-So, without further ado today,
I'm not going to tell a story.
I think Tamas has a story.
So --
And Tamas has actually
worked with --
with Sasha Marshak
for a long time.
When I say "a long time,"
I mean almost 15 years.
And in those 15 years, of course,
he has learned a lot
about his boss.
And therefore,
we want him to share
with us those stories.
So please help me
welcome Tamas.
-Thank you.
And I'm here today
to introduce
not just Dr. Marshak
or just Sasha, as you know,
but I'm -- I don't need to do
a real introduction
because we will hear
soon about this life
of a much more interesting
inside for --
true insider's perspective.
But, um, also many of us
already known Sasha.
He's been here for --
for 10 years now.
And as Josh mentioned,
it has been luck
that he's been here
because he...
And so I had the privilege
to work with him,
which was
a great deal of fun.
And today, I'm really glad
that he's giving this talk,
not only because I think
he's an outstanding scientist
and simply
a very good person,
but also he's really
enthusiastic about things that,
you know,
that -- that captures you.
And this includes,
you know, nature
that he likes to look
at through science
or even more, perhaps,
through math or other --
But also through other ways,
including picking mushrooms
or cross-country skiing
or even more,
well, I was impressed,
devouring apples to the foot.
You know, that -- that is quite,
you know,
it's an interesting thing.
And -- and also Sasha's path
from the northern edge
of Europe to
here was quite --
quite a journey spanning,
as you can see,
a wide range of disciplines.
But -- that give him,
you know, a broad range --
broader perspective
on both in tradition
but also on other
things that I was --
was impressed, for example,
doing fish or going in...was
I -- I think really exciting.
So I'm looking forward to hear
about his adventures
that he's going to share
and -- and his insights.
And I'm really glad
to back up Sasha.
-Thank you very much, Tomash.
Thank you, Charles.
It's a high honor for me
to give presentation
at many a talk.
And it's always an honor
to speak in H114 around here.
Let me invite you to join
for a journey,
ready to transfer journey
through between
pure mathematics and clouds
and aerosols with some stops,
nuclear reactors and
vegetations and even fractals.
Several weeks ago
before retirement,
Warren Wiscombe stopped
by my office
and handed over to me
a folder with documents
dated back to spring '91.
And I want to share with you
some of those documents.
And so this is use ad
that was published
in spring '91.
And, um, I highlighted
a piece of it.
So you can better read it.
Um, that time,
I was in Germany working at
University of Gottingen
as an examiner
from Humboldt fellow.
And, um, I got already
an offer from Toulouse
to work on vegetation,
canopy irrigation
vegetation, canopy.
And then, all of a sudden,
is spotted this ad.
And two words
grabbed my attention,
first is NASA,
and second word, radiation.
And I did not know much
about cloud optical depths
and very little about
fractals and turbulence,
mostly from popular journals
like "Scientific America."
But nevertheless,
I applied for it.
To my surprise,
in a few weeks,
I receive a letter from Warren
Wiscombe who admitted
that he was the guy
who crafted this ad.
And perhaps
he liked my resume
because he replied to me.
And he said
that he explicitly explain
about this position,
that it was not
an academic position
but with well-defined goals
stated by the department
of energy.
But in general, if I like it,
I could consider
his letter as an offer.
Oh, gosh.
I was thrilled.
So it's NASA.
It's Warren Wiscombe,
the guy with distort
mie scattering,
delta-M approximation.
So I reply to him the same day.
And I want to read
to you one paragraph
in my reply
and especially --
And now after 22 years
I can sign under every word
I wrote there
in my poor English.
But I want to read you
one sentence at the end.
It is a great honor for me
to have a job there,
collaborate with people
and contribute to the program.
Thank you again
for your offer.
And I say, "Yes," to it.
Yes, you can.
And it took long 3 months
before I got
a real offer from SSAI.
And perhaps I was
the last applicant
to a NASA job
with a nationality
of the Soviet Union.
And Soviet Union collapse
in 2 months.
And people with ration
prospered, with rationality.
They were more welcome,
probably because of some
political relationship.
And so it took me
2 years to be escorted.
But nevertheless,
my transition
from math to clouds
started there.
