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NE Live@A-Train
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NASA EDGE: NE Live@A-Train Symposium

A-Train Symposium
- Steve Platnick
- Chip Trepte
- Graeme Stephens
- Shaima Nasiri

Come on, everybody! Climb on board the AAAAAAA-Train! NASA EDGE joined scientists from around the world to talk about the cumulonimbus size cloud of data being kicked out by NASA's premier constellation of Earth observing satellites. Calipso, CloudSat, Parasol, Aqua, Aura, OCO, Glory and GCOM-W1 are loaded with multiple instruments designed to give us an unprecedented look at our planet's climate. Plus, a cloud quiz with scientist, Dr. Shaima Nasiri. All with no cover charge!


[intro music]

CHRIS: Out of the gate, we have Steve Platnick from NASA Goddard.

BLAIR: And a special tribute video to Dr. Don Cornelius.


CHRIS: I’ve got to go to my cheat sheet here with my iPad.

BLAIR: Cheat away, Chris.

CHRIS: Symposium provides a forum to exchange information on the latest scientific advancements, using multi-sensor measurements from the A-Train, and is structured along four major things, atmospheric composition and chemistry; aerosols, clouds radiation, and the hydrological cycle; atmospheric, oceanic, and terrestrial components of the carbon cycle and ecosystem; and weather and other operational applications.

BLAIR: If you think that was a mouthful, you should scan a few of these posters here, [laughter] because the scientists have brought their A-game to the A-train symposium.

CHRIS: That’s right.

BLAIR: No doubt.

CHRIS: How long did that take you to figure that out?

BLAIR: Just a couple of seconds.


CHRIS: Now there is a rumor that you actually studied the A-Train before the show.

BLAIR: Actually, I didn’t know what the A-Train was before the symposium. When I found out about it I thought, well, let’s see. New Orleans has a rich musical heritage, of course, and the A-Train is actually named after a Duke Ellington song, ““A” Train.” But I wanted to give a different musical feel and have a new way to remember the A-Train and what it is.


ANNOUNCER: The A-Train. CALIPSO. CLOUDSAT. PARASOL. AQUA. AURA. OCO. Glory. GCOM-W1. Matrix headlights are not paid a promotional fee to NASA EDGE.

CHRIS: I don’t know what to say.

BLAIR: I had originally wanted to do a bunch of dances for each of the satellites but then when I tried one, I pulled a hammy, and I decided to go all graphics.

CHRIS: Did you pull out the VHS and watch some old shows from the 70s?

BLAIR: Yeah, and this is Don Cornelius endorsed by the way.

FRANKLIN: Welcome to the A-Train.


BLAIR: So Steve, I understand we have a land-based A-Train in Manhattan, a subway line. But NASA also has an atmospheric version of the A-Train. What exactly is that A-Train?

STEVE: When we speak of the A-Train, we’re talking about formation flying. We have a set of satellites and they fly along a similar track in space.

BLAIR: Like a train.

STEVE: Like a train. Over the last few years, we’ve had five satellites that are part of that A-Train. Over the next few years, we’re going to add a few more to that. Collectively, these satellites see a broad range of remote sensing data, and synergistically they can work together to tell us more about the earth and it’s systems than any single satellite alone.

BLAIR: What kind of actual data are they looking at from the A-Train?

STEVE: Clouds; land surfaces; green vegetation; deserts; ocean; sea ice.

BLAIR: So it’s like a scenic train, a train ride across the atmospheric country side.

STEVE: This would be one of the most scenic trains you’ve ever been on. That’s right. Unfortunately, it’s difficult to get a ticket. It’s kind of hard.

BLAIR: Ah, it’s always tough.

STEVE: In addition to that visible imagery, we cover the whole spectrum of data that’s useful for science. When you talk about a satellite, normally we call those a mission, for instance, the AQUA satellite or the AQUA mission has multiple instruments on them. With that package on the satellite, we have a broad range of instruments, which include microwave, infrared, visible, and so on but we don’t have everything. You can’t put everything on one satellite. So, other satellites in the A-Train, such as the AURA, which is toward the end, actually has those instruments that see into the UV.

