- I'd like to introduce
to you Rick Anthes

who is a former student
here going way back

into 1962 when he was
an undergraduate student

from out of state, I believe.

Is that right?
You came from Virginia.

And then he went on to a
masters degree and a PhD.

His talk today is gonna be
more of a general topic

just about philosophy and
modeling and what we can do

with weather prediction overall,

called "Demons and Butterflies,"
so here's Rick.

(applause)

- So without further ado, and
you'll see why in a minute,

I'm going to get started,
and with a prologue.

Actually more than half
of the slides are not even

directly related to my talk.

This is kind of interesting.

The prologue is very long,
and the title of the prologue

is "Sandy, Harvey, Irma and
Maria: A Photographic Essay."

And I hope this is relevant
from a societal point of view

about what the real talk
is going to be about.

So hurricanes, you've
seen many pictures.

They're absolutely
beautiful from space.

They're just majestic,
artistic almost.

They're just amazing to see.

And so many people, this
is one of the reasons

I got interested in hurricanes

because from a distance
they're just wonderful.

But when you actually
experience one,

it's anything but beautiful.
Hell on Earth.

And I'll start out with Sandy.

This is a split image of New
York City on a typical night

at top, and the
night after Sandy hit

in the lower half where almost

all the city is without power.

So now I won't say anything

for the next hundred
slides or so.

(somber music)

♪

And then, I'm trying to
inject a tiny bit of humor

at the end of this.

There's Hurricane Nate
where the chief damage

was blowing a bunch of
pumpkins out of a field.

But anyway these are picture
stories that I don't think

I get from the news.

You hear of 27 deaths and
a billion dollars of damage

and this kinda thing and
it doesn't until you see

the variety of people who
are affected by these things

and the amount of time it
takes to rebuild their lives.

So what about Hurricane Sandy?

And I was inspired to do
this talk right after Sandy

which was 2012.

It formed in the western
Caribbean late in the year,

October 22nd, almost
this time of year.

It made landfall in New Jersey
on October 29th, very late.

The largest Atlantic
hurricane on record.

And these are statistics.

They just don't tell
the story you saw.

53 people killed,
$32 billion in damage

which is about 1/3 of
the government sequester

at that point.

So how good were the forecasts
of Hurricane Sandy?

They were superb,
they were excellent,

they were unbelievable,
and it wasn't by chance.

How many more lives
would have been lost

without these
excellent forecasts?

You saw the damage
that was done,

and it's a remarkable thing
that only 53 people were killed.

Probably thousands would have
been killed if that storm

had come in unannounced.

And imagine if people
weren't prepared for it

what the loss of life would be.

Never before had a hurricane
approached the East Coast

from the east in late October.

Came in from the east.
Never before.

Here is a set of tracks,
the historical tracks,

of hurricanes that came
within 200 nautical miles

of New York City

from 1851 to 2011,

the entire record up until 2012.

You see every one
of those tracks,

by the time the storm hit
past the Virginia capes

was heading off to the
north and northeast.

The Hurricane Sandy
came up the coast

and, if you were a forecaster
who had no satellite

observations or models
and you saw that track,

everybody would have forecast
continuing moving out to sea.

Instead it did something that
no storm had ever done before.

It turned to the
left and came in

and made landfall from the east.

Never ever before happening,
and yet it was well predicted.

How can that be?

Here's a forecast

from the European Centre for
Medium-Range Weather Forecasting

9 1/2 days before landfall.

And on the left you can
see that their outlook

nearly 10 days
before landfall

had this grayish area
off the Atlantic Coast

where already they
were forecasting,

some of their models were
forecasting major events,

significant probability
of the severe windstorm

affecting the Northeastern
United States.

Then by the time
three days later,

6 1/2 days before landfall,
the various models

were forecasting those
tracks that you see

in the middle panel.

And already almost a
week before landfall,

the models were
forecasting this left turn,

which again had never
happened before.

So this is not an empirical
model based on past data.

It is a model based
on laws of physics

and mathematics
and observations.

And then the observed track
is shown on the right panel.

So extremely well forecast.

People had many days of warnings

and were well prepared.

A lot of people say, "Well, we
got through that disaster.

