 Hello everyone from wherever you're joining. Thank you for being here today for this NCAR Explorer series conversation, quantifying and improving travel safety using computer technology with Dr. Curtis Walker. My name is Dr. Dan Z. Lowe and I'm an education specialist here with the National Center for Atmospheric Research or NCAR, which is a world leading organization dedicated to understanding our system science. So that includes our atmosphere, weather, climate, the sun and the importance of all of these systems to our society. I'm really glad y'all could be here today to learn more about the impact weather has on roads and emerging transportation technologies. For this event, you'll be able to ask Curtis questions throughout using the Slido platform. If you scroll down this webpage, you can see the Slido window just below where you were seeing the live stream video of this event. If you haven't already, go ahead and click on the green join event button and then you can ask questions on the Q&A tab and answer poll questions on the polls tab, both of which are found in that blue bar across the top and definitely be sure to join Slido to add your thoughts to our work cloud question. What do you think of when you hear road weather because we're going to get to that very soon. This lecture is also being recorded and will be available on the NCAR Explorer series website. With us today, we have NCAR scientists, Dr. Curtis Walker from the Research Applications Laboratory or RAL. Dr. Walker is a project scientist specializing in research at the intersections of transportation, weather, climate and artificial intelligence. He brings over 11 years of surface transportation meteorology research, development and project management experience. Dr. Walker earned a PhD in Earth and Atmospheric Sciences with a meteorology climatology specialization from the University of Nebraska-Lincoln in 2018. He has since worked on many surface transportation projects to advance technologies that will improve road safety for drivers, including a collaboration with the Nebraska Department of Transportation to develop a winter severity index. Dr. Walker is also a member of many professional societies, including the American Society of Civil Engineers, the American Meteorological Society, Association of American Geographers, Intelligent Transportation Society of America, Transportation Research Board and American Geophysical Union. So Curtis, can you turn on your camera and give us a quick hello before we check out that work cloud question? Yeah, sounds great. Thank you so much, Dan. And it's certainly great to be here with you all. Again, welcome wherever you're watching and I'm looking forward to tonight's conversation. Awesome. So Paul, can you pull up that work cloud for us? All right. So one of the answers I haven't seen potholes. Weather and its impact on road safety and drivability, automated vehicles, hazards, safety. Curtis, how does that sound to you? I like potholes. That's the number one in my book. But yeah, I think we definitely have a great audience tonight and all of those, we're gonna talk about all of them. And yeah, potholes are indeed a weather related issue with freeze and thaw. Yeah, they are. We're just gonna at the end of construction season two to fix all the potholes. So thanks for those responses to that work cloud question. We have a poll question coming up in just a little bit. So make sure y'all have answered that. But so Curtis, you work in the research applications live here at ANTAR focusing on surface transportation and weather. So could you tell us a little bit about how you became interested in doing this type of work? Yeah, absolutely. And to answer that question now, I'll share some slides here with you all that really review the work. So yeah, let's dive on in and hit the road, as I say. And so to lead off tonight, we'll be talking about improving travel safety using different forms of technology. And this picture goes back to November, 2019, my family's Thanksgiving trip from Colorado back to Nebraska. And this is what the interstate looked like, I-80 traveling across Nebraska. And boy, this was before the era of Zoom Thanksgiving and Zoom holidays with the pandemic. But I certainly would have rather not do that drive because you can tell it was certainly a mess. And so maybe a little bit more kind of about my background, you've heard a lot of this already, but I did want to share, in addition to my current role at ANTAR, more recently in my professional history, I was an advanced study program postdoc at ANTAR, really paved the way for my opportunity to be here in my current capacity now. And also I had a brief stint with the Colorado Department of Transportation as a maintenance and operations meteorology and weather operations intern. And so that was a really great opportunity to combine the research and science that I've learned with the actual real world boots on the ground forecasting. And my academic history, Nebraska, where I earned my graduate degrees, but before that I grew up in the New York City area. And so it was actually there on the East Coast that I really found this love and passion for all things transportation as well as inclement weather. And so to walk you through maybe a little bit of photo history of my upbringing, this is a picture from my neighborhood in Queens, Queens, New York. I'm sad that the Mets lost the playoffs, of course, but this is an image from the Long Island Expressway