) cc >> Tom Zinnen: Welcome,
) everyone, to Wednesday Nite at the Lab.
) I'm Tom Zinnen. I work here at the
) UW Biotechnology Center. I also work for UW Extension
) Cooperative Extension, and on behalf of those folks and other
) sponsors, Wisconsin Public Television, Wisconsin Alumni
) Association, and the Science Alliance here at UW Madison,
) thanks for coming to Wednesday Nite at the Lab.
) We do this every Wednesday night, 50 times a year.
) Tonight, it's a special pleasure to be able to introduce to you
) Steve Ackerman. He's the director of the
) Cooperative Institute for Meteorological Satellite
) Studies, otherwise known as CIMSS.
) He's also professor of atmospheric and oceanic
) sciences. Founded in 1980, CIMSS is an
) academic research collaboration between UW Madison and the
) National Oceanic and Atmospheric Administration, otherwise known
) as NOAA. You may know him as one of the
) weather guys, along with Jon Martin, and they are both
) regular guests of Larry Meiller on the air at Wisconsin Public
) Radio. Steve is originally from
) Long Island in New York. He is one of the great examples
) of the Wisconsin Idea. Over the years it's been a great
) pleasure to get to know him. About eight years ago at a
) conference when we were thinking about the future of science
) outreach, he was the final speaker.
) He used a slinky as a demo and he said whatever you do in
) science outreach to organize it here at the university, the last
) thing you can't do is sit there. And I've always remembered that.
) No matter what we build, it's got to work and it's got to
) move, and I really appreciate that metaphor.
) Another great story that he's going to get to share is the one
) tonight about the Suomi satellite, and it's pretty
) extraordinary to think that some of the innovations that you'll
) hear about tonight go back to the innovation of videotape as
) instant replay in the early '60s and Packer and Bear games
) on Sunday afternoons. [LAUGHTER]
) I'm looking forward to hearing that story for the second time.
) Please join me in welcoming Steve to Wednesday Nite at the
) Lab. [APPLAUSE]
) Steve Ackerman: Thanks, Tom. Yes, Tom has settled my accent.
) You'll hear it with particular words like water.
) [LAUGHTER] I am from Long Island though
) I consider myself a Wisconsinite for the following reasons.
) I enjoy really good beer, really good cheese,
) really good brats, cold winters, and the Green Bay Packers.
) [APPLAUSE] Got to be a Wisconsinite.
) So I'm going to talk about the role that Wisconsin has played
) in defining the field of weather satellites, both historically
) and then what we're doing today as well.
) I want to begin by also thanking, and I'll do this at
) the end as well, some folks who have contributed some of the
) images I'm going to be showing. We're going to do a lot of
) stuff, and I'm only going to show you the tip of the iceberg
) because we do a ton of stuff with weather satellites on this
) campus. So I'm not going to be able to
) show it to you all, but hopefully afterward we can talk
) about some of the other things that we do or that you might be
) interested in that I haven't touched upon.
) What I'm going to do in terms of the outline is first talk about
) the beginnings of satellite meteorology and the role that
) the University of Wisconsin has played in the development of
) that. I'm also going to talk about
) what we're doing today. So it's not just about what
) we've done in the past, although we've certainly built a great
) foundation for us to build upon. I'm then going to talk about
) where we, and really you, are headed into the future with
) regard to satellites that study the weather, where we're going
) to be. As Tom mentioned, CIMSS is a
) collaboration between the University of Wisconsin and
) NOAA. And so we work with NOAA in
) defining the capabilities of the next generation weather
) satellites. And we'll talk about what's
) coming down the road. And then along the way, we'll
) actually learn a little bit about weather satellites, their
) orbits and what they look like and how they're used.
) So, historically, it all began with the international
) geophysical year, or the IGY, which began in 1957 to 1958,
) specifically July '57 to the end of December in '58.
) And both the US and Russia announced that they were going
) to launch and Earth satellite in order to support the IGY and
) look back at Earth and study it. And that began the space race.
) So that was the start of it all. Of course, we all probably know
) that the Russians were sprinters and they got out way ahead of us
) really quick with the launch of Sputnik in 1957, in particular a
) successful launch of Sputnik. And at the bottom there I have a
) nice URL where NASA actually goes through the whole history
) in all the details. So I'm going to kind of give a
) broad brush of that. 1957, the US began its race with
) the attempted launch of the Vanguard rocket, and it
) exploded. It didn't work.
) It didn't get very far. And, in fact, it prompted
) nicknames like Kaputnik, Flopnik, and Stayputnik.
) [LAUGHTER] So some of you may actually
) have heard those things. So not a great beginning in
) terms of if you think of it as race.
) We're stumbling and they're halfway around the track.
) In 1959, there was a successful loss, a loss, sorry,
) of the Vanguard satellite, and it actually had a weather
) instrument on it. The Vanguard II had an imager of
) clouds. So its purpose was to look at
) clouds and to image them. However, because it wobbled a
) lot, it really wasn't any good. And so you're going to be
) hearing me, as I go through, a very distinct wording in terms
) of what UW has done in terms of observing Earth from space.
) This was the first one, we were not the first one, this was the
) first one, although they were not successful.
) The other thing I want to point out is at the very bottom of
) this New York Times article. Again, Long Islander so that's
) where I'm going to get it. You'll notice it says that it
) was about 20 inches and about 21 pounds.
) So that's about maybe this big. Not a very big satellite.
) We'll look at the satellite progression in size as we go
) through this as well. And notice the price of the New
) York Times in the upper right: five cents.
) So, historically, pretty good. June 1959, Vern Suomi along with
) Robert Parent, who was an engineer, designed a radiometer
) to measure the Earth's radiation budget.
) And that flew, well actually that was on a satellite in 1959,
) and you can image, the way that I'm wording this, what happened
) to that satellite. Kaputnik, right?
