-Good afternoon.
And, always nice
to see you here.
Today we have, 
an exciting speaker.
And, I can't wait,
to hear his story,
long journey here at Goddard.
Of course, some of you know,
Joel has been here for 38 years.
-38.
-Probably going to 39.
So...
-Yeah.
-Yeah. That...nine.
And -- and to help us,
of course,  introduce,
Joel is, 
one of the, the --
the associates who has been
working with Joe
for a very long time.
And if we squeeze
all those years
Joel has, uh, worked
into 12 months,
we can almost say
that she's been here
around 10 months
out of the year.
So that tells you how long,
she has been here.
So Lena Iredell
is going to introduce
or make some remarks about Joel.
Then, after that, we'll --
we'll be on --
on our way running.
So please help me
welcome Lena Iredell.
[ Applause ]
-Thank you.
Hello, everybody.
I started working in Joel's
group over 30 years ago.
And I can honestly say
that there's never been
a dull moment.
Joel finds the research
he's involved with exciting,
interesting and always
a fascinating puzzle
that he's determined to solve.
Whenever someone in the group
describes something unusual that
we see in our data,
we know that he won't rest
until he thinks of a reasonable
set of conditions
that caused it
and then find a solution
for the issue of concern.
I can't count the number
of times that Joel
has walked into the office
in the morning
and said that he woke up
in the middle of the night
and had an epiphany.
So this must mean that
Joel truly has a dream job.
Joel, incredibly, can juggle
many ideas at one time.
I remember many years ago
when there were five of us
in Joel's group
who all shared a large office.
Joel would walk
into the room
and go from desk to desk
to find out what new results
we had to share with him.
He would get excited
over the results discovered,
ponder the next step
in the research
and then move on around
the office to the next person,
who was usually working on
a completely different project.
By the time he took
those first few steps
to that next desk,
you could already see him
switching gears in his mind
and was peppering them
with questions and ideas.
It was an incredible
sight to watch.
After years of working
with Joel,
I am amazed at his sorting
and filing system.
What looks like just a pile
of papers on his desk
is a unique
and astonishing system.
Joel could walk into his office
and, within seconds,
find anything
he's looking for.
And the only time
I've ever seen this system
fail was many years ago
when we were
all moving offices.
Joel happened to be away
at a conference on moving day.
So Joyce carefully
unpacked and set up
his office for him,
shelf for shelf,
drawer for drawer.
However, the new office
was an identical mirror
image of the old one.
And it took Joel weeks
to find anything.
Something you might not
know about Joel is
he's a big sports fan.
He watches baseball,
football games
and knows an incredible amount
of stats for college
basketball and football.
He's always willing
to describe the latest
and greatest plays
of recent games
and how that changes
the standings.
It's almost with the same
level of enthusiasm
that he follows the polls
and political candidates.
And if you've ever been
to Joel's office,
you know that he loves
classical music.
You might not know
that he's also
a big fan of music
from the 1960s
and is probably the only person
that can understand
what Bob Dylan is saying.
But if you really want
to see Joel's face light up,
ask him about his grandchildren.
To him, his family
is everything.
And they are indeed
his pride and joy.
Please welcome Joel Susskind.
[ Applause ]
-Okay.  the title
of the talk you see
is "Journey from Chemistry
to (who would have thought it)
Meteorology."
I'll be talking about
how I got from there
to -- to here.
So, as a child,
growing up in Brooklyn,
New York, I --
I used to love to mix
things together to see --
to see what happened.
I had
a 9-year-older brother.
Here's a picture of me
and my brother.
I think I was 6 months old here.
I found this.
It's a great picture.
And, he had a chemistry set
when he was young and --
to, make hydrogen sulfide
in the kitchen
and also to copperplate
the silverware.
I actually spoke
to him last week.
I told him about this talk.
He said,
"Those were the only two things
I ever did
with my chemistry set."
But my mother,
was not amused.
So she didn't let me
have a chemistry set.
But I had a friend
living down the block
who had a, uh, uh, he --
he had a biology set.
It had some --
had some chemicals in it.
He had ferrus sulfate,
light blue crystals.
My mother had some ammonia.
I'd mix 'em together
and see the colors change.
I'd like to mix things
where fumes would come out.
So -- so this was my first step
in a -- in,
in a long journey.
Okay.
I went -- I went
to high school in New York
in Stuyvesant High School.
It's in Manhattan.
I'd take a subway ride.
It was a special school.
You had to take
an exam to get in.
It was a special science
and engineering high school.
And I especially loved chemistry
and mathematics over there.
I had this wonderful teacher,
Mr. Atkin,
for honors geometry and honors
intermediate algebra.
Now, in New York,
the way they graded you was,
from 0 to 100,
100 was a perfect sco--
 a perfect grade.
With him, got 100 on everything.
You couldn't get more than a 95,
for your grade
unless you solved
the prize problems.
And I solved every
single prize problem
that we had except for one.
In fact, I -- I should've known
this was gonna be a tough one.
We usually had a day
to do a prize problem.
They gave us a week
to do this.
And I spent all week
and couldn't figure it out.
There was one genius
student who got it.
So I'm just gonna show you
the problem right now,
and, expecting everyone to,
of course, get it.
So the problem is --
is -- is -- is --
is -- is this working?
Yes.
The problem is simple.
Uh, uh, um,
here's a piece of paper.
Uh, it's on its side.
Um, we have this huge
triangle somewhere here.
And, uh, they go, you know,
it doesn't meet on the paper.
And the problem
was to construct
the angle bisector
of these lines.
Well, you don't -- you don't
know where -- where they meet.
And you can't do anything...
... Anything you had
to do was on the paper,
you couldn't do
anything off the paper.
So I -- I -- I --
I'll give you a hint now.
And -- and it's something,
I mean, we knew.
It was --
it was a theorem that said
the angle bisectors
of a triangle,
all of them meet
at a single point.
So that should be enough...
I mean, we didn't
even get that clue.
That should be enough
to help you solve this problem.
But, um, I'll just let you know,
my last view graph,
I'll give you another hint.
Okay.
So let's go on.
So I -- I --
I went to college
in Columbia in Manhattan.
I, uh, uh, uh, um,
I took the subway
a little further.
And I started majoring
in both math and chemistry.
Those are things
that I loved.
As a sophomore, I took organic
chemistry and I hated it.
For one thing, I'm a real, um,
k -- klutz in the laboratory.
My wife, Leslie, who's here
can certainly attest
to that for anything
around the house.
So, uh, actually,
I got a B in that class.
It was the only class
I got a B in.
But, uh, uh, uh, th--
there were about 100 students
in the lecture class
of organic chemistry,
this big lecture room.
And, uh, we only had
10 chemistry majors.
Everyone else was --
was, uh, um, premed.
But, uh, uh, uh,
the best thing
I remember about that class
was every Monday,
it was 10:00,
a bunch of us chemistry majors
would sit together,
weren't paying any attention
to what the teacher,
uh, uh, spoke about
and we'd talk about
the wonderful thing
that happened in Sunday's
Bullwinkle show.
So that -- th -- th --
that's my fondest memory
of organic chemistry.
Uh, in my junior year,
I took a class
in physical chemistry.