Let me show you
the transition in phases.
This is professor
Gennadi Vainikko.
I learned from him not only math
and radiative transfer
but also the ethic of science
and science in general.
And then Warren Wis-- Oh, sorry.
In addition to atmosphere
and clouds,
he also taught me
about the United States.
And I still use his advices.
And in between,
it was professor Juhan Ross.
I learned from him
about radiation in vegetation.
And it was from
our own Bob Cahalan
who I learned a lot
about fractals
and cloud structure.
And my transition
wouldn't be successful
without my good friends
and colleagues
and coauthors of many papers,
Yuri Knyazikhin now
from Boston University
and Anthony Davis now from --
from JPL.
And as all science,
those six people
are different nationality.
Gennadi Vainikko is Finnish.
Juhan Ross is Estonian.
Bob and Warren American.
So Bob likes to say
that he's Irish.
And, um, Yuri Knyazikhin
is Russian.
And Anthony Davis
is Canadian.
And, um, now let me switch
to radiative transfer.
So in this slide,
I listed a few branches
of physics that deal
with radiative transfer.
And in addition to well-known
in this building,
atmospheric physics
and vegetation,
there are also astrophysics
and nuclear physics.
Indeed, almost all knowledge
we get about distribution
of stars or abundance
of elements in space
comes from simulating
of radiative transfer.
In nuclear physics,
since neutrons
moving in a reactor obey
the same laws
as radiation interacted
with atmospheric particles,
then radiative
transfer plays,
indeed, the key role
in nuclear reactor design.
And historically, historically,
perhaps astrophysics or,
be more precise, star,
the study of star photosphere
were the first scientific branch
that initiated
from the mental research
in radiative transfer.
And the first paper
goes back to 1905 by Schuster.
And the whole and --
So it started the beginning
of 20th century
and associated
with such big names
as Schwarzschild-Milne,
Edgington
and later Chandrasekhar,
Ambartsumian, Sobolev.
A lot --
Next step in the development
of radiative transfer
came from nuclear physics.
And these people, they call
it not radiative transfer
but transport theory.
And during Manhattan program,
the Manhattan program
and after the Second World War,
motivated mostly
by weapon development
and later nuclear reactors,
having almost unlimited
supply of funding,
having the core --
the core bunch of physicists
working on this project,
they were able
to make models just right.
And the progress
was really rapid.
And in addition to the first
analytical matter
developed in astrophysics,
nuclear physicists developed
a lot of approximation methods
and that we keep using
in atmospheric physics.
So in addition
to famous Monte
Carlo method that was developed
in Los Alamos
at the beginning
of '40s by Stanislaw Ulam
and the first paper
together with Metropolis
was published in 1949.
So was developed
during Manhattan project.
Actually, reading biography
of Enrico Fermi,
I read that Enrico Fermi was
the developer of Monte
Carlo at the beginning
of surface
work in the fusion theory.
However, he never
published any --
any paper on Monte Carlo.
So in addition to Monte Carlo,
it was also an approximation
and the spherical harmonics
method that now we widely use
in atmospheric physics.
And in parallel
with nuclear physics
but with much less funding,
atmospheric physics was
contributing
to the development
of radiative transfer method.
And it was much slower progress
before the satellite era,
before the remote sensing.
And we all should be proud
because part of
the fundamental results
in radiative transfer
were developed here
at Goddard in Building 22.
And you have the youngest branch
is radiative transfer
in vegetation.
And it goes back
to perhaps '60s, '70s.
And -- and --
it was a generalization of --
to the medium,
to the oriented plates.
And most of the result
that were obtained
from this discipline
in radiative transfer,
they were based on the equation
of radiative transfer.
So this is a standard
stationary equation
of radiative transfer
that describe energy
conservation
for radiance at point
R in the direction omega.
It simply says that a beam
while traveling
loses some energy
due to absorption
and gains some energy
due to emission
and the distribute
energy due to scattering.
So as simple as that.
And all discipline deal
with this equation.
But the devil
is in coefficient.
So coefficients
are all different.
So let me start with familiar
to us for clouds.
So scattering face function,
as we know,
depends not only on
two directions,
coming and reflection
but the scattering
angle between them.
