00:00:00:12 - 00:00:25:11 Speaker 1 So okay, we're going to have a series of quick presentations on each of the science disciplinary discipline areas. And so I'm Paul Lundgren and I am the Solid Earth Lead. And so if I go to outline the arrows, don't. 00:00:26:21 - 00:00:30:09 Speaker 2 The arrows just. 00:00:30:18 - 00:00:31:04 Speaker 3 Right here. 00:00:31:15 - 00:00:33:02 Speaker 1 Yeah I prefer didn't. 00:00:33:02 - 00:00:34:09 Speaker 2 Do that time. 00:00:34:23 - 00:01:09:11 Speaker 1 Okay so and everybody's are presumably following the similar format. So there we have a number of questions. You know, Andrea went over earlier the overarching science goal which is out of Earth's surface structures respond to tectonic and climate forces and what are the implications for geologic hazards. So I'm and then there are a number of sub questions. Of course, in solid Earth, primary hazard producing processes include volcanoes, earthquakes and faulting processes, landslides. 00:01:10:02 - 00:01:50:07 Speaker 1 All right. And then landscape change, which is especially connected to climate and then energy, mineral and soil resources, and then finally, infrastructure and damage assessment. So and I won't go into all the some questions, but which will be a topic more for the breakout session. And so the goals that we have, of course, we're really in general interested in bare topography, although budgetary structure is not just a noise source for us, but it's also something that can be important, especially with landscape change and landslide. 00:01:50:07 - 00:02:30:21 Speaker 1 Or it might influence the a more integrated approach to the science and hazard assessment. And then and then for these were we need these at a precision accuracy and spatial temporal resolution to meet both the science and the application needs. And so we need to accurately characterize and forecast, you know, the goals from a science perspective. Rather, we need to be able to forecast these hazards in a societally or socially relevant time frame, which is very much direct out of that from the technical survey. 00:02:31:21 - 00:03:03:10 Speaker 1 We need to assess then when disasters had happened, assess the impacts of these disasters on the earth system and society following an event forecast sea level change along coastlines in the world and then understand the processes and interactions that determine the rates of landscape change and then improved discovery of energy, mineral and soil resources. I So and then those are the science goals. 00:03:03:12 - 00:03:40:13 Speaker 1 The application goals are fall under that basically these same topics there's there's and there's of course more specific ones in the report and that will be going over to in the breakout session. But we're focused on again, volcanoes, earthquakes, cycle landslides, vertical land motion. Both are as relates to coastal, you know, relative sea level rise, but also inland in places where large scale, especially groundwater extraction or other fluids leads to large landslides, subsystems. 00:03:40:13 - 00:04:26:14 Speaker 1 Typically, I ground moon related to mining or resource extraction or activities and then land surface change that includes everything from sinkholes, cavern collapse, land deposition that might occur from floods or debris flows and including belt formation and things like permafrost to thermal karst. And then space archeology also falls within this as an application global. So in terms of the gaps, you know, there is, as has been articulated, this need for modeling simulations, investigations. 00:04:27:08 - 00:05:01:21 Speaker 1 So for and and again we've identified various activities for volcanoes, earthquakes and faulting and landslides. So a lot of these involve doing physics based model simulations that help us assess really what are the sensitivities of our model outcomes to to this to the quality of the topography data, whether that's the spatial resolution, vertical accuracy and precision or the temporal sampling. 00:05:03:04 - 00:05:41:09 Speaker 1 So and those can help help us understand really how good a part or be do we need to do the science and applications? And and then again, there's also role of the civil rights landscape change in resource extraction. And so finally getting to the end here, the solid earth measurement needs this is from this is from the the the report that was produced from the initial one year study. 00:05:41:24 - 00:06:21:00 Speaker 1 And the key thing here is I just wanted to show that in terms of the various parameters and this is shown earlier aspiration and threshold needs and that, you know, solid earth was one of the more had some of the more stringent requirements that were in the report. I mean, many of these are shared with other disciplines. But, you know, often the aspirational need was really for very high resolution in horizontal and vertical resolution and also very stringent requirements on the revisit time. 00:06:21:12 - 00:07:07:14 Speaker 1 And so this will have, of course, going forward will have a lot of impact on on architectures and how can you achieve things that I think at least for solder processes, you know, we all recognize the need for high resolution based teams at some cadence, but for a lot of the hazards that we deal with and for the end users such as USGS or other agencies, having the ability to sample very frequently at low latency is very important for evolving hazards that have high to high topographic change, such as, you know, volcanic eruptions or other processes. 00:07:08:04 - 00:07:41:00 Speaker 1 And so that that can put potentially a lot of pressure on innovative architectures that can help, you know, get the technology there maybe in a more focused mode and during extreme events. And and then, you know, we were asked to, you know, identify some airborne campaign sites. There are a number that have been identified here. Will these will go over in the in the breakout session. 00:07:41:00 - 00:08:05:19 Speaker 1 But, you know, I think a lot of these are going to be sites that we tended to try to identify that would have potentially a lot of crossover with other with other disciplines. And and well, I'll let you read that as we transition. So hopefully the instance. 00:08:08:22 - 00:08:36:21 Speaker 3 Thank involved thanks for parking quick. Let me set a timer for the rest of the speakers, but if you can try and get ahead of schedule, that's awesome. Our next speaker is not bad is Brooke Medley. And then just so you can cue up, it's going to be the song Saatchi Marks the Mark and Laurie Magruder. You're going to clap at 8 minutes. 00:08:36:24 - 00:08:58:07 Speaker 3 You've heard it before that you get a pass. I'll do my best. I don't typically drink coffee and I just had about a third of a cup, so this might be pretty fast. Yeah. So I'm here on behalf of the Cryosphere Group. My name is Brooke Medley. I'm in NASA's Goddard. And I just also wanted to acknowledge what I'm presenting is really just a summary of a lot of other people's work. 00:08:58:07 - 00:09:22:20 Speaker 3 So especially the other folks who are in the credit team as well as the folks who wrote the original study report. Okay. So our overarching cryosphere question is how are the components of the frozen earth changing and what are their impacts on the regional and global climate system? And we sort of define frozen earth as ice sheets, glaciers and ice caps, sea ice and permafrost. 00:09:22:20 - 00:09:43:02 Speaker 3 And so it sounds like we might have some overlap among the groups for where permafrost exists, but I think that's perfectly fine. And I will let everyone read sort of their favorite, favorite topic here. I won't go through all of them, but they kind of follow the same question of, you know, how are things changing, what are the drivers, what are the processes behind it? 00:09:43:05 - 00:10:27:11 Speaker 3 And then ultimately, what's the downstream impact of those changes? Okay. So four goals are overarching goal is to improve our ability to measure the current state of the cryosphere and project its future state in a warming world. And so again, we sort of pass these out into the different sort of subgroups, ice sheets. We want to monitor time variance, surface mass balance processes, things like precipitation, compaction, runoff of glacier dynamics, ice shelf processes, rifting, calving, things like that, and also generate a potentially time varying high resolution DRM that can be used by ice sheet modelers for model initiation and calibration. 00:10:27:11 - 00:10:54:05 Speaker 3 Similarly, the glacier and ice caps folks would like to quantify glacier dynamics at a seasonal scale and surface mass balance processes as well. Jumping to sea ice. They're interested in quantifying time varying sea ice thickness distribution, snow distribution and sort of state of the melt. Is it frozen? Is there open water? Is it a melt pond? What is the sort of current state of the ice? 00:10:54:19 - 00:11:16:23 Speaker 3 And finally, for permafrost, quantify the seasonal freeze of the active layer and monitor secular permafrost degradation and thermal karst evolution. So again, this is all very much not written in stone. These were just kind of my off the top, off the top of my head ideas. I think there's so much more that needs to be done in sort of specifically for krill. 00:11:16:23 - 00:11:42:07 Speaker 3 But to start out with, I would really I think the ozone development is really rich for the cryosphere. We're kind of getting there. We have a lot of ice sheet model developments for model developments. How can we take it to the next step and put it in a forecasting framework or even maybe like an RC like framework that doesn't do sort of the full RC expectations, but some sort of more basic level to understand the impact on our modeling capabilities into the future. 00:11:43:04 - 00:12:11:13 Speaker 3 Other things like assessment of the spatial signatures of drivers on elevation change. What do these different things look like? Can we leverage those patterns to understand more things about or to understand what we actually need out of? STV System Trade Space. There's this whole discussion on, you know, we can't get everything we want, so what is it that we exactly won and that'll be I think a big one impacts of changing geophysical properties on elevation retrievals and and so on and so forth. 00:12:11:13 - 00:12:35:11 Speaker 3 I won't jump on everything, but we'll talk a lot about this at the break out. Again, this was largely just pulled from the report with a few tweaks to it for our measurement needs. Again, we're going to kind of run through this. I hope that it's really just a launching off point for conversation so that we can then identify where we missing things and where there are potential future science gaps that we can address. 00:12:35:11 - 00:13:00:21 Speaker 3 But the sort of mean I think maybe the most stringent requirements that we have are things like duration. Ice evolves at a very slow time scale. So it's hard to really, you know, learn much outside of the seasonal signal unless you have a longer duration mission. And then then obviously the rate of change accuracy, its ice changes at a very, very small amount. 00:13:00:21 - 00:13:26:09 Speaker 3 And so it's really important that we can actually tease out the signal from the noise needed experiments. I think there's two upcoming sort of tangentially related crayon airborne campaigns, Art six and Snow X, I think. I don't know if Snow X is done yet, but at least somewhat coincident. And then I would maybe jump down to the bottom for data operation Icebridge we are lucky community. 