I loved it.
We were learning
a little bit
about quantum mechanics.
And, uh, I also took a class,
um, turned out to be a mistake.
Maybe not a mistake,
I took a class
in advanced mathematics class.
In fact, it was
a graduate class,
uh, geometric topology.
It was extremely abstract.
It wasn't helped by the fact
that my professor,
world-famous genius,
uh, was from Japan.
I couldn't understand
a word he was saying.
Anyway, uh, um, I --
I -- I -- w--
w -- w -- I -- I -- I -- I --
I remember we had a...
Um, I didn't know
what was going on.
We had a quarter-final.
And I think I got
10 out of 100,
which was a good score.
Many people got 0.
But I decided to drop the class,
uh, and -- and --
and -- and to continue
with chemistry.
There were a couple
of superstars
that ran up to the board
and were showing the professor
how to do it.
So -- so I figured
I'd stick with chemistry
and, uh, forget
about mathematics.
Uh, my goal, then, uh,
as it was for a long time --
I wanted to go into academia.
I wanted to be
a professor of chemistry.
So, of course,
I was going to graduate school.
So I was gonna go
to get my PhD somewhere.
I applied to a number of places.
I narrowed it down
to staying at Columbia
and going to, uh, to Berkeley,
UC Berkeley in California.
And my professor said,
"Joel, uh, uh, uh,
why don't you go to Berkeley?
Uh, um, it's good to see a --
a -- a new, uh,
you know, a different
chemistry department,
what different people are doing.
And, also, uh, you'll find it
interesting over there."
Well, sure enough,
I -- I -- I got -- I got to --
I got to Berkeley in -- in 1964.
I was there from '64 to '68.
And for those who know, um,
it was a pretty
exciting place at that time.
Uh, we had a lot
of protests about,
uh, about the Vietnam War.
Leslie and I took part
in -- in many of these.
By the way, it was
exciting another way.
You mentioned, uh,
you mentioned Bob Dylan.
You know, Bob Dylan would come
to our campus and give...
Uh, Joan Baez was there,
you know.
We -- It -- It --
it was a lot going on culturally
and all kind of things
like that.
So -- and anyway,
I got a huge culture shock
coming from New York
when I went to California.
Um, uh, uh, uh, I flew, um,
from New York
to San Francisco.
And they had a special deal
at the time.
Uh, if you fly --
if you fly across country,
they'd give you
a free helicopter ride
to the Berkeley heliport.
You go across Oakland Bay.
So I took -- I took the, uh,
I took the helicopter ride.
And then I took a terk--
uh, taxi toward UC Berkeley.
Uh, uh, um, it was this big
street, University Boulevard.
It's a six-lane street.
And my taxi is going
in the left-most lane
heading that way.
And a pedestrian puts a foot
in the crosswalk on
the other side of the street.
And the taxi driver stopped.
It blew my mind.
Coming from New York, you know,
if you're in a taxi
and someone walks...
No one would dare
walk in front of you.
But if they did,
they'd hit you.
So, I mean,
I couldn't believe this.
So with all this stuff going on,
I did have a big
culture shock going --
going to California.
And, uh, just to follow up on
that, my friend, Dennis Travis,
who was one of the super
geniuses in math at,
uh, at, uh, Columbia,
he came to visit me.
We had this big street,
downtown Berkeley,
again, a six-lane road.
He put his --
he put his foot into it,
and it, uh,
not in the crosswalk.
And he immediately
got a jaywalking ticket.
So, you know, s -- so,
so much for liberal Berkeley.
Well, um, I found
a professor to do --
to do research, uh, with, uh,
Professor William Gwynn.
Uh, um,
and his research group
performed laboratory
gas-phase microwave spectroscopy
with an emphasis
on making advances
in the quantum mechanical theory
of vibration-rotation coupling
and its effect
on absorption line positions
and intensities.
And, uh, and -- and I was
particularly interested...
Um, we link the motions
of one part
of a molecule with another.
I was particularly interested
in the effects of wh--
what's called internal rotation.
Here, let's take --
take a look at this.
Internal rotation
of a methyl group,
this is a CH3 group,
with respect
to something else.
This is the molecule
I worked on,
ortho-fluorotoluene.
Toluene is a benzene ring
and a methyl group here.
And it -- it -- it rotates.
It actually vibrates.
Interesting things happen.
Uh, and we have --
you have a certain
symmetry, uh, uh, here,
but if you stick
a little fluorene on there,
it changes the symmetry
a little bit.
And I was particularly
interested in breakdowns
in symmetry.
So -- so I wrote
a thesis on, uh, on --
on this -- on this molecule.
Um, a -- a -- a --
as I've already said
and, uh, uh, uh,
I was a klutz in the lab.
But, uh, my professor,
he insisted that --
this chemistry department,
he insisted
that all his students
take their spectra
and analyze them.
My interest is -- was the --
was the quantum mechanics.
He said, "If you --
if you don't -- If you're not --
you're tying one hand
behind your back
if you're only
doing the theory."
Okay.
So, uh,
let me say something
about quantum mechanics.
There were two different
approaches to quantum mechanics
that were published in 1925.
One was by Schroedinger
and Heisenberg.
They were totally,
totally different.
Schroedinger's
approach found solutions
to differential equations
and Heisenberg's approach
used no explicit math
but involved the use
of matrix representations
of different
geo-physical parameters,
such as the position vector, x.
That's where something is.
And its momentum, p.
Yeah.
Uh, um, now,
these two different approaches
were later shown to be
scientifically equivalent.
They came from totally
different places.
But they're absolutely
equivalent, one to the other.
And, fortunately for me,
everything our group
did was in matrix mechanics.
I'm a wiz at matrix mechanics.
But to this day,
I couldn't solve
a differential equation
if my life depended on it.
So, uh, I would've been
in a different field
and probably not here
giving my track
if we did
differential equations.
Well, let me say a few words
about Professor Gwynn.
Uh, he was --
he was an incredible genius.
Uh, he had a --
a -- an enormous intuition
that I admired
and learned a lot from.
Now, you can't teach
somebody to have intuition.
But you can teach somebody
how to use intuition.
Uh, and -- and, I mean,
I saw how he thought.
And -- a -- a --
a -- all of the research
I do, uh, uh, um,
is based on use of intuition
that, uh, uh...
In quantum mechanics it
was especially the significance
of off-diagonal matrix elements.
Now, he -- he didn't --
he never really spoke
to his graduate students
until he could learn
something from you,
toward --
toward the end.
So we had a wonderful
research group.
And, uh, uh, um, uh, usually,
we were there for 4 years.
So I was in my first year with
two other graduate students.
And we had second year,
third year, fourth year.
And I interacted with them
and learned a lot from them.
Professor Gwynn, you know,
we were having a great time.
I was partying,
beer and pizza every night.
And after 3 1/2 years,
he comes and says,
"Joel and the others,
be done in 4 years."
So I finally --
finally had to do something.
But, uh, a -- a --
also while I was there, he --
he arranged the postdoc
for me with his colleague,
Professor John Overend
in the chemistry department
at University of Minnesota.
So, you know, I had
to get something done.
I finished my thesis.
