
These are not tornadoes, but they are twisters
and they are twisted.
The cool thing is 10 years ago today
was a Wednesday and my son was born this morning.
I got to come and do Wednesday Nite @ the Lab at that night.
And this is his little toy.
I thought it was very appropriate that we got to have
both right-handed and left-handed tornadoes
on hand here.
And I'm going to ask you how come all our tornadoes in the
Midwest go this way and hardly any of them go this way?
Tonight, we are under a tornado warning here in...
Dane County and other parts of southern
and southwestern Wisconsin. No joke.
It was really great coming to Wednesday Nite @ the Lab
tonight to have Wisconsin Public Radio
break in and tell us to seek shelter.
So, we have, tonight, Leigh Orf from the Cooperative Institute
for Meteorological Satellite Studies.
He was born in Springfield, Massachusetts,
grew up and went to high school in Morris, Illinois,
which is down on Route 47,
and they would know nothing about major tornadoes storms.
- Right. - It's a very flat part of Illinois.
He came up here to UW-Madison
to get his bachelor's and PhD in meteorology.
Then he was on the faculty at University of North
Carolina in Asheville and then at Central Michigan University.
In 2015, he came back to be here at UW-Madison.
Tonight he's going to talk with us about
supercomputer modeling of tornadoes.
We probably lose more sleep to tornadoes
than to other kinds of natural disasters in Wisconsin.
I'm looking forward to seeing some of the scariest video
I've ever seen on TV, or at least on the web.
All through the simulations of his supercomputers.
Please join me in welcoming Leigh Orf to
Wednesday Nite @ the Lab.
[applause]
- All right, let's see if we can wake this thing up.
There we go.
Okay, well, thank you very much for the invitation,
and I'm really happy to be here.
I am going to talk to you about supercells and tornadoes.
And I've got a whole bunch of cool video
and stuff to show you, so we'll just get right down to it.
So when you're asking a scientist what they do or why
they do it, generally speaking you have some goals
to try to better understand the physical world.
In this case, I'm trying to understand a few things here,
if I just sort of break it down.
First of all, how do tornadoes form?
We can watch them form when people have video cameras.
We can take our Doppler radars and scan storms.
But the processes that go on inside of storms that produce
tornadoes are still not very well known.
So computer models are very useful tools to answer questions
about things that go on inside of simulated thunderstorms,
and that's how I do this.
Once a tornado forms, it's like the balance of forces
that keeps it going.
This is an important thing as well.
And finally, why do some supercell thunderstorms,
like the one that was warned today,
and it's to our southwest.
I don't think it produced a tornado.
We were issued a tornado warning for that part of the county,
but I don't think a tornado, I think it was seen on radar.
This is called-- Assuming I'm right,
that would have been a false alarm.
We issued a warning, but no tornado happened.
And we tend to err on the side of safety,
so we tend to issue warnings whenever we think there's going
to be a tornado, even if we haven't seen one yet.
And this is a problem because, if you're like me, you're like,
well, I heard the sirens go off four other times
and there's no tornado, why should I listen to this one?
Well, the fifth one's a tornado, so you have to be careful.
We're trying, we're working on this,
but it's a real challenging problem.
So, really, why do some supercells produce long-lived
devastating tornadoes while most others don't?
And we don't know yet.
We have some ideas, and this research really aims to take a
computational approach to answer that question.
So my process, how do I attack this problem?
I have been very interested in using computers to solve
physical problems since I was in high school.
I was in high school in the '80s.
Computers weren't so big and powerful,
but I already saw that you could do
some very interesting things with computers.
Once I realized you could program them to simulate
physical things, I was hooked because I liked weather and I
liked computers, so I thought, hey, why not use them both?
So I'm simulating a storm, growing it in a virtual lab
you might say, and just seeing what happens.
When I run a simulation, I don't know
if it's going to produce a tornado or not.
And I'm only showing you the ones that produced tornadoes,
but I want to be clear about that.
Like the real weather, it's hard to predict whether the tornado
is going to form in even the simulated storm.
And that's probably good.
It means our model is behaving a bit like the real atmosphere
in that it doesn't always produce tornadoes.
So it was a real challenge to get this work to even happen.
To get the tornado to even form.
It took a lot of getting things to work on these very powerful
supercomputers that are not exactly user-friendly.
So I'm at this stage in the--
I'm burning as much computer time as I can
to create as many tornadoes as I can so I can study them.
You'll I've done a lot of work visualizing and animating,
putting the motions so we human beings
can understand what's going on better.
We're just starting to get to the quantitative analysis part.
That means when you get into the real numbers, not just movies.
And then, of course, we also want to always compare our
results to observed storms because the real atmosphere
is really guiding us on this.
Why do we study these types of storms?
They're the least common but most devastating.
So most supercells, about one in four to one in five supercells
produce a tornado.
And most tornadoes are short-lived
and not very damaging.
Very few tornadoes produce the kind of damage you saw
like in Joplin, Missouri, or in El Reno, Oklahoma.
I can think of a whole bunch.
Plainfield, Illinois, is one that's near and dear to my heart
because I grew up in Illinois,
and Plainfield was in our football conference.
And I actually played football on that football field,
and I saw, from an aerial photo, just garbage everywhere
because the high school had been destroyed.
That was in 1990.
So despite advances in observational and computational
meteorology, such as what I do, we still don't really know why
some storms produce tornadoes and others don't.
So one of the ways to approach this is just to simulate a whole
bunch of storms and look at what happens in the ones
that do produce these long-lived tornadoes
compared to the ones that don't.
But I do want to first talk about ordinary thunderstorms.
The ones we're more familiar with.
The more common thunderstorms.