However, because of
the diffraction theory,
half of all energy
goes in a small angle
in diffraction peak
and thus requires
hundreds of term
in Legendre polynomial.
From the other hand,
in vegetation --
in vegetation, dependence
on wavelengths much weaker
because scattering centers
like leaves much bigger
than electromagnetic
wavelengths.
However, because
of leaf orientation,
extinction coefficient
depends not only on location
but also on direction.
And the face function,
so more spherical,
much more spherical,
depends on not on --
not the scattering angle
but on absolutely
really of boss angle.
That what makes
the small difficult.
In nuclear reactors,
in nuclear reactors,
the solution depends
not only on location
and direction
but also on energy.
And this term in addition
to integrating over four pi
over all directions,
we have to integrate
over energy.
And, however,
all coefficients
are well defined
because man made.
So we know we can get
inside there.
Okay.
Let me put on hold
radiative transfer
and switch to the geography.
In contrast to Elene Rammer
who gave many a talk recently,
I was born not
in sunny California
but way beyond polar
circle in Murmansk.
And you know that Murmansk
is well-known,
remote sensing town
because one of the leader
in aerosol atmospheric
optics our own Oleg Dubovik
lived there for 16 years.
And recently, I was invited
to Michigan Tech
to give a colloquium there.
And Professor Raymond Shaw,
who introduced me then,
he said that for many year
that he runs
this seminar series,
he never introduced a person
who was born north than him.
And he was born
in Fairbanks, Alaska.
And the third point
that recently
I got involved in blowing
snow started as a member of
iSAT science team together
with Steve...
And we analyzed the effect
of blowing snow
on the altimeter
accuracy for iSAT.
And also we're able
to detect blowing snow
from satellite like iSAT,
CALIPSO and even MODIS.
Similar to Maryland people,
in Murmansk,
we also have days
when schools are closed.
But these days are
called blowing snow days
rather than snow days.
And if in Maryland kids
enjoyed blowing snow days,
been outside playing
snow ball,
making snow man, then, honestly,
it was not a fun
for blowing snow days
to be outside
because of very strong wind
and almost zero visibility.
Sometimes, you cannot
open a door
from your apartment
building to get outside.
And now, um, I can take --
I can --
I have my score to settle
with blowing snow.
And now I can
take revenge to it,
be able to detect this guy
from satellites.
And so I'm very
happy about that.
And in addition
to cross-country ski
and ice hockey
during long blowing snow days,
I like chess and like math.
And so I didn't
have any equations
where to go out graduating
from secondary school,
of course, university
math department.
So by passing Moscow,
I ended up in Tartu in Estonia.
Tartu University is one
of the oldest universities
in Eastern Europe.
It was founded by Swedish
King Gustavus in 1632.
And recently,
I found a list of records
that what classes
I took during 5-year study
at the department
of mathematics.
And, of course, most of it,
almost all of it,
was math, logic,
geometry, algebra, analysis,
differential equations,
math, physics,
probability theory,
game theory,
numerical analysis,
even ill-posed problem.
And I was really liked
and hated that.
We were taught radiative
transfer theory,
which is not very common.
And that became, actually,
the whole career.
But there are also two
classes that kind of,
um, sound weird
for this audience.
It's scientific atheism
and scientific communism.
And, you know, everything
depends on your professor.
We were lucky that
scientific atheism class
was taught
by a professor expelled
from Leningrad University
for his liberal views.
And he was also a consultant
of Dresden Art Gallery.
So he tried to share with us
his knowledge
and interest in art
and during a special
Renaissance time
from 14 to 17 century.
So it was really fun.
I have to admit
that communist class
was pretty boring
and that thick book.
So another event that happened
with me important
when during my Tartu
University year
that I met a girl.
And we married.
And we were 21 years old.
And by the graduation
from Tartu University,
we had already
a half-a-year-old daughter,
who is now a teacher in Seattle.
So it was not that
rare to marry at 21.
And -- and many
of my friends
got married that -- that time.
My professor, as I said,
was Gennadi Vainikko
who taught me a lot of math
plus also radiative transfer.