00:13:26:14 - 00:13:46:20 Speaker 3 We've had kind of a really long heritage of airborne measurements, specifically of ice elevation, but also with other geophysical measurements. So I'm hoping that we can also leverage that. And then when we start to think about potential targets, I think we can be really lean and really focused on what we actually need because we can leverage that heritage. 00:13:46:20 - 00:14:13:14 Speaker 3 So hopefully we'll have some good discussions on that. And I want to just leave this up here, too, to try to move things along. So thank you. Come to the Grail group. Thank you so much. It is worth pointing out these talks, summary talks here it is set up for the breakouts where we want your feedback. You're going to quack at 8.3. 00:14:13:15 - 00:14:40:22 Speaker 1 Thank good tester. Okay. I'm a slow talker. Anyway, you. I'm guilty. You'll see a completely different set of slides here, but I'm going to cover the same issues. This is basically our team working on the multi sensor, multi-platform, simplest topography and vegetation, a structured data fusion information system. This basically address this the gaps that was identified in the decade. 00:14:40:22 - 00:15:07:22 Speaker 1 I mean, information is studies on how do we bring different type of information to really solve this problem of vegetation and structure and surface topography. So we followed that roadmap of the study and looked at tree major observations that was identified. One was with the light or one was with these stereo photogrammetry and the other one was with the Insar capabilities. 00:15:08:04 - 00:15:42:04 Speaker 1 And you see at the bottom that there were several type of configurations, both from airborne and satellite, to really tackle this problem. Information and studies from the beginning decided that in order to really solve this problem with the requirement that goes from one meter in terms of resolution to 100 meter and in terms of the dynamics covering days to months to season to annual and and all the other information that comes from it, you really required a complex platform. 00:15:42:19 - 00:16:12:18 Speaker 1 So another thing that comes out of this thing is the vegetation is extremely somebody said it is not noise. And for us the signal is extremely complex system on the ground. It actually not only nature has its own complexity of different structure and across different topographical variations, but also extremely different dynamics. You see that graph at the bottom that shows vegetation grows up to a certain years and after that year it doesn't stay constant. 00:16:12:23 - 00:16:45:14 Speaker 1 It basically have a huge dynamic that changes constantly over time. So we need to solve this problem. And for our system we put something together which basically let me just go. But we basically decided that to create the information system and models that allows the group as a whole to do the book, the on see and also look at the multi platform configurations and and also information formulation. 00:16:45:14 - 00:17:12:07 Speaker 1 Is that an implementation? We need to have a simulation component which we use electromagnetic modeling both for later and radar to do the simulations of the whole topographical informations. We need to have that feeds into a data fusion system which uses A.I. in order to really solve this problem globally and get rid of all that complexity of the vegetation. 00:17:12:07 - 00:17:40:21 Speaker 1 It's by bringing information. It's not going to be done purely with the analytical model anymore. Then we have something which actually simulates the multi sensor trade studies, which was also identified in the incubation, and that's the objective three. And then last it goes to the information system for basically putting all the datasets together and actually solving and allowing the community with the toolboxes to really look at this problem for different regions. 00:17:40:21 - 00:18:10:05 Speaker 1 So I'm going to actually just give you one example of each for the simulation that needs to be actually looking at leads, solving the problem of putting trees together and the electromagnet to simulate both the observation from the multi sensor, multi frequency platform and also lighter to do the problem. And there are example of this that you can see, I don't think this is being. 00:18:13:22 - 00:18:14:14 Speaker 2 Installed. 00:18:15:18 - 00:18:43:11 Speaker 1 Right here. So you can actually put trees on the on the system and then actually simulate the whole topographical information. This is an example of tomography that comes from the radar and then actually different range cuts across the surface. You can see different aspect of this forest. And this simulation is extremely important because it can be exactly equal to the observation platforms that we develop. 00:18:43:11 - 00:19:34:01 Speaker 1 And currently at JPL with the Vegetation team, we are using UAV SAR data to basically collect observations and also with light out to really solve this problem together. So the information system basically is the public facing system that everybody can have access to both simulations, the models, a lot of Jupiter notebooks and and so and the tool boxes that people can actually look at the data collected across the globe in different board types with different type of topography to try to see what is the sensitivity of each of these configurations to really get and the accuracies that we want from the or identified in the exhibition space. 00:19:34:01 - 00:19:56:01 Speaker 1 I'm going to stop here and then all the science questions and stuff. You see that the graphs will actually discuss those during the breakout group. So you can actually. 00:19:56:01 - 00:20:11:13 Speaker 3 Thanks to Science Marksman, I believe your next the brighter arrow. 00:20:12:05 - 00:20:35:22 Speaker 1 Could be all right so the actually this is the hydrology group the group name I didn't put it on the slide here, but it's on the second one. So first I'd like to thank all the people that work with me on preparing the, the content of those slides. So here they are. And the plus, plus plus is all of you guys that will be in the record session this afternoon. 00:20:36:16 - 00:21:15:12 Speaker 1 So our request to read here is you. So here we want to take advantage of the fact that hydrology is very diverse in terms of applications and expertize and community that you are and those online as well can participate and give their opinion on from different perspectives. So this is a really important meeting for us. So here we'll be trying to refine the science questions and make compelling, develop compelling applications for industry, establish the region priorities gaps, and also discuss technology maturation this afternoon. 00:21:16:11 - 00:21:49:10 Speaker 1 So the question for the hydrology group is how will water availability and flow change with climate and increasing increasingly dynamic landscapes? So whether the the terms the storms impact the landscape in drought draws fires, those all impact the hydrology cycle whether it's natural or anthropogenic from water diversion projects, dams, fires and so on. Everything is included here. So that's what we mean by increasingly dynamic landscapes. 00:21:50:01 - 00:22:25:14 Speaker 1 The challenge here for hydrology, which was identified in our white paper, the report is that hydrology is particularly interdisciplinary and reconciling all the measurement needs from the different communities may be a challenge. So hydrology looks at everything from mountains to sea, everywhere there's water. So that includes snowpack, snow, snow, dams. We're not talking about sea ice. It's more about freshwater availability, lakes, rivers, reservoirs all the way to the coast, wherever there's water. 00:22:26:13 - 00:22:51:15 Speaker 1 That's what hydrology is concerned with. So I drove hydrology has three elevations up to three elevations, depending on what you're looking at. The first one, of course, is water level and the second one is ground elevation, which is also shallow bathymetry or whether it's inland or on the coast. But we did it in a different route. We'll see how it works this afternoon. 00:22:53:00 - 00:23:18:19 Speaker 1 And then we also have a vegetation structure or vegetation height, which in wetland because you have vegetation and you have water. So we're also concerned with those types of landscapes. So we'll have to not only look at this diversity of expertize and sciences, but also the diversity of potential ways to look at it. So we'll have to discuss those different technologies and what can be done with each of them. 00:23:19:17 - 00:24:00:00 Speaker 1 I took this from the report and these are the goals and objectives. And then we defined as serving our hydrological science community. The first one is the first goal here is how is the water cycle in fresh water visiting the changing with time and all that increasingly dynamic landscape and how those changes. And the second one is how those changes in the water cycle and freshwater changes impacts other issues like bio jim geochemical cycles, ecosystems and services that these systems do provide. 00:24:00:17 - 00:24:27:24 Speaker 1 Then we also looked at the fourth goal, which is how does the water cycle interact with other Earth system processes. So that's the Earth system as a whole, how the interplay of the water cycles interacts with these other cycles. And finally, we look at more the colder regions where you have a different dynamic and how the, for example, the melt of the permafrost impact the water cycle. 00:24:27:24 - 00:24:48:05 Speaker 1 So there you see that there's a lot of overlap with different groups. And so this so I'm sure some of you would be split between where should I go in the breakouts? So we can discuss that and see where you best. And just for the sake of time, I'm not going to go through this, but these are aspects we will discuss during the breakout session. 00:24:48:05 - 00:25:20:04 Speaker 1 Then we'll look at the knowledge gaps and the potential gap filling activities. So we did identify a some of those gap filling activities most of them have or related to overseas airborne campaign field campaigns and so on, and also existing existing instruments, spaceborne instruments. So we will also look at applications and ideologies. So there is of course the science aspect, but applications in hydrology are really important. 00:25:20:11 - 00:25:50:17 Speaker 1 Water is life after all. So the STV Hydrology Guarantee came up with a few measurement requirements. Here is a summary of that. So the median measurements for spatial resolution is five meters with a vertical accuracy of ten centimeter, which is very ambitious. All right. So I'm not going to go through this all in details, but there's a lot of datasets that do exist. 00:25:50:17 - 00:26:17:01 Speaker 1 We came up with this list. The group of people I listed earlier, we came up with whatever existed up to date. We have data sets of those types. Every star, the first one, every SA two, there's all of the above campaign datasets and everything is on the web already. It's all available to all. So there's a very high potential of measuring the using what already exists. 