"It'll never happen
again in my lifetime

"and it can't
happen to me again."

Well, this is this year.

Jump forward nine years to 2017,

or five years I guess it is.

Through September there
have been 15 separate

$1 billion weather
and climate disasters

just through September.

So we're on a track
for a record year.

And you see they're
all over the country.

There's hurricanes,
there's tornado outbreaks,

there's fires, all
kinds of things.

And it's just no question
this is not just anecdotal.

There's no question that
the frequency of severe

weather and climate
events is getting higher.

And why is that?

Well, this is not a
talk on global warming,

but, "It is global
warming, stupid."

And that's all I'll
say about that.

So the lecture is
"Demons and Butterflies,"

but I'm trying to
set the context

for something that's important.

What is this?

It's a fortuneteller.
It's a wizard.

Somebody that tells the future.

Amazing people would
believe this guy

before they believe science.

But foretelling the
future has always been

a fascination of humanity,

and prophets over the ages

have been worshiped
and vilified.

It's said that-- You've already
heard this joke I'm sure.

A meteorologist says
weather forecasters

are the second oldest
profession in the world.

People want to know what's
gonna happen in the future.

So foretelling the future
has always been a fascination

whether it's forecasting
the stock market

or forecasting a football game
results or whatever it is,

people love to talk about
forecasting the future.

And you see it in these
common expressions.

I should have known.
I should have seen it coming.

In retrospect, it was obvious.

20-20 hindsight.

The signs were there
for all to see.

Sixth sense.

Premonition.

And in today already
walks tomorrow.

The present is big
with the future.

Good detectives such as Sherlock
Holmes and Hercule Poirot

deduce what has happened and
sometimes what will happen

from a few observations.

Foretelling the future can
be based on past behavior,

empiricism, or the natural
laws of mathematics,

physics, and chemistry.

But all predictions,
one way or another,

are based on observations.

Whether you're a fortuneteller

or a mathematical
modeler of hurricanes,

you're using observations
one way or another.

Well, the philosophy
of forecasts

goes back many years

and Gottfried Leibniz,

famous for Leibniz's rule in
mathematics, if you know that.

I think we all learned that
in our calculus courses.

1646 to 1716.

A very interesting quote.

"Everything proceeds
mathematically.

"If someone could have
sufficient insight

"into the inner parts of
things, and in addition

"had remembrance and
intelligence enough to consider

"all of the circumstances
and take them into account,

"he would be a prophet and see
the future in the present

"as in a mirror."

So read that carefully.

You have to have insight,
remembrance, intelligence,

and to consider everything.

This is foreseeing models in
a way, very complex systems.

If you could
understand everything,

you could predict everything.

And even more direct,

the Marquis de Laplace.

You know about
de Laplacians, right,

Laplace in mathematics.
He was a mathematician.

He dreamed of an intelligent
being, an intellect,

which was later dubbed,
I guess by his colleagues,

"Laplace's Demon,"
who knew the positions.

He dreamed of an intelligent
being who knew the positions

and velocities of every
single atom in the universe.

And using Newton's
equations of motion,

he could predict the motion
of each one of these atoms,

all the molecules that
the atoms are part of.

They didn't know about smaller,
subatomic atoms at that time,

but predict the future
of the entire universe.

And this long quote
at the end is actually

very prescient in
terms of the theory

behind developing numerical
weather prediction models.

We may regard the present
state of the universe

as the effect of its past
and the cause of its future,

an intellect, which at any
given moment knew all the forces

that animate nature and
the mutual positions

of the beings that compose it.

If this intellect,
think supercomputer,

were vast enough to submit
the data to analysis,

could condense into a
single formula to move

one of the greatest
bodies of the universe

and that of the lightest atom,

for such an intellect,
nothing could be uncertain,

and the future just like
the past would be present

before its eyes.

The condition of every
one of us in the room,

every molecule,
every wave out there,

and all around the world,
including the molecules

in life itself, if you knew
exactly where they were today,

according to Laplace,
you could predict

everything in the future,

how humans would behave,
when they would die,

how many children
they would have,

how many children the
children would have,

and so on and so on.

A perfectly deterministic
system if you knew

where everything was and
you knew all of the laws

that we follow.

That was Laplace's view.