These are often called air mass thunderstorms.
Under certain conditions, though,
these rather sort of harmless-looking thunderstorms
can produce very brief, very strong winds,
and they're called downbursts or microbursts.
I studied these while I was here at UW for my PhD.
And I wanted to at least give a little bit so you can sort of
compare and contrast the difference between a supercell
and something like this. This is, I think, Phoenix.
This was taken in the monsoon season in the Southwest.
You see this big blast of rainfall from this cloud.
You can see sort of a hole in the cloud.
The rain streams down, hits the ground,
and spreads out, boom, in all directions.
That is not a tornado. That is a downburst.
But I wanted to sort of show you that sometimes
pretty boring-looking thunderstorms
can produce some pretty strong winds.
These winds can sometimes get into the tornado range,
but usually they knock some trees down.
Here's a simulation of a thunderstorm
that produces a downburst.
Here is the cloud field and the rain field.
This is from some work I did a few years ago with some wind
engineers who were interested in looking at how damaging winds
from thunderstorms affect the built environment
such as power transmission lines and things like that.
So I cooked them up a thunderstorm
that produced a specific kind of downburst,
and I'll show you a little animation of this.
This was done by a guy at NCSA.
So he took my data and he animated it.
This is cold air sinking.
That's what forces these downbursts.
Hits the ground and it spreads out into a cold pool.
This is a cold blob of air that is generated
by evaporation of rain primarily.
It spreads out, like if you'd dump your milk
on the kitchen table it'll spread out in a puddle.
In this case, it spreads out and you see some very strong winds
along the leading edge of this.
But again, this is not a supercell.
This is not a tornado.
This is a downburst, but it is another type of thunderstorm
that produces damaging winds.
This is the kind of storm I'm interested in.
This is a supercell thunderstorm.
I chose this one because it has some interesting features in it.
So this one produces a very big tornado.
You can see it on the ground.
Here are different parts of the storm
that meteorologists will often refer to.
The mesocyclone is a fancy word for rotating updraft.
So all thunderstorms contain updrafts.
That's air that's rising upwards, like, you know,
think of billowing smoke from a campfire rising up in the air.
The air inside, the air is rising
because it's positively buoyant.
It's lighter than the air surrounding it,
and there's an upward force that helps it rise.
Now, this whole updraft is rotating because of how the
storm is interacting with the environment.
That atmosphere surrounding it, which is,
in cases for supercells,
contains a lot of environmental wind shear.
So you perturb this cloud growing in this highly sheared
environment and it causes things to become
organized in interesting ways.
Now, supercells are typically broken down into flanks.
The forward flank, that's where the storm is going.
So this storm is moving probably towards the right,
towards the east or the northeast.
The rear flank is where the storm has been.
Now, you typically see a lot of meteorologists studying this
region of the storm for a couple of reasons.
One reason is it's safe.
Safer than the forward flank because if you're in the rear
flank, the tornado has already been there
or at least been in that vicinity.
It's moving away from you.
One result from my research, if you want to call it a result,
is the fact that we really need to be studying this region a lot
more carefully even though it's a lot more dangerous
and a lot windier, typically.
But that's where a lot of the interesting action is.
This cloud here is called a tail cloud.
And you'll see me refer to this several times in this talk.
Notice how it seems to be sort of streaming into this updraft here.
And that is not necessarily exactly how the wind is going,
but it does sort of hint to that kind of wind motion.
And you'll see why I do that in a moment.
Now, this is for my simulation.
This is from a 30-meter simulation.
You can see there's your mesocyclone and your tail cloud,
forward flank, rear flank, tornado.
It's all there but it's a simulation. Okay?
This was something I grew in a computer.
So if, how do I do this? How do we do this?
You need a good model, in this case a model is a piece of software.
It's written in Fortran.
It's developed by a scientist at the National Center for
Atmospheric Research, which is funded by
the National Science Foundation.
This model was written to utilize, very efficiently,
big computers that are made up of, essentially,
hundreds of thousands of individual computers, really,
is what it kind of boils down to.
Tens of thousands of computers
each of which has multiple cores.
So you have to write code that can actually exploit
that kind of hardware.
You need a good set of initial conditions.
We turn to the actual atmosphere for our conditions.
We sampled, or in a sense we sampled the environment adjacent
to a storm that produced a real nasty tornado.
We used that to grow our cloud. You need a supercomputer.
If you see one on the street or something, let me know. I'm kind of looking for one.
I wouldn't mind having my own personal supercomputer.
But in reality, blue waters, again, NSF sponsored.
It's at the University of Illinois.
About a $250 million machine, the machine and to maintain it.
Very nice computer.
And the software, I wrote a bunch of software
to handle the immense amount of data that came out of the model
and I wanted to be able to visualize it
and animate it and do all these cool things.
And it took a fair amount of work.
And you do need a little bit of luck because,
like I said earlier, you don't always get the storm
that you are trying to simulate. This is radar.
I was just looking at my radar app on the phone
because it was always tornadoes all around us, supposedly.
This is reflectivity.
This shows you where the rain and the hail is.
Here is a blob that sort of centered on where the tornado is
because meteorologists sometimes call this a debris ball.
This storm is kicking up lots of debris.
It's in a pretty rural area,
but it's not knocking trees down and such.
And that shows up as a signal on radar.
This is radio velocity.
The radar is over here somewhere.
And this couplet indicates rotation.
So this radar is telling us, not he left there,
that we have rotation going on.
And these two images are at the same,
they're just two different fields
but the same exact location, the same exact time.
So this is what tour model supercell look like.
If you look at radar much and you're interested in supercells,
you see this nice hook echo,
this big area of heavy rain and hail.