00:26:17:10 - 00:26:39:20 Speaker 1 And sometimes we have all these datasets that have been acquired. There's a lot of funding for acquiring data, but not so much for analysis. So I think that's maybe a place where we can identify what needs to be done with those datasets. What can be done to is datasets. All right. So just I leave you on this. Let's talk about water and this afternoon. 00:26:41:07 - 00:26:47:07 Speaker 1 That's it. 00:26:47:07 - 00:27:21:16 Speaker 3 Thanks, Mark. Lori Magruder is our guest speaker in this session. Thanksgiving. My name is Lori Magruder and I'm from University of Texas at Austin. And here for Coastal Geomorphology, we had somewhat been rebranded from that incubation study, which used to be coastal processes. So we're trying to disentangle from from what hydrology is doing to what now we're doing is coastal geomorphology throughout the coastal processes. 00:27:21:16 - 00:27:50:16 Speaker 3 So if I say the word water or inundation, I didn't mean to bark. I didn't. And that's that's yours. So let's see how this goes. A lot of what I'm talking about is, again, a synthesis of what Chris Parish and the coastal processes folks, the elevation study did and came up with and then the breakout session. I'm very excited to engage with interested parties on how to refine those with the new branding and figure out how we can be optimized. 00:27:50:16 - 00:28:25:17 Speaker 3 The complementary nature of of all of the processes in the study world. So I've got a few questions area. The overarching question was how our coast changing through both anthropogenic influences or natural storm surge and weather events and what are the impacts of that? And so what we're thinking about are these influences and how they might affect the marine ecosystem and include benthic habitats and coral reefs and and seagrass as examples. 00:28:25:17 - 00:28:58:09 Speaker 3 But also we're looking at because the geomorphology component is what what's happening in that whole sediment lifecycle of erosion and subsidence or accretion and how is it changing or how is it just morphing? What are those budgets associated with the impacts or influences? And then there's also a component of wanting to understand how to protect, you know, safety of navigation and marine navigation and where that hazards and how that changing over time as well. 00:28:58:09 - 00:29:27:03 Speaker 3 So some of the goals are as I think this is a consistent message of we want to assess the current state of your service in order ecosystem and then you want to model it and then you want to accurately predicted. This is one of the cases where you about an and they didn't mean it but it's okay and I should mention I think what a lot of all of these figures here are just kind of stock images because they've got to be the size of pretty or not into the words. 00:29:27:03 - 00:29:55:08 Speaker 3 So anyway, the goals are to model and predict again the coastal sediment lifecycle erosion and then thinking about the vulnerabilities to the marine ecosystem but also to coastal communities and how can we make them more resilient. Think about this is we need seamless data because still human biology. So we need topography and we need both imagery and then we need some smart way to smash them together without introducing errors. 00:29:55:08 - 00:30:34:14 Speaker 3 And so there's a in schools to figure out how to do that effectively. I think a lot of the science gaps, again, if you think of science after that and that umbrella of knowledge gaps and the state of our system of methodology gaps, like what's the hardware we're using algorithm gaps. We can just as the example previously of how do we what algorithm can we do to put might not be together in this way and and then measured against obviously and these are all kind of gaps that that some of the synthesis of the innovations that he came out with. 00:30:35:05 - 00:30:58:09 Speaker 3 We don't really understand all of those together what the sensitivity of our in and out through will be relative to the uncertainties of all of the data that we're taking. So we really need to explore and figure out sensitivity studies to that. To that end, and then also think about uncertainty, propagation and really understanding what the planning product is that in the analysis can be done. 00:30:58:17 - 00:31:26:13 Speaker 3 And another one of the gaps to highlight is decoupling. The longer your work by more geo morphic hardware change and then decouple that from from normal seasonal seasonal variability. So most of the measurements are what I'm interested in would be shallow water within tree vegetation structure, in some cases lines demography in the water surface types of sea water, surface heights. 00:31:26:16 - 00:31:53:07 Speaker 3 You know that some hydrology measurement as well, or these are similar to monarchs that might help us with gaps associated with what are our data ones that we're using and how do we put the data together. And also there's some really neat technologies coming out of characterizing the surface, the water surface, and then deriving the imagery. We don't have a direct measurement, and that's where the technology is of stereo imaging and radar. 00:31:53:07 - 00:32:18:09 Speaker 3 So those are things that we can use as filling measure the gaps. I got some of that aspirational and threshold measurements that are relevant to coastal measurements. Obviously the max bathymetric depth is important. 30 meters usually being defined here. Florida, 83 to 20. This goes a little bit beyond that but ten so gets you from the nearshore and intertidal zone that we might need. 00:32:18:18 - 00:32:54:24 Speaker 3 And and and another one to call out is vertical accuracy, which is pretty high for underwater, depending on how deep you are. So those are going to be some interesting things to track down and how we need to refine those and those requirements. These are from the the science usability matrix in the incubation study needed experience. I mean, ultimately because we want to do sensitivity studies and understand propagation of uncertainties, we need to take data at different temporal and spatial resolution and then figure, figure that out. 00:32:55:09 - 00:33:23:12 Speaker 3 And we don't have a lot of that. The satellite coverage or satellite systems that we have out there is set to be very well. But we all know that the sometimes the pencil line, the minority, I don't necessarily satisfy the spatial needs. And we need to figure out maybe do certain trade studies in terms of what what really means coincident in time to say that we use commercial imagery and and you can do satellite imagery techniques with the bicep2 data. 00:33:23:12 - 00:33:46:02 Speaker 3 You know, where where do we find the sweet side of of this will satisfy the needs for for characterizing the environment and also doing change over time. And we do like the data sets we need to go about the events. There's not a tugboat in there starting to be more fatty, dense, available in certain areas, but we need to submerge them before we can can really move forward. 00:33:47:15 - 00:33:53:01 Speaker 3 And I'm sorry when I've said all that, everyone that maybe we're ahead of schedule. 00:33:53:12 - 00:33:59:07 Speaker 2 Thank you. 00:33:59:07 - 00:34:19:07 Speaker 3 Thank you. We're actually not doing too bad schedule wise right now. Before we move on, we're going to save our discussion for the breakouts and then the discussion later in the day, unless there's a really pressing question. We're going to move on, though I wanted to get a sense of the size of the breakouts because the biggest set of people will be in here. 00:34:19:16 - 00:34:22:15 Speaker 3 The other rooms are all hosted, the same number of people. 00:34:22:15 - 00:34:24:11 Speaker 2 So this 50,000, those. 00:34:24:11 - 00:35:13:20 Speaker 3 Will be the label Howard is hoping us. So for Solid Earth who who wants to go to the Solid Earth breakout roughly ten. Okay. Or 12 cryosphere breakout then to know that two three, four, five, six about the same vegetation they hydrology looks like we're pretty evenly set up. Okay and then coastal 1000. All right, Howard, they're all all fit and all the rooms. 00:35:13:21 - 00:35:21:18 Speaker 3 I'll let you guys figure that out. Pietro Melillo. Melillo I'm probably not saying the right fit around them. Exactly. You got the AP and everything. 00:35:22:00 - 00:35:22:20 Speaker 2 Right? Yeah. 00:35:22:20 - 00:35:23:06 Speaker 3 Excellent. 00:35:24:00 - 00:35:40:17 Speaker 2 That was pretty good, actually. Let's see. 00:35:40:17 - 00:35:45:19 Speaker 3 Well, he's setting up. I just wanted to say the speed at which they went through the science products gives me some confidence we're going. 00:35:45:19 - 00:35:51:00 Speaker 2 To get through this in the next couple of years and for the next decade, something. 00:35:51:00 - 00:35:52:16 Speaker 1 Yeah, wrong screen. 00:35:53:06 - 00:35:55:07 Speaker 2 It's nothing saying. 00:35:56:03 - 00:35:57:00 Speaker 3 Oh yeah. 00:35:57:12 - 00:36:15:05 Speaker 2 That might be so. Oh, right. Okay. So before we start, I'm picturing I'm with the University of Houston, Texas, and I'm the application. 00:36:15:11 - 00:36:16:02 Speaker 1 For the. 00:36:16:08 - 00:36:35:13 Speaker 2 Next TV. When we recognized this panel, we thought that, you know, when it comes to application questions, we do really need to involve stakeholders. And so the we recognize this session here is to invite you talk stakeholders about different fields of expertize. 00:36:35:24 - 00:36:38:22 Speaker 1 And so we'll have about five talks. 00:36:39:16 - 00:36:57:03 Speaker 2 The first one would be related to Cryosphere, the second one is Earth Science, and third one will be coastal processes. The fourth one would be wildfire is the last one. We'll have an international speaker we'll have from DLR, which will present reasons about that. 00:36:57:03 - 00:36:58:23 Speaker 1 And then the banks products. 00:36:58:23 - 00:37:18:23 Speaker 2 Which is the actually the only basis and again parameter music is space and today. So we don't want to do let's start with the first speaker here, Steve. He will then be start remotely. So I would be flipping slide for him and yes please I need need yourself and the stage is yours. 00:37:20:01 - 00:37:23:08 Speaker 4 Thank you. Pietro, can you hear me? Okay, the volume. Okay. 00:37:23:08 - 00:37:23:23 Speaker 2 Yes, we can. 00:37:24:21 - 00:37:45:09 Speaker 4 Thank you. Thank you so much for the invitation and for being with you today. Apologies for not being there in the room. My name is Eli Deeb. I'm a research scientist at the Cold Regions Research and Engineering Lab in Hanover, New Hampshire. I'm going to chat about a few cryosphere applications that we are pursuing with other partners both government and Department of Defense. 00:37:45:09 - 00:38:13:05 Speaker 4 U.S. Department of Defense. I took the liberty to of next slide, please, Pietro. I took the liberty of adding some information based on our engagement with the National Snow X program. I know Snow is somewhat of an orphan observable in that maybe not often. I think it is under the hydrology subgroup, but there are a lot of applications in both hydrology and cryosphere, snow on glaciers, snow on sea ice. 00:38:13:05 - 00:38:44:08 Speaker 4 So for the last seven years, we've been supporting the National Snow Program with remote sensing observations of snow in the western United States and the North Slope and Fairbanks, Alaska, and really taught the mission of a few or the few, the future satellite mission of snow. And in fact, this resulted in two mission concept proposals. One, both radars, one, exploring volume scattering, one exploring phase based approaches. 