or Interstate 495 through Queens that runs from Midtown Manhattan out to the East End of Long Island. And anytime there was a snowstorm, my dad and I would always go out, take pictures and I would just be in awe of the carnage and the impacts there. And so we see here, a van facing the wrong way. These are pictures from the infamous day after Christmas Blizzard. We didn't have a white Christmas, but we had a white boxing day, I guess, or day after. And associated with the snowstorm, it really snarled public transportation, huge impact. So we had buses that were stranded in snowdrifts. We had law enforcement vehicles that were covered in snow and plowed in quite well. And really, from a young age onward, I've always had this passion and interest for both transportation and weather. But as I went through my career, certainly as high impact weather events occurred like the unfortunate impacts of Hurricane Katrina in 2005 in the New Orleans, Louisiana area that turned streets into rivers or closer to home in upstate New York where I also have family. The remnants of tropical systems, Lee and Irene in 2011 that resulted in significant inland and river flooding. And more recently, the impacts of Hurricane Sandy and its aftermath in the New York City area that completely blacked out significant portions of lower Manhattan and resulted in some pretty significant flooding. And then as my travels took me to Nebraska, not my photo, but that of Matt Coker and the twin tornadoes of Pilger, Wissner in 2014. And so adding on this passion for, okay, from winter weather to hydrologic extremes to severe convective weather, how do all of these affect roads and people? And we know that when we look at winter weather impacts, whether we're talking decades ago or just in the last decade, the impacts here along Lakeshore Drive in the Chicago, Illinois metropolitan area are the exact same. And we know that every year there's going to unfortunately be some kind of extreme weather impact somewhere out there in the world and in the country. And so now that my travels have taken me to Colorado, certainly Colorado is no stranger to the extremes, whether it's from plowable hail that can fall in the spring or summertime, to the unfortunate impacts that we've had to deal with, locally in the Boulder County community more recently of wildfires. And so this was actually a time lapse that I took from my phone in the NCAR parking lot looking at the Cowwood fire. Now this was back in 2020 and then you can see the smoke plume here. And this was certainly much closer to home. I live in Southwest Longmont. And so at one point there was that question of, could the fire get to where I live? And of course at the time the answer was no, the fire stays in the mountain, right? But we of course know those of us local in the Boulder, Colorado area, we unfortunately had to deal with the impacts of the Marshall fire, not even a full year ago. And that was absolutely a case where the fire did not stay unfortunately on the foothills but did come into communities. And so this was a picture from my apartment complex back during the Cowwood fire but really taking stock of how do we move people? How do we safely evacuate people from all kinds of weather hazards? It really comes down to safety and people's lives and also the economic implications as well. And so when we think of transportation meteorology in the news to put some highlights, it ranges everywhere from the relatively minor inconvenience in the grand scheme of things, being stranded on the road or having a really tough trip home for the holidays to unfortunately the more catastrophic incidents such as vehicular pile ups and loss of life that are associated with these hazards as well. And then also it's important to consider the economic impacts and implications of these events as well. And so this was an article from a couple years ago now that shows interstate 80 through Wyoming and because of the truck traffic, the supply chain, keeping the toilet paper on the shelves that we need so much that every hour IAD is closed the trucking industry loses about a million dollars. And there are times that IAD has been closed upwards of one to two weeks during say a really severe winter season. And so all of this from the impact perspective to the personal experience has really been what's gotten me interested in this field. Sorry, Curtis. No, well, now I was gonna say just when we look at the impacts of adverse weather on the roads, I really like showing this chart because it really puts numbers on it and it really quantifies it. And so what we have here is the number of average annual fatalities and increments of a thousand on the vertical axis tracked during the period from 2007 to 2016. And the takeaway here is that when we look at the number of direct fatalities that are tracked by the National Weather Service from weather hazards, things like flooding, lightning, tornadoes and hurricanes, never to diminish any loss of life. But when you factor in the indirect fatalities that are attributable to weather related crashes on the roadways, you're dealing with an order of magnitude greater. Now these crashes could be that they happen during precipitation, rain, snow, sleet or what have you. They could have happened due to a pavement condition that was something other than dry. So perhaps the roads were slick or icy or slippery in some way. And then there's kind of the third hazard environment if you will in which it