And then Niels Bohr,
the famous physicist,

had a much simpler
statement which sounds to me

like Yogi Berra, more like
Yogi Berra than Niels Bohr.

"Prediction is difficult,
especially the future."

True, I think we can
all agree with that

even though we may question
Laplace's Demon a little bit.

Well, Bjerknes, Vilhelm
Bjerknes, getting into our field

in 1904, the father of
the Norwegian school

of weather prediction,
said the following.

"If it is true, as any
scientist believes,

"that subsequent states
of the atmosphere develop

"from preceding ones
according to physical laws,

"one will agree that
the necessary and
sufficient conditions

"for a rational solution of
the problem of meteorological

"prediction are the following.

"Number one, one has to know
with sufficient accuracy

"the state of the
atmosphere at a given time."

Those are the observations, and,

"One has to know with
sufficient accuracy the laws

"according to which one
state of the atmosphere

"develops from another."

That's the mathematics and
physics of how motion reacts

to forces at a given time.

And this is definitely the basis

for numerical
weather prediction.

Well, then some 50
years later, along comes

brilliant mathematician,
actually a meteorologist,

who became brilliant in
the field of mathematics,

one of the few ones that
ever did this, Ed Lorenz

from MIT, got into chaos theory

and is alleged to have
said, at least interpreted

to have said, "Does the
flap of a butterfly's wing

"in Brazil set off
a tornado in Texas?"

This is now in popular
mythology, in popular speeches,

is the butterfly effect,
and it's the idea

that you can never measure
everything to a sufficient

accuracy to make a
good prediction, a
perfect prediction.

There's always gonna be
a butterfly somewhere

that you don't know,
you can't follow,

and the butterfly flaps
its wings and that sets off

a cascade of events
that lead to something

as severe as a tornado in Texas
or a hurricane in New Jersey.

In the '70s, Greg was talking
about why I was developing

numerical models and he had
the so-called Mesoscale,

and a lot of the larger
scale dynamists said,

"that you're wasting your time
because the smaller scales

"of motion are never
gonna be predictable,

"and why are you trying
to do Mesoscale models?"

So I was trying to think
of a rebuttal to this

and I came up with the idea
that in many synoptic situations

large scale situations,
the small scales are forced

by the larger scales.

So if you know the large-scale
waves, they produce fronts

in more or less the right
place, smaller scale events.

They produce areas favorable
for convection and so forth,

so that if you know the large
scale initial conditions

and you can predict them,
they will lead to

small-scale phenomena,
create small-scale phenomena,

even before the small-
scale phenomena exist.

And so you actually
see that today.

People are forecasting
tornado outbreaks

three or four days before
tornadoes actually occur,

even start to occur,
because the large scale

has predicted well
and it predicts

the environment of tornadoes.

So, that was my argument
for the fact that there

was predictability in the
smaller scales of motion,

which according to
predictability theories

should be less predictable
than for the very large scales.

Anyway, if we look at some
real data in this case.

These are forecast accuracies
of the European Centre

for Medium-Range Weather
Forecasting, ECMWF.

Since 1981, has had the best

global prediction
model in the world.

And the United States has
tried desperately to catch up

to this model, but has
always lagged behind it

by about a half a day's
worth of forecast.

What this shows is the--
You don't need to understand

what the skill scores are,
but 100 at the top

would be a perfect forecast,
and 30 would be like

a correlation coefficient of
30, wouldn't be much value,

but still some
value over guessing.

And the colors are different
links to the forecast.

So the blue envelope is
I guess day three forecast.

So the day three forecast
tend to be very accurate

and they've been increasing
in accuracy with time

going from about 87% in
the Northern Hemisphere

in 1981, to nearly 96%
in recent years.

And they've leveled off.

They aren't getting much
better, because apparently

the 3-day forecast
is close to as good

as it's ever gonna get.

The lower part of that
green envelope at the top

is the Southern
Hemisphere forecast.

And you can see that it
was much worse in 1981

than the Northern Hemisphere.

And why is that? It's because
the Southern Hemisphere

doesn't have as much data.

There are a lot more oceans
and they don't have

as many balloons and so forth.

But that gap is closed, until
today it's almost nonexistent

and that's because
of satellites.