Uh, this is a profound
lesson I learned.
I finished my thesis.
And I spent 2 hours
talking to him,
showing him this, showing him
that, showing him this.
He says, "Joel,
do you mean to say this?"
I said, "How can you say that?
I've been talking to you
for 2 hours."
He says, "You weren't talking
to the reader for 2 hours."
And that's
a very profound lesson.
If someone can misinterpret
something, they probably will.
So I had my first postdoc
at the University of Minnesota.
I was there for 2 years,
'69 to '70 with John Overend.
And, uh, this was a big change
and a little change.
He was doing
high-spectral-resolution
infrared gas-phase
molecular spectroscopy.
Uh, uh, and infrared
spectroscopy is a --
it's a higher frequency
so investigates,
uh, vibration,
rotation transitions
between molecular
quantum states.
Microwave is just rotation.
It's a lower frequency.
And I was still enamored with --
with my, uh, internal rotation
of methyl groups.
So I went full --
f -- f -- full out.
I studied ethane, CH3CH3,
two methyl groups.
And I derived my own
theoretical approach
to analyze this data,
which, of course,
I was using matrix mechanics.
Nobody else, the professor,
nobody else did
anything like that.
But, uh, but, um, I --
I -- I showed them
how to think that way.
And, uh, uh, uh, it --
it -- it --
it's an extremely complex
problem with different states
coupling different states
and this and then this.
But I looked and said,
"Hey, look,
this is what's important.
These off-diagonal matrix
elements are important.
Those are not."
People would write pages
and pages and pages
of equations.
I do a simple matrix calculation
and get a better result.
So, um, by the way, this --
this type of resu--
research was
called chemical physics,
and by everybody else
in the chemistry department,
was considered
to be physics.
Um, now, as I said, uh,
my career goal was
to be a professor of, uh,
of chemistry somewhere,
physical chemistry.
But there weren't any
positions available at the time.
So it's not what I wanted.
But this led to
my second postdoc position.
And -- and my second postdoc
was at Florida State
in Tallahassee.
Uh, I was with Professor
Robert Hunt
in the physics department.
Now, I went there,
I didn't want another postdoc.
But he said -- he --
he heard about my work,
"Joel, it's very important.
We want you."
And there was a promise
that in a year or two,
they'd have
a professorship in --
in Florida State so --
so I went there.
And, again, we were doing
high-spectral-resolution
and IR gas-phase
molecular spectroscopy.
But I learned
three huge differences
between physics and chemistry.
One is, in chemistry,
what we were doing
was considered physics.
In physics, at Florida State,
everything we were doing
was considered chemistry.
So, uh, that's
one big difference.
Another difference is,
in physics,
you're either a theorist
or an experimentalist.
They could tell that
I was really a theorist,
not an experimentalist.
So they wisely didn't
let me near the lab.
So I was analyzing data
that other people were taking.
And another difference
is that, in chemistry,
you'll notice I had
a couple of carbons,
ethane, two carbons.
In physics, if it's more than
one carbon, that's chemistry.
So I had to work on methane,
CH4 and CO2,
doing the theory of absorption
spectra of those gases
taken by others.
Um, uh, uh, uh, um,
I also got involved
with, uh, w -- w --
with, uh, the chemistry
department at Florida State.
There was a nice professor
over there doing
very interesting research,
uh, uh, um, that
I'll talk about
in a minute, uh,
molecular spectroscopy.
And I was told
a faculty position
might become available
in chemistry.
So I said, let me give it a try.
So, um, I did
some work with --
with the chemistry professor
and his group.
Uh, chemistry, uh, uh, uh,
it's ultraviolet,
so it's higher frequency
at its electronic transitions.
And that group was
studying ethalane --
ethylene, C2H2.
Uh, note, it's two carbons,
they could do that
in the chemistry department.
So, um, now this group,
uh, uh, see -- see, eth--
ethylene in the ground state,
electronic state is planar.
It's in a plane.
The first excited state,
it's twisted like this.
And they couldn't
figure out why.
I mean, they could tell
from the spectra.
So I looked at it.
I said, "Come on, of course
this is the case."
I ran up to the blackboard.
I wrote some off-diagonal matrix
elements and said, uh,
"Sure, look."
They -- th--
th-- they were --
they were shocked.
Turned out here that, you know,
I was doing work
in both chemistry
and physics department.
They both loved
what I was doing,
really important but,
uh, uh, times were tough.
They didn't get the funding
to hire either a --
a -- a -- a professor
in chemistry or physics.
So, um, and, uh, meanwhile,
I was looking
at faculty positions
all over the place
and not finding anything.
So I was, uh, this was --
this -- this was a bit
of a crisis at the time.
I had a 4-year-old child,
no job prospects.
I didn't know what
I was gonna do.
Uh, spoiler alert,
it worked out for me.
Here I am.
A -- and, uh, um, you know,
I'll tell you, now, how --
how I got from there to here.
And it was some interesting
things that happened.
Um, uh,
but also let me just say...
I -- I -- I -- I --
I -- I have three sons.
I've told them all.
And I'm telling others.
Sometimes you don't know
how things are gonna work out,
you know, but they do.
Things work out
for the best.
Okay.
So now, uh,
a very important thing happened.
I mean, I told you,
I didn't have a job, nothing.
But racquetball was an enormous,
wonderful thing.
My professor, Hunt,
played racquetball
with John Gille.
Some of you may
recognize the name.
Uh, uh, um,
he was a professor
in the meteorology department
at Florida State.
And he had developed
a satellite-born, limb-scanning,
broad-band IR radiometer
to measure trace constituents.
And, um, John was leaving
Florida State to go to NCAR.
And, uh, he was, uh,
he knew, I mean,
Bob Hunt was telling him
about what I'm doing.
"Joel, we need
somebody like you."
So he said, "I think
I can get you --
I can get you a position
at NCAR, regular position."
Okay.
It's not -- It's not professor
but sounds interestingly enough.
So -- s -- s --
so I went with him, at the time,
to the first Radiation
Symposium in Fort Collins.
This was in 1972.
And then I gave
a seminar at NCAR.
And then John said,
"Joel, I'm sorry, uh, I --
I -- I didn't get
the funding that I have.
I can support you for 2 months
to do some calculations for,
uh, uh, uh, um,
for, uh, for the --
for the spectra
of nitrogen dioxide.
I want to measure
nitrogen dioxide.
It's an interesting molecule.
Do the calculations."
And I can perform
the work anywhere I wanted.
Well, interestingly enough,
I -- I met this fellow,
Steve Schneider.
Some of you may
have heard of him.
He made quite a name
for himself in --
in -- in -- in -- in climate.
I met him at NCAR.
And he had just left the Goddard
Institute for Space Studies,
yes, to go to NCAR.
I said, "Ah,
why I don't go to GISS?
I mean, I live in New York.
I have family over there.
It's as good a place
as any to --
to, uh, you know,
to do something for 2 months.
What's gonna happen
after that, God knows."
So -- so he told -- he told me
to go see Joe Hogan at GISS.
Well, so I went
to Joe Hogan's office at GISS
to tell him I needed a desk
and some computer time at GISS
to do molecular
spectroscopic calculations
for John Gille.