could have been low visibility conditions with fog or blowing dust or high winds that could blow vehicles over. And so when we look in the United States about 21% or one in five highway crashes is in some way attributable to weather conditions. And so in addition to the safety implications, this is millions of crashes per year, hundreds of thousands of injuries and significant economic, environmental and societal cost as well. Yeah, which leads us right into a poll question we had asked for audience about some of this, that's the stuff that we were just talking about. You know, what percentage of highway crashes can be attributed to weather? So Paul, could you bring that up for us real quick? Yeah, and it looks like a lot of our audience, I agree about a quarter of, you know, those highway crashes can be attributed to weather, which is quite a lot actually. Like that's a lot more, I guess, than I expected. So you've talked about it a little bit, but like what exactly is this kind of service transportation weather and like why is it so important to understand? Particularly, I guess as we start moving more and more towards automating our transportation needs. Yeah, certainly, let me go into maybe in a bit further detail here and jump back into the slide deck. Let's make sure I go into the slide deck, there we go. And so, yeah, to address that more broadly, it's not just the safety implications or the operational pieces of the hazards, but there's also the infrastructure as well. So this is a picture actually from that same Thanksgiving trip. So, you know, clearly I should have just not gone, right? But this is actually a bridge that washed out in the area of some family due to some shifting and mass movement associated with recent rainfall that had occurred. But when we look broadly at the weather impacts to roads and drivers, it really takes kind of two forms. There's the vehicle impacts, so the loss of control or traction or friction or your handling and maneuverability. Being able to do those things that you're used to doing on a dry road during a sunny day, but then there's also the driver and human behavior factors that can unfortunately also include human error as well such as your situational awareness and heavy rain and blinding conditions. Maybe you're not aware of how close you are to the vehicle in front of you or you are following too closely for the conditions. All of those things that law enforcement like to tell folks when they get pulled over. But there's also then traveling too fast for the conditions or having to take abrupt or evasive action because maybe the vehicle in front of you starts to slide or maybe an animal crosses your road in front of you as well. And so, you know, when we look at the prospects as you mentioned Dan about automated vehicles and the future of our transportation and mobility space, we see that self-driving cars certainly have the potential maybe to overcome some of these challenges. So in the vehicle impact space using artificial intelligence to be able to appropriately adjust the engine power, the torque and the braking to compensate for road conditions, we can program those algorithms to try to have that human experience. And perhaps one of the bigger assets is the ability for these platforms to communicate with other vehicles and to be able to identify and react to changing conditions. Now, in terms of that human dimensions piece, we can use again the suite of sensors that are on these self-driving cars from LiDAR and radar and digital base maps to really have this concept of localization. Basically knowing where your vehicle is relative to fixed objects but also other objects out there as well. So being able to identify objects and other vehicles beyond what the human eye might be able to detect and then also adjusting the speed appropriately for weather conditions. Now, for those of you that might not be familiar with the suite of mobility technology out there, I do wanna review just at a high level, you might hear these two terms of connected vehicles and autonomous vehicles or even putting the two together. And an autonomous vehicle is one in which it operates in isolation from other vehicles and it relies on its internal sensors and algorithms. So again, whether this is cameras or LiDAR or radar, it does the processing of sensing the world around it and safely navigating that world. Whereas the connected vehicle communicates with other vehicles and or with fixed infrastructure. So for example, maybe your vehicle knows, hey, this traffic light's about to change against me and so I'm gonna start slowing down so I don't run a red light or have to slam on my brakes. And then it's that connected automated vehicle or CAV that puts these technologies together to navigate its environment. Now, in terms of the levels of automation that are out there, there are five and currently in terms of the commercial commercially available vehicles out there, not to pick on Tesla, but that's the one that everyone knows. And Teslas fall in level two or partial automation in which they can do many of the driving functions autonomously, but they still require the human driver. So there are no commercially available higher level autonomous vehicles out there currently. There's a lot of testing going on and in select pilot cities, many of which are in the Sunbelt, AKA not harsh weather conditions. There are certainly a lot more higher levels that are out there. But it's when we get to these highest