Satellites are global.

They measure globally, and
so the Southern Hemisphere

gets just as good observations
as the Northern Hemisphere now.

And this is a dramatic
testament to the power

of global satellites of
which Wisconsin, of course,

is the leader in the world
in satellite meteorology.

The red curves are like
five-day forecasts

and the green curves are
seven days or whatever.

It doesn't matter, but the
forecasts are getting better

at all-time scales.

And the gap between
the Northern Hemisphere

at the top of each of the bands
and the Southern Hemisphere

is diminishing because
of global observations.

Absolute truth, positive
truth of the impact of

global models and
satellite observations.

So another one of the reasons
I'm bringing up the resolution

of these global
models one at a time

roughly at the same time
scale as the bottom,

so in 1981 the European Centre
model had a horizontal

spacing between data points
of about 200 kilometers,

100 some miles.

And what you're seeing
here is the resolution

getting finer and finer,
the pixel size getting
smaller and smaller.

The model is resolving
finer and finer scales.

And that's the picture
of a hurricane.

When it gets down to 16
kilometers and the last one is

10 kilometers, you can
actually see Hurricane Katrina.

But that's what Hurricane
Katrina looks like

at 250 kilometers
on the far left.

It's just a blur, a smudge.

So you can't even resolve
hurricanes back in 1981

with these models,
but currently you can,

and that's a testament to
the value of computer power

and good models in addition
to the good observations.

So the predictions are
getting better all the time.

The next slide shows a
different way of judging

how good models are.

This goes back to 2008

technology of
geostationary satellites.

It's the Meteosat observations.

And if I didn't have
these things labeled,

even in 2008, you
have trouble telling

which is the model and
which is the satellite,

if you didn't have
these labeled, right?

And you could probably tell if
you stared at it long enough,

if you were an expert,
if you were either an
expert in the models

or in the geostationary
satellite imagery,

but just looking at it, the
casual person is gonna say

that model on the
right is damn good,

even without looking at numbers.

And so you know the model
is doing something right.

And we were looking
at one of the

tornado models this
morning or this afternoon.

In the tornado model,
the clouds were so accurate,

you just know that
they're right,

even without a lot of numbers.

But this is actually one
way of verifying models

is to look at the
pattern recognition.

Humans are very good at
seeing whether something
looks good or not,

whether it's right or not,
and you can see it there.

And that was quite
a few years ago.

The models in the geostationary
satellite like GOES are,

what do you call
them now, GOES-16?

(mumbled response)

Yeah, is a much
better resolution

on the satellites and much
better resolution on the models.

So they still look
very good together.

But numbers are important.

Here's a record of
official hurricane

track errors over
time from 1970,

before models and
before satellites

out to 2016, the
latest data I had

from the National
Hurricane Center.

And the track errors
are in nautical miles,

which are very close to miles,
from zero to 700.

In this case, a
low number is good

because the track error, the
error, the position error

of the storm is smaller.

And so you can see that
the different forecasts,

the red curve is 24 hours,
the green is 48 hours

and so on up to 120 hours,
the dark blue at the top.

With some year-to-year
variation, all of these

official forecasts
are getting better.

And they're starting
to maybe converge.

Of course, you can't
get any better than zero,

so at least the one-day, the
two-day, and the three-day

forecasts are getting
pretty darn good

to less than a 50-mile error
in position of the storm.

So anecdotally the
forecasts are getting better,

like Hurricane Sandy.

Statistically they're
getting better.

We know why, it's
computer, it's models,

it's satellite
observations of all kinds.

It's better physics, it's
scientists working on this.

This is not an accident.

It's not because of
political philosophy or

the people are better
or anything like that.

It's pure science, physics,
measurements, education.

This is all something that
we did, we as a community,

as a university community,
as government centers

in Europe and the United
States, we did this.

This is not forgone,
this is not an accident.

It's the results of
mathematicians and physicists

and chemists and
computer scientists

and educated people and
supportive graduate students

and government grants
doing all of this.

And it's saving
thousands of lives,

maybe hundreds of
thousands of lives,

just in this one little area,

this little tiny area
of weather prediction,

it's science and education.

It's not philosophy,
it's not praying to God.