When we studied
the discrete ordinates matter
from Chandrasekhar 1960 book,
as a matician,
he mentioned that,
"Look, these physicists,
they never prove
that this matter convergent.
They never state the conditions
for when it would converge.
They never estimate the rate
of convergence and the best
convergent rules that could be
used for this technique."
So ironically, that became
the topic of my dissertation.
After graduating
from Tartu University,
I went to Novosibirsk.
That time,
Novosibirsk and especially
the academy town
20 kilometers
from Novosibirsk
was a scientific paradise.
And in general,
it was much more liberal,
of course, liberal
in Russian metrics,
than Moscow and Leningrad.
And the Monte Carlo school
was one of the best
in Soviet Union.
So I started working
in Institute
of Computational Mathematics
and Mathematical Geophysics.
The leader
of this Monte Carlo school
was Professor Mikhailov,
one of the key authors
of a famous Marchuk
atmospheric radiative transfer,
Monte Carlo atmospheric
radiative transfer book.
And, um, that time,
the approach to Monte
Carlo Russian approach
and American approach
were a little bit different.
While Russian was
highly mathematical,
then American one
was highly practical.
And, um, Russians
objected to use
any variance
reduction technique.
Variance reduction technique is
some acceleration
of Monte Carlo code
because of slow computers
and to force
to get maximum information
from each photon trajectory.
And so they objected
any variance reduction technique
if it's not
rigorously justified.
And even they said
that if you have a nice idea,
and I think up how to accelerate
your technique,
your solution,
but later because
of a lot of randomness
you can have an arrow
that you know --
you don't know
the source of it.
And, um, even radiative
transfer processes,
which is purely
physical processes,
they consider it as a solution,
approximate solution,
of radiative
transfer equation.
So first from internal
radiative transfer equation
that I showed you, oh, before.
So they converted
to integral equation.
And then this integral equation
was represented
as a series of integrals
with increasing dimensions
by scattering order.
So called Neumann --
for Neumann series.
And then each of them,
Monte Carlo was nothing else
but a random convergent rule.
And, um, so that helped me
a lot later on,
sorry, later on to use this
Monte Carlo to solve
radiation problems in clouds.
And, um, in spite of my
interest to Monte Carlo,
I got my PhD,
and my dissertation was
in discrete ordinates method.
And so this book
that we publish,
and it has jointly
with Yuri Knyazikhin.
And part of this book
is part of my dissertation.
And it's mostly
about convergence rate
of discrete ordinates method.
So in general, the main results
and a lot of people use DISORT.
And so the equation
was we increased twice
number of streams,
how much accurate
our solution would be.
So that's type
of equation.
So anyway, after graduating
from Novosibirsk University
and getting my PhD there,
I got a job back to Tartu
and studied
working in Institute
of Astrophysics and Atmospheric
Physics 20 kilometers
south from Tartu
on radiative transfer
in vegetation problem.
My scientific adviser
was professor Juhan Ross.
And many of us who deal
with MODIS surface
BRDF that showed this name
Ross Thick-LiSparse.
And this is the same Ross.
This is the same
Ross who goes here,
Ross Thick-LiSparse.
And, um, Ross is known
for development
and mathematical model
of radiative transfer
in plant canopy.
So it just plane
parallel layer
with still a bit medium
but with small
flat-oriented blades
like simulating leaves.
And I was young.
And, um, we had
a lot of funds -- fun.
So in radiative transfer
in vegetation canopy
and beating
slow Russian computers
with at once variance
reduction technique.
And we even tried,
believe you or not,
to do some inversion
using Monte
Carlo to estimate vegetation
canopy parameter.
Of course, it didn't have
any practical sense.
But it was fun
to take derivative
over four trajectories.
So anyway, we were ready
to publish a book,
"Photon-vegetation Interaction"
under my name and Juhan Ross,
addition in part of our results
where included in this book.
And then in 1990, in 1988,
Tartu was already open town.
And a group
of American scientists
and NASA scientists led
by Ghassem Asrar,
visited us, mostly to
discuss writing this book.
And, um, this is
a round-table discussion.
And you can see
our own Piers Sellers
sitting in the corner.
So that was in Tartu 1988.