levels of automation from four to five that we talk about, completely removing the human driver, completely removing the steering wheel as well, but weather presents certainly a challenge for all of these technologies. And so case in point, looking locally, our local transportation agency, RTD here in the Denver area actually tried an automated shuttle out and this is a blurb from a report in which there were numerous service disruptions because things like snow, heavy rain and steam generated by melting snow led to the vehicle to interpret those weather events to be obstacles in the path of the vehicle. So obviously maybe it was safe that it wasn't trying to drive these snowflakes, but that's not very reliable in terms of the expectations of people in society. And so some of the maybe future challenges of this space is when we look at the weather community and the automated vehicle community is, who's gonna provide the weather information? Is it going to be the National Weather Service? Is it going to be private sector entities that may be formed business relationships and partnerships with one another? And then we also get into the nebulous waters of who's gonna collect and store this information? Is it gonna be the property of the vehicle manufacturer? Your Ford, your General Motors? Is it gonna be the property of you, the owner? Or is it gonna be the property of the insurance company so that they can call through the data when things do go wrong and make determinations about claims and what have you? So it's really gonna be an exciting but also a challenging world in terms of the future that awaits with looking at how can we better integrate weather into these autonomous mobility platforms? Cool, so it sounds like we would really need the car to know. Quite a lot of things. I know we had a poll question for our audience about this. So Paul, could you bring that question up about what our audience thinks self-driving cars will need to know to safely drive down the road? Yeah, so it looks like 100% said all of the above. So current weather conditions, other vehicles on the road and the wetness and dryness of the road. Any other things that a car might wanna know, Curtis? I really like all of the above. It's the catch-all and teachers in school always tell you, never select all of the above, right? It's a trick question, but not in this case. Your vehicle's gonna need to know everything. Yeah, great. So I don't see any audience questions quite yet. So again, anybody in the audience has a question, please go ahead, pop it in the slide or we'll definitely try and get to it. And also just another, we have another poll question coming up here soon in a little bit, but Curtis, I imagine that like what an emergency vehicle might need to know is very different than like maybe what I would need to know as I commute or as I get on a bus to commute. So have you thought about some of those like different transportation use cases and how things might be different for each? Yeah, absolutely. And that's actually where some of the fascinating research possibilities are. And so, you know, to demonstrate this, I want the audience and you to, you know, really think about these next few use cases. And so when we think of this automated vehicle space, you know, let's consider different resolution. And so let's say in these four cases, right, we have freight and supply chain. So we have the truck that's going from New York to LA, carrying your iPhones or something like that. Then we have transit, kind of that urban space, maybe regional, so commuting from the suburbs to the city center for work. And then we have kind of this finest scale, your emergency services. So you never know when someone's going to call 911, but when they do, you have to get there quickly, safely and efficiently. And then there's kind of this catch all high impact weather. And so in this space, when we, you know, think about that, that largest spatial domain, this kind of nationwide weather needs for freight, for trucking, for supply chain and logistics, we really need long range planning. So one of the common themes you're gonna see at the heart of all of these is route optimization. How do we get from A to B using the least amount of energy, whether that is electric powered vehicles, gas powered vehicles, or what have you in the future? And also how do we get there safely? So least costs, fastest time and safest. Now, when we go to that kind of more regional scale, thinking of transit, again, regional weather information, kind of this mid range planning horizon, what are you gonna need for your, you know, say self-driving buses or bus fleet this week, or think of it as a school district as well, getting kids to and from. Again, route optimization is gonna be essential. And then again, to that really localized, kind of short range or tactical planning horizon, route optimization again is important, but consideration of the weather is essential. And then last but not least, when we get to this high impact horizon, there's the question of with all the sensors, with all the knowledge and all the technology, your vehicle might know, hey, there's a road here and I can be on it. Even if I can't see the road because it's raining heavily, it's snowing heavily, I know that there's a road and I'm getting from point A to B. However, your vehicle has to also know, just because there's a road there doesn't mean you should be on it. And so case in point here, whether you