00:38:45:05 - 00:39:21:05 Speaker 4 But these same systems in wet snow can can detect the surface of the snow, and thus potentially snow depth over time. Here, I'm showing some applications that we deployed using an airborne lighter system, deployed on a based system to follow the National Snow Arts Program across various locations during several, several winters. Next slide, picture. So here is really an application of that method where we derive snow depth over time from airborne light. 00:39:21:05 - 00:39:46:06 Speaker 4 Our observations in the upper left hand corner is the Dry Creek experimental watershed and buoys near Boise, Idaho. At the top of the screen is a snow tunnel chart of the snow accumulation and ablation phase of that watershed or that one snow tunnel state. The point observation. The green lines are airborne light our datasets that were collected as part of a crawl now said effort. 00:39:46:06 - 00:40:11:14 Speaker 4 Boise State University also was part of those efforts in the upper right hand corner is an example of the point cloud data and the fidelity from the airborne light, our data sets and then in the middle portion of the slide, I'm showing just our attempts of adjusting all of these airborne light, our datasets using open source, automated registration techniques. 00:40:11:14 - 00:40:51:04 Speaker 4 So our team is really engaged in the open source community and trying to develop tools that could be transferred across groups and across agencies. And on the right hand side of the slide, you'll see just a depth difference from March 21st of excuse me, March 3rd, 2021 to November 24th, 2020, where you can see some fine scale changes in the snow depth over that time period, indicating areas of stand up distributions snow wind redistribution scour and the such that slide please. 00:40:51:04 - 00:41:24:18 Speaker 4 Our team also does a lot of work with glacier ice sheet, mass balance and dynamics applications. Here is an example where we've been deploying some instrumentation regle ground based LIDAR scanners at the terminus of HALIME Glacier. It was featured in a science article. Next slide. And really it's it's showing the use of this unique technology to collect high temporal and spatial resolution of the calving front of call time glacier on southwestern Greenland. 00:41:25:08 - 00:42:01:19 Speaker 4 Next slide, please. So this work is part of a larger effort that is funded by the Hastings Simons Foundation both originally started at University of Maine is now also we're we're working with the University of Kansas and Leigh Stearns. And you see an example on the left hand side of the deployment of these instruments. And then in the middle you see the examples of the laser, the light, our acquisition of the terminus of these glaciers and the high fidelity in both space and time that can be collected of of these data with the moulins in the middle. 00:42:02:02 - 00:42:31:03 Speaker 4 We're currently looking at how to scale some of these observations in relation to Spaceborne observations with feature tracking from optical remote sensing. But the prospect of using something like science excuse me, surface topography and vegetation to do this is really exciting. Next slide, please. The other point I just wanted to mention is with these observations and a plethora of observations become big data. 00:42:31:03 - 00:43:09:15 Speaker 4 So I wanted to just introduce this concept of geospatial repositories and data management using this engaged grid program. As an example, we've developed an open source geospatial porter portal to archive point cloud data, surface elevation data across various platforms and including USGS, NOA and other analysis assets. It's deployed in a web based, cloud based environment using Amazon Web services and allows the end user to access these data and download a small piece as needed. 00:43:09:24 - 00:43:41:13 Speaker 4 Next slide please. One example of this is University of Texas in Texas Austin being funded by this MDA program to generate a product from the ICESAT two data sets that are now available within this geospatial portal. So really just wanted to give a nod to opportunities where we could work with other operational partners to get these type of NASA datasets into a geospatial portal. 00:43:42:04 - 00:44:09:17 Speaker 4 Final slide. I think the actual so we were asked to list some of our wish, what our wish list was or needs or desires. And really based on what I've shown. Yes, we can generate products at large spatial scales to look at snow depth or glacier mass balance as far as surface elevation changes over time. But can STV really help us bridge the gap in some of our process? 00:44:09:17 - 00:44:46:14 Speaker 4 Understanding of the global cryosphere, some of our spatial and temporal requirements for snow hydrology or for some of the snow products I mentioned, we're really looking at tens of meters and spatial resolution and depending on the application, we're looking at a single observation, perhaps at peak snowpack to several synoptic observations. The accumulation and ablation phases. From the glaciology perspective, we're usually maybe at larger, larger spatial scales, hundreds of meters, perhaps less frequent for ice sheets, but more frequent for some of the fast moving glaciers. 00:44:46:20 - 00:45:14:05 Speaker 4 And to capture some of the dynamic conditions. And I just through this last bullet for damage assessment, through some of the NCAR applications, if you think about some of the pre and post events of of destruction and damage across the globe, they're really looking at high resolution and temporally recent observations. And some of those examples from the Gaza and Ukraine can show application areas for operational partners. 00:45:14:22 - 00:45:57:04 Speaker 4 So what would it be a game changer with respect to Cryosphere? I think from my perspective and some of my colleagues perspectives, I pulled some of my teammates really the ability to have open, transparent, reproducible, automated workflows to really deal with these large volumes of surface data over time would be game changing. This this includes not just scientists looking at these questions in the cryosphere, but also data scientists, cloud computing resources and other i.t resources and really moving away from the single investigator work for these larger into interdisciplinary efforts that incorporate in-situ remote sensing and modeling. 00:45:57:20 - 00:46:22:12 Speaker 4 And I think we can look to the nice R mission in collaborations with the Alaska satellite facility where they're facilitating data production, dissemination and community engagement through cloud based data and tools. So at that, I'll leave it and perhaps there will be some questions at the end or I'll plan to join the cryosphere breakout session. Thank you, job. 00:46:23:06 - 00:47:10:19 Speaker 2 Thank you. So if you ever petitions, please do write them in the chad or ask them at the end of the talks. So let me know the second presentation. The second speaker is an Steve DeLong from USGS. I think this presentation will be mine as well. So he's here. Thanks. I'm in one the USGS, I'm a geologist. I work in earthquake science and I'm going to try to represent both the earthquake science community and then sort of a jiomart community as well. 00:47:10:19 - 00:47:35:21 Speaker 2 So I'm going to do three examples. They're all sort of like our best examples of earth sized applications. I'm going to give them just a super quick run through of USGS event response, how we use surface topography and vegetation data for that, and then just the mailbox. So first example is regional scale landscape change. This is a landscape response to an extreme weather event. 00:47:36:04 - 00:48:00:09 Speaker 2 So extreme storm event in actually northeastern Minnesota that affected over 8000 kilometers, square kilometers area. And there was initial there was light air collected a few months before this event. This event occurred and there was lots of flooding and landslides in a fairly low relief area, but it was underpinned by glacial sediment. So it responded a lot to this event. 00:48:01:05 - 00:48:18:12 Speaker 2 And so then the challenges with this sort of large scale change of action are that first you need a lot of data to cover the extent and then you need a lot of accuracy and resolution within those data. And when you start to compare two different datasets, you start to see the limitations of the pre and post data. 00:48:18:12 - 00:48:54:19 Speaker 2 A lot of actors sort of emerges from the analysis. And so I'll show some pictures and after I go through all these bullets here, but we used a few tools to sort of improve that pre and post later with Bayesian streamlining, there's actually a commercial software package. We've collaborated and been a developer on that, and then we did some sort of brute force ICP alignment of pre and post event data where it was warranted and we used what we call a correction service, so we extrapolated an area that no change to get rid of some of that noise in error and the changed model. 00:48:55:04 - 00:49:15:23 Speaker 2 We then from this result which is just a huge amount of pixels of elevation change, we use object based image analysis to segment segment and classify the maps of the landscape change. It's really hard to know what to do with a billion measurements, elevation change. So we try to simplify it and classify it in a way that was intuitive for land managers and geomorphologist. 00:49:16:15 - 00:49:39:10 Speaker 2 And so in the end, we map elevation change and level detection of, you know, between ten and 15 centimeters plus minus at a resolution of one meter across the entire area, and trying to do as good a job as we could of error budgeting. And the goal here was to do volumetric sediment budgeting because actually that has implications for things like reservoirs and water quality. 00:49:40:05 - 00:50:12:05 Speaker 2 So if you take the data and delivered from the vendor, this is commercial line, our data contracted by the state and you subtract them, you get a map that looks like this and you can see a lot of the information there is really about internal problems with one or the other or both. Our data sets after the work we did on it got rid of a lot of back noise and there's a little zoom box there where you can see the real geometric change emerges in the white areas are change less than plus or -15 centimeters. 00:50:12:15 - 00:50:20:23 Speaker 2 So we have the advantage of having lots of lower the topography than we know didn't change this event that we can use to guide us. 00:50:21:19 - 00:50:23:04 Speaker 1 Zooming in too, to. 00:50:23:07 - 00:50:54:07 Speaker 2 Get a sense for the sort of landscape change that we're working with here in the upper left is the free event satellite image. The upper middle is the post-event satellite image. So you can see that a of vegetation was removed due to during landslides. Upper right is a study that was done rapidly by a local professor just who just had access to Google Earth basically, and mapped landslides and deposits on the second row there, pre and post topography. 00:50:55:08 - 00:51:18:10 Speaker 2 You can see a whole lot there, though. If you notice, there's a channel in the leftmost one in the middle row and not a channel in the middle. And that's because there's so much aggregation in the valley. The result of the elevation change mapping is on the middle right there and red is erosion, blue is deposition. So you can imagine a map like that over 8000 square kilometers. 