And we can zoom in a little bit and we can take a closer look,
and our tornado has just started here.
We get this nice cyclonic tornado,
but we actually have an anti-cyclonic tornado in this simulation as well.
So some tornadoes do rotate, at least in my model,
and the wrong direction, quote-unquote.
The truth is anti-cyclonic tornadoes and cyclonic
supercells are not unprecedented.
There have been several observed but they are kind of uncommon.
So if you're wondering the size of the problem,
the simulation I'm going to talk about mostly
is about two billion grid cubes.
We break the atmosphere into cubes, essentially,
and then all the cubes are in space and kind of communicate
with one another to solve the laws of physics.
I have gone down to higher and higher resolution
to where I have about 18-and-a-half billion grid cubes.
We're talking some serious computation here.
And when you go from 30 meters to 15 meters,
it doesn't take twice as much computation,
it takes about 16 times as much.
And there's laws, there's reasons for that.
But this is why you need supercomputers.
It's a very big problem, so I'm going to give you a quick overview
before I get into all the cool stuff.
So we have a tornado that's on a digital ground,
if you want to call it that, for just under two hours.
It's similar to the one that was observed, which is gratifying.
You see a lot of things that aren't tornadoes but they look
like little mini tornadoes in the simulation
when you actually look at them in the vorticity field,
which is a field that shows the atmosphere air spinning.
We see very interesting things.
I will just explain it in my poor human way
before the computer shows you.
You see vortices accumulating and sort of merging together
as a tornado forms, and a feature we're calling,
and it's kind of got a funny name,
streamwise vorticity current.
Let's just call it the SVC.
You'll see it better when I show it on the video,
but it's a feature that shows up in these simulations
that I think helps to maintain the supercell
that helps to produce the tornado.
But first, some actual footage.
I want you guys to get used to looking at actual supercells,
and then we can come back to the model data and compare.
So these are designed to sort of get you familiar
with what's going on here.
So this is repeating loop over and over again.
We're looking from the south towards the north.
This is the forward flank of the storm, the rear flank.
This is our rotating updraft. You can see that it's rotating.
And here is a tail cloud.
This is the tail cloud we've been talking about.
Notice how this rain here is kind of kicking out.
Here's a different view.
So you can sort of see this motion
that kind of goes like this: rain falls, cools the air,
gets sucked back into the storm and helps to form a cloud.
I think that's what's actually happening here.
The storm is almost recycling itself.
But you keep-- Here's your rotation.
You can see I've sped this up a little bit so you can see the action.
We'll take one more look at this storm a little closer up, but,
again, see how this cloud is sort of boiling,
almost like it's boiling up off the ground?
You've got air here, but it's a mixture of cool,
moist air and warm, moist air mixing together,
and that can actually form a cloud when the conditions are
just right and you're seeing those conditions right there.
But you're also seeing a tremendous amount of rotation
and some explosive updrafts.
You can sort of see the updrafts shooting up.
It's easier to see on the computer screen.
This is one of my last sequences.
Here, again, is the forward flank.
Notice how the air is kind of streaming in
and forming this little cloud here?
There's a theme here, and I am focusing on things that I've seen in the model.
But I'm seeing this in several, I just went out to YouTube
and looked for time lapse of supercells
and I saw a lot of things that my model shows
because I'm not a field meteorologist.
I do collaborate with field meteorologists.
But anyway, you sort of see a pattern here.
And this is my favorite one, my last one.
Here's your tail cloud, your forward flank of the storm,
the mesocyclone, and just look at this kind of streaming right in.
Right here is where you'd expect that tornado
to drop right out of that cloud.
And that's what, I'm not sure if this particular one did,
but now I'm going to start showing you
some of the simulation data.
But that should give you at least a general idea of what to
look for or where I'm looking in the storm.
And here's one more that actually shows a tornado form.
This low-hanging cloud that you see here is called a wall cloud.
It often, it's not always there with supercells.
Usually it's there, but it's like this little collar that
kind of goes around the tornado if the tornado forms.
And the tornado typically descends out of that region where the wall cloud.
The wall cloud is probably just air
from the tail clouds being wrapped around.
But meteorologists give it a name because we do that thing.
That's what we do. Give things names.
This is some footage from my collaborators, Bruce and Kathy,
who are also doing research with me.
Here's sped up four times.
You're seeing, here's a tornado,
and you can see these little multiple vortices.
You can see little hints of them.
So you see one tornado that just looks like a funnel,
but there's actually little, sort of like a razor,
a Norelco razor, if you know what that is.
You've got little vortices kind of rotating around one another.
This is in real time showing the tornado
when it sort of looks like a big, tall stove pipe.
And this is an interesting event.
This is in Pilger, Nebraska, in 2014.
Two tornadoes from two supercells.
The tornadoes eventually end up merging.
But you can see the tremendous power of this thing.
This was an EF4, the second highest category.
And you can see, if you look closely,
debris being sort of swung out there.
This guy is like, oh, yeah, it's just another day in Nebraska,
just driving down the road.
I'm like where did he start?
Because it's like he must have just been there, right?
But you can see stuff flying out.
But, yeah, you know, pretty powerful stuff.
And this is the kind of storm that we're trying to simulate.
And some of the things that you see here,
and here's the final sequence I'll show you of observations, this is that monster tornado.
It's weakened and then this other tornado has gotten stronger.
This is sped up a little bit but you'll see,
and sorry for the shaky video, but this guy will kind of come
in here and merge with that one.
So you see vortex mergers, you see all sorts
of interesting things, tail clouds.
So this is sort of giving you an idea what we see with our eyes.
Now we get back to the simulations.
So you've seen this before.
Here's the domain size, 120 kilometers on a side,
20 kilometers tall. This is the full model.