) That also did not make it. That exploded as well.
) I think there were like seven Vanguard rockets that blew up on
) the launchpad or shortly after. But that was okay in some
) regards because they got to build another one and made it a
) little bit better, a little bit better instrumentation.
) Also, notice the time period here.
) '57 didn't work, '59 we got another one up there ready to
) go. So the building of these
) satellites is really happening really, really quickly.
) Today that doesn't happen. We're talking about the next
) generation weather satellite being launched in 2015.
) we're all excited about it but that's three years away and
) we've been working on it since 2008.
) So things get more complicated and it takes a little bit longer
) to get them in orbit. So that was June of 1959.
) October 1959 they built another instrument.
) And that's up there, up on the right-hand side.
) It was launched on Explorer VII, October 13, 1959, and it
) measured the Earth's radiation budget and it was a successful
) launch, and therefore we call it, as I have in italics there,
) that it was the first successful meteorological satellite
) instrument ever to observe the Earth.
) And it was designed, again, by Vern Suomi and Robert Parent.
) So we are the beginnings. It all began here.
) And, in fact, Vern has become known now as the father of
) satellite meteorology, and this is the first step that he took
) in doing that. He began his work actually
) looking at energy budgets. So energy budgets are looking at
) energy exchanges between objects by studying energy budget of
) cornfields. So he went from the cornfield,
) very small, all the way up to looking at the planets.
) This is kind of the global energy budget.
) Something we still look at today.
) And, again, if you think about how objects warm up and cool
) down, it's all about energy gains and energy losses, how
) that occurs. And even when we do that on the
) planet scale, we have to look at are the energy gains and what
) are the energy losses. And if we look at the Earth as a
) whole, those energy gains and losses in terms of exchange with
) the environment are all happening at the top of the
) atmosphere. It's all happening with space.
) And so we have to measure what's coming in and what's going out
) at the top of the atmosphere where space is because that's
) the end of our environment, our closed environment, and then
) that's the surrounding areas. And so if we want to look at,
) again, the reflected solar energy, up there in the upper
) left, I guess you would say, as well as the outgoing long wave
) radiation, that could only be done by satellites.
) And that's what his experiment was to look at, and we still do
) this today. The instrumentation that he came
) up with was actually pretty innovative and pretty darn
) clever. What it consisted of, in simple
) terms, is basically a white ball and a black ball.
) And what would happen then is that as energy fell onto the
) Earth from the sun and then got reflected back off the planet,
) this solar energy or you can think of it as visible, in terms
) of the black ball, all that energy would be absorbed.
) And then we can see how much that heats up.
) In addition, the Earth, like us, we're all emitting infrared
) radiation. And, again, the black ball is
) going to absorb that. So that's going to absorb all
) the energy, and that's going to tell us the total amount of
) energy that's leaving the planet, both reflected solar and
) emitted infrared radiation. The white ball, any of the solar
) visible energy that's going to hit it are going to be reflected
) off. So that won't be absorbed.
) But the infrared will still be absorbed by that material, that
) white material, that white paint.
) And so this one tells us, the white one tells us how much
) infrared energy is being absorbed or, therefore, going
) out to space. And then the black one tells us
) the total. We subtract them, we get the
) solar component, and then we get the Earth component, the
) terrestrial component. So pretty straightforward way,
) and it worked very successfully. It made 4,000 observations a
) day, which at the time, of course, was a lot.
) They only looked at 400 to 1000 particular data points were
) actually collected, and they were transmitted down to Earth.
) And each one of these little white dots represents where an
) observation was made, and then the yellow dots represent the
) ground stations where the data can be beamed down to.
) Once those satellites get launched, they're gone, they're
) up there. So we need to transmit the data
) back down to ground stations and pick it up.
) They learned a couple of things with this very simplified
) instrument, and actually we have, if I can jump back just to
) show you, this guy right here, this satellite up there, that's
) the satellite right there. So, again, take a look at the
) size of it. It's probably about this big.
) And if you come to Science Expeditions this weekend and go
) over to the Space Science and Engineering building, you can
) actually see a mockup of that satellite.
) So we actually took the plans and reconstructed it to scale,
) and it's about this big and maybe 80 pounds.
) Two people can easily pick it up, although you won't be able
) to. [LAUGHTER]
) So don't touch it because it was expensive to make that.
) So what he was able to do, Vern and his colleagues, a lot of his
) PhD students, was to start looking at what is that energy
) balance, that budget at the top of the atmosphere and how did it
) correlate with weather systems. And, of course, what they found
) was, this is the way it works, the weather systems move around,
) that changes the energy budget at the top of the atmosphere.
) And that's important because as we try and get energy out of the
) tropics and to the polar regions, it's these weather
) systems that do that, and we can measure that now from satellites
) and seeing what happens. This is all the data.
) Fred House was one of his PhD students and we had a 50th
) anniversary and he brought this book over with him because this
) is how data storage was done in 1960.
) You put it all in a book. We've had, actually,
) computerized some of this, typed it in by hand, so that we can
) actually make maps now and animations to show what the data
) actually looked at. But that's how we collected data
) in 1960. And then we went to the magnetic
) tapes, the big reel ones that you would see in sci-fi or maybe
) in your own labs. And then we went down to
) cassette disks, and now we're to these little tiny little things
) that it's amazing how much data we can store.
) This thing now would be nothing to analyze by today's standards.
) Again, that was the beginning of the Suomi-Parent instrument that
) measured the energy budget at the top of the atmosphere.
) And then there were subsequent missions that flew on other
) satellites. Now we're going on this
) satellite called TIROS through the '60s and early '70s.
) Making each time doing a new launch, making a better
) measurement, a more accurate measurement, a more precise
) measurement. And then, I like to think of
) this energy budget era of actually making very precise
) measurements of the energy leaving the planet and being
) absorbed by the planet as beginning in the 1980s with the
) Earth radiation budget program. And this is an image of the
) Earth Radiation Budget Satellite.