00:51:18:10 - 00:51:45:10 Speaker 2 It's tough to interpret. So we used object based image analysis to sort of classify this into places where valleys eroded. Valleys are great in those slopes graded or eroded, and then we can classify them by the depth of erosion begin at shallower, steep landslide and have the depth of deposition and different slopes. And then on the lower right is just what an area that was previously vegetated looked like after these landslides. 00:51:45:15 - 00:52:14:02 Speaker 2 And this is all glacial or clustering sediments. These are fine grained, fairly weak sediments that are prone to this sort of shallow sliding, these extreme events index example is repeat airborne ladder. This was led by the Scott at Arizona State University. And so in central California, the San Andreas fault creeps and that means it slips without earthquakes. So there's a constant motion at the surface. 00:52:14:02 - 00:52:38:16 Speaker 2 And this is good to know because it can impact infrastructure, but it also lowers shaking hazard, which is a big concern for earthquake hazard. And so this has traditionally been measured using on ground surveys and there's been measuring more recently using SAR, but we use topographic difference. And so basically pre and post light our topography to measure concrete at a high resolution. 00:52:38:23 - 00:53:17:09 Speaker 2 So the input data are one meter resolution. These are analyzed at 30 meter window size using point to plan iterative process point algorithm. And this allowed us to generate three braids every 400 meters at about 1 to 200 aperture went across the fault zone along 150 kilometers involved. And the time period of this measurement is from the initial before in your scope and volume selected light hour 2527, 2018 when USGS selected lighter some of these year dominating results so thanks for them later. 00:53:17:09 - 00:53:40:08 Speaker 2 And so this provided maps of deaths, the meter scale displacement and maps of shear strain and provide the basis for new understanding where an active ball trace was and enhanced and allowed us to get our students to go out there and do some really detailed ball trace mapping. Using this as one of the inputs. So what what does that look like? 00:53:40:08 - 00:54:05:19 Speaker 2 So the upper blue dots are in the right lateral San Andreas Fault motion. So we can see is a long 115 kilometers of a fault. We've got many, many measurements of the right lateral motion of the ball. And then you can just notice in the Y axis there that we're talking about centimeters scale per year creep rates. So we have to be able to detect things that are a fairly low defamation rate. 00:54:06:01 - 00:54:28:11 Speaker 2 And there's a map of the displacements in the bottom seven to. And just a moment, this slide just compares all the different ways of measuring both feet. I think one interesting point is the bottom compares the top outs of the black dots or the results of this study with a couple of previous SAR based results. And probably every one of these is wrong in some way. 00:54:28:11 - 00:54:56:18 Speaker 2 We'd like to think homo derived three brains are perhaps the most defensible, and this can really be compared to a long legacy of measurements along this fault on so in detail an okay so in detail the maps of displacement looked like the middle row there and then the bottom is sheer string and I'll just show you that what this looks like in the field. 00:54:56:18 - 00:55:19:08 Speaker 2 If you look in the right, there's a fence that's some decades old that's actually offset right laterally. And this is what lighter derived topography looks like along with both. And so I'll skip this one in interest of time. Basically the point I wanted to make with this is that there are sometimes fluvial systems that need to be monitored and very high spatial resolutions. 00:55:19:09 - 00:55:53:07 Speaker 2 This is Yosemite National Park two Ami Meadows in the high country that the National Park Service needs to know how stream banks are changing. We provided this with ground based measurements and this is this right now would be great to do remotely rather than on the ground. So typical earthquake response we use. If an earthquake happens somewhere, we'll make maps of the vault rupture and measure offsets using a combination of SAR and satellite imagery and then eventually we'll do more like 3D topographic different thing. 00:55:53:07 - 00:56:22:21 Speaker 2 And I just want to point to a recent work that's been come out for from the Turkey earthquake led by I wasn't involved as I made the folks in the USGS in office. So there's a really nice set of studies that are coming out describing how we respond to earthquakes. So just to echo some previous comments, more and more, we're looking at larger regional spatial scales, tends to 105 meters and require, you know, accuracies that the meter ness, meter level we require, you know, a meter. 00:56:22:22 - 00:56:56:20 Speaker 2 So spatial resolution literally means the standard for this type of things and has been going for years in these sorts of data by involving other organizations. But we would love to be able to see this in higher frequencies and again, sort of repeat so we can actually see things like cascading hazards as hazard events progress through time. And there's lots of interesting details of things we'd like to be able to measure in event and all things. 00:56:56:20 - 00:57:17:03 Speaker 2 All right, thank you, Steve. The next one is Monika from USGS and public policy now of Lovejoy. So if I put your your name, see, you know. 00:57:20:19 - 00:57:58:11 Speaker 5 Thank you for inviting me to present the why you are so interested in elevation elevation data in coastal zone and I'll speak about accuracy scale and resolution regarding with coastal zone. So mapping matters. So we speak about accuracy, but actually it is uncertainty since is qualitative versus quantitative. But it's way easier when we speak with our own stakeholders to say that this is obduracy for a map of or an event event. 00:57:58:15 - 00:58:33:21 Speaker 5 Instead of speaking about uncertainty, we have some standards. Nobody likes standards, but actually help us to quantify things. And we have standards for accuracy for of from 1941, which are still valid today for the graphic maps. Oh, I saw espresso standards from 1990. Most of them, as you see, they're about accuracy regarding the mapping or the counter interval. 00:58:34:01 - 00:59:13:15 Speaker 5 And when we enter into the digital age from the late 1990s, we speak about our image alarm and see standards. Standards also came from our error distribution. We wanted you know, to be as close to normal go Gaussian distribution or is zero mean so then we can have all those. I'm zero regarding you know 68% confidence interval, 90% confidence interval, 95% confidence interval and so on. 00:59:13:15 - 00:59:43:24 Speaker 5 And we realize that in this situation, standard deviation of error will be equal with our MSE. So we speak about our MSE, even if all the time we do not have this ideal case. But if we know the vertical error message, we can answer or very particular questions. So we are very interested in and some of them are what is the smallest increment of water level in relation. 00:59:43:24 - 01:00:14:22 Speaker 5 Why you speak about that? Because it's a contouring operation on a medium. So if the vertical accuracy of the medium is one meter resolution, we cannot speak about 20 centimeters, you know, inundation on that landscape. Also, what is the minimum learning timeline? You know, we speak a lot about what happened by 2100. You know, can we plan for that? 01:00:14:22 - 01:00:47:11 Speaker 5 But this means that what is the time interval required for a certain rate of sea level rise or inundation or whatever, or for the cumulative sea level rise to reach a minimum water level increment that. It is affordable to the beam we have. Right. So we can see that and we can see that if we have a dam which has an accuracy of ten centimeters, that means an almond sea of about five centimeters. 01:00:47:11 - 01:01:30:12 Speaker 5 Right? Then we have a minimum water interval. We can map, which is 40 centimeters. 40 centimeters will accumulate in over 40 years for a certain sea level rise. If we go from an accuracy of ten centimeters to 15 centimeters, the time interval already jumps from 40 years to 60 years. So today I cannot plan for 2050 if my accuracy is ten centimeters on the dam, which is really good if you think of it. 01:01:30:20 - 01:02:03:10 Speaker 5 Right. So what is about that? Also, we need to think about what is the minimum significant topographic change. We already heard some things about that. So it's really very important. So what coastal assessment questions we usually are required to ask is if the dam of purity is a certain, you know, centimeter level and we want to look at impacts of a certain centimeter know water level in the nation. 01:02:03:20 - 01:02:38:22 Speaker 5 This, for example, what is a given storm surge search on top of Artemisia, we can see about it. And let's look at an example. We work with USGS, for example, major Montreal angle Republic of the Marshall Islands. We did a survey there in 2017. We use commercial drones to the arrival ground bears and we'll look at some variable dams, global dams for the same area. 01:02:39:03 - 01:03:13:01 Speaker 5 We know what is the outcome of sea for all of these situations. We collected over 60,000 GPS points, so it's real validation for all of this. And as we see that only four or SFM dams we can have on a sea level rise or inundation of less than a meter for most 60% or 95% confidence interval only for from the drone survey. 01:03:13:08 - 01:03:47:13 Speaker 5 We are almost there. We will look at the Digital Globe Advance Elevation series and all of the global dams will support only meet our level inundation. So also resolution and scale matters depending what we are interested in global dams, the commercial ones are about ten meter resolution. The free ones we can have the access are usually 30 meter resolution at a regional scale. 01:03:47:14 - 01:04:19:07 Speaker 5 We still will be better. We have, for example, for us the USGS National Elevation Datasets, which are three meter resolution with or without problems, depending on how you look at or a ten meter resolution. And at the local scales, which we really can be at one meter resolution, we can be centimeter scales or very small areas using less of them and things like that. 01:04:19:13 - 01:04:53:13 Speaker 5 And the purpose why we're interested in that is what is the smallest feature we can identify in a resolution, right? And what is the minimum significant topographic change? And here we speak both vertical and horizontal. So if both datasets are perfectly co-located. We can look, for example, at Dunes Movement or we can look at erosion of the top of the bluff in horizontal space. 01:04:53:17 - 01:05:27:12 Speaker 5 But we are also interested in vertical differences. And as an example, it shows a very small, shallow landslide on the shores of Lake Michigan. It happened in August 2019. You know, the bluff is about 30 meters high. The landslide is about 5 to 10 meters wide by September. So only in a month. Half of the pulse of this landslide was eroded by October. 