I'm zooming in now, and you can see that tiny little tornado
there, well it looks tiny, we have to simulate the whole storm
at basically the resolution that that tornado is at.
So you have to resolve, when we say resolve a tornado it means
we have to have enough grid cubes that are making up that
tornado and we don't know where the tornado is going to be
so we've got to throw a lot of grid cubes at it.
So the whole thing requires
a tremendous amount of computation.
All right, so this is one of my newer simulations.
It's going to run for like two minutes.
So here we are, here's the mesocyclone here.
Here's the sort of tail cloud that's kind of forming.
You can, this is the forward flank region where the rain is
falling, and sort of sweeping in here is the rear flank.
So keep your eyes right about here.
You see all these little sort of like
little tags in the cloud field.
One of those is going to become a tornado.
And it's going to happen right about now.
So there it is. Interesting.
All right, now what led to that?
Well, we can't see it just looking at the cloud and the rain.
Although I did this because I wanted to show what we see in
the model, which is our eyeballs see cloud rain.
So here I've done something, here's a forward flank and the rear flank.
This is the surface temperature perturbation, so cold is blue.
Cold air, the storm is producing cold air, it's in blue.
Here's the forward flank and the rear flank.
So this is the surface, like looking at a weather map.
This is reflectivity, which is radar, what the radar sees.
And this is the updraft.
And what happens prior to this tornado forming is this updraft
one kilometer above the ground gets really strong, okay, that's one thing.
We're looking for precursors to the tornado formation
so that we can hopefully forecast these down the road.
So the thing I noticed, one of the things I noticed when I
studied the updraft is it gets really strong.
You see these dark reds?
You have updrafts a kilometer above the ground
exceeding, say, 150 miles an hour.
That's air going up. Okay?
One thing that this simulation has showed me
is that the updrafts in these things near the ground
are tremendously strong.
Stronger than I think we may have assumed in the past.
Now I'm not saying that the model is right necessarily,
you know, but it's really hard to measure updrafts a couple
hundred meters above the ground if you're not using a model.
So we have to sort of assume the model is probably doing the
right thing, but we still have to find these things in Mother
Nature, and that's the job for my cohorts in the field
who are field meteorologists.
But as I've been talking here, this thing is just churning along.
Now, it looks like it's just sitting in place,
but the whole storm is moving towards the northeast
at about 40 miles an hour.
The reason we move the box with the storm is so it doesn't just
go away and we lose our storm.
So a trick we use--
We simply move the model domain with the storm.
It's actually not really a trick, it's pretty easy to do it.
It's what all cloud models do for this kind of environment
where the storm is actually moving.
Now you can see it's gotten wider.
It's hard to see in here but there's little multiple vortices spinning around,
and you can sort of see them show themselves once in a while.
And I've got some slides that show this better.
But it's just cranking and cranking.
It's sucking up all that cold air.
And this is another thing that was kind of a surprise to me
is that this tornado is actually thriving on
lifting this cold air.
Cold air is heavier than warm air, okay?
Given the same conditions, one's colder, one's warmer,
where the cold air is a little heavier,
you'd expect it to be harder to lift the cold air,
and it is, but the thing is,
the updraft is parked right over this boundary.
So it's sucking up cold air, but it's also sucking up air
that's rotating and it's tilting it into the vertical.
And that rotation, that enhanced rotation, is helping, I think,
to drop the pressure which is helping to make the updraft
stronger which is helping to form the tornado.
So that's what we have to get quantitative into this.
Now, eventually, yes, this is a dangerous condition
if you're in a real tornado.
It gets wrapped in rain. This is extremely dangerous.
If you're a storm chaser and you're in this mess,
you're in grave danger because there's still a tornado in there.
It is weakening but there's a tornado in there.
And, you know, the chaser, some of the chaser accidents and such
that I've heard of are people driving in heavy rain or getting
blown off the road from strong gusts of wind
because they're too close to the storm.
There seems to be a new sport of extreme storm chasing that I'm
not entirely crazy about because all you need is a cellphone
and a radar app to get to where the storms are now.
It's actually quite easy to chase.
Here I'm doing the same sequence as before but I'm just,
this thing that looks like a kayak is actually
the updraft being volume rendered.
The updraft is what I'm focusing on because it seems to be,
it's like the heart of the storm.
The updraft is being forced by other things,
but the updraft itself is what's responsible
for a lot of the action.
So we don't have a tornado yet. Here's the cloud field.
Here's the radar reflectivity, and here's that cold pool again.
But you'll see, it'll show up.
I clipped it at one kilometer above the ground.
So what you start to see yellows here,
that's the updraft getting stronger.
That's the updraft getting closer to, say,
40 meters per second, say 100 miles an hour or so.
Now the tornado's there.
The tornado has a signature in the updraft field as well.
There's actually a small downdraft.
It's in the center of the tornado.
Just like a hurricane, most tornadoes have eyes.
In other words, a sinking downdraft in the center.
You'll see evidence of that in a little bit.
But, it's just cranking along.
And part of me looks at this and I'm amazed at how--
It's almost like this isn't moving.
It's just locked in place. It's smooth.
There's definitely some things that we can look at
that are sort of in balance that we can
try to look at the part that's balanced and then sort of
figure out what's going on with the tornado.
But it just goes and goes.
And this is 10 frames, let's see.
This is one frame per second.
30 frames per second for your eyeballs.
So you're seeing-- I'm saving data every second.
So you're seeing a lot of information.
This was the hardest part of this problem so far has been
finding a way to save data that frequently
and not go crazy because it creates, you know,
a couple hundred terabytes of data.
That's a lot of data that goes into these things.
