All right, great!
So I want to spend a few
minutes with you this afternoon
talking about how Nimbus
as the granddaddy of
learning how to take
measurements of precipitation
and how far we've
gone in the 40 years
since Nimbus started doing
precipitation measurements
from space.
So I will start by going
to the next slide,
so I have a little bit
of an outline here.
I am going to talk a
little about the history
of the Nimbus measurements
and instrument launches
for that data.
I will explain in as
layman terms as I can
how these radiometers work
to measure precipitation
and then I am going
to talk about
some of the science results
that have happened in terms of
precipitation from Nimbus,
all the way
through the decades,
to the Global Precipitation
Measurement Mission,
which I am the
Project Scientist of,
and we launched
in February,
and I will show you some
of that data as well,
and we'll... we'll
talk about that.
So a little bit of a
launch history here,
so there were these...
and you've probably
already talked about this;
sorry, I had to speak out
for another important
event in the afternoon,
but here is some of
the history here.
Really the Scanning
Microwave Spectrometer (SCAMS)
was the beginning of taking
measurements of precipitation
from space; before
then they weren't
really looking at
that type of data.
And then so over the
years, you know,
microwave sounding units
have been launched.
In 1997 the Tropical Rainfall
Measuring Mission was launched,
which the whole focus
was on precipitation.
It had a radar built
by the Japanese.
AMSUs, ATMS have
come out of this.
And then as we just said,
the Global Precipitation
Measurement Mission
was just launched,
and it has great capabilities
to be able to measure rain
and snow from space.
All right! So this is
the SCAMS instrument;
some of the things I
want to point out here;
a 110 km footprint.
That's gigantic!
Think about trying to
resolve a convective core,
a thunderstorm of the
summer at 110 kms,
it's going to be
completely washed out.
So yes this was
good and it got data,
but it was pretty big.
It also had these
series of channels,
and 31 gigahertz was
basically the only channel
that was sensitive
to precipitation,
the falling part
of precipitation.
The 22 was sensitive to water
vapor in the atmosphere,
but it really didn't see
the precipitating stuff.
The other channels were
just for temperature
at different altitudes,
so this would typically
see around 4 kms,
11 kms, and 18 kms,
these different
channels here.
Okay, so the next
slide is GPM.
So GPM launched
just this year.
Has 13 channels; V and H,
that's two channels each,
so we have 13 channels,
we go from 10 gigahertz,
which is sensitive to
very heavy rain rates,
up to a 110
millimeters an hour.
And then we go all the way
down to 183 gigahertz,
which is sensitive
to the falling snow
and down to 0.2
millimeters an hour.
So we have a range from
0.2 millimeters an hour,
all the way up to 110,
and then we also are
able to detect
and estimate falling
snow with this device.
We also have the Dual-Frequency
Precipitation Radar,
which has two
frequencies; Ku,
which is about
35 gigahertz...
no, 13 gigahertz;
and then Ka, which
is 35 gigahertz.
And really what... the other
thing I want to point is
we get down to 5
km footprints.
So now you can start seeing
those little pop-up storms
that hit one part
of your neighborhood,
but not another.
You know, when you drive
through it's raining
and then it's dry.
So this... at
that resolution
we can start seeing the
regional scale cloud effects.
Now, there are some parts
of our scientific community
that want us down
at 1 km or less
so you can get at more
detail with the processes,
but this is pretty good.
So I've already
talked about
the 0.2 to 110 millimeters
an hour in snow,
let me talk a little
bit more about here.
It was designed for
a three year life.
Five years of fuel
was put on it,
but based on our projections
we might last 10-15 years.
Now, TRMM
launched in 1997,
has just run out of fuel.
So like 16 or 17 years.
And we hope to last
at least that long.
But you don't know
with the solar cycles,
the solar cycles
cause drag
and it pulls the
spacecraft down,
so you need fuel to
boost it back up,
to keep it at
its altitude.
All right!
The other really cool thing
about this is this instrument,
the radiometer,
when I talk to high
school students,
middle school students,
the radiometer is
like taking an x-ray
through the cloud,
so you see where
there is lots of rain
and lots of ice with
this radiometer.