It's doing something about it.

It's helping God by doing
something for ourselves.

And yet you have people
that want to cut funding

in these areas.

The total NESDIS budget is
about $2.1 billion a year,

the satellite budget
in the United States.

$2.1 billion, it sounds
like a lot, right?

We've had 15 $1 billion
disasters already

in nine months of this year.

And they, they want to cut it.

I'm trying to design the
satellite system for 2030

and beyond, when I'll be dead.

I still think it's important
even though I'll be dead.

I have children, I probably
won't have grandchildren,

but many of you
have grandchildren.

They're gonna have children.

And we need to be
preparing for the science

and the forecasts
of 2030 and beyond.

That's what I'm trying to do.

Let's get back to what
might be possible.

Let's get back to what
some fun stuff is.

All right, well this
is an interesting thing

that's actually fairly old
technology in the modeling area

and visualization
compared to some of the
things you can do now.

But what it is, is a five-day
forecast using a massive model,

a very high resolution
advanced model

of Cyclone Nargis
which is a major storm

that developed in
the Indian Ocean.

And this shows the five-day
forecast in this model

of this genesis of this storm.

It's kind of an interesting,
beautiful depiction

but these are basically the
wind flow at different levels.

The greenish colors
are low level flow,

and the reddish colors
are the upper level jets,

and so you see a
kinda low level flow.

You're looking at the
Indian Ocean there.

And you can see with
time this vortex develops

in the Indian Ocean.

And there you can see it.

That's Cyclone Nargis in 2008.

You can see this developing

with no hint of anything of that
scale in the initial conditions.

The large scale just did it.

It was predictability
of that tropical cyclone

five days in advance
by this global model

in the right place and pretty
much at the right time.

Not exactly in the right place
or exactly at the right time.

And this is becoming or
could become a routine.

You saw this in Hurricane Sandy
which is a real data case.

So there's the cyclone
well developed.

You can see the low-level
inflow and the outflow,

global model and
initialized with real data.

So again, we know
what we're doing.

Okay, here's a
climate model for,

I say quote "September 2000"

which might be September
2500, 50 years from now,

for an entire month.

And again, does this
pass the reality test?

Do you see hurricanes
forming in the Atlantic

and moving toward Florida
or toward New Orleans?

Does this look realistic?
Well, look at that.

That looks exactly
like Hurricane Katrina.

There's more forming
in the Atlantic.

These large-scale models,
with the righty physics

and right ocean
interactions, can produce

hurricanes in the right
place and time ontologically

at least at the right seasons.

Okay, so I am gonna
start wrapping this up.

Anyway, the summary is
getting away from Laplace's

abstraction and being able
to predict everything

all the time at all scales
and every human's behavior

and all their children's
behavior and all that.

There is evidence that you
can have greatly improved

forecasts of such severe
weather as tropical cyclones.

I hope you remember the
prologue, but these are really

high impact events,
days in advance.

And then the boring line

but you've got to keep
pounding this home

to the politicians
that this is not by accident.

We need high resolution models,

probably at four
kilometers or better.

We need improved physics,
it means understanding.

We need more PhD students
working on these problems.

And we need interactive
ocean-atmosphere models.

We need improved and initial
conditions in the atmosphere

temperature, water vapor, and
winds-satellite observations

are absolutely essential here.

We need better data
assimilation techniques.

That's ways of using these
strange forms of data

from the satellites and
we need faster computers.

So again this is one slice
and one aspect of society

but we know how to do it,

and we just don't have the will
to do it, it seems sometimes.

So back to the answer,
the big picture.

Who wins, the butterfly or
the demon, Laplace's Demon?

Well, there is a
different between

what is theoretically possible,

that's what's called
predictability,

and what can ever
actually be done,

and that's actually predictions.

The demon may be
theoretically possible,

and that's the question
I think for philosophers.

Not for us, because
we'll never be there

in a practical point of view.

But the butterflies are
ultimately going to win.

And I see there's no reason
not to help the demon a little,

in such beneficial activities
as weather prediction.

And that's the end of my talk.

Thank you very much.

I'd be happy to take
any questions or outrageous

statements or challenges or
denial or whatever

alternative points of view.

Thank you.

(applause)