BLAIR: Interestingly, why is it called the A-Train?

STEVE: First of all afternoon. It turns out we’re in a low-Earth orbit and all these satellites are flying a similar track. This low-Earth orbit is called a polar orbit, meaning that we’re flying across the poles. That allows us to map out the entire globe. Because we’re flying at a low orbit and we’re moving so fast, we do an orbit in about 100 minutes.

BLAIR: Okay. That’s pretty fast.

STEVE: That’s pretty fast. And as you’re making that orbit, the earth is rotating underneath you.

BLAIR: Okay.

STEVE: If you time that correctly, you can make so you travel over the equator at the same time for every observer on the ground. Let’s say you’re standing on the equator and you’re looking up. Someone says, hey the A-Train is going directly over your head. By your watch, you would know that’s about 1:30 pm by solar time.

BLAIR: So they’re basically always flying or capturing data in daylight?

STEVE: That’s a good question. Of course, as they make they’re orbit they do go across to the nighttime side. So, when I say there’s a 1:30 pm afternoon crossing, there’s a compliment to that. If you were on the nighttime side of the equator, you’d see them pass by at 1:30 am.

BLAIR: Oh, good. For a second I thought the A-Train didn’t have a sleeper car.


CHRIS: Guys, Franklin, we need to get one of those monitor systems they have at Goddard in our studio.

FRANKLIN: We definitely need one in our studio. It would make you guys look a whole lot better.

BLAIR: It’s going to take more than a wall of monitors to make me look good. I can say that for sure.


CHRIS: On deck Chip Trepte talks about the A-Train Symposium.

BLAIR: And Graeme Stephens talks about the Peace Train.

CHRIS: You mean Cat?

BLAIR: Oh wait, no, Cat sang about the “A” Train… out on the edge of the atmosphere, there rides an A Train.

CHRIS: Chip, Steve told us a little bit about what the A-Train is all about, the constellations. What is the A-Train Symposium all about?

CHIP: Ah, good question. Science is important in the sense that you want to learn things but it’s also important that you share that information with other scientists in the public as well. The symposium, here, is a meeting where we get together. Scientist from all around the world, we come together and talk about what we’ve learned and what we don’t know. This was a good opportunity. We had a meeting three years ago and thought it’s a good time to do it again. Bringing the A-Train together is kind of in a bottoms-up where a good science idea allowed for a mission to develop then the combination of other satellites. So, they’re flying in formation actually came together. I wasn’t this grand vision that started years and years ago. It was by piecing together each of these things slowly. And then we got this great constellation, different instruments, and we’re learning things together. Take for example, clouds. Clouds change so rapidly and there’s not one single way to measure a cloud. So, we needed to put these different instruments together.

CHRIS: And plus, I would see it would be really challenging to have multiple satellites in this constellation or train taking different measurements. How difficult is it to take all those data sets from each individual satellite and try to come up with a model to predict the climate?

CHIP: That’s why we’re here. It’s one thing to learn about one instrument. It takes a lot a work to do that but now it’s trying to combine that where you have very specific details; a lot of understanding with that and trying to merge that with others. It’s not a simple thing to do it right. It takes time. And again, talking together, and sharing information is a place where you build on that information.

CHRIS: Another big component of the symposium is the education aspect of it.

CHIP: Yeah.

CHRIS: I understand there’s going to be a lot of scientists going into schools, talking with students, and also you’re providing teacher workshops.

CHIP: Yeah, that’s right. It’s a fun thing we’ve added to the science symposium. We’ve got a 3-day teacher workshop that we’re bringing in local teachers from the area and sharing some information, resources, the web information but we’re also bringing scientist from the meeting into those workshops to allow them to have some interaction. On Friday, we’re going to go out into the schools as well so we can make contact with the students and allow them to have a question, answer period. This is a fun thing where we get to give a little back to the community.