) Actually, it's not an image, it's a photograph because it was
) launched from the space shuttle on October 4, 1984.
) I like to show this because this is what got me to Madison.
) I was at Colorado State working on my PhD, I had just finished
) it, and Bill Smith, who was the director of CIMSS at the time,
) heard I was job hunting, and he flew down to Colorado and said
) have I got a job for you. He was involved with the Earth
) Radiation Budget Experiment, but he was into something new,
) making new types of satellite measurements and he didn't want
) to run that program anymore so he asked me to come up.
) And so I did stuff with dust. I looked at Saudi Arabian dust
) outbreaks, and that almost got me on Good Morning America.
) Almost. And I'll tell you that story
) later on. That also now paved the way for
) more satellites, that whole GY experiment got us into it,
) showed that we can do it, and began to map clouds with visible
) images again, launched on these satellites called TIROS.
) And, again, off to your left you can see the satellite image over
) here. This is the satellite before it
) was launched. Again, look at the size.
) Now we've gotten a lot bigger. You probably can't have one
) person pick it up. Also, the other thing I find
) nice about it is it's not in a clean room.
) Those guys don't have hair nets on or anything like that.
) It's all just out there. You won't find that anymore.
) These satellites are all built in clean rooms, and it looks
) like they're doing medical operations.
) And this was one of the first pictures brought back about
) Earth. There you go.
) Not too bad. Not what we see today, but it
) was the beginning. And, in fact, if you took a
) whole bunch of these satellite images, you could paste them
) together and make a global map. So wherever it's white, there's
) clouds. I'll outline with my mouse here,
) this is North America right here.
) Here's South America down here. I'll probably try and do it with
) both that and the pointer. So there's South America down
) here. There's Africa, of course.
) And when I first came here Vern was still alive and still very,
) very active. And so when I happened to see
) this first image, I kind of looked at this and scowled.
) And he almost hit me. [LAUGHTER]
) Because when they got this, the scientists were thrilled.
) They were excited. This is the first time they ever
) had a global picture of cloud cover on any one particular day.
) They had never seen this before. Never seen hurricanes over the
) Pacific region, for example. And now there it was.
) They could see it. So let's talk about a little bit
) about the satellites themselves because you can see these things
) are stitched together, and you can image that this is one
) picture right there, and it's stitched to another picture
) right below it. So how do we get those things?
) So let's begin by talking about the two primary types of weather
) satellite orbits that we use. The first one is what we call
) the geostationary orbit. And in this orbit the satellite
) is about 36,000 kilometers away from Earth, and it's located at
) the equator. And at that distance and at that
) location, it can spin around as fast as the Earth turns.
) And so it's rotating around with the Earth, so it looks like it's
) fixed in space. The nice advantage of that is
) because it looks like it's fixed in space, it could look down on
) North America and North America remains fixed.
) And as cloud systems move through, they move.
) So it's really good for tracking weather systems.
) And if you watch the evening news or go on the Internet and
) they show you images of satellites, that's usually from
) a geostationary point of view. It's good for tracking moving
) weather systems. Of course, the disadvantages is
) that if you're over the western hemisphere, you don't know
) what's going on on the other side of the Earth.
) Also, you don't really know what's going on at the poles
) because you can't see them because the Earth gets curved so
) you don't see them. So what do we do?
) We launch a satellite in a different orbit.
) We call that, generally, polar orbiting satellites because as
) the Earth spins, it orbits from pole to pole.
) It just goes around and the Earth spins underneath it like
) that. So we call that polar orbiter.
) The advantage of that is two things.
) One is that you can get the entire globe twice a day, one
) during the daytime and one during the nighttime.
) The other advantage is that it's at a lower altitude.
) So it's at 850 kilometers or so. So as you get closer to
) something, if you don't have glasses on and you have good
) eyes, you can see more detail. So in these satellites, these
) polar orbiters, you get better spatial resolution.
) You can see things better. Of course, if you only get twice
) a day, it's really not good for weather patterns, per se, in a
) given location because you don't see it a lot.
) Another advantage, however, is that you get 14 shots of each
) pole every day. And so that's a good way to
) track weather systems in polar regions.
) So thinking of the geostationary satellite again and wanting to
) track weather systems, again Suomi, the father of satellite
) meteorology, came up with the idea of let's put a camera on a
) geostationary satellite. And then in that way what will
) happen is if the camera works, unlike that first launch, what
) you'll see is the satellite will be relatively fixed and the
) clouds will move. And so one of the many great
) quotes of Vern Suomi is: "The weather moves,
) not the satellite." And so that was the Suomi Spin
) Scan Camera, and even today the types of the imaging that we do
) from satellites in geostationary are based on this instrument
) that he developed. And it was flown on ATS I.
) That was the name of the satellite.
) You'll also see me, a lot of times when I talk about
) geostationary satellites, we call them GOES.
) It stands for Geostationary Orbiting Earth Environmental
) Satellites, satellites or environmental satellites
) I should say. Again, there's another image.
) Not so bad, a little fuzzy, but you can imagine if you have a
) couple of these images separated by an hour, you're going to get
) an animation just like you would in a cartoon.
) That's a bit about satellites. Let's talk a little bit about
) how we use satellites to infer things about the Earth or the
) scenes that we're looking at. And there's two ways that we
) make observations in terms of weather observations.
) One is what we call direct methods, also called in situ,
) excuse me, for in place. And so those measure the
) properties of the air that it's in contact with.
) So a good example of that is a thermometer.
) A thermometer measures the air temperature that it's in contact
) with. So that's a direct measurement.
) Indirect measurements are what remote sensing is, and in that
) case we don't have physical contact with the thing we're
) trying to measure. Usually we're measuring energy
) being emitted by that object, and through the amount of the
) energy that we get and the patterns that we get, we can
) infer something about this scene.