So the updraft gets very close to the ground.
You can see downdraft air. The blues are downdraft.
So what ends up killing this thing is you start to see a
lot of downdraft air impinging upon this updraft.
And you'll see it coming in from the forward flank over
here and some from the rear flank over here.
But you see these big curtains of blue, that's precipitation.
That's rain falling.
You can see it here in the reflectivity image.
So we're looking towards the west, but see this wall here?
You're seeing the downdraft associated with this heavy precipitation.
And, eventually, as you'll see,
it just gets kind of clobbered by rain.
We've already seen it in the previous sequence,
but eventually it just kind of gets,
this updraft gets eroded to the point where
a tornado just can't be maintained.
And this is the big one.
When you see this big push of precipitation that starts to
come in from the side, this is when it starts to get obscured,
and it eventually gets weaker
but not before doing some EF5 damage.
This thing is rain wrapped, doing EF5 damage,
which is probably the absolute worst possible situation that
you could be in if it was coming at you because you can't see it.
It's in rain, and, in all seriousness, you know,
go to your safe spot, go to your shelter,
go to your basement in the center somewhere,
a small bathroom or something.
But it does eventually fizzle out.
And I have some other slides of it fizzling out.
But, essentially, it gets sort of clobbered by downdraft.
The precipitation kind of falls and it sort of--
In a sense it seems to be sort of breaking the balance of
forces that we had for so long.
Okay, so this one is kind of fun too.
What I'm doing here is-- This is actually precipitation.
I've just colored it in psychedelic colors
just to entertain you.
Well, no, actually not.
The colors indicate the speed,
whether the wind is going up or down.
Rain that's falling, the rain can fall through the air.
You can have rain falling through the air
where the air is going up. You see what I'm saying?
Air, you know, think of a large raindrop.
I might fall at, say, 30 miles an hour.
But if it's in an updraft going up 20 miles an hour,
it's going to fall at 10 miles an hour.
So you can have rain that's falling up,
and there is rain falling up here.
But anyway, remember the earlier sequence I showed you
that showed that rain kind of coming in
and forming that little tail cloud?
Well, this is precipitation. And I see a similar feature.
So here you have this precipitation that's falling,
and it's not falling very fast.
It's probably like small drizzle droplets,
and it's actually being pulled up into the storm.
And that's where you see our tail cloud form.
And I've seen this in more footage and I'm starting to
think there might be something going on here.
So you have rain falling up. Ah! Crazy.
If you get a strong enough updraft, you can lift it up.
In fact, that's why hailstones get so big because hailstones
can fall, say, 100, maybe 80 miles an hour
but you can have an updraft that's 90 miles an hour.
In fact, the hail formed because it was suspended
in the air for so long.
Now here's vorticity.
Vorticity is the measure of spin and shear.
But the thing I want you to realize is this is the magnitude
of vorticity, and I'm shading it by the vertical components so
that when you see red, that's cyclonic or counterclockwise.
So this is, like, the heart of the mesocyclone.
It's rotating this way.
And when you see blue tubes, those are anti-cyclonic.
So, yes, there are a lot of anti-cyclonic tubes
or vortices in this simulation.
At some point, I blessed this thing and called it the
streamwise vorticity current.
I'm sort of joking, but this is the feature we've found
that seems to be important.
You'll notice as I change the view in a second,
this thing kind of wraps up around the tornado but is not intersected.
And there's a tornado. It formed.
So, somewhere in that movie is the solution to tornadoes.
Well, maybe not. So I'm continuing to go.
There's our super strong updraft.
There's our streamwise vorticity current
that's bringing this air up and around.
And there's our tornado.
Look at that, it actually lifts a vortex almost off the ground
and then turns it erect, and then it kind of,
look what it does to it.
See, the blues guys are rotating the opposite direction
of the red guys, and they don't interact very well.
They're like pointing two magnets, north poles to each other.
They sort of fight against one another.
But when you have red guys coming in,
they just get assimilated into the circulation of a tornado.
So that's interesting as well.
So this, what you're seeing here, no one's really,
this is like the first time anyone's managed to do this
with this kind of fidelity and this kind of,
showing it in this kind of form.
And I've even managed to go to higher resolution.
I'm going to share some of that with you shortly.
But, there's a story to be told here.
I need a fleet of grad students to dive
into this data, and they'll be happy for a long time
because we have tons of data to analyze.
And, in fact, I'm in the process of generating more data.
And isn't it interesting how in the rear flank of the storm it
kicks in a bunch of I call of phosphorescent spaghetti.
But, essentially, it's very turbulent in the rear flank
where you have all these pulsing downdrafts.
So where you see this blue and red mass, it's just turbulence.
It's just air that's kind of going off
in all sorts of different directions.
But sometimes those turbulent eddies and that turbulence forms
these vortices, and there's another blue guy
who's going to get swept around the red guy.
So, yeah, it's pretty cool.
You can start to see it's getting eroded a little bit
as this downdraft air starts to get ingrained in there.
But it goes and goes.
And these features here that I think are important,
we need to find them in nature.
And that is going to be a challenge because this is,
if you're actually, the storm is coming right for us
in the room from this direction.
It's coming to the northeast.
And if you want to study this region,
you better deploy your instruments and get the heck
out of the way because it's coming right for us.
But I'm very serious when I say this is a region of the storm
that we need to study harder to see if what this model
is telling us is out there in nature.
And I want to also stress the fact
that I've studied one environment.
For all these simulations, it's like one storm
being simulated in different ways.
I'm really interested at looking at several environments,
not just one storm, so that we can start to draw more general
conclusions because I want to be clear.
I don't want to make any grand conclusions about this
simulation because it's just one environment.