CHRIS: Potentially, one day you might inspire and encourage some of these students to go into earth science fields.

CHIP: You know, we’ve got some college students here right now and they’re inspiring us with their questions. We’re always learning, not only from the elders but we’re also learning from the young folks coming on.

CHRIS: What do see for the future of the A-Train? What’s the potential length for this satellite train.

CHIP: You never know. Space is a harsh environment and anything can happen. But right now, many of the satellites look like they have the promise of operating for several more years, 3, 4, 5, 6 more years. We can continue collecting this data and get a long record. Again, what is important is careful measurements for a long period of time because the climate, weather systems, they’re very complicated and it takes a lot to try and understand what’s going on.

CHRIS: What’s CloudSat all about?

GRAEME: CloudSat is a satellite that flies a radar that views clouds. We’re interested in clouds for a lot of reasons. Principally, imagine Earth without clouds. Actually, we wouldn’t be sitting here talking if Earth didn’t have clouds because clouds provide the source of fresh water that we live on. So if there were no clouds, there would be no replenishing of the fresh water on our planet. We wouldn’t be here today.

CHRIS: It’s all part of the water cycle.

GRAEME: It’s all part of the water cycle.

BLAIR: What role does CloudSat play? What kind of data does it gather? I get that they’re important but what are we learning from CloudSat?

GRAEME: Fundamentally, CloudSat flies a radar that’s unique. It’s unique because it has a sensitivity that allows us to see the full structure of clouds. It’s like the equivalent of a CAT Scan on weather systems. It flies through, scans the weather systems. You can see the organisms as they work inside the weather systems and how the storms evolve, and what provides the lifeblood of the storms, which is what we call the heating, the diabetic heating. That’s what CloudSat provides us with; it provides us with an unprecedented, unique view of the inner workings of storm systems.

CHRIS: So essentially, you’re able to look through clouds with the radar?

DR. STEPHEN: Look through clouds, and into clouds. One of the key things we try to do with CloudSat is to measure how heavy clouds are.

BLAIR: I never thought of them having weight before.

GRAEME: Well, they do. They have weight. They have water. And why in the heck would we care about water? In particular, if you think about this, if you take all the water in the oceans of Earth, wrapped it around Earth and made it a big, blue ball, that would form a layer all around Earth about a kilometer deep.


GRAEME: If you took the water vapor in the atmosphere, which are these principle greenhouse gases which are really critical for shaping our earth’s climate, and you compressed it down and made it a layer all around Earth the same way you did to the oceans, it would be 30 millimeters deep. If you took all the water, clouds, and rain, critical for giving life to earth and everything about earth, and compressed it down and wrapped it all the way around Earth, it would be less than 1 millimeter deep.


GRAEME: This tiny, tiny, tiny fraction of water but it shapes our Earth’s climate and sustains life. That’s what we’re trying to measure. We’re trying to measure that and how it evolves, storm systems evolve, and how it’s likely to change in the context of global warming, is what we’re trying to understand.

CHRIS: Right.


BLAIR: Up next we have Shaima Nasiri.

CHRIS: You mean Nasiri.

BLAIR: You did it right. See, I did it wrong even though it’s written in front of me.

CHRIS: Let’s get to the segment.

BLAIR: You mentioned a lot of instruments on the different satellites. Why the focus on the particular instruments?

SHAIMA: Each of these instruments offers something different. As Graeme was talking about CloudSat, CloudSat is able to see through clouds. It can give you information about very light precipitation but what it can’t tell you about is the very thinnest cirrus clouds on top, the upper layer ones. It can’t see those. They Calipso, the LIDAR instrument, can though.

BLAIR: Okay.

SHAIMA: That can give us information about cloud top heights, about where clouds are, about aerosols, which we want to avoid if we’re looking at clouds.

BLAIR: Got something against aerosols?

SHAIMA: I prefer clouds. Everyone has got their preference. These are instruments that give us vertical information. They just see along a track. They don’t see anything broad here. Where as the MODIS instrument has about 36 channels and it has very good spatial resolution.