) And so now I'm going to ask you a question.
) I'm going to tell you, obviously, the one on the left,
) the image on the left, is of a thermometer.
) The image on the right, I'm going to tell you that it's an
) aluminum pan and it's got water in it.
) What can you tell me about that water?
) [INAUDIBLE] It's boiling, it's steaming,
) how fast it's evaporating. How do you know that it's
) boiling or evaporating or steaming?
) You can see it. >> You can see it.
) You can see the steam that's coming out into here, right?
) I could argue that, well, no, I just threw dry ice into that
) water. That generates a cloud, a fog,
) as well. But what happens to that cloud
) when it spills out? In generally sinks, right.
) It generally doesn't go up. What's the other thing in here
) that tells you that this water is probably boiling?
) It's on a stove. >> It's on a stove.
) Exactly. You can see the hot thing on the
) bottom in here. If you look real close, you can
) see that thing right there. That's red because it's red hot
) because it's emitting a lot of energy.
) And so this is kind of what we do with satellite meteorology
) when we're looking at imagery. We got some information about
) what it is and then from that entire picture, we deduce
) something. And a lot of times we can't even
) see it. We might not see that it's
) raining, but we're able to deduce that, wow, that has a
) convective over-shooting top, it's got to be raining down
) there and there may even be a tornado.
) We also make measurements at different wavelengths.
) So a lot of times when I show you a satellite image, you'll
) hear me talk about the wavelengths that it's measured
) at as well as the region of where it is.
) So this is a visible image made by a camera.
) It's at the wavelengths that our eyes detect at.
) So this is what it would look like if you were standing on
) this building taking a picture of our building.
) You can see, of course, all the antennas that are sitting on the
) top. We're a 15-story building.
) And, again, this type of thing when we're measuring solar
) energy from a satellite, we're either going to directly look at
) the sun and find out how much energy is coming at us from the
) sun, or if we look at the planet, Earth, we're going to
) look at energy that's reflected. So that's what our eyes see.
) We see reflected energy. Can you tell me how many floors
) this thing had? >> 15.
) 15 because I told you, right? But you wouldn't be able to tell
) by this imagery unless you can really zoom in and actually
) count them, you might be able to do that.
) What can you tell me about this power plant thing?
) Is it running or is it not running?
) Don't know. >> You don't know.
) Right. You can guess that maybe it's
) not running. You don't see any smoke coming
) out. If you saw smoke coming out,
) that might be an indication that it was running.
) But otherwise you don't. Satellites, in addition to
) measuring visible energy, and a lot of times you don't see that
) in the evening news because you are watching the evening news
) when? >> At 6 o'clock.
) At 6 o'clock and the sun's going down.
) Or at 10 o'clock and the sun isn't up and so you can't get
) visible imagery. So a lot of times you see
) infrared imagery because you can get it at day or night because
) we're always emitting energy. So if we look at this same scene
) in an infrared camera, it looks like this.
) So where red regions are hot, and the dark blues and the
) blacks are cold scenes. So the sky is clearly cold.
) Now what can you tell me about the tower?
) It's on. >> It's on, right,
) because you can see the hot air coming out the top.
) It emits a signal for sure. Notice the antennas at the top
) of the dishes which collect satellite imagery.
) They don't beam things up, they collect the image that comes
) down from the satellites. You can hardly see those because
) they're nearly the same temperature as the ground.
) Notice this floor right here in our building is hot.
) That's where people like to say, it's not where--
) the computers are actually on the floor below.
) This is where the administrators are.
) [LAUGHTER] Actually, this is the floor,
) the heating floor. This is where all the heating
) and cooling material is. That's why it's so hot.
) But that's our joke. So you're going to see
) satellites, we make measurements all the time at a variety of
) different wavelengths. Most of the geostationary
) satellites that you'll see on TV, those instruments have five
) different wavelengths. Usually you see the IR.
) If you watch the weather channel, you may see the water
) vapor imagery. That allows you to track water
) vapor in the upper atmosphere, and thus track jet streams.
) You very rarely see the visible and you very rarely see the two
) other ones which are used for cloud height detection, and then
) the third one is used for fire detection.
) You can pick up where fires are. Alright, so now let's talk a
) little bit about making satellite imagery because I'm
) going to show you a bunch of satellite imagery in a little
) bit, and I'm probably not going to go into the detail of how
) they were composed so I want to give you a sense of it now.
) A lot of times you'll see satellite imagery in black and
) white. Those very early ones I showed
) you were all in black and white. And they're usually at a single
) wavelength. And the gray scale usually
) represents energy. And so at infrared imagery that
) you see on the evening news, the white generally means it's cold,
) it's not emitting a lot of energy.
) The black generally means that it's emitting a lot.
) So it's usually clear sky. Clouds are usually white in
) imagery. A lot of times in order to
) enhance things we'll color code them.
) So we'll just put color on them. So if we're interested in things
) that are really cold, we may just make them blue instead of
) white because we can do that with our computers.
) And I'll show you a couple of scenes that might do that.
) Another thing we do is we make measurements at more than one
) wavelength. So, for example, we may make a
) measurement at red wavelengths, one at a green wavelength, and
) one at a blue wavelength. When we do that, then we can
) take a computer monitor or television and take the red
) observation and put it through the red gun, the green
) observation and put it through the green gun, and the blue
) observation and put it through the blue gun.
) And what you end up with is a true color image.
) It will look like what it looks like when you're up in space
) looking down. Of course, we don't have to use
) those wavelengths. We can use any combination of
) three different wavelengths through three different guns,
) and we do that to highlight certain features, like a lot of
) times we'll use it to pick out snow from clouds because at
) visible wavelengths clouds are white, snow is white so you
) can't distinguish it. And I'll show you an example of
) that later on. When we do that, when we're not
) using red, green, and blue to generate our composite color
) imagery, we call that a false color imagery because that's not
) the way it looks like according to our eye.