And Joe Hogan said,
"Joel, you know,
John Gille t--
told -- told me..."
told -- t -- told -- t --
told Joe Hogan about me,
"And we need
somebody like you."
You know, I had looked
all over the place.
I walk in off the street
and there we are.
So -- so it turned out
that I received
another postdoc position
at GISS in New York,
National Research Council
postdoc position,
postdoc number three,
uh, uh, uh, to work
with Dick Stewart,
who some of you
may know from here.
He's recently retired
a few years ago.
I'll get to that.
And Dick was studying
sources of pollution over land.
And, of course,
I was hired to do quantum
mechanical calculations again.
After 1 year there, Milt Halem,
some of you might know
the name Milt Halem,
at GISS, told me,
"Joel, I need you --
I need you to support
my research,"
his research
to compute atmospheric
transmission functions,
uh, for the VTPR
broadband sounder
which flew on NOAA 5.
Transmission
functions are calculations
to show how the atmosphere
absorbs in these --
in these spectral bands,
uh, as a function
of temperature, water vapor,
what have you because
if you can analyze the data,
you have to know the physics.
So at that point,
I became a contractor to
support Milt Halem's research.
And you'll note that there was,
uh, there was no meteorology
and I'm still doing
quantum mechanics here.
Well, still no meteorology,
but I began to move
in that direction.
I got into temperature
profile retrievals.
You know, I was computing
transmittance functions
for these --
for these instruments.
Now, okay, now that
I measured this,
why don't --
determining temperature.
And, uh, I had a wonderful,
uh, uh, uh,
a wonderful colleague,
Mous Chahine.
Uh, he worked with, uh,
with -- with Milt Halem.
Uh, he was --
he was one of the rare,
rare collaborations,
uh, uh, of JPL and Goddard.
And then he and I became
very strong collaborators.
And he said, "Joel, you know,
we're really unusual pair."
Usually, they're
competing for money
or something like this.
Anyway, unfortunately Mous,
uh, passed away s--
suddenly 2 years ago.
That was a big loss
to everybody.
But, uh, um,
he was doing theory of,
uh, of, uh,
of how to analyze
these satellite sound --
the broadband radian's
measurements,
infrared under
partial cloud cover.
And, uh, uh,
in following Mous',
uh, theory,
I developed
and implemented an algorithm
to retrieve temperature
profiles from VTPR.
And we got to
analyze the VTPR data.
It's just a 15 micron band
but, uh, uh,
broadband channels.
But Mous' theory
also showed that,
hey, you could do even better
under partial cloud cover
if you have channels in both
the 15 micron band
and the 4 micron band,
which is another --
another band at --
at a higher frequency.
Now Nimbus 6 was launched
in 1975 carrying
a HIRS infament --
instrument, which held --
had both 15
and 4.3 micron channels.
And I -- I developed...
I was reeling in --
g -- getting into
temperature retrievals here.
I developed
an improved algorithm
based on Mous' work
where we use the 15
and 4-micron channels together.
And I showed that
we could do much better.
And -- and -- and --
a -- and they showed
that we could do much
better retrievals
in VTPR under cloudy
conditions having both.
I'm gonna talk about
the Green Tree restaurant.
And -- and it's a wonderful
Hungarian restaurant
a few blocks from GISS.
One day I had lunch
there with Lew Kaplan.
Yes, uh, Lew Kaplan, uh, w--
a tremendous pioneer
of satellite
sounding research.
He had been from
the University of Chicago.
But then he came back
to the government.
So he was working
with us for a while.
I had lunch with -- with him,
with Mous Chahine and Milt.
And, uh, Lew took a napkin
and scribbled
a couple of equations on it.
And these equations showed,
hey, if you have high
spectral resolution
and, look, in between
the absorption lines,
you could get much
more information
about what's happening
in the atmosphere.
You get sharper
waiting functions.
The -- the --
the response to the atmosphere
is much narrower
and also, uh, if you --
if you also have
high resolution,
uh, uh, um,
measurements at the high end
of the 4 micron band,
uh, between 2378 and 2390,
this would really do,
uh, wonders for you.
And, uh, this napkin
led to the design
of the AIRS instrument,
flying on EOS Aqua.
And AIRS can be thought of
as the Green Tree Restaurant
Sounder.
It was the inspiration
for this.
Okay.
In 1977, Milt Halem,
uh, moved his group
to Goddard here.
And, uh, um, he became head
at that time
of the Goddard Modeling
and Simulation Facility.
Uh, it wasn't
a branch or anything
because he was
a civil servant.
And I was his first
civil servant.
So there were
just two of us.
We had a bunch of contractors
and what have you.
A -- and -- a -- a --
and I was put in charge
of analyzing satellite
sounder observations.
That was my --
that was my responsibility.
Shortly afterwards,
he hired Bob Atlas, uh,
who some of you might know.
And he was in charge
of data assimilation research.
Eugenia Kalnay in charge
of global weather research.
And Jagadish Shukla in
charge of climate research.
And what I was doing then was
doing temperature retrievals
from, uh, uh, from --
f -- f -- fr -- first from --
first from, uh, VTPR and --
and -- and -- and --
and then from HIR-- uh, H --
HIRS and, um,
passing them on for Bob Atlas,
for his use in data
assimilation experiments.
In 1982, GMSF became Goddard
Modeling and Simulation Branch.
That's after we hired
some more civil servants.
So we were a branch.
A -- and we were located, um,
in the ground
floor of building 22.
And I spent most
of my career over there.
It's only a few years
since I'm here.
Uh, three doors down the hall
from me was a young biologist,
uh, Piers Sellers.
Piers and I became
very friendly.
And I really liked
his sense of humor.
Well, now I'm gonna talk
about the TOVS, uh, Pathfinder,
Path A data set.
TOVS, the TIROS Operational
Vertical Sounder,
was first launched,
TIROS, then in 1978.
It was comprised
of a HIRS2 instrument,
which was a somewhat
advanced version of HIRS
and also the Microwave
Sounding Unit, MSU,
and it later flew on
NOAA 6 to 12
and also on NOAA 14.
NOAA 13 failed at launch,
NOAA -- the launch.
Uh, NOAA 13 had two things
going against it.
One was the unfortunate
number of 13.
Two was that was
the only launch I attended.
So I went there.
And, uh, and the launch failed.
But nevertheless, uh,
we develop this
Pathfinder Path A
retrieval algorithm
that contained further
improvements
in retrieval methodology
and, in particular,
the use of mi--
uh, microwave observations.
We never had microwave before.
And they're a very useful
part of the suite.
And, uh, um, we analyzed --
we analyzed TOVS' data
using this methodology
for the period 1979 to 2002,
uh, uh, uh, when, uh,
TOVS on NOAA 14
began to degrade.
And we have this --
we had this big data set.
And it included surface
skin temperature,
temperature and water
vapor profiles,
total ozone, cloud heights,
fractional cloud cover
and Outgoing Longwave Radiation,
OLR, which is a product
that's computed
from everything else.
And it contains a daily,
5 day, monthly mean values
of all of these products on
a 1-degree-by-1-degree
lat-lon grid.
So I began to learn
a little meteorology
by looking at the products.
