 All right, thanks very much. It's a real privilege and pleasure to be able to be here. I don't have any charts, I just got some notes for myself that I'm gonna use. Just to start off, like, you know, gee, 50 years, it's getting to be a while. And even if you think about, you know, 15 years or so between the first and the last launch and then 15 years that the last one worked, that meant that there's 30 years that there's actual real operations data acquisition going on, which is an enormous time and sort of what it meant to our collective discipline. It's important, I think, that we recognize that we've got both the individual spacecraft and the series aspect. Because this is one of the things where, you know, the fact that there are multiple ones succeeding themselves is really big and then, really, the series didn't stop with numbers. The series, or at least for most of the things, for a lot of things, the series continued in the future. So if I try to think about, all right, so what do they really do for us? So I was trying to break this up into a couple of different categories. So the first is I'll say, well, everybody got the planned results. Now there are things that people set out to do for specific reasons and they worked. We got them. We had a path to the future, both for a little bit for research and for operations. So it becomes almost like a biblical chronology that you can think back for a lot of instruments streams, you know, limbs begat clays, which begat hurdles, sams begat isams, living to hurdles, to herbs, to series, to RBI, toms, to, well, toms and toms and nomi and noms. And there's a whole bunch of these things. There's so many of the data sets that we've all come to know and love really got their start with the Nimbus series. And when people are looking at long-term earth system evolution, in many cases, and creating, working with the multi-instrument, multi-platform data sets, that's the way that we have to address earth system evolution because we rarely are in the position of having anyone data set long enough to say, well, we'll just look at evolution with one. We've got to put them together. Well, for a lot of those things, Nimbus is where it all started. You know, you look at a lot of those, there's kind of the quintessential trans diagrams that you'll see. The left side of that is Nimbus. And there's some that transition to operations, so that was always good as well because the transitioning things from research into operations continues to be a challenge for the nation. What are some other things that did? Well, I was calling it unplanned results. Maybe it's not unplanned, but unanticipated results. Things where the smart folks, many of them actually work here, looked inside the data and found things in the data that maybe not weren't supposed to be there, but weren't supposed to be products, but people were really clever and figured out how to do that. And I think Tom's, that you'll probably hear more about from Paul, it was a really good example. I'll try to say a little bit more about that. Then you get some combined products where people would say, well, if I take this from the Nimbus sensor and this from something else, I can do something else. That's the real definition of synergy. But two of the things that Nimbus really did for us. One is that we talk about the kind of the discipline of earth system science, but as a discipline, it's really pretty new. Before Nimbus, I think you'd have meteorology and oceanography and a bunch of other things, but people did their own thing. But I think it was really when Nimbus came along and you successfully worked your way up to Nimbus seven, which was probably the one that really came furthest along of that. That's what let people really look at different parts of the earth system at the same time and begin to do the interdisciplinary work and look at how the different components of the earth system relate to each other. And also to begin to sort of integrate cause and effect so that you have enough comprehensiveness of measurement that you could look at different parameters and how they would vary in time and space and test them against models and see whether the pictures that one had held together in the face of data that essentially were now comprehensive enough that you couldn't get the right answer for the wrong reason anymore. You know, if you were getting it right, it had to be pretty much because it was right. And those are the kinds of things that Nimbus let us do. The other thing is it let us do a lot of that in 3D. You know, I think it's hard for many of us to think back before that time where for a lot of things, especially like, say, for 3D atmospheric constituents, you know, we'd have some ballooning profiles and some aircraft trajectories, but we really didn't have 3D climatologies let alone over a period of long enough that you could look at, you know, what happens from one season to the next? What happens from one year to the next? And those are some of the things that we got. So I don't wanna say a lot about the details of many of the things because the speakers who are here after me are way more knowledgeable about those particular applications. So, you know, I'll just maybe say a little bit about those instruments that, you know, perhaps won't be represented by some of the people who are here. And, you know, I mentioned limbs. Seven months of data, but going from, I think it went 84 north to 64 south, weather, ozone, NO2, nitric acid with three-dimensional distributions. So there's a whole bunch of things that had temperature as well. So things that that let us do and when you combine that with the SBUV profiles and, you know, I really said we had this three-dimensional distribution of ozone with a good spatial resolution working its way down into the lowest stratosphere and then with the NO2 and nitric acid being able to look at partitioning and be able to look at chemistry and say a little bit more about some of the things that that meant. You had SAMs with the methane and N2O profiles so that one was be able to get a sense of these radiatively and chemically active source gases and the vertical rates of decay, which would provide some information about further chemistry and the balance between chemistry and dynamics in ways that were very difficult to have addressed before without that kind of information. It had SAM2 with the poly stratospheric cloud measurements and say after the ozone hole was discovered and people were trying to figure out what was causing that, having the distributions of poly stratospheric clouds from SAM2 became really important to providing information about the climatology of the particles that were the surfaces for the heterogeneous chemistry. And one of those, I guess, you know, I'm not gonna say much about Toms and SBUV, I figure Paul will cover that. Those were kind of the planned products, but then you also had the, you know, unanticipated ones like the, say, Toms aerosols and Toms UV reflectivity. The, you know, I mean, maybe they were planned all along, but, you know, they were people thought of that or it started out like, you know, sometimes one person's noise is another person's signal