) So let's do some image interpretation.
) With image interpretation, as I like to say, usually I like to
) define things like the wavelength that it was made at,
) when the observation was made, and then where the observation
) is made. And the reason whey we need to
) know those things is because of what we're trying to do.
) We're trying to take an observation that this scene is
) and turn it into some information about the scene so
) that that information we can gain knowledge of what it is
) we're looking at and down the road hopefully do some sort of
) action. So we make an observation of a
) cloud scene, we can gain some information about it, like what
) is the temperature and how high it is, or is it made of liquid
) water or is it made of ice. If it's liquid water but its
) temperature is below freezing, that's some knowledge that tells
) us this is dangerous for flying into because if you have liquid
) water that's below freezing and a plane goes into that, you're
) going to get icing on the aircraft.
) And they don't want to do that. And so the action would then be
) the pilot doesn't fly there. So that's what we're trying to
) do. So given this picture right in
) here, who wants to tell me what it is?
) Black lab. >> A black lamp?
) Lab. >> A black?
) Black lab dog. >> A black lab dog.
) Okay. Anything else?
) Landscape? >> Landscape.
) Possibly Madison. >> Possibly Madison.
) Why don't you know? What are some of the reasons why
) you would not know? [INAUDIBLE]
) Yeah, you don't know what the, I didn't tell you what the
) three Ws were. So the wavelength is, this is at
) visible wavelengths, so you can see it.
) This is what you would look like visually.
) When, it's taken in the morning. Where, it's taken in Madison.
) Now, does that help tell you what it is?
) [INAUDIBLE] >> Nope, not campus.
) [INAUDIBLE] Nope, the reason why you
) can't still figure it out yet is because I also haven't told
) you the fourth W. The fourth W is "wesolution."
) [LAUGHTER] You want to know how big
) these pixels are, right? We have digital cameras with
) pixels, megapixel resolution is usually what you think of when
) you're going to buy it. So you want to know how big
) these things are. These little pixels right in
) here are little. They're on the size of maybe a
) millimeter. Now, does that help you?
) It helps you eliminate a lot of things.
) It's no longer a lab. It's no longer Madison.
) It's actually that little square.
) [LAUGHTER] That little guy right in there.
) So this stuff here is butter. That there is maple syrup.
) It's all melting. It's on my breakfast table
) because it's morning. My action is, I'm ready to eat
) this. So this is the other thing that
) I want to remind us of is that a lot of times when we zoom into
) things, like looking at particular clouds, a lot of
) times we also want to back out and say what's the total scene
) look like, what's the big picture, what's the global
) picture, so we can put things in perspective, otherwise we get
) kind of lost of where we're at and where we're going.
) So, now let's get back to satellites again.
) Where are we today? So one thing that Vern did when
) we started getting out images from geostationary satellites,
) so the clouds were moving, not the satellites, you could then
) track it through animations. So in this image right in here,
) this is an example of, and I'm going to point to a couple of
) little wind barbs there that may be a little hard to see.
) These are where we track the winds.
) So looking at how things moved, we're able to determine how fast
) they moved because we can measure the distance and we knew
) the time between the images. So the change in distance,
) change in time, that's speed. And, initially, this was all
) done by hand by this woman right in the right-hand corner.
) This is what here job was. And you can see that it took a
) long time to do this. This, actually, the idea of
) doing this, as Tom was alluding to earlier, came from Vern
) watching football games when they started doing instant
) replay. So you would see, I remember it
) as a kid so some of you will remember it.
) Watching a football game, all the sudden the magic of instant
) replay came. It's just like, the scene went
) by, let's look at it again. And you could go back and watch
) the guy running for a touchdown or get hit or whatever.
) And you can do that as many times as you want.
) And so Vern said we need to do that.
) We need a computer image that can animate things and, however,
) we also need to be able to track it.
) So we don't want to just watch it, we want to make measurements
) on it. And that led him to develop what
) was called McIDAS which stands for, remember this was in the
) '60s, it stands for Man computer Interactive Data Access System,
) McIDAS. But, of course, it wasn't just
) men working, there were women working on this as well.
) But McIDAS actually kind of Scottish, sounded kind of cool
) I guess. Good name.
) So, now if you spent a lot of time, you could develop a lot
) more winds than this. But we were actually constrained
) because what we want to do with our satellites besides look at
) imagery is we want to get them into weather forecast models.
) We want to provide information which means once the satellite
) image comes down, we have a certain amount of time in which
) to get it to the model, and if we don't get it in that time, it
) doesn't get in and it doesn't get into the forecast.
) And that's why there are so few. Today is a different picture.
) This upper right now, each color, you can see, first of
) all, there's a heck of a lot more arrows.
) You can see the different colors.
) They come from different satellites.
) So the yellows and the greens, those are US satellites.
) I'm sorry, the greens and the reds are US satellites.
) The blue is European satellites, and the yellow is the Japanese
) satellites. These are from the
) geostationary. We also do this now with the
) polar orbiters. We divide winds over the polar
) regions. And this all goes into weather
) forecasting models. In fact, most of the satellite
) data that actually gets collected is never seen by human
) eyes. It all gets processed in
) computers and gets fed into numerical weather prediction
) models, and it makes weather predictions better.
) How do we know it makes weather predictions better?
) Should we do a demo of how it works?
) So this is how it makes works. This is how it makes things
) better. I've got a ball here.
) It's a standard racquetball. I used to play racquetball.
) Now I don't anymore. It's too tiring.
) So I switched to curling. [LAUGHTER]
) Which is actually more tiring than you think.
) The sweeping, it really raises your heart rate!
