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