I mean, I never learn --
I never took a class.
I never did anything.
But it was just obvious,
looking at the things
that we had,
that I could see
what's going on.
And I'm relatively self-taught.
And some of the really important
things that depicted
were the short-term climate
trends over this period,
interannual variability
and especially the effect
of El Niño on anomalies.
Um, so I began
to get into meteorology
in spite of myself
here, somehow.
So now I want to change --
change tack a little
and talk about holiday parties.
Uh, following
a tradition from GISS,
uh, Milt Halem always had
a holiday party
around New Year's day.
There was always
plenty of good food,
good beverages of all types
if you get my gist here.
Real good, um,
camaraderie between everyone.
And dur -- during the party,
Milt always spoke
of all the great things
that our branch
did during the past year.
He gave out awards.
And somebody always
took pictures of the event.
Uh, uh, uh, this tradition
continues to this date.
This Sunday, I -- I had
my holiday party in my house.
And a number of us were here.
It was supposed to be
the second week in January.
But a little snow
got in the way.
So -- so we just
had it last week.
But I'm going to,
uh, show some pictures
now of some of these parties.
Some of the people
at these parties made
quite a name for themselves,
uh, uh, uh,
especially a young child.
So here.
Let me look at this.
This -- this is a party.
Uh, can you see that?
This is --
this is a party from 1979.
Here's Milt Halem,
our branch head.
Here's Dave Atlas,
who was -- who is --
who was our division, uh, chief.
He was -- he was, uh...
Steve Platnick n --now.
Layman Baker was someone, uh,
one of our civil servants
who is at NOAA right now.
Uh, this is a picture.
Um, Eugenia Kalnay,
uh, Lou Uccellini.
Lou was not in our branch.
But he was just down the hall.
And we were very
friendly with him.
So we did a lot.
Yeah.
He's, uh, he's --
they've made names
for themselves here.
Uh, here's, uh, 1982.
Milt is giving me a --
a -- an award of some sort.
Um, here are two
pictures from 1983.
Michael Ghil,
he was a professor at UCLA.
But he spent a lot of time
with us, uh, doing, uh,
he was a climate scientist,
collaborating.
And here's Eugenia Kalnay
and Jagadish Shukla.
And here's this
little kid, Sergey Brin.
Uh, um, he --
he was the son
of Yevgenia Brin who --
who -- who --
who supported Bob Atlas
in data assimilation work --
work -- work.
He worked with --
with -- with, um, uh,
Bob Rosenberg here.
And I -- I -- I always thought,
uh, he was a pretty bright kid.
I thought he had a future.
Uh, so, uh, you know,
so he made out okay.
Here's, uh,
some more pictures.
Here's a picture from 1984.
Uh, here's Marvin Geller.
He became the next
division chief.
Um, uh, Jagadish Shukla
was at this picture.
Yale Mintz, who --
who was a professor at UCLA
who collaborated a lot
with us on climate research.
And here was Piers Sellers
at -- at that party.
Piers was not part of his group,
but, you know, down the hall
and we were friendly
and this and that.
Here's some more pictures.
1988.
Here's a picture of Chuck Cody,
uh, who was
the deputy to,
uh, to Marvin at the time.
He's still here.
He's since retired
from the government.
Picture of, uh, myself
and my charming wife,
Leslie, who -- who --
who hasn't aged a bit.
Here -- here --
here's one from --
from -- from -- from --
from, uh, uh, uh, from 1990.
Bob Atlas is here.
Uh, here's Bob Rosenberg.
He -- he hasn't
changed a bit.
And here are his two
older daughters,
uh, uh, Sarah and Bonnie.
His youngest one, uh, uh,
Melanie, uh,
wasn't born yet.
And here's one from 1999.
Uh, uh, um,
here's Milt again,
uh, myself, Bob Atlas,
Layman Baker and Jeff Chen.
He was the project man--
the project manager
of the contract
that was supporting us
at the time.
Okay.
Well, there was
some organizational changes
that I address.
Um, Milt became the chief
of the Space Science
and Computing Division in 1984.
And he asked me to join him
and become a branch
head over there.
And I turned down the offer
because I was having
too much fun
doing what I was doing.
Let me give you this anecdote.
He was very disappointed.
He told me this right
before Hanukkah.
I said, "I'm not gonna do it."
He said, "Joel, I wish you
a miserable Hanukkah."
Anyway, at that point --
at that point,
Eugenia Kalnay became
the branch head of GMSB.
Uh, Eu -- Eu -- Eu --
Eugenia left to join NOAA.
Uh, uh, Eugenia was followed
by Ray Bates as branch head.
He was from England.
And then he went back
to England.
And, uh, at that...
In 1992, Franco Einaudi,
our old division chief,
um, who I have memories of...
I'll leave out the word, fond.
Uh, dissolve,
uh, uh, dissolved GMSB.
And he split it into two groups.
One group...
Remember when I was
telling what we were doing.
We were doing satellite
research, data assimilation,
climate this and this and this.
So he -- he --
he made one group
called the Satellite
Data Utilization Office.
Uh, I was the chief of that.
It was an official
government management position.
Bob Atlas worked
for me and others
and the Data
Assimilation Office, DAO,
which was, uh, the precursor
to the Guided Modeling
and Analysis Office.
Um, in 1995, Franco
dissolved my office.
And I then became a senior
scientist in the laboratory
for atmospheres
as the informal head,
spiritual leader,
of the Sounder Research Team,
in which I was and remain
the only civil servant.
I mean, it's called the team
because there
are no other
civil servants in it.
So there's no government --
I'm not a manager.
Now, I've had, um,
some of you may know from now,
I've had some Mission Support
Contract responsibilities.
Starting from GISS,
there was always
a Mission Support Contract
in place
to provide support
for Civil Service
Scientist research.
The contracts are typically 3
to 5 years when they're rebid.
I served on many source
evaluation boards, uh,
performance evaluation boards.
And I actually liked that.
I don't know, I'm...
Uh, now, there...
I'm sorry?
-That's 'cause we're so good.
-Well, whatever.
I enjoy -- I enjoy this kind
of responsibility.
There was a single contract
supporting GMSB
when it split into SDU --
DO and DAO in the same contract,
because the new one
wasn't bid yet,
supported both S --
SDUO and --
and, uh, um, and DAO un--
until, uh, after the split.
And then, uh,
separate RFPs came out.
So I became
the Contracting Officer
Technical Representative,
COTR, on the --
on the SDUO contract,
later the SRT contract,
and, uh, have been the COTR
on other support
contracts as well.
Um, this is one
of my favorite stories.
People know I tend
to repeat things.
Uh, something
happened in 2008.
The Laboratory for Atmospheres,
LAS, was having
their holiday party.
They have an
annual holiday party.
Jim Irons was the deputy
director at the time,
deputy lab --
lab -- lab -- lab chief.
And I said, "Uh, Jim.
I can't come on that day."
I supposed to have
some surgery.
Well, the day before the party,
the surgery was rescheduled.
So, um, I said,
"Okay, Jim. I can come.
But I hadn't bought a ticket."
"Don't worry. Just come."
Um, uh, at the party,
Jim said I could do
a little favor for him.