) So if you've taken physics, at some point intermediate physics,
) you've developed the equations that can describe how this ball
) is going to fall, how fast it's going to fall and the speed at
) which it'll hit the ground. And if you've gone a little more
) advanced, you've also then calculated how high it will
) bounce after it leaves the floor.
) That's all just driven by equations and then solving those
) equations. And the same thing in weather
) prediction. Weather prediction is just a
) whole bunch of equations. Granted, they're extremely
) complicated and really hard to solve, but, basically, that's
) all we're doing. We're just taking the
) atmosphere, applying all these equations, and then moving it
) forward in time and see what it's going to be like.
) Same thing. So, let's do a forecast since
) we're talking about weather. Let's let you do a forecast.
) So what I'm going to do is I'm going to release this ball.
) I'm going to drop it, and what I want you to do is to predict,
) now of course you're not running a model, but another way we do
) forecasts is by seat of the pants.
) Ah, I think it's going to be nice tomorrow.
) And I want you to predict how high you think it's going to
) bounce. And so in order to do that, what
) I'm going to do is I'm going to place the ball on the ground and
) I'm going to raise it up. And when it gets to a height
) where you think that's how high it's going to bounce, I want you
) to clap and I want you to clap loud.
) Okay? Everybody got it?
) What height is the drop from, though?
) I'm going to drop it from this height.
) Okay? Alright.
) About that height because I may be up here.
) Ready? Here we go.
) I'm going to go down and here we go.
) Hopefully you might be able to see.
) Clap loud. [AUDIENCE CLAPS]
) All right, so most of you were probably between about
) my knee and my hip. That's probably what it was.
) So, again, since we're doing weather we can validate.
) Now we're going to validate it. >> Oh!
) I cheated, didn't I? Right?
) What was wrong? >> You didn't drop it.
) I didn't drop it. I dropped it but I didn't just
) release it and let it go. I changed the initial conditions
) is what I did. And this is what satellites are
) really good for in improving weather forecasts because in
) weather forecasts we've gotten so good now with our models that
) we need to know what the atmosphere is right now
) everywhere. And you can only get that with
) satellite observations. And so that's what satellite
) observations do. They bring in the initial
) conditions globally. What are the winds globally?
) What's the temperature globally? Where are the clouds globally?
) Where's it raining globally? And then the models can use that
) to make their forecast. And, of course, if it comes in
) wrong, you get it wrong because the initial conditions were
) wrong. So we have what you call,
) you bombed the forecast. [LAUGHTER]
) Which most of you did. I don't want to hear any
) criticisms of weather forecasters.
) [LAUGHTER] Oops, what did I type?
) Alright, close your eyes. I hit "end" instead of "page".
) So now I have a lot of these slides you're not going to get
) to see. Alright, so that's our cloud
) drift winds. Again, looking at where we are
) today, this is where we were in 1961.
) We had about two satellites up there.
) This is where we are today. We've got a bunch of
) geostationary satellites up there, all international but
) data is shared through the World Meteorological Organization.
) We got polar orbiters going on, and we got a bunch of NASA
) research satellites going on. And they all come into our
) rooftop. And then it leads to space junk.
) They're all satellites. I think I actually have one up
) here called Jason. If you can see that.
) There it is up in here. That guy stopped talking to us.
) And so we have no control over it.
) So it's just floating around in an orbit, and it's big.
) And it's really bad because it's in a good orbit.
) Yeah? >> Didn't a country recently
) launch a satellite? And how does one negotiate for
) space for your satellite? >> Yeah, so launches and
) negotiations occur through the World Meteorological
) Organization and through the United Nations.
) And through those, there's probably a space agency that
) each country runs. Of course, this didn't even have
) any of the spy stuff up on there.
) So spy stuff, I'm sure you do it and that's the way it is.
) [LAUGHTER] And if we find out there's
) one up there, we probably shoot it down, and then they
) launch another one. This is where we are today with
) imagery. If you think back to that first
) black and white one that I showed you, this is what it
) looks like now every night on the evening news.
) And this is produced by our data center, and of course, we don't
) have to just look over the US. You could do this globally and
) watch global cloud patterns move around.
) This is over a two-week time period.
) And every day on our website, this gets thrown up there and
) people come and get it and put it on iPhone apps and things
) like that. This is good information.
) We also generate a lot of forecast products.
) And so this is one where we take several wavelengths,
) 19 different spectra wavelengths that our eyes can't see,
) infrared, and from that we can determine the stability of the
) atmosphere. And by stability we determine
) whether or not it's inducive to convective precipitation.
) So anywhere where it's red, that's a region where the
) atmosphere can just blow up and a thunderstorm can develop,
) a severe thunderstorm. And wherever you see these dots,
) that's where severe weather occurred, either hail, tornado,
) or high winds. If you look at the bottom,
) you'll notice that this was done in 2001 is when it was done.
) So we've been doing this a while.
) And, actually, what we're doing now is trying to take this big
) red area and confine it and make it smaller.
) So we know that's a region that's favorable but where is
) the thunderstorm really going to develop.
) So we have people working on those type of techniques.
) We also do climate studies, and I should put climate in quotes.
) Here's where we've looked at Lake Superior, and we've taken
) satellite going back to the mid-1980s, like 1982 or so, and
) derived the cloud cover over Lake Superior and all the Great
) Lakes regions, in fact. And what you see in that is an
) annual cycle, an up and down cycle going on where it's
) cloudier over the wet lake in the wintertime and not as cloudy
) over the summertime. And what we're looking for here
) is trends. What do we see in this, what is
) that? '82, '92, 2000, 30-year period
) or so. And so there actually is a very
) slight trend in here. And so we like to joke that in
) 400 years Lake Superior will be cloud free.
) [LAUGHTER] We've also now, because the
) satellites have gotten better and the spatial resolution
) has gotten better, have actually looked at cloud cover,
) oh, darn, I didn't put up the scale on this, as a function
) of season for the state of Wisconsin.