With the radar that
the Japanese provided,
you get layer by
layer information
about the particles
within the cloud
and I like to describe
that kind of
as a CAT scan.
And just like doctors use
CAT scans and x-rays
to understand what's happening
within the human body
and diagnose
what's going on,
we use our information to
understand the layers
and levels within the cloud,
and we can use
that information
then to improve weather
forecasting models
and climate
change models,
because those models have fairly
simplistic representations
of precipitating particles
within the cloud.
All right!
Okay, so how do passive
microwave radiometers work?
I am not going to
talk about the radar,
the CAT scan one,
I don't have enough time
to talk about that,
but let's talk about the
passive radiometers,
because Nimbus
really started
this passive
device thing here.
So really what they are,
they're instruments
that they see everything
in their field of view,
all the way down
to the surface,
and if you've got
the right frequency
you can also see into
the soil a little bit.
So what happens with ice is
you tend to get scattering,
so cosmic background
comes down,
there's ice particles,
ice scatter,
reflects back up
to the spacecraft,
and you see a cooling
in your brightness
temperatures, that's
the measurement there.
Rain can cause scattering,
which causes the cooling,
but it also has a
mission and absorption,
which would
cause a warming.
So if you're
looking at an ocean,
the ocean has a really
cold background,
very reflective of the
cosmic background.
Then you get a
cloud over it,
you see a big spike of warming
in the brightness temperatures.
So that's how you can
tell you've got a cloud.
And the temperatures
change based on
whether there is rain
or if it's just liquid.
The surface also
contributes.
You can have scattering
from say snowpacks
or you can have a mission
absorption from, you know,
asphalt or trees, or
warmer bodies like that,
and so all of
that contributes
to the brightness temperature.
So what do you get?
So this is some work I did
probably 15 years ago,
and what this plot is, and
I will hopefully kind of
make this simple is, so I
had a convective rainstorm
and so... and the
solid line there,
so this is frequency from 10
gigahertz to a 1000 gigahertz,
and then this is the
brightness temperature value.
And so like this is
a convective rain
and solid precipitating snow
is in this dotted
line right here.
And then anvil cloud,
so it's an anvil cloud,
no precipitation
falling out,
is in this dashed line,
and then a shorter dashed line
is high
relative humidity,
and then low relative
humidity with the dot dash.
So you can see that if
you are here in this,
you know, 180 or so
gigahertz range,
you can see
distinct patterns
for all of these different
cloud conditions.
But when you're down here,
there is really only
two distinct patterns,
the rain or all
the other cases.
So the idea is to try
to get the channels
that are sensitive to give you
the most degrees of freedom.
So SCAMS had
these channels;
I already mentioned
these channels before,
so you can see that
we're, you know,
sensitive to high
relative humidity,
so now you know you've
got cloud water;
sensitive to the rain,
which is the solid thing here,
and then this is kind of
all the other pieces,
so you could kind of learn a
little bit of information.
And then you could use
additional separation
between this one
and this one to get at
the rain in the cloud,
I mean the rain.... yeah,
the rain in the cloud.
And then this provided
additional information
at the 30, 37... no,
these were the...
the temperature
sounding channels.
So they're not
you see them all coming
together right here,
so you're not getting
much information
at all about the rain.
That's all about...
those channels are for
temperature sounding.
All right!
So typical precipitation
radiometers today
have channels from 10
through 89 gigahertz,
and you can see here
when you add the 10,
you get this
very heavy rain;
when you add the
90 gigahertz,
you get a lot more
separation there.
So you're basically
getting two or three
more degrees of freedom for
being able to distinguish
between convective
rain, falling snow,
high relative humidity,
low relative humidity,
and things like that.
So the GPM radiometer
actually added three
or added these additional
frequencies up here,
at 166, 183, + or
-3 and + or -7.
So we're getting a
lot more information
and that allows
us to resolve
between these different
characteristics.
We don't have any
radiometers way up here yet.
There are instruments
that have those channels,
but not designed for this.
All right! So let's go
back to the science.
This is the Science
Team meeting
for the SCAMS
instruments in 1977.