Even in my craziest dreams
that time,
I could not imagine
that in 3 years,
I will be here
at Goddard working
with the best scientists
in the world.
So anyway, that what happened.
And, um, in 1989,
I got awarded a fellowship,
Alexander von Humboldt
fellowship.
And we moved to Germany
for a little bit
more than 1 year
working on radiative transfer
and vegetation for institute
of bio climatology.
And you already heard my story
how I got a job here
in Greenbelt.
The first month has been here
was really hot,
hot, humid, weather,
outside, cold inside.
I get used to be
other way around.
And, um,
a lot of problem
with everything,
passing driving test,
language, physics of clouds,
fractals, turbulence
and being escorted.
Anyway, I got through
all this thanks
to two papers
by Bob Cahalan
published in '89.
They were the first paper
that I was able
to read and understand.
And I got some understanding
of structure of clouds
and even the use of fractals.
And, um, I will be talking
about this paper
a little bit later.
But meanwhile, let me show you
another paper by Bob Cahalan
that played really
important role in
my understanding
in simulating the clouds.
This is bounded cascade
paper published
by Bob in '96.
We start with uniform slab.
And then you divide it in half
and transfer fraction
from one to another,
randomly choosing the direction.
Then we keep dividing, dividing.
And so this
is a typical cascade --
cascade, well known.
The beauty of this model
was that he weight
that we use to transfer energy
from one part to another
were decreasing.
So this will give you
my annotation,
what was different
in that notation.
And as a result,
we got a model
that was still intermittent
at first step
and then smooth and bound
at higher step,
remaining very much
what's going on
with liquid water.
So we took it over from Bob.
And, um, did together
with Anthony,
Bob and Warren much
mathematical study
with this model
and publish it in phys ref.
So this was kind
of my transition
from pure math
to atmospheric science,
of course through physics.
And, um, in this model,
so let me give you one example.
So we proved that this model
was continuous and self-affine,
self-affine property,
meaning that zoom
in a piece looks,
statistically,
the same as the hole.
And so this
a fractal property.
So we took this model.
We took this model.
And here we rescale
it here in horizontal.
Then it getting
smoother at a scale.
Again, it's getting
smoother and smoother
so because of
the decreasing weights.
However, you were scale
in vertical axis,
you were scale
in vertical axis,
then the behavior
of this function
is statistically the same
as the original one.
However, it's only
a small piece,
smaller and smaller
pieces of it.
So we were able to prove
the self-affinity.
And this model
had two parameters, P and H.
P is responsible
for intermittency
and H for smoothness.
And we were able to show
that this is a scale invariant
and wave member spectrum
or also power spectrum
or energy spectrum,
which is nothing else
but Fourier transform
of outer correlation function
that tells you how
correlated pieces of the cloud,
um, of the model is.
And we found that
with some parameter
H equal to one third,
our spectrum behavior will be
found in many time series,
measurements of liquid water
behavior using aircraft
and ground-based measurements.
So now let me share
with you some of Warren
Wiscombe's philosophy
on science development
that we tried to follow.
Science is tool driven,
used to say Warren.
Even if you have
great ideas,
if you don't have
appropriate tools,
your ideas might die.
So now we had a simple model
that can simulate or mimic
the very ability of liquid
water in clouds,
horizontal variability.
We had
well-justified and proof
radiative transfer model.
And by that time,
we had best computer create too.
So we were well-equipped.
And we could move forward.
Let me give you an example.
This is a piece 60
by 60 kilometers of marine
stratocumulus from Cahalan
and Snyder '89 paper.
And now after beautiful
MODIS picture,
this looks pretty boring.
But believe me, that time,
it was state of the art.
And analyzing this plot,
trying to get --
trying to study correlation,
so this is energy spectrum or,
as I said, Fourier transform
that tells you
about the correlation.
It's a log-log plot.
And what we see first,
it's so-called cloud streets
about 8 kilometers
of this spike.
But it's not what
I want to focus.
So we see that this
is a straight line
on a log-log plot
with this exponent
with a slope minus five third,
the same as we observed
in liquid water in --
in this particular liquid
water pass data
measured from the ground.
So for large scales,
we have the same behavior.