) So this is average 10 years for December, January, February, the
) winter. Upper right is spring.
) Lower left is July. And then the fall is the lower
) right. Very cloudy in the winter.
) Not so cloudy in the summertime. This is like 40%-50% cloud cover
) is what the blue is. And the reds are like 80%-90%
) cloud cover. And so it was a surprise to me
) in terms of the summertime being so cloud free.
) That's not what I would have expected.
) Because these instruments are getting better, we can actually
) zoom in and do composites over Madison.
) So this right here is Lake Mendota.
) That right there is Lake Monona. And, again, so here's the cloud
) scale. Reds are 70% or so, blues are
) 40%-50%, and so what we see is that even these little clouds
) are generally, on average, clearer than the surrounding
) land region during the summertime.
) And here's one snapshot that just demonstrates that in terms
) of validating it. This is the type of stuff we
) couldn't do before because the satellites weren't as good as
) they are now, particularly since 2000.
) Of course, we can go to Hawaii too.
) This is the cloud cover in Hawaii.
) This is Hawaii right there. This is its chain right there.
) Dark blues means that it's low cloud cover.
) Reds means it's high cloud cover.
) Wind direction is pretty consistent from here, from the
) northeast down to the northwest. And so what happens downwind of
) mountains? You don't get clouds.
) So it's cloud free. And in this region right in
) here, there's some up-slope that goes around when the clouds whip
) around the island. And so you end up with a local
) spot of really high cloud cover. So if you're going to go to
) Hawaii, the big island, don't buy a house down here.
) You want to go some place else. I like Hawaii because I want to
) go down there and validate this. [LAUGHTER]
) That's I want to do. So that's the lead side of the
) Hawaiian Islands, and that's the up-slope part of the islands
) which generate cloud. The newest weather satellite, as
) Tom has noticed, is a satellite that was named after Suomi.
) So NASA and NOAA got together and in order to honor Vern Suomi
) and the fact that he's the father of satellite meteorology
) named this satellite after him, and it's called the Suomi
) National Polar Orbiting Partnership Satellite, or Suomi
) NPP. We're calling the Suomi Sat,
) so feel free to do that as well. This is the satellite in
) general. Again, this is a real picture of
) it. And these are the different
) instruments that are on it. People in Wisconsin, in the
) Space Science and Engineering Center and CIMSS, deal a lot of
) this imager, veers, and this imager down in here, Chris.
) This imaging here does radiation budgeting experiments.
) So that continues what I had shown earlier back in the 1980s
) and even back into the 1950s. This satellite here is the size
) of a school bus. So they've gotten really big
) now. >> Is it open like that too?
) No covering on it, much of it's open.
) Yes, right, once it gets launched.
) And this is all done in a clean room.
) So it's satellite bus in a clean room.
) So it's really big, big buildings.
) Yeah? >> It kind of looks like it's
) wrapped in yellow tape. That's not true, right?
) Sort of. It's wrapped in yellow stuff,
) but it's not tape. Anybody know what it is?
) Gold foil. >> Gold foil.
) It's wrapped in gold foil. >> And that...
) That keeps reflecting energy out so it keeps it from
) heating up. You don't want it to get too hot
) when either the sun shines on it or when Earth's energy shines on
) it. Yeah?
) Where's it put together? >> Where's it put together?
) There are private industries that do this.
) I think Ball in Colorado was where it was all assembled.
) So the different instruments are build by different companies,
) and then the bus, which we call the bus which carries all the
) instruments, I'm pretty sure it was built by Ball.
) And they integrate it and test it and then launch it, and
) that's why it takes so long because there's a lot of
) instruments in there. I want to just show you some
) imagery. So this right in here is a true
) color image. So it's three wavelengths, red,
) green, and blue. Shooting it through a gun.
) This is what it would look like if you were up in space.
) Clearly this is the east coast of the US.
) And you can see Long Island in there too if you look real
) close. [LAUGHTER]
) And this is the complement of that in the infrared region.
) And if you look real close, you can see the Gulf Stream coming
) right up in there. And so you can imagine fishers
) really like this because you can identify where those gradients
) are in warm temperature versus cold temperatures.
) I'm going to show you, here's another true image.
) This is Great Britain right around in here and Ireland and
) here is Spain and Portugal and northern Europe.
) Here's a scene right in here. So again, in addition to do RGB
) color images which make very pretty pictures, we also develop
) products. And so this is looking at the
) cloud height and it's separating.
) The yellows and the reds are liquid water clouds, and
) the purples and the blues are ice clouds.
) So they're cold. And so, again, you look for this
) region right in here, for example, this is an ice cloud,
) right next to it is a water cloud.
) If I go back to the visible, you can't tell because they're both
) white. So this is the advantage of
) bringing in new spectral observations that our eyes can't
) see that. Of course, we can do it
) globally. This is what it looks like.
) This was yesterday, I believe. 4-10, was that yesterday?
) Something like that, in 2012. So that's what the picture
) looked like globally stitched together.
) It looks a little bit better than what that first stitching
) together that I showed you. And, of course, we can do it for
) every product. So white regions are regions
) where a pixel, we're confident that that pixel is cloudy.
) Green regions is where we're confident that it's clear.
) Red regions are where we think it might be cloudy.
) And blue regions are where we think it might be clear.
) We don't know because we can't tell because some information
) tells us it's cloudy and some information tells us it's clear.
) So we don't know. The good thing is you don't see
) a lot of red and blue in there. So that's the good thing about
) it. And then of course from that we
) can get other cloud properties like how high the cloud is.
) We can tell how much water is in the cloud, whether it's ice or
) liquid water, and we can tell you how big the particles are in
) that cloud. These are all things that we
) need for climate modeling as well as for weather modeling.