Dick Stewart,
the COTR of the LAS,
uh, Support Contract
was retiring.
"Maybe, Joe, you could --
you could just be acting COTR
on that contract for a --
for a while.
Uh, we'll find somebody."
And, of course, the somebody
became me but only
because I said,
"I like it. I'd do it."
So, uh, so then I became
the COTR on two contracts.
One was the LAS
Support Contract.
And one was the SRT
Support Contract.
And a new RFP came out in 2012,
uh, called Support
for Atmospheric Sciences,
uh, SAS.
That's the current contract
that we have right now.
And the SAS contract
combined both the SRT contract
and the LAS contract.
Why?
They were both coming
due at the same time.
You could have
one less SEB
and one less COTR
and what have you.
So, uh, so I worked
with Jim Irons to --
to write the RFP for that.
Uh, um, and, uh,
somebody had to be the COTR.
So I drew the short straw again.
And it became me.
And, uh, believe me,
it's a big responsibility.
But I -- I enjoy it and,
in particular,
the challenging responsibility
of interfacing
between procurement
and government scientists.
I thought Karen would
appreciate that comment.
Okay.
Now, um, now,
let's get to, yeah, um, to AIRS.
And the real fun begins here.
Uh, um, AIRS,
the Green Tree Sounder,
was launched on EOS
Aqua in 2002.
And the main goals
of AIRS was --
were improving numerical
weather prediction,
uh, and monitoring
climate processes.
It's -- It's a passive
high spectral resolution IR
spectrometer
with spectral characteristics
that were consistent
with the pioneering
work of Kaplan and Chahine.
I was involved
in the design as well.
And, uh, Mous Chahine was
the Air Science team leader.
And I was an original
Air Science team member.
Uh, I wrote the theory --
the basic theory of how
we analyze their data.
It's -- it's given in a --
in a -- in a paper over here.
It's not important.
Uh, this was
a prelaunch algorithm.
Uh, we...
And it was fundamentally
different from anything
that anybody had done before.
And there's
a reason for this.
It's both good and bad.
It's fundamentally different
because I never read anything
that anyone had done.
So I always come up
with a fresh approach
to how to do things.
I did that
in quantum mechanics, too.
It was always totally different.
This was a prelaunch paper.
We've had versions
four to six of the AIRS Science
Team retrieval algorithm.
Uh, uh, they all
basically follow
what I did with
incremental improvements,
each time a little better.
You learn.
You make it a little better.
And the different geophysical
parameters are solved for,
sequentially using observations
in different channels.
This is another story
that I -- I really liked.
Uh, uh, in June 2001,
um, Piers Sellers
returned to Goddard
as the deputy director
of the Sciences
and Exploration Directorate
after an illustrious ca--
career as an astronaut.
He is a wonderful guy.
And I saw Piers, uh, uh, uh,
coming into the building.
And I said, "Piers, it's great
to have you back at Goddard.
Goddard needs somebody like you
with a great sense of humor."
And this is no joke,
Piers said, "Joel, that's you."
I -- I was really honored.
But, um, I'm nothing like Piers.
Uh, his sense
of humor is --
is much better than mine.
And he's provided
outstanding scientific
leadership here at Goddard,
uh, which has been made
even more effective,
I believe, by his marvelous
and self-deprecating
sense of humor.
Now, as you know, he's been
dealing with some health issues.
He seems to be doing well.
And we all wish him the best.
Now, I want to say something
about a typical
AIRS Science Team meeting.
We have AIRS Science
Team meetings every 6 months.
And, um, a new version
of the AIRS Science
Team algorithm,
which we use to reprocess
all the old data
and all-new data,
typically comes out 3
to 4 years.
In a typical Science
Team meeting,
many scientists come up,
including myself,
having done research
with the data
that we produced,
talking about how great
AIRS Version-N data is.
It's so much better
than anything we've ever seen.
And I get up there
and say, "Yes.
But N+1 is gonna be
much better."
And this --
this is all the time.
The, uh, AIRS Version-6
is what's currently operational.
And the, uh, Version-6 data
set covers September 2002
through January.
We probably have
February by now.
But we haven't,
uh, looked for it.
Um, no.
February's not done yet.
Through January.
And, uh, Version-7,
God willing,
is due for release
in the near future,
we hope maybe in 6 months
or something like that.
Here's a picture of
the original AIRS Science Team.
And, uh, there are a number
of, uh, members here.
I don't know if you can see it.
Um, uh, here.
Is this working?
Here's Mous Chahine.
Here's me.
There's a couple of people.
Oh, -- oh, Larrabee Strow.
If anybody knows Larrabee,
he's changed a little bit.
But he -- he was --
he was there.
Uh, we have a number of members
from NOAA, uh, Dave Wark,
Bill Smith, uh, Hank Revercomb,
um, um,
Larry McMillan,
Mitch Goldberg,
they were all
on the original team.
Alain Chedin was from France.
Here's George Aumann
from GPL.
Um, uh, this --
this was Roberto C--
C--Calheiros.
He was -- he was from Brazil.
They built the, uh, uh, uh, uh,
um, uh,
special humidity
microwave sounder that failed
after 3 months.
But, uh, one of the most
important people on this team
who really never did
much science but --
was Catherine Gautier.
Uh, she was fantastic
because she was a professor
at University of California
and Santa Barbara.
And she hosted many
an AIRS Science
Team meeting over there.
And it's just like heaven,
being over there.
Okay.
Now -- now, I'm gonna talk about
some of the science
that we've been doing.
Uh, before that, this.
Um, as I said --
as I said, um,
the AIRS was designed primarily
to measure temperature
and, uh, water vapor profiles.
But the Science Team
Retrieval algorithm, we said --
we -- we determined many things.
And, uh, um, we determined
a lot of other parameters,
uh, uh, uh,
atmospheric profiles of ozone,
other trace gases,
cloud properties,
Outgoing Longwave Radiation.
A -- and, uh, this enhanced,
uh, capability,
um, has caused
some tension
between the AIRS Science Team
and some other science teams
in the sense
that there are instruments
there designed
just to measure ozone,
just to measure OLR,
just to measure this,
just to measure that.
And we're putting out
a competing product.
And sometimes
they looked at us
and, uh, with --
with, um, distrust, at best.
But, in fact,
I feel very strongly
that being able
to measure something
from two different instruments
from two different ways,
and if you get the same results,
uh, it's very strong.
It supports the results
of both instruments a lot.
So, um, so I -- I --
I'm gonna show some, uh,
some results, uh, very soon,
uh, uh, um, about -- in --
in two of the areas where
we had some initial issues.
One was Outgoing
Longwave Radiation,
for which CERES was designed.
And the other was total Ozone,
for which OMI and OMPS
were designed.
And I'm gonna show examples
of how well our products
agree with theirs.
And, uh, um, we're now
getting along very well
with these teams.
I can see another one coming up,
uh, that's, uh, CO2,
uh, getting a good CO2 product.
The OCO team doesn't want to
hear anything
about what we're doing.
But I think, one day, we'll find
that we're very complementary,
one to the other.
Okay.
So now I'm gonna
show you some results.
So, um, the first three
are climate related.
The last is data
assimilation related.