01:05:27:12 - 01:06:06:17 Speaker 5 Almost all of it was eroded by November. Nothing was there anymore from that. So if we're interested in volumes and what happens with this landslide and with all transport of sediment, we need not only very high temporal datasets, but you need them very quick after the event happened because some of those changes are ephemeral. So if we are not quick to capture them, they are gone forever. 01:06:08:08 - 01:06:48:21 Speaker 5 Now let's look at the bottom three near-shore bathymetry and top over symmetry. But symmetry means to things depending who you speak with. It's either water depth or underwater terrain elevation. So when oh my god, I have to watch. So the global double bill symmetry, it is the cause for our needs. And if we look at the topography metric glider, we can have it at the one meter resolution maximum depth would be 4050 meters if we are extremely lucky. 01:06:49:08 - 01:07:21:11 Speaker 5 Otherwise it's about 20, 30 meters and we can have local and regional TV, dam and USGS offer some of those for USGS US coast with the Coastal National Elevation Database. So satellite, the right of symmetry has a long history of development. It really sped up since Landsat and central to a data became freely available, but you need external sources to derive with symmetry. 01:07:21:19 - 01:07:53:04 Speaker 5 And lately, in the last two years, there is now one symmetry module for the same state of pipeline which can be arrived directly to be them incorporated and you don't need any external data and there is there the publication, we just have it, you know, a month or two ago regarding this new possibility. And those are our wish lists and dreams, which I let you read because I'm out of time. 01:07:53:08 - 01:07:59:07 Speaker 5 Thank you. 01:07:59:07 - 01:08:35:21 Speaker 2 Thank you so much. Morning. Of course, we will have time to discuss this in the breakout sessions. The next speaker is Peter G from UC Irvine. Let me just going to talk about wildfires, this energy source in 10 minutes. Right. So thanks for having me here. I'm associate professor, human environmental engineering at UC Irvine, where I live from here. 01:08:36:11 - 01:09:05:06 Speaker 2 So I'll follow the same format as the last speaker speaking. What you mean the problem was a few pictures just to see like what kind of implications are you talking about? So on the left hand side you can see this long exposure. The bottom reveal embers, firebrands and their particles that really did you invite environments and they can really be very nasty within the end of our environment and we can drop them for very long distances and can deposit on structures and new fires. 01:09:05:22 - 01:09:28:11 Speaker 2 This picture shows a fire climbing of a tree, which you would bring that up when put a firefighter or a land manager, because this is where the fire becomes really, really, really intense and severe. These two pictures show fire behavior in complex 30. This is this picture is actually the aftermath of Granite Mountain Fire in Arizona, which actually had 19 firefighters. 01:09:28:12 - 01:09:58:07 Speaker 2 And it is so and this picture is here shows that prescribed burned to the ground by one of my students mission and so on them. This actually has some implications for our study and we hope so just to begin with I know from the influx community line of direction where we want to understand how ecosystems and atmospheres exchange carbon and model influences. 01:09:59:00 - 01:10:19:14 Speaker 2 So these measurements are kind of as you might, homogeneity, but the fact is most of the measurement towers are located near. So I'm going to do some gaps in every important concern. And I tameness in the landing site. So we wanted to understand how does these you know, what what is what does this presence of these gaps are in this group? 01:10:20:01 - 01:10:40:21 Speaker 2 So we published these papers where. You look at the wind flow near this data, some gaps, and you can see this is the forest structure in Berkeley, DC here, this dark brownish color and you get these little circulation. You also studied what happens. I mean, of course, these edges and gaps are also present and complex theory. So if you have those, you also get additional recirculation structures. 01:10:41:01 - 01:11:08:15 Speaker 2 But the interesting thing is then the terrain also interacts with with the vector emissions that come from these gaps. So whenever you see gaps, remember that we have these circulations and perturbations and then the the geometry, the composition, the density, all of it has some implications for that. So how does it relate to wildfires? Here you can see simulations of fire behavior using 3D fuels in the one meter resolution. 01:11:09:01 - 01:11:35:02 Speaker 2 This is a large area simulation model. So we can see the fire going here. But also, if you notice here, you get the fires coming towards the velocity field, vertical cross-section of that. So you can see these kind of the re medians popping out of the sides of the fire. So because of the presence of data, some gaps, you have this interpolations by the fire industries and pollution start interacting with them. 01:11:35:22 - 01:12:00:12 Speaker 2 So that's one of the main take away there. And this actually has implications on, for example, effectiveness of the rings and structures that we created as a part of management of some of the communities. So we did one study we published in France Ecology Management this year really studying the effectiveness of a rig in the Creek Fire region that you can see the engine very differently inside and outside of the region. 01:12:01:01 - 01:12:31:02 Speaker 2 So this kind of flows. Our mission matters, starting to feel like forest treatment. As I mentioned, this heterogeneity is a really important rate. But then again, as a demonstration, we started a fire simulation and actually get an important statement from Arizona and then within the forest. So again, creating a lot of these entities and their sound that the fire in the regional forest and higher intensity because it didn't work together. 01:12:31:14 - 01:13:03:12 Speaker 2 But then as you can see after the zero downtime it has burned larger area saying that so the of spread in the forest is increased. So what's happening here so the edges and the gaps and that's basically producing more wind and introducing more turbulence inside the canopy. And what you're not showing here, but you can look at this paper, but it also investigated that the sparks are going to be can also actually be more drier in some cases and reducing to less fuel moisture. 01:13:03:22 - 01:13:36:03 Speaker 2 That means you can also get high fire intensity you to spark this. Let's look at the effect of the flames and moving from vegetation structure during events. So, I mean, as we all know, fire behavior is a confluence of civil weather entry. And so those models of fire behavior like the are thermal model in that work are so many operational models still in place in the participants bedrooms that the rate of spread is the ratio between the heat received and the heat required to burn a certain amount of fuel and. 01:13:36:03 - 01:14:01:13 Speaker 2 So it's a pretty good model. The idea is that with wind and then with terrain, we are increasing the efficiency of the fire and heat up and that's downstream. So you've got to increase the rate of spread with this wind factor and the flow factor estimation rate. But then it does work. And some of the reason models like Wind is that they actually do more Einsteinian simulations to get those effects. 01:14:02:03 - 01:14:24:07 Speaker 2 But what we can do, more advanced simulations of, for example, this is a vehicle data and returns from Pikes Peak, Colorado. So again, I think it's really pretty complex and we also have the treatment. So you have both you can play, right. So we want to see how this fire is. There are thermal model, correct. So how does it play like when it goes up slow brings it down. 01:14:24:07 - 01:14:52:07 Speaker 2 So that idea is that fire would be in faster upslope are faster out and go slower downslope because of the efficiency effect. And so we did that. We had a downslope and we change the slope in upslope with the same vegetation structure. And we do find that once the fire that slope, it does go faster upslope. But the problem is whenever you encounter that full complexity of the period in all of that, they go out of the way. 01:14:53:05 - 01:15:10:01 Speaker 2 So here you can see very different fire and we give you a realistic the actual terrain. And then we actually take the terrain and make it more complex. In this case, we are making it like four times more complex, as you can see here. And the graph section showed the wind speed. So wind is very, very different, of course. 01:15:10:12 - 01:15:32:11 Speaker 2 And this has real implications because most of our models think about this slope and that state. But so here is the complexity of the model. And so, for example, can have real implications and disasters implications. For example, this article shows that California fires are breaking the rules because they're not burning. You know, they are. They're going down so very fast. 01:15:32:11 - 01:15:52:09 Speaker 2 And these can be firefighters. These are the firefighters, these rules, so to speak, to create defensible spaces, to fight the fires so the wind fire to be more like this. Complexity, for instance, might be unpredictable and it can catch people by surprise. The problem is, and it's important for the community is we don't have good data on this. 01:15:52:09 - 01:16:16:24 Speaker 2 And the last thing I would mention is the role of air transport. So we in fact, if we look at the data, most of the damage structures in the wild environment interface in California and in the West in general, that's not really come from the maintenance comes from these firebrands or embers landing on structures and we have a group. 01:16:17:00 - 01:16:44:07 Speaker 2 And so this is forensic evidence. For example, this is paradise. And that burned down. You know, you can see like the footprints of like fully destroyed houses, but you see trees still standing. It's the measurements that the ember dynamics is actually the more dominant mechanism here. And California, for example, has regulations like you should have like a primary buffer zone and secondary buffer zones around your houses. 01:16:44:22 - 01:17:10:05 Speaker 2 And but the problem is we published the study in Ecosphere where you looked at how much of the way did I lose somebody in four days on public and really focusing on complexity on the slope and the aspect is about 40, 50 to 40% difference at which data. So that means the wind is really complex and this means we also have to revise some of these regulations and the guidelines. 