At this point, things have gotten all rainy
and the storm is getting weaker.
Here's a still image to give our eyes a rest for a few minutes.
This is just at the point where the tornado is forming.
This is that sort of upward, the horizontal vorticity that gets
tilted into the vertical that we're giving a name, the SVC.
This little vortex right here is going to become a tornado.
This is actually an anti-cyclonic vortex.
And you see all these other guys here?
Some of them are spinning one way
and some of them are spinning another way.
Most of them are spinning cyclonically,
and they help to form a tornado.
But you see a lot of vortices that are anti-cyclonic.
So, again, the red guys are cyclonic, going this way.
Cyclonic, cyclonic, cyclonic. Okay, that's good.
These three sort of merged into a tornado.
Here's a tornado looking stronger.
Here's an anti-cyclonic vortex. So it's rotating this way.
So you have one rotating this way, one rotating that way.
And this guy gets sort of lifted up a tornado
like we've seen before.
But this is something that surprised me.
There's a whole lot of rotation going on at different scales.
Here we go again.
That vortex I just showed you got lifted and tilted.
Now it's a horizontally-oriented vortex.
It looks like a hairpin.
There's a vortex going through that one.
It's like all twisted up. It's so cool.
Same kind of thing going on.
Anti-cyclonic, anti-cyclonic, cyclonic, cyclonic.
And then more phosphorescent spaghetti.
So a lot of turbulent action going on.
How much is of that is important
and how much of it is just happening?
Well, we don't know yet.
We really only started looking at these simulations
at this kind of resolution recently.
So here I'm just going to let some air parcels go
and watch where the air goes.
Just drop parcels near the ground.
And immediately you can see both the tornado
and the streamwise vorticity current.
So this is, I'm just letting parcels go
from near the surface.
Like everyone throws up a big beach ball every two seconds,
only the beach ball is completely negatively buoyant.
And boof, boof, boof, watch the beach balls go.
So this is our SVC. This is our tornado.
This is the gust front, the rear flank gust front.
It all kind of shows up, and I've shaded it by speed.
The air is moving faster here than it is here, for instance,
but it's moving really fast in the tornado,
as you might expect.
So all these different ways of looking at the same thing.
This is that streamwise vorticity current thing
if you're looking at radar.
Here's your hook echo. You'd expect it to be sort of streaming in
from the north sort of and wrapping around.
Again, this is something that if we're going to find we're going
to have to look pretty hard for it.
Here's a figure that kind of shows there's that SVC thing.
These are just parcels being let.
Notice as it's rotating as it goes.
Here's our tornado, and we'll just kind of go up
the tornado and watch.
This thing is rotating, but also the path is rotating.
So it's really kind of cool.
The path of this thing is a helix,
but it's moving in a helical direction.
So it's moving helically, but it's also shaped like a helix.
It's like rotation all the way down.
But this is an interesting feature.
Basically, the storm is organizing vorticity in an
interesting way that I think is helping to make it stronger.
And this is something that--
This is where we're going with this.
Here's a really cool sequence.
This is the best rendering of a thunderstorm
I've seen by David Bach at NCSA,
one of the folks I've collaborated with.
There's the model grid.
We're going to plop the storm down.
This is just a cloud field so there's no rain in this.
But David has done a very good job of using shadows
and such to make it look really spiffy.
There's the overshooting thunderstorm top,
mammatus clouds, and we let this go for about 800 seconds.
There's our tornado on the ground.
We're going about 30 times real time.
60 times real time.
And you can see that explosive growth
you would see with the time lapse of the supercell.
And, like I said, this is very, very nice rendering.
So David took my data and made it look really good.
Here's the streamwise vorticity current thing.
I'm just releasing air parcels, letting them go,
and seeing where they end up. Notice how they rotate.
They're rotating horizontally and then they turn upwards
and now they're rotating vertically.
And tornadoes are all about vertical rotation, right?
Rotation about a vertical axis.
Here you have rotation about a horizontal axis that then
becomes tilted into the vertical, and that's important.
That's an important process in this simulation
and probably in real storms as well.
So, yeah, you know, sometimes you can't even see the tornado.
It's wrapped up in this mess of turbulence
that sort of squirted out from the rear flank of the storm.
More parcels.
So here I'm releasing them in different parts
of the storm to see where it's going.
So here is that forward flank region of the storm.
That's the streamwise vorticity current in red.
The green is in the cold pool air,
and it seems to go straight into the tornado at the bottom. The yellow is similar.
The blue, the light blue is in rear flank of the storm,
and it seems to take a more circuitous path
and doesn't necessarily get right into a tornado.
But looking at this really slowly,
you can see the very sharp turn upwards and around, you know,
the SVC is going like that upwards.
And over here, the greens are just going,
taking a very sharp turn right at the tornado base and getting into a tornado.
So we're going to be looking at where the air is coming from,
where it's going, what are the properties of the air,
what modulates the properties of the air.
All the different things we can do.
Like I said, a fleet of grad students for a long time.
Lots of stuff we can look at.
And onward and onward.
Yeah, so this is the 30-meter simulation
I've been sharing with you.
This is the research we've published.
We've already done more simulations.
In fact, some of the stuff I'm going to show you
at the end of this talk has been seen by nobody else
but one of my collaborators.
You'll be the first to see it.
I just rendered the frames yesterday.
Here is a tornado again releasing air parcels.
Notice, remember when I said that there's a downdraft
in the center of a tornado?
Well, the air is getting caught in that downdraft
and then immediately recirculating upwards.
And you can see that.
I'm just letting parcels go a kilometer-and-a-half
above the ground, and they get caught in the downdraft
but they make a really nice tracer for the tornado.