have destructive hail, baseball size or larger, or perhaps you have a tornado crossing the road in front of you, maybe you have a flooded road or any other type of hazard, your vehicle should know that it needs to stop because that is gonna be the safer course of action or maybe it needs to detour or reroute entirely. And so again, back to this question of, that's a lot of other decision making that your vehicle has to do other than let me drive straight, drive the speed limit and not hit the car next to me or in front of me. And so it's these different use cases that really push the envelope. And so it culminates in the automated vehicle space and this concept of operational design domain or ODD. And ODD can be simply described as, it's the specific conditions for which an automated vehicle is designed to safely operate under. So think back again to those levels of automation. And depending on the level we'll somewhat govern what the ODD is. And so this can be factors like road type, are we on a two lane interstate 75 mile per hour speed limit or are we on a six lane highway and a busy urban area with a 55 mile per hour speed limit and lots of traffic? And again, weather is gonna be a part and a component of that ODD as well. And so to understand this concept of operation of design domain, we really wanna be able to quantify the weather into different regimes. And so this is where I'll spend a good chunk of, kind of the rest of the time we have here talking about some of the ways in which we are able to quantify and categorize the weather. Yeah, go ahead there. Oh, I was just saying as we move into this next section I know we had asked the audience a question about a time when maybe they were traveling in severe weather and what one piece of information did they really wish they had that would be helpful during that trip? So Paul, could you pull that up really quick as we transition to this next section here? Yeah, so will I make it out alive? And that's a great, I don't know, Curtis, if you've had this experience but I've driven in some pretty gnarly snow storms before. How icy are the roads? Yeah, that's a super great one, particularly when we think about like, ice that you might not be able to see on the road very easily. How the weather will actually interact with the roads? Will it freeze? Will it melt? Is the snow going to stick or is the rain going to freeze? Road conditions in real time on my journey, will it rain? Other drivers can be very helpful. All it takes is a split second to have an accident. Do you have any initial reactions to any of that, Curtis? Yeah, I really like the will I make it out alive. For me, this is the curse and the burden of my field. I'm always, when I drive now, I'm like, oh, I don't want to be one of my statistics. I don't want to slide off the road when it snows or rear in the person in front of me because I couldn't stop in time. And so, certainly in our hybrid and virtual environment, it's much easier to maybe not drive during inclement weather. But yeah, I do try to avoid not so much rain, even though rain is actually a bigger hazard even than snow and ice. But definitely if there's a forecast of snow and ice out there, I try to stay off the roads. And so I agree with all of these. And my hope is that in the future, we'll be able to provide answers to all of these questions. Maybe to make it out a live one, the answer will be don't go out and you'll stay alive. Yeah, and thanks to our audience for sharing your thoughts on this. So yeah, so back to you, Curtis. We're about to dive into the journey of your research. So can you tell us a little bit about more of the specifics about the work that you do? Yeah, absolutely. And so let me lead off with the slide. I left this concept of weather, severity, and disease. And so generally summed up whether severity and disease are a way in which we can quantify the weather conditions, their severity, their impact for a variety of applications. This can be for risk communication. It could be for decision support. It can be for emergency planning or recovery and resilience. And so let's look at some of the weather severity indices that folks might be familiar with already. And so really when we look at this severity index space, we have a variety of scales out there that are in some way weather severity. Now, some of these might be more of an intensity or more of a wind speed scale, but in some fashion, they're a way to communicate perceived risk and impact. And so we have the Saffir Simpson with hurricanes and tropical systems. We have the tornado, the EF scale, which categorizes damage. It's a damage index. So after a tornado has hit a community or hit structures, National Weather Service folks go out there and categorize what they're seeing. More benign weather conditions, such as heat and cold extremes. We have the wind chill or the heat index, which heat and cold extremes absolutely lead and contribute to loss of life as well. And then maybe some scales that folks are less familiar with would be things like, say, the drought index. Certainly those with agricultural interests are much more underwater resource, are much more sensitive to this one. And then where I'll spend quite a bit of time is talking about more recent tools, such as winter storms, severity indices. But before I go more into those, I do wanna show my favorite index. This is a picture taken by my graduate advisor, Mark Anderson. I've taken a similar picture now though myself, but this is outside the National Weather Service in Cheyenne. And in the office, if the chain isn't at a 90-degree angle, it's broken, notify meteorologists. 