01:17:11:01 - 01:17:42:17 Speaker 2 We cannot mention the nominal distance, adverse event transport if you have a wildfire. So what are we doing then we will adapt. So yeah, we are doing some fieldwork because as I mentioned, data is poor. So we are doing our own fieldwork with our drone, our lighter thermal cameras, multispectral cameras using prescribed burn. Sorry. Yeah, this so you can see we are doing prescribed burns and we are collecting our own data with sonic anemometers, etc.. 01:17:42:19 - 01:18:12:00 Speaker 2 This is a map of the same forest as you can see here, maybe the lighter. So we are trying to work simulations with those, but we're also tracking embers using nighttime burns in those areas because again, the understanding how embers behave and reject and how would they transport be there if you hadn't done certain. It's a little in the literature and we are also taking them back into a lab and testing to see whether they really samples are cylinders of the models that as you can see, they are not. 01:18:12:18 - 01:18:44:09 Speaker 2 So this is the wish list to do. Again, read that long short. I want to mention that we really need a lot more data on distributed data on complex data and we need a lot of robust measurements. We need a lot of measurements also. So not just the pre structure. So we need what is happening inside the canopies because a lot of fires are prescribed burning for example, or some of the slow moving fires that are happening below kind of can do some of this. 01:18:44:22 - 01:19:14:02 Speaker 2 And we also need for data, so we need the spatial data in higher resolution but in time. And then lastly we need a lot more information on the atmospheric boundary land and it's been was mentioned before, which is also important for wildfires. So he Diego is here and the operations that's it is very much so last talk it's from Irena Zinke. 01:19:14:24 - 01:19:19:15 Speaker 1 The first Eric's science coordinator. 01:19:19:23 - 01:19:26:02 Speaker 2 And I think you were trying to present in your own if he if that's. 01:19:27:04 - 01:19:33:08 Speaker 6 Just me, this would be great. If I can share, I will just try now to share screen if this works. 01:19:33:19 - 01:19:36:24 Speaker 2 Yeah, I should not be perfect. 01:19:37:03 - 01:19:38:07 Speaker 6 Do you see my screen? 01:19:38:20 - 01:19:44:19 Speaker 2 Yes, we do. We didn't know we could do that, actually. 01:19:44:19 - 01:19:45:10 Speaker 6 Is it okay? 01:19:46:09 - 01:19:54:01 Speaker 2 That's the. Sorry. Yeah. Yes. We're putting you on screen in the room right now. 01:19:56:04 - 01:20:21:04 Speaker 2 And please, it means I'm very free to ask questions in mine in the chat and save your questions for the big conversations. Or if I write it only in 10 minutes. 01:20:21:21 - 01:20:43:14 Speaker 6 Okay, great. Thank you. Thanks a lot. So. So, yes, my name is Arina Hein Sick. I'm the science coordinator. The tandem mission, which is, as Peter was already saying, the, the only bisecting mission in a single pass configuration in the moment that exist. It's clear that it's not only me which are presenting this, but it's a whole team which is behind. 01:20:43:14 - 01:21:12:00 Speaker 6 And here you see only some of them and which are responsible for the processing of the data and management of the mission itself. So Tandem Sad is a bicep technician where we have to satellite illuminating the same area on the spot on on the earth's surface and like these we have with no time difference in interferometer which which we call the single pass interferometry, the mission has been launched. 01:21:12:00 - 01:21:42:12 Speaker 6 So the second, let me say satellite has been launched in 2010, which means already it's quite a long standing emission, which means we are now operating this mission around certain years. So therefore this date still is everything is quite operational, working continuously and we still hope that we will be operational until 2029. These are just some measures here to see, okay, that we have still enough hydrazine battery capacity. 01:21:42:12 - 01:22:20:10 Speaker 6 So great. And the instrument performance which is most important, is very, very stable over the whole years with a signal to a 0.2 DPI per square meter. The main idea or the main, let me say objective of of the tandem mission is tandem says already is a derivation of a digital elevation model, a surface model where we in principle have a global coverage and hosting of 12 by 12 meters the absolute height accuracy we were hoping to have around two meters. 01:22:20:10 - 01:22:56:14 Speaker 6 Now we even see that over very, very flat areas. We have less than one meter high frequency, which is great. So the acquisitions are is summarize acquisition between 2011 and 2015. This is because of the the fact that it takes very, very long to have a global coverage. So we have in principle connected to global coverage and then additional, let me say, acquisition acquired over very, very difficult areas like for it, for for example, very steep terrain, but also very dense, vegetated areas. 01:22:56:14 - 01:23:24:21 Speaker 6 And also like Greenland and Antarctica, a very, let me say, covered areas where we have a lot of snow and ice. So the completion was in 2016. These dam is now in principle the the basis of the companion custom that is freely available on the server of of Copernicus itself. So you can just download it in and said so it's it's available edited. 01:23:24:21 - 01:23:54:23 Speaker 6 So this is so let me say the tandem extend is a is a non edited product that we have and the Copernicus dam has been then by the by my company because we we are a dual use tandem extant because of dual use mission where we have company contribution and the company was in principle Airbus was then deriving or making of added value of this original dam let me say. 01:23:54:23 - 01:24:19:06 Speaker 6 And and now is distributing it over the Danica's we have also other demo products here you see a little bit a kind of series of products. So starting from 2010 to 2014 as a time step here we have the tandem ecosystem that I was just showing and we have in addition also developed upon our dam, which is a 90 meter of of Antarctica dam. 01:24:19:20 - 01:24:44:11 Speaker 6 Then we have a centimeter edited them now, which is said the tandem extend is only a raw dam, but we have all the gaps still inside. But we have now an edited version and I will explain a little bit later on what kind of layers we have here in addition and then in addition, what we have also value added products that we have derived from the tandem extension itself. 01:24:44:11 - 01:25:10:11 Speaker 6 And the one is a forest, non forest map in the spatial resolution of 50 meter. Then we have the global urban footprint, which is also the spatial resolution I think of 30 meters. And then we have the bird settlement footprint which is a3d let me say map, which is based on an a 90 meter spatial yes, spatial resolution. 01:25:10:11 - 01:25:34:21 Speaker 6 And then we have the tandem coastal line product which all the coastlines are mapped. And at the end what we have us as a hydro set project which are in principle all the water mass that we have derived from tandem are produced inside. But it's not only tandem XP is the combination between Tandem X and also optical data. 01:25:34:21 - 01:25:57:09 Speaker 6 So that's a value added product. And then what we have newly this is a line which is more highlighted with a green color from 2017 and 2021, which we are still acquiring. Now also data. We are now on the way to produce an updated dam which we call the end of 2020, which is replacing and the tandem ecosystem, the original one. 01:25:58:00 - 01:26:24:12 Speaker 6 And what we have now produced is kind of timestamps of the different dams that we have in the 30 meter spatial resolution. This one be called the tandem ecosystem change maps. And all the the maps that you that are here displayed at the moment are already described. And we have more visualized also on this, a QR code. You can see it on the Jewish service at DLR de. 01:26:25:05 - 01:26:46:04 Speaker 6 You can have a look and the description of all the maps that you see. Let us go a little bit through to products that I just picked out. And these are the edited to kind of extend the centimeter one why we have edited I said before what we have original is an unedited demo of where we have gaps inside. 01:26:46:04 - 01:27:12:18 Speaker 6 You see it on the on the left side in the image where we have a black patch in this green colored surface areas. And what we also have are let me see, very bad declassified water areas in the Ed Dam. You see now the water areas, the principal, the flowing lines of rivers going towards the surfaces. And also what we have covered is in principle the gaps. 01:27:13:02 - 01:27:51:03 Speaker 6 And therefore, we have also produced and edited maps. As you can see here, very, very principle of figuring out or in principle displaying which kind of areas we have really edited by hand or using other sources of of areas. So that's said the the 30 meter dam is urging me to posting we have produced it now in two different vertical debt states and the one is the WGC A34 in Ipswich and the one is the great ETM 2008. 01:27:52:01 - 01:28:19:23 Speaker 6 We have produce to them, let me see kind of additional layers, layers, information, layers, you see it here displayed on, on the lower part where we have the edited them and we have always in addition a layer which is called height information. This is a height elevation Basque that is the original one from from the unedited them. And then we have a layer which we called editing mask. 01:28:19:23 - 01:28:56:04 Speaker 6 These are the pores that we have edited so that you can reconstruct what are the edited areas and regions and then we used also the land cover map. We have the vegetation and the and the water area combined inside maps so that this is also displayed and you can check it, cross-check, let me say with the edited them and for sure all of these in principle are also displayed as an example fire so that you can have a look very quickly if you just like to see it in digital coded format, that's editing masks. 01:28:56:04 - 01:29:28:21 Speaker 6 So These are all the regions, all the colored areas in this map, the region that we have edited. Again, on the side you see the legend where we use different data sources to do the editing of the different areas. And then what you see here now is editor Dam. That is the final dam in this 30 meter spatial resolution and displayed here is a huge sheet displayed what and now tandem 30 meter dam change maps. 01:29:29:05 - 01:29:50:16 Speaker 6 So that's another product that had like just shortly to display. So as I said, we started in 2010 and since then we have we had different, let me say, equity acquisition strategy. The first one was was clearly dedicated to the derivation of the global dam. So therefore, we have in 2011, 2012, the first and the second global coverage. 01:29:50:22 - 01:30:33:03 Speaker 6 And we had additional parts like Antarctica and steep terrain in 2013 and 2014. Then we started to have the science phase where we had particular phases where we have very long baselines in order to have a higher sensitivity to certain heights, smaller heights, if you like, to map, and then so on, you see and. Then we started in 2060 again to have a specific here where we just focus on Greenland and Antarctica expeditions from 2017 to 2020, we started again to have a new expedition to do an update of the tongue, to make stem, to derive the dam 2020. 01:30:33:03 - 01:31:04:14 Speaker 6 And from 2020 now until 22, we have a principle, we call it also the 4D phase where we have different time steps, not only the dam but also the time not included into certain regional area. And you see already the the areas are smaller now because we are now in a phase where we cannot Yeah. In principle run the system continuously and with full power because we are still trying save a little bit fuel and energy. 