I think it's a really cool, really cool way
to visualize a tornado, I think.
Okay, for this next sequence that I'm going to share with you,
blue is downdraft
and this sort of green/gold color is updraft.
And this is going to show you the tornado dying,
and this is radar reflectivity at the surface.
So towards the end of the simulation,
the white tube is the tornado.
It's the vorticity at the surface.
You'll see the tornado is doing okay.
You can see it all the way up into the cloud.
But watch all this blue sort of just kind of circle the tornado.
You'll see it get really blue here.
And yes it's raining heavily,
but I think there's some other things going on.
And, boom, the tornado is gone.
Really, it gets stomped by that downdraft,
and it really ends quite abruptly in this simulation.
Not all of our simulations have that, but that one does.
Okay, so we talked about the 30-meter simulation,
now I'm going to go to a 20-meter simulation.
So this is higher resolution, more grid cubes,
so you'd expect to see more detail,
maybe better-looking clouds.
The simulation has gone on quite a bit to this point,
but at the time I started rendering frames,
this is an anti-cyclonic tornado.
And you'll see it kind of goes away and then,
boom, there is that one.
And I see this in some of my simulations where the
anti-cyclonic tornado weakens and that one strengthens.
Like a see-saw kind of a thing going on.
And I'm still trying to figure that out.
Here's that precipitation off to the right, streaming inward.
You don't see it as clearly as before,
but one of the nice things I like about this rendering is
you can see the individual vortices inside the tornado.
So this is multiple vortex tornado.
We think that most strong tornadoes especially have
multiple vortices in them, so you can see them in there.
You can see clear air between those cloud,
those tubes of cloud, which is what the vortices look like.
Now I'm going to slow it down a little bit and zoom in.
And I'm also going to compare it to a guy who got stuck
on the side of I-39 for the--
It was in 2014, Illinois.
I can't think of the-- Fairdale. Fairdale, yeah.
You've probably seen this video if you're a tornado freak.
So this guy is on I-39.
This thing is coming basically right across the street this way.
The reason I show this, and yes, he was okay,
is that you can see, if you look carefully,
you'll see little breaks between the vortices
where you can see the sky behind it.
And that's just kind of showing you that--
There's one, there's, you're seeing multiple vortices here and here.
So in other words, when I see this looking like that,
I'm like this is probably,
we're probably on the right track here.
But we always have to look to Mother Nature
when we do this theoretical work, okay?
We have to validate our data.
Here I'm doing vorticity again, blue guys and red guys.
So the blue guy sort of fizzles out,
and here comes this screaming red guy.
And I use a different threshold here
to show you just the tornado.
But I'll slow it down a little.
But notice how it's like two different vortices
wrapped around each other, spinning around.
So each one of these is a little tornado sort of.
The are really rapidly rotating air,
and then they're sort of dancing around each other.
And, you know, when you hear about tornadoes hitting your
region and one house gets obliterated and the house across
the street doesn't, it's because you've got these tiny little
vortices, and it's almost random.
It's chaotic. Okay?
So that's the reason why, I think, sometimes you see
almost random damage when it comes to like populated areas.
This is also looking at spin vorticity.
And I'm going kind of fast here, but I'll slow it down.
But, you know, lots of action.
I've seen actual radar data that looks a lot like this from the
May 31, 2013, El Reno, Oklahoma, tornado that actually managed to
kill some professional storm chasers.
They were caught unaware of the fact
that they were in a two-and-a-half-mile wide tornado.
And one of the multiple vortices came down and it hit their car.
But this kind of sort of random chaotic looking stuff,
we've seen this in the field.
We're pretty sure that this is not something
that is just happening in models, okay?
So this kind of activity where you have all these
multiple vortices, these are all spinning cyclonically.
Oh, that one's anti-cyclonic.
So you occasionally get some that are spinning
in the other direction, but they're short-lived.
Okay, so I just ran this simulation a few days ago.
Just got the data and I just rendered some frames,
and some of them are ready for this talk.
And I did this on purpose. I wanted to get this ready for you guys.
So nobody else has seen this. This is 15-meter.
I can say with pretty good certainty,
that nobody else has done a simulation
at this scale of a thunderstorm. I don't think so.
This is like almost 20 billion grid points at 15 meters.
Again, the sky is anti-cyclonic where I happen to start,
but look at the tail cloud region
and how much more realistic it looks.
You're starting to see convective elements.
The blue is just where the cold air is.
The boundary between the cold air and the air ahead of it.
But I'll let this go. But this is pretty exciting.
And you can see the explosive upward growth.
You can see these elements kind of going up.
And you see that in the field all the time
when you look at the base of the storm.
So yes, we get a tornado. It's cyclonic.
It's EF5. It's very strong.
You see the same kind of things with these sporadic downdrafts
in the rear flank of the rain.
You'll see rain coming around to wrap it.
But notice how messier and sort of more detailed things are.
As you go to higher resolution, you start to resolve things more
and you start to see more of that detail.
And you can see there's an interesting
boundary between cold air and warmer air over here.
We didn't see that boundary over here in the 30-meter simulation.
It was over here somewhere.
So I think one of the results of increasing the resolution is
it changes the structure of the cold pool just a little bit.
Again, we've got a lot of work to do to unpack all this.
But it goes, oh, hello, what are you?
[laughter]
Hello, there. Let's dance. Whoo.
And this is cool. I mean, really.
I've never seen anything like this.
So that was an anti-cyclonic.
It came and went. That's cyclonic again.
And the simulation stopped right here.
So I've got the rest of the simulation sitting on a
supercomputer ready to go.
I just need to get it going again.
So we haven't finished that one yet.
We don't know how the story ends.
And here is that vorticity thing again.
