Wisconsin Public Television University Place Wednesday Nite at the Lab  Steve Ackerman Satellites Over Wisconsin  April 11, 2012 UP714   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]