Anybody in this room
in this picture?
Just one, all right!
I want to shake your hand,
you're -- right here,
yeah, okay.
You want to stand up and
see how you've changed
over the years
Love the hairstyles,
love the hairstyles.
I hope that there is
no pictures of me
that are floating around
in about four years.
But no, this is great!
This is a great
Science Team.
You know, these
names here,
I've seen in papers my
whole live, you know,
key papers for
precipitation science,
so this is great.
And this is some of the
data that they came up with.
This is the cover
of Science in 1977,
where they were able to do
the first global images
of the water vapor;
they separated water vapor
from cloud liquid water.
And so this is the
results that they got,
and good enough to get on
the cover of Science
at that time.
But the lead author
was Dave Staelin,
who actually happens
to be my grand advisor,
does that make sense?
One of his graduate
students was my advisor,
so I am related to
this work, right?
Unfortunately, he passed
away several years ago.
Anyway.
So now here is the TRMM
and GPM Science Team.
We're massive.
We had almost 200 people at
our last Science Team meeting.
We have international
representation;
we had about 16 people
from Japan come in.
They are our partner so they
do work carefully with us,
but we had 12
other countries
represented at the
Science Team meeting.
And the reason is, is
because we can't rest on
just measuring precipitation
in the US or Japan;
precipitation is a
global phenomenon.
And we really need to know
where it's precipitating,
how is that precipitating
changing during climate change
or other patterns.
We need to know
both globally.
We need to know at
the local scale,
at that 5 km scale, and we
need to know it frequently.
So one of the great
things about GPM is that
we design the instrument
so carefully
that we're using it
to basically
inter-calibrate
all the other precipitation
sensors out there.
NOVA is giving us data,
international satellites
are giving us data,
and we will have rain
rate estimates
everywhere in the world,
every three hours.
And as you can imagine,
that's great for applications,
you know, for
predicting floods,
for landslides, for
improving our climate models
and our
precipitation model.
All right!
And I'll just note
that Arthur Hou,
Dr. Arthur Hou,
who was the Project
Scientist for this mission,
passed away
about 11 months ago,
and we also had a very
nice memorial symposium
for him at that time.
So what have we
learned from TRMM?
So this is TRMM's
climatology,
millimeters per day.
It's averaged
over a whole year,
over about 11...
9 years of data.
So you can see
these patterns;
heavy precipitation just
north of the equator
and the ocean, a little
bit down here, you know.
And then this is the
standard deviation
among all the inputs.
So you can see that, well,
maybe there are some problems
measuring our rain here,
maybe a little
bit in there.
There are some issues
with the waves,
the different waves of
developing this data.
And then Bob Adler put
this data set together.
Another interesting
thing is,
okay, we know that El
Ni–o and La Ni–a,
typically they
measure that index
based on the sea
surface temperature
and how that changes.
With the long
record of TRMM,
they actually can use an
index of precipitation
to see when the precipitation
starts to change,
you can actually tie that
to El Ni–o and La Ni–a.
So the red here is basically
indicating an El Ni–o effect
and the blue is La Ni–a.
And so what you can see
here is basically
when the precipitation
okay, so this is cut off,
but this is +1 and a -1.
Once you get above...
and so these are...
average over precipitation...
let me calm down.
All right!
So the average
over precipitation
over this long time period
from 1979 through 2014;
this is using GPCP data,
which goes back
and forth in time
goes back in time as the
earliest data we have got.
And then so this is an
average over allotted time,
and that's the
0 line here.
And so these
precipitation changes,
if it's 1 standard
deviation above
or 1 standard deviation below,
you can start telling
what's happening.
So in El Ni–o we have
we tend to have
more precipitation,
and these are boxes in the
El Ni–o/La Ni–a region,
so it's not global, it's
just a focused area.
So what you see is then
you have an increase here
and a very low decrease
in precipitation.
So this basically says,
there is no La Ni–a,
we've got an El Ni–o,
and so you can kind of
see the up and down,
going back and forth
between precipitation
and El Ni–o and La Ni–a.
So this is just
a different way
to look at predicting
El Ni–o and La Ni–a.