SHAIMA: Yes. That is in AQUA.

BLAIR: Good call, Chris.

SHAIMA: And it gives us information about what’s going on in the channels I look at 1 kilometer.

BLAIR: Okay.

SHAIMA: The AIRS instrument doesn’t have as good a spatial resolution but it has enormous spectral resolution. So it has information in 20378 channels.

BLAIR: That’s better than my cable hookup.

SHAIMA: Exactly, exactly.

CHRIS: Is it in HD?

SHAIMA: And more that you want to watch too?

BLAIR: Really?

SHAIMA: Hmm, no not quite.

CHRIS: Not quite. Okay.

SHAIMA: If it had better spatial resolution we could call it HD.

CHRIS: Okay, in the future.

SHAIMA: Yes. That would be great.

CHRIS: We have a cloud game.

BLAIR: Yes, we do.


CHRIS: Let me give you this because that’s the answer key.

BLAIR: Okay.

CHRIS: What we have is; we’re going to set this up. We have 11 pictures. This is just arbitrary. We just picked 11 pictures.


CHRIS: We want you to name the type of cloud it is.


CHRIS: All right.

SHAIMA: What’s my perspective? Am I looking from an airplane? Am I looking from the ground?

BLAIR: You’re looking from a show live in New Orleans.

SHAIMA: Well, it matters. It matters. It’s some type of cumulus cloud, maybe a cumulonimbus, maybe a cumulus congestus, maybe a happy cumulus.

CHRIS: Okay, you’re an over achiever. We just wanted cumulus.


SHAIMA: Okay, all right.

CHRIS: Next one.

BLAIR: Okay, next cloud.

SHAIMA: That’s a classic cumulonimbus. Or if you live out on the eastern plains of Colorado, you’re going to call that a thunderhead.

BLAIR: Interesting. Did you say cumulonimbus? Is that what you said?

SHAIMA: Yes. Yes, although there’s cirrus up top.

BLAIR: Well…

CHRIS: So there’s double clouds.

BLAIR: Okay, she got cirrus so she gets half credit.


BLAIR: No, no, wait a second.

SHAIMA: You think it’s cumulus congestus? Oh, is it raining. Is it nimbo?

BLAIR: No, no, I just have cumulus.

SHAIMA: Oh, okay.

BLAIR: I didn’t have the nimbus.

SHAIMA: Oh, but there’s a really deep convection there.

BLAIR: It’s really hard to quiz a genius on her own topic.

CHRIS: Yeah, we’re probably wrong.

SHAIMA: Okay, we’ll go cumulus again. There’s vertical growth.

CHRIS: We’re going to go based on what you said.

BLAIR: Let’s move to the next one.

CHRIS: Move on.

BLAIR: Yeah, okay.

SHAIMA: Maybe stratocumulus?

BLAIR: Okay, well, yeah, I’m going to have to say that’s wrong. That’s clearly an altocumulus or alto, depending on what part of the country you’re from. And I’m making that up because I don’t know if it’s said differently anywhere.

SHAIMA: It may be. Hmm. See, this is where we need more information about mid-level clouds.

BLAIR: Okay, all right. Then we’re even.

CHRIS: Do we need a lifeline on this or can we go onto the next one?

SHAIMA: No, no, we can go on to the next one but I think some people might disagree with you.

CHRIS: Okay.

BLAIR: Well, there’s no doubt about that.

CHRIS: This is a tough one.

SHAIMA: No it isn’t.

BLAIR: Okay. What is that?

SHAIMA: Ah, it’s a condensation trail and there is cirrus in the back.

BLAIR: I’m so glad you said that because I never knew what contrail was talking about.

SHAIMA: Oh, yes, condensation trail.

BLAIR: So we’re all learning here. That’s good.

CHRIS: Okay, next one.

BLAIR: You got that one right by the way.

SHAIMA: Cumulus congestus.


CHRIS: Cumulus what?