We have the same behavior.
But for smaller scales,
with a scale break
around 200, 300 meters,
we got a much steeper slope.
We got a much steeper slope.
In this paper,
two explanation, physical,
instrumental was provided.
But it was not very
satisfactory explanation.
First of all, we could not
model yet this process.
And you only understand
what's going on
if you can model it.
So having all our tools,
we were able to model
the same behavior.
So we took this again log-log
plot of wave number spectrum
versus wave lengths.
And so we model it
horizontal distribution
of cloud optical depths.
And then you reuse
one dimension
radiative transfer pixel
by pixel.
Then this nonlinear
transformation
will keep the same slope,
will keep the same slope.
However, if you use
Nadir radiative transfer
with photon traveling
between different pixel,
with photon traveling
horizontally,
we could see that
the slope started bending
and reminds of the same
that we observed
from Landsat image.
And we were able to estimate
this scale break
and relate
it to the distance
photons travel
horizontally between exit --
between entry and exit points.
So this is kind of estimate
for a photon
horizontal transfer.
And we were able to derive
analytically the scale break.
It was nothing else
but a harmonic mean
of two characteristic scale
of cloud, cloud
geometrical thickness,
H, and photon
mean free pass,
in this case transport
mean free pass.
And since we related
to the photon
traveling horizontally,
then it's a simple
green function problem.
When we estimate a matter
with a pencil beam,
like delta function,
like delta function.
And then we measure a spot.
This is namely the average
distance of photon
traveling horizontally.
So we call it radiative
transfer green function.
And everyone shows movie
in this room.
Let me also follow
that tradition.
And, um...
You see the spots.
It's a characteristic
of cloud property.
The geometrical
and optical thickness of cloud
because the ratio,
geometrical over
optical thickness,
give of the distance
photons travel
between scattering.
Anyway, so we --
Everything goes by spirals.
So we are back to
nuclear science community.
And we wanted
to report this result.
And this people
in nuclear science,
they're a little bit snobbish.
And they don't trust
to any theoretical result
in radiative transfer
that comes not
from their community.
And so we had hard time
trying to report this --
this result.
And we reported them
to two legendary figures
in radiative transfer.
One is Jerry Pomraning
and another one George Titov.
And both of them admitted
that this kind of radiative
smoothing scale
is preternatural.
And they never seen it
published anywhere else.
So we were really happy.
And I started my presentation
with Warren Wiscombe.
So let me share with you
another picture of Warren
and me in Oklahoma
alongside the theoreticians.
We look at this instrument
that measure flux
so that we actually simulate
in our computers.
And -- and so that time,
Warren insisted
that we need to take part
in field campaign
in order to see
how everything works.
It's not just strange
alien objects.
And no radiation
talk can be given
without referencing
to our godfather, Chandrasekhar.
And let me give you
my favorite citation
from Chandrasekhar 1960 book
that I tried to follow
in my radiative
transfer research.
So in a study of the equation
of radiative transfer,
we therefore have
two objectives.
First, the development
of approximation methods
of solution which will have
sufficient flexibility
for adaptation to
any mathematical situation.
And second, the development
of methods of sufficient power
and generality
which will enable us
to discover the various
integral relations.
So that's what
we are doing now,
analyzing current MODIS,
CALIPSO and other data.
And I will finish showing
you a group of young,
maybe not very young,
but very talented people
with whom we are --
we have the same
scientific vision
that comes from Chandrasekhar.
And we are working with
iSAT, CALIPSO,
MODIS, ARM
and Discover projects.
Thank you.
-Now we've got time for
a few comments and questions.
Okay, one question.
-[ Speaks indistinctly ]
-Honestly,
I never work with microwave.
Probably spectro difficulties,
variations in
speckle-cell scattering
is made legible there
and then difference
in absorbing properties.
So that's probably
the main challenge.
But I have to admit,
I never work in --
in microwave region.
Mostly I worked in solar.
It's not only visible
but also in near infrared.
But I'm sure in this building,
we do have real expert
in microwave remote sensing.
I am not.
-[ Speaks indistinctly ]
Um, is that, like, how --
how to reinvent yourself
totally from scratch or what?