) Another instrument that UW is critical out is this Chris
) instrument. This is a global picture of it,
) of an image at one wavelength. This thing makes measurements at
) over 2,000 wavelengths. So, again, very hard to visually
) picture that, so what we try and do is turn it into information.
) So what we'll do is we'll zoom in to that region near Central
) America. That's the image on the right.
) And we're going to pick out once pixel, which is about six miles
) on a side, and from that, because we have 2,000
) observations, we're going to derive the temperature profile
) of the atmosphere and the moisture profile of the
) atmosphere. And that's what's going to get
) fed into a numerical weather prediction model.
) I like to show this because at the last American Meteorological
) Society meeting when this data was just becoming available you
) could only get it from UW Madison because we were the only
) ones who could generate these pictures.
) And as we look at each really detailed issues about
) calibration of these instruments, the people who make
) those calibrations work and solve any calibration issues are
) here in the Space Science and Engineering building.
) Looking at what's coming, a couple of exciting things are
) coming down the road. It's called GOES-R program.
) If you noticed sometimes on some of the imagery at the bottom it
) said it was GOES 13 or GOES 15 or GOES 8, those represent the
) satellites that are being launched.
) The US always has two satellites running, one to look at the east
) in part of the US, and the other to look at the western part
) because we're such a big nation, particularly if you go out to
) Hawaii. So the GOES program, the GOES
) satellite, is given a letter while it's being developed.
) Once it's launched and gets up and is tested and it looks like
) it's working, it's given a number.
) So that's how we know whether or not it's under development or
) working. So University of Wisconsin, in
) partnership with our NOAA colleagues, particularly those
) who are stationed here at Wisconsin in the same building
) as the CIMSS scientists, are working to develop the
) observations and the analysis tools for that next generation
) weather satellite. And, in fact, most of the images
) here that you see on the left are all made up images.
) We took weather forecast models, ran it at really
) high resolutions, really high temporal resolutions,
) and simulated the satellite observations.
) And then simulated current observations as well so that we
) can compare what does it look like.
) Actually, this is from a European satellite which looks a
) lot like GOES-R. This is what it looks like.
) This is what our simulation is. So then we can develop
) algorithms to test and to develop products like cloud
) altitude and cloud top pressure. Or cloud water content.
) Just to give you and idea of its capabilities, this is kind of a
) current scene over the continental US.
) We get a little picture like that.
) We can zoom in and make it bigger if we want to.
) Currently, we get that either every 15 or every 30 minutes
) depending on what's going on. With the GOES-R we get that
) every five minutes, an entire picture of the continental US.
) If we want to zoom in on a mesoscale feature like a
) hurricane, again that's a zooming in of that hurricane, we
) will, with GOES-R, get a look at a hurricane or a thunderstorm or
) severe weather every 30 seconds. Right now we can't do that.
) We can take a little break and go look for it for a couple of
) minutes and then leave because we don't have the capability to
) routinely monitor it. Now we'll be able to continue
) looking at the US and then look down here every 30 seconds for
) where a storm is developing. Why is temporal resolution
) really good in terms of distance between it?
) I'm going to demonstrate that with an experiment we did with
) GOES-10, so you know it's a successful image.
) Here's an image of the Texas region, and you'll see I have
) things marked like wave, sheer, convection, and over-shooting
) top. These convections and
) over-shooting tops are thunderstorms.
) These waves here are atmospheric waves, cloud patterns.
) They're going to look like waves.
) And this guy here is sheer so you're going to see clouds going
) in different waves. Just look at one region.
) What I'm going to do is I'm going to show you current
) capability, which is every 15 minutes, and then I'm going to
) show you what it's going to look like every minute.
) So not even every 30 seconds, and you'll see the big
) difference between why we want high temporal resolution.
) If you don't like flickering, close your eyes because this is
) what it looks like today. That's every 15 minutes.
) It's kind of like, eh, annoying. This is what it looks like when
) we get every minute. You can really track things and
) see what's going on. You can really pick up that
) sheer. You can really pick up the
) bubbling of the thunderstorms and where their location.
) And this is what we're going to get routinely coming in the
) future. Other exciting thing is we're
) going to get a geostationary lightning mapper on this GOES-R.
) We're not involved with it directly a lot.
) Our colleagues in NASA are really the ones developing the
) algorithms. But this is going to measure
) lightning from space, and, therefore, not just cloud to
) ground, which is what we measure now with ground stations, but
) from cloud to cloud and within a cloud.
) And studies have shown that changes in the frequency of
) lightning appear to be correlated with things like
) tornado development and touchdowns.
) So we'll be able to monitor that.
) You see all of the sudden a change in the lightning
) frequency, that might be a spot where you would expect a tornado
) to develop. So we've come a long way
) from remote sensing. It's definitely changed the way
) we view our world. Everything's done now from
) satellites, pretty much. And I want to end with a quote.
) CIMSS celebrated its 25th anniversary in 19, I'm sorry,
) what would that be? We started in '80.
) 2005 is when we celebrated our 25th anniversary.
) So we decided to have just a little celebration.
) I thought that would be a good idea.
) But then people from around the world hear we were having this
) little celebration and wanted to come.
) So they called me up and said invite me because if you don't
) invite me, I can't get my government to pay.
) [LAUGHTER] So we had people from all over
) the world, many of them former students, coming here to
) celebrate, not just CIMSS, but the fact that UW Madison, and
) this is a quote, "...the Mecca of satellite meteorology."
) In other words, if you're going to do this, particularly
) if you're in Europe, if you're going to be in satellite
) meteorology, you have got to come and visit the University of
) Wisconsin because this is where it all began, and this is where
) the expertise still exists in many of the instrumentations
) we use. So with that, I want to say
) thanks to those folks again who really helped, particularly all
) the CIMSS and SSEC scientists who worked to make some of these
) images. And thank you.
) [APPLAUSE]