Now, you have all heard,
read it in the papers...
Matter of fact,
I read it about 2 weeks ago.
So I told Lina,
"Make this plot."
So, uh, so we read
in the paper
that, uh, um,
2015 is the warmest year
on record.
Well, our record
only covers from 2000 --
from -- from -- uh, uh, um,
from 2003 through 2015.
I'm just using whole years.
And this is a plot of the, uh,
the global mean,
annual mean surface
skin temperature
as a function of year,
the difference of the surface
skin temperature
from the average
of the 13 years.
And, um, if you take
a look at it,
these are the values,
ups and downs.
But there's definitely
an upward trend here
and, in particular,
an upward trend here.
And here I have the slope
of the least fit,
uh, straight line.
Uh, these squares fit
to this time period.
And there's a trend of
.0123 Kelvin per year.
Now, this -- this was from
the surface skin temperature.
Surface skin temperature
is really very easy
for us to measure from AIRS.
The radiation that's
coming from the surface,
we have these
so-called window channels
where there's very
little absorption.
You have to account
for the little bit
of absorption
and, of course, clouds,
but we're very good
at accounting for clouds.
We do soundings on up
to 90% cloud cover.
Um, so this is
from skin temperature.
And then there's
another product that,
only recently, I didn't
think we could do.
But we're getting
very good results.
We don't live on the skin.
The skin is the,
you know, the ground,
the ocean, whatever.
We live in the air.
So we also determine
surface air temperature.
And it's a --
it's a -- it's a --
it's not a new product.
It's a newly
looked at product,
a newly appreciated product.
You'll see that we're getting
very much the same result.
The trend is
almost the same.
We're not really
sensitive to radiation
that's coming very
near the surface.
But the algorithm
is very smart.
It somehow knows what to do.
And we get very accurate
skin air
temperature differences.
So here -- here, uh,
this is the glo--
global, uh, uh, mean.
But I want...
I'm showing you
an annual mean temperature,
uh, for 2015,
minus the average of --
of, uh, of the annual
mean temperatures
of all of these years.
And this is the spatial
distribution of that.
Reds and these greens
here mean it's warmer.
Uh, um, blues and, uh,
this color here
means it's darker.
The dark -- It's colder.
The darker the color,
the -- the bigger the effect.
And yeah. This here is
.19 Kelvin global mean,
warmer than the average.
And there's global warming
if you want to think that way.
But it's not global.
People seem to think
that everywhere,
everything is
happening the same.
That's not the case at all.
The warming that's been
taking place, primarily,
has been in
the far north here.
And it's been
taking place here.
Greenland, for this
particular year,
and I'll show you something
alluding to trends in --
in a minute, has been cooling.
I don't know why.
I'm not any...
I call myself
an observationalist.
I see it.
You gotta figure out why.
But, uh, but clearly,
this has been cooling.
Antarctica has been cooling.
But it is very clear,
you know, this is --
this is what we're getting
for skin temperature.
It's being added...
This happens to be
an El Niño year.
And you can see
this effect of El Niño
in the surface skin temperature.
It's adding to this trend.
But the trend was there
before we had the El Niño.
What's interesting is this plot
for the surface air temperature,
uh, they're independent,
so much so that,
if my answer here is too warm,
my answer here
is gonna be too cold.
They go in
the opposite direction.
And you can see that
the spatial plots,
you know, are --
are very much the same.
So this is one
topic of interest.
Now I'm gonna switch gear
and go to another.
Uh, I mentioned
Outgoing Longwave Radiation.
Here, I'm using
the word ARCs.
It's average rates of change.
It's a short-term trend.
Uh, you know,
Bill Lau had told me,
"You can't talk
about trends for 13 years."
So it --
it's the slope of the --
of the least squares fit
to the anomalies.
And -- and -- and, uh,
this is over the --
over the whole time period.
And here's a -- a spatial plot.
Warm... I'm sorry.
Red means OLR is increasing
over this time period.
Blue means OLR is decreasing
over this time period.
And first, let's look at AIRS.
And you'll see something.
Now, OLR is the radiation
going to space.
If the surface is warmer,
everything else being the same,
there's more going to space.
So typically, at high latitudes,
it's following what
the land is doing.
You'll see the effect
that 2015...
But the trend
was warmer over here,
cold over here, cold over here.
What's happening
in the tropics
primarily is the effect of,
uh, uh, uh, of, uh, uh, uh,
of, uh, uh, uh, uh,
of El Niño activity
on the distribution
of clouds and water vapor,
is you have more high clouds.
Radiation comes from the clouds.
The OLR goes down.
So here, you're seeing
some really big patterns of OLR
that we compute
from our products.
Here's CERES.
This is a measured product.
Uh, you'll see
a very close relationship
between what's being
measured by an instrument
that's just measuring flux,
what we're determining
from our products,
what we're computing from that.
This is the difference
between them.
Uh, the spatial
correlation is .975.
The agreement
is really, really good.
In the --
in the global mean sense,
we have OLR decreasing
by minus .01.
They have minus .018.
The difference
is .008.
Um, it's insignificant.
It's amazing that we get
this type of agreement on a --
a 1-degree spatial scale.
What I'm not showing you is
the absolute values also agree.
These are just trends we agreed
are better than 3 watts
per meter squared.
And CERES wasn't
designed to --
to -- to -- to get results
3 watts per meter squared.
So this -- this is
very important.
But now I'm showing
another...
So this is a validation of both.
And we can explain why
these things are happening.
And now I'm showing
another use here.
And this is the OLR
coming from the MERRA-2
analysis from GMAO.
Uh, it's the trend.
They -- they produce
their own OLR.
Uh, it's computed
from their products.
And if you look, you'll see that
there's very good agreement.
Uh, uh, their analysis
is showing less coming
from, uh, uh, from Greenland,
more coming from here.
These patterns
are very much the same.
But if you look at the patterns,
it's interesting.
So, you know, what they're
doing is very good.
And why are we doing this?
We're interacting with --
with the --
with the GMAO.
Let's diagnose what's going on.
Where do we think it's good?
Where do we think it can
use a little improvement?
If you look at
what's going on here,
you'll see this feature is
much bigger than this feature.
This feature is much
bigger than this feature.
Uh, uh, um, this feature
is much bigger
than this feature.
So while they're a model,
their -- their --
their analysis is
clearly responding
to effects of El Niño
and cloud distribution.
Somehow, it's overdoing it.
Also, they have this huge,
uh, value, uh,
of decreasing OLR over Africa
where we just have
a hint of it here.
So something is being
exaggerated there.
But it's really very, very good.
Um, um, another...
And -- and by the way,
we looked at...
I don't have time
to show everything.
We looked at the clouds.
Uh, uh, um,
their changes in clouds
are very similar to ours.
But somehow, they got
an increase in clouds here,
which lowers the OLR.
We didn't get that.
And also, their global
mean changes minus .1.
We're minus .01.
And that comes from the fact
that, for some reason,
their cloud cover
has been increasing
over this time period,
including over Africa.
And ours is not.
And that's consistent
with this measurement.
Almost done here.
How am I doing?
Not bad.
Uh, I'm going to go
to the third,
the science topic,
and that's total ozone.