All right!
So from TRMM to GPM; TRMM
was launched in 1997,
as I said.
It just recently
ran out of fuel,
this summer it
ran out of fuel.
It will probably last into
the spring, maybe summer,
before we have to turn
off all the instruments.
We can actually
still take data,
even though it's
slowly descending,
TRMM has shown the
importance of having data
for predicting floods
and landslides
and other things like that,
and we know that those
operational users
are already taking in
GPM data for that.
I've already talked mostly
about this information,
but one of the really
interesting things
is because TRMM had
operational users,
GPM made it a requirement
to get the data out
to the public as
soon as possible.
So one to three hours
after an event
the data is on our website.
It's freely available to
anybody that wants to get it.
And that's really great,
if you're, you know,
an emergency
management planner
and you know that
the last nine hours
ago you had 3 inches
an hour of rain
and six hours ago it was
4 inches an hour rain,
and now it's 2 inches,
and you've got a
flooding basin,
you can say let's get
our people out of here,
you know, let's evacuate.
And this is also going
to be used by...
for hurricanes as well.
So the other thing to
point out is TRMM
only went from + or - 35¡
latitude; GPM goes higher.
So we can actually track
things like Hurricane Sandy
and look at it as it goes
into the extra tropics
and mid-latitudes.
So this is really great
for the state of the science
in terms of
precipitation.
So this is an event,
the March 17 snowstorm
that was here in D.C.,
and if you were
in the area,
they shut down
a lot of things;
Goddard was closed.
And this is some of the data
that we were able to get.
This is actually only
about two-and-a-half weeks
after we launched
and this is the data.
It took us a lot longer
to render this data
than to actually take it.
So off the coast
of the Carolinas,
you can see
this rain event;
the reds and the
greens are rain,
and then over inland
you have this very cold
falling snow
shown in blues.
And so you can
see the x-ray,
that really long strip of data
that goes from there to there,
that's just projected onto
the surface in terms of rain,
but also the CAT scan
like data from the radar.
And really interesting
things to note, you know,
the rain actually has a higher
cloud top than the snow.
It's a much
shallower cloud.
You can see that there
is a melting layer here,
so above the melting layer
you have all your ice
and below it it's
melting and raining.
And so this data gives
us great insight and,
you know, we can use this
stuff to start measuring
snowpack information to
help us understand
our water resources.
Many areas of the
world do use this data.
Need to know how much
it's precipitating
so that they can monitor
their freshwater resources.
All right!
So I am already
talking about this.
So I've already talked
about flood monitoring,
landslide hazard forecast.
We can use this
in all models;
freshwater management,
crop forecasting.
So all of these
instruments can tell you
when it's raining and
when it's not raining.
If it's not raining you've
got drought conditions,
which affects your
crop productions.
So some of the TRMM data
has actually been used to
send food to Africa early,
because they knew that they
were having a drought
and they would not be able
to product enough
food in time.
Other interesting
things; where it rains,
you get puddles; where
you have puddles,
you get mosquitoes,
and if you're in some
parts of the world,
those mosquitoes
might have malaria.
So we've actually done
some really interesting
stuff with the TRMM data.
And that's all I have.
[Applause]
Oh, I am sorry, yes,
oh, two more things.
There is a GPM model over here
so you can actually come up
and take a look at
the spacecraft.
And then the other thing
was Chuck gave me an email
from some of the
first people
that were working on Nimbus,
Garrett Campbell,
which I was in the
previous presentation
is one of the names,
they have some new
scientific results
based on some of the first
observations from Nimbus.
And this is Dr. Garrett
Campbell and David Gallaher.
They have recovered the
first observations
by the Nimbus series of
satellites, August 31, 1964.
They've used that image
and many more to
derive CI's content
in the 1960s
around Antarctica.
And they're showing very large
fluctuations around 1964-1966.
And they published
this... and they...
recently they published
it along with
some collaborating information
that showed up in the
ice core measurements
from the Antarctic.
So what they're
saying is Nimbus
is still being
scientifically used
and still producing
new results.
So good for the
scientists out there!
All right! Now I'll
stop talking.
[Applause]