75-degree angle, beware of low-flying trains. But if it's perfectly perpendicular, welcome to Wyoming, it's a windy place. That too is a severity index, right? But when we look at and focus on winter and winter storms, this work I'll take you through was, it was a roller coaster ride. And we know that a winter season could be a roller coaster where we have maybe lots of little storms or we never know, maybe we'll have that big one. And so when we sum up over the course of a winter season, all that's occurred, we could have an average winter, we could have an above-average winter, cold and snowy, or maybe a below-average winter. Now, below average, just because it's warm doesn't mean we didn't get a lot of precipitation as well. So maybe it wasn't a lot of snow, but perhaps a lot of rain fell instead. And so this is the project that really had an opportunity for me to kind of take off in this field in my career, in which we developed one of these indices, a winter severity index for the Nebraska DOT. And so as part of this project, we identified storms from our baseline period, a 10 winter season period from October 2006 through April of 2016. And we determined the important meteorological or weather variables and combined them to classify a winter severity index or a storm index first, and then kind of a seasonal statewide index. And so when we computed the final index, we could look at it from a variety of spatial and or temporal scales from monthly and seasonal to daily, and again, from statewide or more targeted regions within the state as well. And we're able to compare that index with, well, what were the winter maintenance operations? How much did the DOT spend and things like that? And so this image here shows you the six category framework that we developed for the Nebraska DOT. And the takeaway is that in the six category framework, we looked at various parameters, things like road access or conditioning or traffic speeds or whether or not the DOT is meeting their objectives. And we came up with a way to frame from these lower impact or kind of trace and marginal scale up to a higher impact or moderate and high impact storms where you do have significant issues. Now, if you're wondering out there, why six categories and why these headers? Well, we really wanted to borrow and leverage the state of Nebraska is in tornado alley. And so they were very aware of the storm prediction center and those categories that they put out for whether or not there'll be a tornado or large hail or damaging winds. And so we simply borrowed that framework that existed and adapted it to winter storms for comparison. Now I'll say since this work, the National Weather Service has developed a winter storm severity index of its own out there, but it's been certainly great collaborations and great conversations looking forward. And so in terms of classifying the weather parameters, one of the challenges we had was we didn't wanna just make an index for Nebraska. We wanted to make an index that any state would be able to use and adapt on its own. And so you'll notice here, you might say, oh, where are the numbers? Why are there just text? And it's because again, we wanted this flexible framework. And so case in point snowfall, a dusting of snow for a place like Nebraska or Chicago or New York is going to be a relatively minor impact. However, a dusting of snow for a place that's not used to snow, perhaps somewhere in the Southeast, Atlanta, Oklahoma, Texas, not to pick on anyone that's not used to snow from a climatological perspective, but a dusting of snow in those places is gonna have a much more significant impact than it might in other places out there in the country. And so we came up with this framework to be flexible. The other thing we did was we directly engaged with our Nebraska DOT partners and had them help us by ranking and assigning weights to the different parameters we were looking at. And so they told us that these first three here from things like snowfall amount being the most important are the most impactful to their operations followed by the rate, how fast the snow is coming down and then the wind speed as well make up the top three most important parameters. And then when we look at some of the other parameters, the spatial extent, how much area the snow is covering or was being impacted by snow while still important was of a much lesser importance. And what this weighting allows us to do then is to combine all of the input data and come up with a single number to represent the storm. And by coming up with that single number to represent the storm, going back to our six categories that we developed, what we see here is when we compare any winter season to another, we could look at say in this case, the 2009-10 winter season and we see that there were a lot of those little storms and a few of those bigger storms, we could compare that to another winter where we see a similar distribution of the events but we noticed that the numbers from say year to year can be much smaller where maybe we had a winter where we didn't have any of those biggest or most impactful storms, but we still had some. And so to show you this full framework of the Nebraska Winter Severity Index, what we called NWINS for short, again, we combine those seven parameters to classify the