Um, Gordon LeBeau, who's here,
who's been extremely
helpful with us,
uh, he was tasked, actually,
by headquarters to look --
to see if they can complement,
uh, uh, uh, uh, um, uh,
ozone results coming from --
from -- from the UV instruments,
the state-of-the art
instruments,
with, uh, with other instruments
like, uh, like AIRS.
We have some of...
Ours is an IR instrument.
Theirs is a UV instrument.
So it, um...
We obviously have some potential
additional information.
For example,
we can get it at night.
They can't get it at night.
So we can get it twice
a day and,
more important, polar winter.
That's real --
They have no idea
what's going on
in the polar winter.
And we can get it
in polar winter.
But the important thing
is they have to...
Before you can use them
together, they have to agree.
Gordon looked at our stuff
for Version-6,
"Joel, this looks real good.
But what about this
and what about that?"
And we made
some improvements.
We came up
with a version called --
called 622, uh,
which we ran at JPL
for 2 months,
August 2014 and 2015.
And here, we, uh, uh, uh,
are showing the --
the -- the total
ozone monthly mean
for AIRS version 622
for August compared to OMPS.
The agreement
is actually very, very good.
The spatial correlation is .96.
You do see this
feature over here.
And we know what
that feature is about.
It's Saharan dust, uh, silicates
absorbing the ozone band.
So if you -- you see them,
you can confuse it for ozone.
We think we have
too much ozone.
We've been doing
further research.
We have something called 627,
soon to be seven,
where we don't improve on this,
but we certainly know
when it's happening,
and we flag them as bad.
We just gave you...
Did we give you a month?
I'm assuming we gave you
a year of 627.
And it's gonna look
a lot better.
And Version-7
is gonna be better yet.
But even so, this is...
You don't just want to look
at 1 month.
I mean, you -- you want to study
the whole time period.
A big thing
you'll notice is, uh,
is there's not much down there.
You can't...
Uh, we're getting very
reasonable, good results here.
And this is -- this is
the interannual difference.
Uh, um, big features
are happening from,
uh, between 1 year
and the other.
We have an excellent
agreement, uh,
uh, um, uh, uh,
a correlation of .96.
So we're really excited
about this one.
Okay. Last science result
and then I'll finish up.
And I -- I -- I mentioned that
we were doing results
also on data assimilation
that goes way back
to when Bob Atlas was here.
And we've picked up
on that work.
Uh, Bob Rosenberg works
with me, Oreste Reale.
And, uh, uh, uh, uh,
and, um,
Erica McGrath from GMAO
have been helping us
a lot on this.
And we're using, uh, uh, uh,
a GEOs-5 data
assimilation system.
But we're looking at ways
of possibly improving
the way AIRS data
is handled in the analysis.
So I'm showing a result.
This is the average of --
of a forecast scale
for 52 forecasts,
uh, run every day,
from September 10th
through October 31st.
This is the 500 milli-bar
height anomaly correlation.
It's judged against
the incep analysis.
A perfect correlation means --
a -- a perfect anomaly
correlation
means the anomalies over
the whole time period are --
agree perfectly.
Uh, uh, the bigger the forecast,
you begin to...
You -- you don't degree as well.
A forecast skill of .6
is considered to be useless.
And here, I'm just showing
you the results of what --
of what we got using
the GMAO data,
handling methodology.
And it's very good.
A 7-day forecast has
pretty good global scale.
All -- all --
all the research that we've done
looking at different ways
of handling AIRS
and other satellite data
has actually improved this.
We're continuing to do more,
uh, uh, research on this.
We're looking toward
further improvements.
And, of course, one --
one time period
doesn't tell you anything.
We're gonna be testing it
over different time periods,
over different years.
But we think the potential
is -- is good here.
Hopefully, we can interact
positively with them.
Okay.
And now, I'm gonna...
My next-to-last view graph.
It would be my last.
But the last is another hint
on the prize problem.
So, um, I call this
my retirement plans.
In January 2014...
Many people give these
talks before they retire.
Uh, Bill Lau gave
a talk then.
And he had no intention
of retiring whatsoever.
But Bill told me then --
Bill told me,
"Headquarters, uh,
understands you're retiring."
He said, "I'm not retiring.
I have no plans on retiring."
University of Maryland
said, "We hear you're retiring."
"I have no plans."
"Well, let me make you offer."
So they made him an offer
he couldn't refuse.
So he retired at that point.
I have no plans on retiring
in the foreseeable future.
And there's a couple of --
a couple of reasons here.
The main thing is...
Uh, here's my wonderful
group at SRT.
Uh, uh, uh, um, here's --
here's myself, uh, uh, uh, uh,
Bob Rosenberg,
uh, Joyce Tompkins,
Lena Iredell,
John Blaisdell, Lou Kouvaris,
and Jay Lee,
who's not formally in our --
in our organization
but incredibly
important part.
And as long as I can
continue to support these,
uh, people, we're gonna --
I'm gonna keep working.
Um, and -- and there are
a couple of reasons.
The first is I still love
doing what I'm doing.
I think we're doing
very exciting research.
Um, like Lena and my guys say,
"We're never bored."
Uh, and -- and so -- and --
so I'm really looking forward
to AIRS Version-7.
We hope to get it out soon.
It'll be applied
to both AIRS and --
and -- and CrIS, which is
another instrument that's --
that's flying on MPP now.
And it's, uh, it's on
NOAA satellites,
different but similar to AIRS.
And then also working
with others,
uh, uh, uh, um,
toward the design of a further
advanced high-spatial-and-
spectral-resolution sounders
to determine --
not just improve the soundings,
but also winds.
And we're really excited
about the future there.
Second is I actually
enjoy being the COR.
If I were not in the government,
I couldn't do it.
Karen's cracking up here.
And -- and -- and I especially
like working with Karen.
We have a lot of fun together.
She wouldn't let me retire.
-No.
-And, as I said,
I couldn't leave my group.
Uh, uh, uh, you know,
we're like family.
Uh, uh, uh, we just love it.
So as long as I can keep getting
money, let's keep doing it.
There's a fourth reason
I didn't put here.
Um, my wife, Leslie,
works out of the house.
She says, "Joel, no way
I'm having you home all day."
So I plan to be here, you know,
hopefully, um, let's say,
God and NASA
headquarters willing,
I'll get some more money
and be here
for another couple more years.
Okay.
This is the last view graph.
I hope I didn't go too far.
This is another hint
towards that prize problem.
This is what
I showed you before.
We had a piece of paper.
We had two lines.
Construct the angle
bisector of this.
Um, you know,
someone said,
"Why don't you
just extend the lines?"
You can't do any work
that's not on the paper.
We're a big triangle.
So I said, "Look,
I gave you a hint.
The hint said
all the angle bisectors
of a triangle meet at a point."
Well, it sort of
tells you what to do.
I don't have any angles here.
So you make an angle.
What you do is you draw
a line, make angles.
And, uh, there you have it.
And now construct
the angle bisectors
and now figure out what to do,
which should be easy.
And, uh...easier anyway.
And, um, I'll just say that
if we have some time,
I'll take questions.
But what to do next is not
one of those questions.