SHAIMA: Congestus.

CHRIS: Cumulus congestus? I never heard of congestus.

SHAIMA: It’s when it has a cold.

BLAIR: I was going to say but do you fly over and drop Benadryl in it? Anyway… I have cumulonimbus.

SHAIMA: Yeah, it could be.

CHRIS: Could be?

SHAIMA: Yeah, it could be.

BLAIR: Okay.

CHRIS: It just shows you the difficulty of classifying clouds, like Graeme said earlier.

SHAIMA: Yeah, and so many of these classifications based at the surface depend on our knowledge of where the cloud base is or where the cloud top is.

CHRIS: Gotcha.

SHAIMA: I can’t tell very well from this situation unless I’ve got a good understanding of what my field of view is.

BLAIR: And a true plug of the A-Train, she’s basically saying if I had all the A-Train data…

SHAIMA: If I had the A-Train data, I would know exactly what that is.

CHRIS: Just like that.

BLAIR: Well, there you go.

CHRIS: Next one… I think we have another one.

BLAIR: Yes, we have another one.

CHRIS: Oh, this might be a tough one.


SHAIMA: [sighs] That is a cloud city, isn’t it?

BLAIR: Hey, very good.

CHRIS: Very good. Wow.

SHAIMA: I’m a nerd.

BLAIR: Or a Jedi.

CHRIS: What movie is that from?

SHAIMA: That is from Empire Strikes Back.

BLAIR: Ah, very good.

CHRIS: Double point. We’ve got a couple more.



SHAIMA: Somebody’s going to shoot that teddy bear.

BLAIR: Ah, that’s not a bullet, that’s a rocket.

CHRIS: That’s a spacecraft flying behind it.

SHAIMA: Oh okay, all right.

BLAIR: Yes, we need a therapist.

SHAIMA: Technically, I think we’d have to call that a cumulus type cloud.

CHRIS: A cumulus?

SHAIMA: Yeah, if you can find really cool shapes in it, it’s cumulus.

CHRIS: Oh really?

SHAIMA: Yeah, bunny rabbits… cumulus.

CHRIS: Probably the toughest one of all. Okay.

SHAIMA: I don’t know what that is.

BLAIR: You really don’t know what that is?


BLAIR: Ah, even I know what that is. That’s an “inquirous” cloud.

SHAIMA: [laughing]

CHRIS: It’s a light bulb cloud, right?

SHAIMA: Okay, all right.

BLAIR: “Inquirous.” Golly, I don’t even have the A-Train and I got that one.

SHAIMA: Okay, you got me there.

CHRIS: What’s her point total.

BLAIR: If you take everything into account, she did get 9 right, 2 questionable ones but she even had a lot of insight into those.

CHRIS: The minimum she had to score was 9 out of 11 to get…

BLAIR: She easily got 9…

CHRIS: Okay, that’s good.

SHAIMA: So I’m okay?

BLAIR: There was only one…

SHAIMA: This chair is not going to open up. I’m not going to fall into the ground below?

BLAIR: No, not at all. It’s all good.

CHRIS: Batting clean up is every man in his poster session adventures.

BLAIR: Wow, those notes look dangerously close to the ones you usually give me.

CHRIS: Are those beignets on his plate?

BLAIR: About twenty pounds worth.

FRANKLIN: Um, this is a pretty crowded convention hall. People are here going over their research and data with their colleagues from all over the United States, well, all over the world. As they discuss how they’ve utilized the data that’s come from the A-Train satellites. A study of ice, clouds properties from synergetic use of satellite observation products and modeling capabilities… that’s straight hieroglyphics to me. These beignets are delicious.

SHANA: You are announcing something?

FRANKLIN: Only if you have something to tell me.

SHANA: [laughing] Not me.

FRANKLIN: Are you a researcher or scientist?

SHANA: Yes, I am.

FRANKLIN: What is your research?

SHANA: My research aerosol. I work with MODIS aerosol.

FRANKLIN: Where is your poster?

SHANA: My poster is there.