-Probably not
the same principal,
the same equation,
different emphasis,
different aspects,
different unknown,
different known,
different simple
and complicated stuff.
So I -- I believe that knowledge
of radiative transfer in general
and solar radiation
in particular
will help to switch.
According to Chandrasekhar,
it should be to any field
that deals with that.
So --
-Sasha, why do you think
that in microwave region,
scattering is not important?
-I shouldn't say,
probably so, yeah.
Say less important.
-Sounds better.
-Sorry.
I didn't want to offend
any microwave person.
-I never worked with microwave.
But I read some paper.
There's a lot of papers
now about microwave
analysis of different
astrophysical objects.
And they see a lot of
optically dense stuff
where multiple scattering
is important there.
And so they solved this problem.
Multiple scattering may be
in a slightly different way
of what you describe,
so called re-emission,
where the molecule, for example,
absorbs the photon
at one frequency of microwave
but then radiating in another
frequency that doesn't...
it still scattered, yeah?
So [speaks indistinctly]
-Right.
Thank you.
-Okay.
Last question.
-What is the next application
of radiative transfer?
-What we are all
doing here, climate.
Climate, energy balance.
And so there's all where
in this building,
at least,
interpretation
of satellite data,
better interpretation
and better algorithm.
But this is not new.
It's improvement.
But you ask what will be
a new direction
for radiative transfer.
Probably help with
a better forecast.
That's probably
the most urgent problem
and how radiative
transfer can help.
It's again, so we need to know
better what we measure.
And in order to know better,
we need better technique
to convert our observations
into real knowledge
about the nature
that would help later
to develop our forecast models.
I guess this is the urgent need
for our community, maybe.
But it's not my field
of expertise.
-About the challenge
of simulating
in a simulation system,
radiances
which are affected by clouds
because I think this is one
of the frontier things
that for people
trying to use radiance
information into more of those.
And of course right now,
they are simulating
on the sky radiance.
So we are discarding
a large fraction
of that measurements
that we have from instruments
such as...
So what do you think
of the contribution
of all that studies
could be of understanding
better the separating clouds
from here out
has and rate
from theoretical perspective
might perhaps help simulate
cloud information in MODIS?
-Very good question.
So -- so far,
all models are binary,
cloud to clear, cloud to clear.
But now, we learn that
it's not negligible
transition zone, gray zone,
which is neither cloud
nor clear that consist
of a lot of small cloud elements
and humidified aerosol.
So -- so probably we believe
that we will
substantially advance
if we can generalize
and move from
binary system cloud
to clear into
a more continuous cloud
to clear system
that includes
the transition zone
or gray zone
that we learn from CALIPSO data,
like 50 percent of all CALIPSO
called clear pixels
are closer than 5
kilometers from clouds.
And then they're affected
by clouds in some way.
So that radiative transfer
can help
this challenging
problem to understand
what's going on
near cloud edges.
-Okay.
I -- I'll pick one more
last question.
Then that's it.
Okay.
-You talked a little bit
another medium
in which might scatter,
snow, ice and water,
that their Earth
atmosphere interface
with laser pulses going into it.
You didn't show that
on your quad chart of radiance.
Did you see that as becoming
an important in...
-Yes, absolutely.
I was really lucky
and happy to work on iSAT
and snow problem
that ice people
finally realized
that in order to get accurate
measurements of surface,
they need to get
through atmosphere.
So they need to learn
about clouds
and aerosol and even
blowing snow.
So that get us employed in this
surface project.
And, um, it's very interesting,
radiative transfer in snow.
I didn't mention it here.
But we can estimate the distance
photon travels in snow.
And because of that,
we have a delay of lighter,
of light beam of delay of pass.
And then we can --
we can, um,
we can wrongly
estimate the surface,
the snow surface,
thinking that --
that surface is lower.
And thus we have
more melting.
So that we need to understand
to be able to simulate
the photon trajectory,
photon penetration
into snow and ice.
It's, again, radiative
transfer problem
and -- and thing we work
with Dave on this as well.
It was very fascinating
and fun stuff.
-Okay. With that last time,
Sasha one more time.
[ Applause ]
We'll see you in 2 weeks.
