 All right, I hope, I don't know, can JT hear me? Hi, JT. Hi, can you hear me? Yeah. Fantastic. Great. Well, we'll put you online here. Thanks for joining us remotely. And, uh, oh, good. Perfect. I was gonna ask if you could see what's happening. I'm not sure what the, what it looks like on your end, but it is, it is all you whenever you're ready. Okay. You guys can't see me, right? You can see your screen slide. That's right. We just see your screen. It might be better off that way. Okay. Um, so this is just a really quick, quick slide here. So, you know, you guys have heard about great, a lot of the past few days, and I was able to see it yesterday, and here's the, here's the, uh, panel discussion on, techniques and, uh, very, very interesting stuff. So, you know, just to, just to give you, uh, a, uh, view from, uh, 10,000 feet here. So, this is kind of what NASA thinks of a golden age of water cycle and fresh water availability observations. We now have a mission that covers almost every aspect of the pressure water cycle. You know, we're getting evaporation, we're getting, uh, atmospheric water vapor, transfer of the land, precipitation, snow, rivers and lakes and so on. We're getting groundwater from graves and, uh, so much of a snap. We've got all these things covered. I think the next question, uh, observationally is, you know, how to add integrated values to these measurements. We're preserving all these things, but, you know, how good of a job are we actually doing at representing every spatial and component? And so, if you could go to the next slide, please. Um, you know, what, what we, one thing we do at NASA, you know, is every 10 years or so, we have this, uh, the Cato survey, and we just had this in, in which we'll, uh, get an opinion by the community of what's happening in the field and what the major questions are in, in hydrology, for instance. And then, um, you know, like these RF5s on, what are the next things that we need to be observing at the unit? And so, those RF5s, so, go into the national account, right, that's like the fifth council, are full of obstacles. So, it has a plot of all of the processes in hydrology. You know, usually these are kind of drawn by hand and done subjectively. And then, if you could advance, please, then what we do is we draw, uh, maybe what missions we have or what missions we're thinking about creating and how they map onto these processes. Right? And so, you know, you can see, like, you know, Grace, you know, we get a little groundwater here, of course, resolutions, maybe mixed groundwater processes and so on. So, give us an idea of what we're doing and where we need to, where we need to head. So, if you could advance, please, but try to make one of these, uh, just for groundwater. And I would have updated this yesterday after I heard, um, uh, from, um, I think it was, uh, Berk there with the electro-magnetic touch on airborne. That was, that was incredibly, you know, that was more about it. But if you could advance again, you know, with groundwater, we've kind of got this going on, where we've got, from space. So, we've got, you know, Grace over there on the right, we get to, of course, the staff. We've got, uh, INSAR, we've got subsidence, you know, around, uh, the kind of middle region. And then, of course, NC3, well minus there over there on the left. And so, with those three, we almost have some coverage of the entire suite of groundwater processes and scales. At least we're getting there. And there's definitely a gap in the middle there probably where, where electro-magnetic or some other techniques could help fill in. But the point with this is that, you know, the framework for which the place these observations into probably has to be modeling, right? And when we have the ability to model across scales, and when we have the ability to model with good uncertainties, then we're better able to place observations into contexts that have units. So, a lot of times I think we're observing things at different resolutions of different scales, and it's hard to get context for them, especially with race, for instance, because such a course overview of what's happening, that it's really difficult sometimes to relate that to what happened on the ground with actual well-off with the common understanding of groundwater. So, could you go to the next slide, please? I'm almost done here. So, one thing I wanted to talk about is kind of the possibility of, you know, how do we understand the uncertainty of a system as opposed to the uncertainty of a measurement? So, typically, right, and NASA were considered with the left side. Considered the uncertainty of a measurement, instrument error, full propagation uncertainty, single variable. How do we do something for one of the sources of uncertainty? We're really on the, we should probably start thinking about things on the right side, which is how does uncertainty propagate across prophecy? How much does a new measurement decrease the general domain uncertainty? So, you know, you've got a modeling system, whether that's a land-service model or a groundwater model, and that model's manufactured somehow. And if modelers can do a good job of estimating the uncertainty in their model, the uncertainty in their parameter values, and then we come in with a single piece of information or multiple pieces of information from observation with uncertainty, then we have the ability to kind of quantify the total domain uncertainty that still exists in that place. So, that's why I'm kind of laying this out as a goal for modeling. I think it's to say, okay, you know, through the combination of models and observations contextualizing, combining unique people's information, but doing so with proper uncertainty quantification, then we have the ability to understand how much uncertainty remains in a system, and therefore where the gaps in information are and where the target keeps oscillating. So, next slide. So this is just, you know, how this would work. You know, you've got some model uncertainty, you've got some observation uncertainty. You can mine it to and, you know, you do a data simulation or something similar, and hopefully you get some results that can include improved best estimate of whatever you're estimating, as well as a combined uncertainty that is reduced from having these two sources of information where maybe the model reveals something about process and the observation reveals something about biases. Okay, so I'm going to stop right there. That's just a quick overview. I think that's where we should head, and I'll leave it at that.