storm events and then to come up with a single number to represent the entire winter season, we simply took each category as a waiting function for then the frequency of events and given the total number of events, divide by 100 to get a nice number between one to 10. And so when we look at that final value, we see here the Nebraska Index we developed and this is data from 2006 through kind of a few years beyond that original development period and the takeaway is this black dash line shows the average for the period. And so when we take the height of each bar or the departure from that average, we get the anomaly. And it's really in this image where we can see that roller coaster plain as day where we have years and periods that are maybe above average, we come back down to a below average period, back up to above, down to below and then back up again at least in these data. And so we can see periods where maybe there's a lot more expense from a maintenance perspective or maybe there's a lot more safety issues from having lots of winter storms to then years where there's not as much of an issue. Now, if we think back to the automated vehicle space, though, it's important to set the baseline threshold of what kind of conditions does our vehicle have to account for in any given year? Is it gonna be experiencing a likely a period of above average winter season or below average winter weather? Now, the other component, as I mentioned at the beginning was we also wanted to make sure we developed a flexible framework that was transferable to other locations. And so we applied the same framework but went with the state of Colorado. And the takeaway here is on the top row, we have five winter seasons of Nebraska storm distributions. And on the bottom row, we have five, those same five seasons with Colorado. And the takeaway is that the framework produces a similar, whoops, the framework produces a similar distribution, although albeit the numbers are different. Now, as you saw in that quick preview there, we can do the same analysis of looking at the year-to-year distribution. We can look at those anomalies, but by using the same framework for adjacent states, we can also do side-by-side comparison. So here we have the Nebraska numbers as the blue bar and the Colorado as the red. Nebraska's go big red, but I wanted to flip the colors up just for a bit. And so by also comparing those anomalies, this lets us get a sense more regionally and geographically of where did maybe two neighboring states, one had an above average winter and one had a below average winter. And again, really giving us that sense and that picture of what's happening, where and when. Yet from this framework at a high level, we can also extend it to other hazards as well. And so a current ongoing project is to apply that winter storm framework now to roadway flooding severity. And so for this, this image comes from the road to Atlantic City, New Jersey during Hurricane Sandy back in 2012. And flooding among the direct fatalities is one of the leading causes. And you'll see here from these statistics, most people unfortunately that lose their lives during flooding events, majority of that is associated with driving. And so that's why we have robust campaigns from the National Weather Service and others such as turn around, don't drown. Never try to drive your vehicle through floodwaters. However, there are sometimes where floodwaters, flash flooding case in point can come up very quickly and catch motorists off guard as well. And so there's always the psychological factor of someone has to be car number zero that makes a decision, I'm gonna stop and not travel the road because I don't wanna end up in a situation that is unsafe. And so what we're trying to do as part of this project is to combine hydro meteorological data from modeling that includes things like precipitation, stream flow, soil moisture and snow melt and couple it with traffic mobility data. So maybe use a reported incidents if you're like me and you like ways and putting those police reported ahead signs, you can also put in weather information such as hail or ice on the road or a flooded road if you encounter it. And also then integrating different information such as vehicle speeds or travel time and traffic volume as well and come up with a similar framework, very similar to what you've seen before with the winter storm index now doing the same for roadway flooding. And so one of the preliminary cases you might notice I have a Nebraska bias in my research but it's cause I have family and friends there that send me the great pictures of, hey, this weather is impacting us or hey, this is happening. And so back in March, 2019 those of us in Colorado experienced the bomb cyclone. However, it resulted in a rain on snow event and some rapid snow melt and some catastrophic flooding in the Midwest and Nebraska, Iowa vicinity. And so for this event, this first image here focused simply on the size of the circles. And so this is from our retrospective modeling of this March, 2019 case and where we see larger circles is indicative of where we had higher flow rates or higher stream flows. And I certainly want to acknowledge my colleagues Aaron Taller and Aubrey Dugger here at NCAR that helped and have led this part of the analysis. Now, where we have the larger circles that's indicative of where we have a flooding problem. Now let's transition to where the color of the circles is important because as