FRANKLIN: Okay, let me go check it out.

SHANA: We are looking at clouds. We want to do cloud screening and we are looking at different resolutions, and pixels and define how we will do the cloud screening.

FRANKLIN: Have you had any beignets yet?

SHANA: I have, thank you.

MADELINE: This project we’re looking at how the physical ocean variables relate to fish populations. So, in coastal upwelling there’s four things that go into it; sea surface height.

FRANKLIN: Did he just walk in front of my camera?


FRANKLIN: Exactly what does your presentation show?

ANITA: I’m looking at the frequency of precipitation from marine status cumulus in the Southeastern Pacific. Looking at how often it precipitates, and how much it precipitates because we really hadn’t had any estimates of this before CloudSat and Calipso.

FRANKLIN: And the amount of precipitation that falls in this area will affect man and what way?

ANITA: Maybe not necessarily man because what I’m looking at is out here over the ocean.

FRANKLIN: You did say that. I’m sorry.

FRANKLIN: First, I want to tell everybody the title of your research here. It’s The Northwest Pacific Ecological Forecasting Multi-Sensored Characterizing Co-variability Among Satellite Derived Variables for Predicting Pacific HAC Distribution. Did you have to use so many words?

FRANKLIN: Talk to me a little bit about how you use MODIS in your research.

JOSH: We were getting the MODIS image and we would over lay it into an OMI image. We were trying to see if you could use MODIS to be able to identify areas of high sulfur dioxide. We were actually able to find about 98% of where MODIS was saying high sulfur dioxide was; actually did have high sulfur dioxide according to OMI images. It was pretty cool.

FRANKLIN: OMI images, talk to me about OMI image.

FRANKLIN: Have you had any beignets?

ANITA: I have.

FRANKLIN: Are they good?

ANITA: Of course.

FRANKLIN: Do I have any powder on my mouth?

ANITA: A little bit.

FRANKLIN: Guys, you should have at least told me I have powder around my mouth.

FRANKLIN: Tell me how you used the A-Train satellites in your research.

MADELINE: In this project research, we’re using a couple of data sets from the A-Train and then actually combining them with satellite data sets that are not from the A-Train. So it’s really kind of an exercise at how to put things together that don’t naturally fit together. We have some things that do, which is the advantage of the A-Train. And then taking things… we have sea surface height from TOPEX/POSEIDON and then having to re-grid that so we could actually put them together and analyze four variables independently.

FRANKLIN: I really don’t know what you just said.

FRANKLIN: I’m definitely going to need some help deciphering the information on these posters. I wish somebody could help me with this. I wonder if she has something for me. Can she help me out? Maybe not. What about these guys? I can’t understand them. So let me move over here…

CHRIS: So what did you learn from the A-Train symposium?

FRANKLIN: I learned a lot. This was like a learning session… well, I’m glad I went to school for liberal arts and education but this is really, really, really good, interesting material. I would say to everybody out there if you have an opportunity, go by the NASA portal and check it out. Look up A-Train and learn what the researchers are doing with the satellites because it’s very interesting. And it will impact you where you live.

BLAIR: Another thing is the Develop Program, which you actually talked to a few of the Develop students. If you’re a student out the and you’re at all interested in clouds, aerosols, atmosphere, anything else, check them out because you’d have an opportunity to study the A-Train, use that data, and actually present papers that other scientists can use.

CHRIS: We have the website below.

BLAIR: Yes. It’s really a good program as we saw first hand during the poster session today.

CHRIS: Not only that, all this data is visually stunning. When you take the data and look at it graphically from an animation prospective, it’s pretty cool.

BLAIR: Yeah.

CHRIS: That wraps another NE LIVE. We want to thank all our guests on the show.


CHRIS: Check out our website at and you’ll be able to download a video podcast version of the LIVE show.

BLAIR: Yes. And hopefully the name botching will be removed.


CHRIS: Ron, keep that in. You’re watching NE LIVE.

BLAIR: Perfect.

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