Now, like I said, there's so much detail here you probably
can't see it all very well.
But this is that same streamwise vorticity current air.
You're going to see the air kind of come up and around.
There is your anti-cyclonic blue guy,
and the tornado hasn't formed yet.
So red is cyclonic, blue anti, and gray is horizontal.
So if it's rolling like parallel to the ground.
And I'll run this a couple times.
But, yeah, I look at this and I can just put this
on my computer screen and watch it all day, basically.
And there's your tornado, so, what I see is well--
It seems like there's this wrap around thing
that happens a lot before a tornado really forms.
It's like something kind of back here just gets strong enough.
It just kind of encircles the region that's sort of trying to
form a tornado, and then, boom, that is completely just,
based upon my observations of this.
Right when you see this kind of come around,
that's when it seems the tornado forms.
So we've got a lot to do, but the fact that we actually did
this is a big deal because it's just really hard to get,
to wrestle a supercomputer into submission, basically,
and get it to do this. It's really hard.
I can't tell you how many times
I failed at this before I got success.
Now you can really see the SVC.
See how it's horizontally rotating?
And then it gets tilted upwards.
This is a really nice way to show that.
Yes, it's a turbulent mess, but it's still, there's overall,
I'll slow it down a little bit here.
You can see how the rotation is basically this way,
and then it gets tilted this way.
And that helps to drive the storm.
There's physical reasons why I say that.
When you have this kind of rotating element,
it drops the pressure a little bit,
which can help to do some other things that help to maintain it.
There's definitely some positive feedbacks in the simulation
that helped this happen.
But that's, yeah, like I said,
I looked at this for the first time yesterday.
I just made this movie yesterday.
So you're seeing it, no one else has seen this before except for my collaborators.
So, all right. Parting thoughts.
If you're not seasick from watching all that. [laughter]
Okay, so the first thing is yeah, we can do this now.
And this is something that has--
I am by far not the first person
to try to do this sort of thing.
We've had people doing cloud modeling like this
since the late 1970s.
The pioneers of cloud modeling, some of them were here
at the University of Wisconsin actually.
Robert Schlesinger in the Department of Meteorology
was one of the first meteorologists to simulate
a supercell in three dimensions.
Robert Wilhelmson at the University of Illinois,
who's also one of my collaborators,
he also made a model that was three dimensional,
and we learned so much about supercells.
So I'm doing the same basic thing those guys started
way long time ago, only to the tenth power.
We're using a more refined model
that has better physics, better numerics.
We're exploiting new computers.
In the '70s, you know, your cellphone in your pocket
had more power than a supercomputer in the late '70s.
I'm not kidding. It's true.
So you can imagine the simulations have gotten more
realistic as time has gone on.
But we can now simulate supercells
that produce tornadoes "naturally."
You don't have to kick it.
We don't have to use a little chamber model
like you see at museums sometimes.
Those simulations have been done for a long time.
We've got the whole storm,
all the messiness involved in a full storm,
and the tornado happens.
The fact that it happens means the model
is probably doing the right thing.
And you'll notice I have all this sort of conservative
language or I'm very well holding back on making any
grandiose conclusions because that's the nature of science.
When you're, you know, doing something that's kind of new,
you want to make sure it's been vetted.
Yes, we've published data, published some of this work,
but there's a lot coming out and I can't keep up so fast.
I can't publish it all right away,
so we're still pawing through it.
But we can do this now. This is exciting.
But we can do it in the model,
but this SVC thing and some of the other things
we've identified, do they exist in nature?
Probably they do.
We haven't looked for them because we didn't know what to look for,
and it turns out the SVC is going to be
hard to find using things like radar
because it's just because of the nature of it.
So, you know, I've been talking to some researchers who are
thinking of sending probes into supercells.
Remember that movie that will go unnamed at this point?
Maybe it was, you know, it was way ahead of its time.
Miniaturization has gotten to the point now where you could
deploy little instruments that measure things.
But there's still a lot of things to work out.
But how are we going to sample that part of the storm? That's a good question.
So, do they exist? I hope so.
You know, because I'm a numerical meteorologist,
I study these things.
I rely on my colleagues to go out and find the things.
It often goes the other way.
Sometimes you'll observe something
and then you have to model it.
In this case, we modeled something
and now we want to go observe it.
So there's this nice give and take in our field where you have
the theoretical, the modeling, and the observational,
and they all help to move the field forward.
So, as I go on, I will simulate different storm environments.
So not just this El Reno environment.
I will try to run at higher resolutions,
try to tweak storms to get them to not form tornadoes so I can
compare to the ones that do and figure out what didn't happen
in the storm that didn't produce the tornado.
That's really important scientifically
because maybe we can see something on radar for instance.
Maybe, for instance, in 10 years
we wouldn't have gotten a tornado warning
for a tornado that didn't happen to our southwest, assuming it didn't happen.
Watch, it'll turn out 50 people died and they're all children.
Ah! No. Anyway, I don't think that tornado produced.
I don't think there was a tornado; I think it was rotation on radar.
But my point is, wouldn't it be nice if,
when you heard a tornado warning,
there was an actual tornado, and you really should take shelter?
And that's where we want to go.
The end game of a lot of research in
thunderstorms and tornadoes is that we can better warn the
public to get out of their way, to get to your safe space
because all the kidding around you want to do about going to
the roof or chasing storms, I mean, people do die.
And we don't want that.
So, with that, I will be happy to take any questions,
and I do post a lot of my talks and stuff on my website.
If you want to slow things down and hit pause,
you can go to that Orf.media, and this talk will also show up
on there and it will also be on the bio sci webpage as well.
So, thank you very much for your time.
[applause]