01:31:04:14 - 01:31:33:17 Speaker 6 And so therefore what we do, we are now collecting smaller parts where we have to get sailing in principle. So the, the new project now is we use a temporal mosaics from 2017 to 20 and we derive out of them is set for each year kind of time steps so that we have the tandem 30 meter dams just shortly to show how these 30 metered change maps are produced. 01:31:34:03 - 01:31:57:03 Speaker 6 But we do we have two kinds of products. The first project is where we have the first dam change. So the the principle, the change the first change that we see. So from 2017, for example, in the last change which is in principle the change that we have received in 2020. And this is visible on the map that you see here. 01:31:57:03 - 01:32:26:22 Speaker 6 So everything that is twice the mapped we can have two kind of changes in the time step. And like this you see now on the left side is the it's of the 2017, for example. And the 2020 is on the on the on the right side. And so the two different time steps where we can see changes, we see we have also product or a map where we can see which other changes occurred worldwide. 01:32:26:22 - 01:32:50:03 Speaker 6 So that's upward from map and the in the lower map is in principle the real ability of the changes that we have seen. And they are also marked separately here, just a very short view on how changes can be observed. This is now an area, New Zealand over an agricultural field. Plus what you see is also forested areas. 01:32:50:03 - 01:33:15:21 Speaker 6 Everything that is in principal red means everything is gone and everything that is blue. It means that something has been raised up. So we have more on this data and we see very strongly that between 2018 and 2019, just using the dam, different dam dams, now produce, we see quite a strong change in things that has been carried out, which is in principle the red colors. 01:33:16:22 - 01:33:38:20 Speaker 6 I'm just going I just have three slides. In addition, just to say what we also now doing is to have not only the dams but also repeat times which are shorter. So therefore we using insert satellite, which is the past satellites like this, we have a repeating past time of 4 to 7 days which we normally have for for 11 days. 01:33:38:20 - 01:34:16:01 Speaker 6 And like these, we can have digital elevation models combined also on a shorter time scale. What we also do, we use different produce like for example the lighter. Yeah, the the light emission GDI baby of using now the GDI of very flat or none of the GDI of the structural functions into the tandem stems in like these. What we derive is very high resolution and continuous measurements of forest heights over huge areas here. 01:34:16:01 - 01:34:43:03 Speaker 6 For example, over Amazonia is 25 meters spatial resolution. Yeah, that's actually all it. Just like to announce that we have this kind of opportunities, open opportunities not only for the digital innovation models itself, but also you can use we call this quest the SES, which are the, the, the regional interferometric products which you can order and you can derive your own database model as I said before. 01:34:43:22 - 01:35:09:18 Speaker 6 And we have also opportunities to combine this not only was tandem makes, but it also is past and now newly we will have a new announcement where we combine it also with AACM data, which is which are this event data also here you can do then kind of interferometric processing? Yes. And it's just like to say we just had our we just completed also our science meeting that we have two in October. 01:35:10:10 - 01:35:35:14 Speaker 6 And also here when I'm hearing now the presentations that have been before meant are the requirements. Also here, it's the it is going towards higher spatial resolution dams, which is very, very strongly requested also from our science teams or science users. They would like to have higher spatial resolution than starting meters. At the moment we have 12 meters, but even this is not enough for them. 01:35:35:14 - 01:35:52:24 Speaker 6 So maybe they would like to really to have a project of six and less meters, a spatial resolution and high accuracy in terms of vertical accuracies. So that's concluding my talk and sorry that I have probably a little bit longer talk. Taking a little bit longer time is expected. Thanks a lot. 01:35:53:10 - 01:36:10:12 Speaker 2 Thank you so much. If individuals for speaking up. So live in Europe 9 hours ahead. So thank you and thank you to all the speakers we were supposed to gather with the BBC to have like a random discussion within 10 minutes. But we're because we're running late. 01:36:10:12 - 01:36:12:04 Speaker 1 We'll just go to lunch. 01:36:12:04 - 01:36:24:24 Speaker 2 And then we will talk and during the breakdown session and I'll just say, Hey, everybody, I'm David. So good. You had a really hard job to try to choose five people to do this session. Right. And I'm pretty sure. 01:36:25:10 - 01:36:28:20 Speaker 1 Most people in this room could have given one of these ten, ten minute talks. 01:36:29:01 - 01:36:30:13 Speaker 2 Talked about three applications. 01:36:30:21 - 01:36:31:20 Speaker 1 And their wish list. 01:36:32:02 - 01:36:34:11 Speaker 2 Including the people on line. And so what we were hoping. 01:36:34:18 - 01:36:35:09 Speaker 1 I pasted in. 01:36:35:09 - 01:36:39:13 Speaker 2 The chat, I don't know if we can actually pull that up here. I think that's the chap. 01:36:39:13 - 01:36:42:04 Speaker 1 And so I put in the list the same set. 01:36:42:04 - 01:36:54:09 Speaker 2 Of questions that every person on the applications panel was provided with. Please think about these and we would love for people to provide answers to these for your specific application because it's just impossible that everybody give a talk. 01:36:54:17 - 01:36:56:20 Speaker 1 So please think about it and throughout. 01:36:56:22 - 01:37:03:01 Speaker 2 Today or positive in the chat, whatever we want to do. Thank you, David. 01:37:03:01 - 01:37:26:07 Speaker 3 Thanks very much. We'll make up for our shortage in discussions this afternoon in both breakouts and after our reports. Breakdowns of the discussion. I want to get back on schedule because Mark globally is in Italy and it's very late there. So you can get lunch in a hotel with a 25% discount or run across the streets with the CEO. 01:37:26:07 - 01:37:33:11 Speaker 3 I really don't have that much time, but I'd like everybody to be back by 115 to respect Marco's time. 01:37:36:22 - 01:38:16:21 Speaker 7 Over the past year or so or even more recently. And and so it is essential to see how the new observe the observing systems existing even previously can the proposed observing systems can perform with the with the new algorithms. And what we expect from analysis is really a something that tell us how well globally a observing system is performing in meeting the the STV targets STV measurements and so we hope to do that to have that output by 2025. 01:38:17:12 - 01:38:44:00 Speaker 7 So at least we can support the proposal to be in STV observing system concept. So this these are the funded RC projects, you see the pieces are I think we have all the pictures of, so I'll just read quickly these five projects. There's the first one, I'm the PI and I'll see framework for STV Multi-Mission design and performance evaluation. 01:38:44:00 - 01:39:13:05 Speaker 7 Mike Wise again, Robert are I'm sure in the room there is also a Rossi whose was a JPL. We have David Jean and I believe he is also he has also some coaches or a little team had the University of Washington advanced information systems to fill the gaps. Keith globally derived the measures of structure informed by ecological theory and observation. 01:39:13:15 - 01:39:44:10 Speaker 7 Paul London has TV volcanoes science application observations, and ALEC was focusing on sea ice and ice sheet with these ozone framework for determining ice, topography, science requirements from a future study mission. And these are the the full list of the ozone subgroup members, probably all of them or most of them. In fact, I'm not that in person, but most of them should be in the room, hopefully. 01:39:44:10 - 01:40:16:11 Speaker 7 And so you're welcome to interact with them today and tomorrow. So the reason why I put this list is also because I was actually fascinated by how many see this oh, here this is essentially indicates ozone. So these are all the ozone subgroup members, but each of them has essentially a role in another subgroup. And you see the colors essentially indicates how diverse the background of the various members in the ozone subgroups is. 01:40:17:14 - 01:40:46:20 Speaker 7 So I'll just cover now quickly for different axes so that we are working on one is the focus now science, sea ice and ice sheet. STV Science requirements. This is a led by Alec Petit who's we can give you more details just very briefly. You can see in this red box there is a comparison between a free run and the in a simulation run. 01:40:48:12 - 01:41:17:02 Speaker 7 And so basically what you're going to get is a to see how well different configurations in the simulation ran depending on the instrument in orbit models and all these things will affect the the performance in this in this red box. So this is focused on ice sheet and sea ice. This is a nasty that we are developing at JPL based on radar. 01:41:17:21 - 01:41:55:11 Speaker 7 It is focused more on simulating the actual radar measurements and extracting STV products from these measurements. These measurements for SDB are specifically multi static measurements. So we are looking at multi baseline interferometry if you like. And so we are essentially implementing the full end to end simulation of the radar returns and adding in the processing chain the also estimation of the product and evaluation of their performance against existing data or existing or against these simulation inputs. 01:41:56:13 - 01:42:34:16 Speaker 7 This is a lighter RC under development by Keith and his team. The goal here to evaluate how light are observing system samples the vegetation structure across the landscape. So here is an inputs of the sources of light and instrument specifications and number of specifics for essentially defining the satellites as well as the scene and as an output. You have the waveform that you see here on the screen and a level two elevation of terrain and canopy flight that is extracted from the waveform. 01:42:34:16 - 01:43:03:07 Speaker 7 And so that's that's also kind of similar to the previous slides, but in in for the later world the other RC here that David Jean is leading is focuses on stereo and I believe Lider fusion. I think I've seen this somewhere and of course all these people, all these members that I listed can give you more details either after this presentation or at the break out. 01:43:03:13 - 01:43:34:01 Speaker 7 And so in this particular case, David and team are focusing they have this new idea of precisely pointing refinement that will will will improve the estimate of essentially the STV products, I believe. And what else to say on this slide. Well, I guess David will will fill you in with all the details during this, this, this, this workshop. 01:43:34:15 - 01:43:59:13 Speaker 7 But, but yeah. So hopefully this gives you an idea of the diversity of the policies that we are studying and also where we are heading with with the study where every input that you may have is more than welcome. So I think this is my last slide. So I'm open to questions now or later during the breakout. 01:43:59:13 - 01:44:00:24 Speaker 2 I think you are open to any. 01:44:00:24 - 01:44:02:07 Speaker 1 Questions, anything on the mine.