and, you know, you start out trying, we gotta get rid of this stuff and then, no, wait, there's actually geophysical information in there and people figured it out. And when I asked around the headquarters to some of my folks as well as a few people in the field, whether some of the things that I should say, one of my guys, David Constadine, did a web of science run and said, okay, give me number seven in the top 10 papers that relate to atmospheric science and meteorology. And I think the largest one by a factor of two was, I think, one of the first Toms absorbing aerosol climatology papers. You know, something that wasn't supposed to be there. But people figured it out and went and did it and there's a whole bunch of stuff that, you know, good stuff that came out of that because it was a unique product. You had the Surface UV and say, you know, talked about EarthSystemScience and 3D. Well, that also began to get us into applications. There's probably other ways that one could do, but, you know, once you started getting Surface UV, people could work down into Surface UV forecasts and actually begin to think about, you know, how people can use that information in ways that weren't anticipated. So, that's, oh, and then combined products. The Toms sage residuals that people used to infer tropospheric ozone. And that became, you know, people found this bulge out over the tropical Atlantic and then began to investigate that. So, there's a whole bunch of things that came out in different ways. I, you know, I'm personally grateful to the Nimbus series, you know, for my career. I arrived at Goddard in December of 1983 to be the chemist in a 3D stratospheric modeling group or as I like to say, I was the right-hand side with all the dynamical terms on the left-hand side. So, you know, I used to say, yeah, call me P minus L. But, you know, I would say, it was, you know, to initialize and evaluate the chemistry in a 3D model. So, what did I do? I, you know, got ozone, NO2, nitric acid, weather vapor from limbs, got the N2O and methane from SAMs, got the ozone from, you know, more ozone from SBUV. I don't know how I would have started that work if it weren't for Nimbus 7. And for me, the timing was great because I think six months after I came to Goddard was when this really fat issue of JGR was published that had all the validation papers for Nimbus 7. And, you know, that was one of the most used journals. And again, you know, it's many other people here and the predecessors were the ones who really made that work. And then when I came to Hécourt is to manage the atmospheric chemistry modeling and analysis program and I started getting proposals from people. Well, you know, I think about whether some of the earliest proposals that I remember getting. And this is 1990. So this is, you know, it was still flying but it was one in a decade after launch. Well, one was reprocess the limbs data. Lims was seven months of data, or six and a half or so. But yet, you know, one in a decade after the data ended, it was like, hey, we can reprocess this. You know, there's a whole bunch of stuff, new spectroscopy, things that people learned. Also, the fact that the, and this is something that's even hard for me to grasp is they had to do a lot of this stuff with really kind of ancient computing. So it was hard to process stuff. So one of the things that the limbs folks said were, you know, we only processed, only used one fifth of the data. We didn't have the computing cycles to use all the data. So now we can actually use all the data. So they went and did that. They reprocessed the limbs data. I remember a proposal to go back and use, look at Nimbus 4 BUV data because I think, you know, the idea is that was close to a decade before Nimbus 7. And if they could go back and clean up the Nimbus 4 data and make it as consistent as possible with the Nimbus 7 data, then you could start comparing, you know, trying to compare things from the early 70s to the late 70s. I remember a proposal that came in to look at the SCR data, I think also from Nimbus 4. And the idea there was, it was infrared. I mean, I'd never heard of SCR at the time. But it had the, sorry, remember, it had the Supreme Grand did it and somebody said, let's go look over Antarctica because if maybe we can pull the Antarctic ozone out from the mid 70s and get some information as to whether or not there was an ozone hole, you know, before the Nimbus 7 data started. That one didn't work out. But, you know, I think it showed the community really, you know, creatively looking to see what they could get out of the historical data. And now when I say it didn't work out, I should be careful. They didn't get the ozone data, but I think they learned more about the infrared radiative transfer over Antarctica than one ever knew before. And I think that became exceedingly useful as one looked ahead. So that one may not have worked, worked quite the way it was anticipated, but I feel like, you know, it was a good investment. And then there were the Tom's additional products like the aerosols and surface UV product. And, you know, that one, I feel, you know, maybe a slight degree of paternity because I was smart enough to say yes to the proposal. Frankly, I'm sure they would have done it anyway, you know, even if I hadn't because there would have been the bootleg stuff, but I'm sure it made it much easier for the people to actually be able to say yes, we got that funded. Sometimes I feel like I'm involved in the drug trade because say, you know, I was a Nimbus user and a Nimbus enabler. And, but the, in fact, you know, it's still there now as I put the word out to some people and said, you know, whether some of the things that I could say and one of the things that I got, you know, because it's a wonderfully responsive community as well as creative, was a preprint, or it may actually be just a manuscript copy of a paper that's either out or will soon be out in JGR where people went back and assimilated limbs in SBUV into the, through GMO, into the GS5 model and they actually looked at they were able to improve the quality of the assimilation with the data. So now, you know, what, 25 years after the data are, were taken for limbs, people are still working with it and finding that improves the quality of the science that they can do today. So that's, that's I think a pretty phenomenal kind of thing in the Testament to the work of everybody associated with it. So I guess I will, I will stop. I would personally like to thank those who made it happen. You know, these are wonderful legacy data sets as well as pointing the pathway to the future in so many ways. I'd like to thank those who continue to exploit it and the people who work with the subsequent data sets because, you know, answering these questions that we have about Earth system evolution, you know, we lie that we get the most out of all the data sets. So for those people who are doing that, thank you. And especially for, you know, really for all those who helped in doing this so that the Earth system science that we know is an integrated discipline can exist today. I don't know how that would have happened without Nimbus. So I'll stop there. Thank you.