I mentioned, the image on the left is from a retrospective model a model we've run historically and the image at the right is from for real-time observations. So we wanna see how well does our model correlate with real-time observations. And the takeaway here is that generally we see a lot of darker purple colors so our model's doing pretty good. However, you'll notice that downstream in places around say the St. Louis, Missouri area we have less correlation there and that's because of human action. So when the Army Corps of Engineer either raises floodgates, lowers floodgates or what have you, the model's not able to accurately capture that human and water resource management component but otherwise does pretty well. Now at a high level, if we look at kind of the mobility side of this case study on the left, we have a trend analysis green road segments are good, yellow, orange and red are bad. We have a lot of congestion there. And when we look at say this event relative to the same period a year later we of course have the caveat with the pandemic. But if we zoom in on a couple of areas at the left and compare that with images on the ground of what was occurring with the flooding situation here and we can see that portions of the Western Omaha, Nebraska metropolitan area, highway interchanges completely underwater. Or if we go kind of south of the Omaha metropolitan area along the Missouri River, we can see this as the community of Platt Smith, Nebraska and there should be interstate roadway here and you can see satellite images of the floodwaters. And then last but not least some of the more rural communities that are outside of the metro area again experienced some significant flooding with water over the hood of a semi-trailer. And so some very huge impacts with this case. But now if we wanna do a deeper dive into some of that mobility data and again wanna acknowledge my colleagues along the way that's put together by Amanda Siemens Anderson. And this shows us, this image at the right shows us those flood reports from waves. And so we see that on the big day of the event we have the greatest number of flood reports and then there's reduction. Now keep in mind here roads are closed at this point people are heeding the advisories stay off the road, stay home and et cetera. There was some secondary flooding over the weekend that resulted due to dams failing and levees bursting as well in the aftermath. But when we look at some of the other data things like ways reports are road closed, we can get a sense of, hey, we can start to see how that flooding is impacting mobility. Now we still have a ways to go in this analysis to actually get to that final point of, okay, now let's put the index together and let's put the numbers. But one final piece I wanna overview is this notion of let's also rely on traffic cameras. Let's use the infinite power of social media whether it's Twitter pictures that people put out there snap stories, Instagram or what have you. Let's use cameras to tell us what weather is occurring when it changes. As one of my other colleagues Gary Wiener has said improve the picture of now. And so this is another analysis with another colleague outside of NCAR Vair Walker-Hannon in which we've done a series of kind of image processing and difference assessments. And so this first case comes from Cedar Rapids, Iowa. And Dan, I'm gonna pick on you here now. See if you're paying attention. Tell me in this image, what do you see from a weather perspective? I'll make it easy. There's a beer truck in the middle. It's beer clock, right? Yeah, so it looks like there's a little bit of standing water kind of in the breakdown lanes but the actual road looks kind of okay. Like dry-ish and like I don't know if the sky in the background you can kind of see in that upper left hand corner looks, I don't know, not clear but not super overcast either. Yeah, no, pretty good summary. All right, same shot about 90 minutes later now. Tell me what changed or what do you see now? Ooh, while there's a raindrop on the camera so I'm assuming it's now maybe raining and that the roads are kind of damp and wet. Yeah, you don't see that defined standing water anymore and you're getting a little bit of shine. If you look over here, you can even see some road spray coming up from that traffic on the other side there. And so absolutely, yeah, pretty good job. And so if we move this forward and do a difference of these images, we can start to understand things like, okay, not only has the beer truck moved on but if you look at the road showing up in kind of this blue cyan color, we can tell our algorithms, hey, there's been a change in the state of the road. We can teach our algorithms, hey, this was a change from a dry road to a wet road or we can do it with say snowfall as well. And so this next case coming out of Nebraska, I won't pick on you again this time, Dan, but in this case, again, a similar story where we have an image, we can see things, there's a truck in the median there actually, there's a freight train below. But if we look at the bridge deck, notice that the kind of breakdown lane to even the exit lane here, this is traffic coming towards you. Lincoln's over here and Omaha's kind of behind the camera, but traffic going Lincoln to Omaha there's quite a bit of snow in the breakdown lane, little bit of snow in that left lane. 90 minutes later, we see that there's much less snow on that bridge deck. And so all of this to summarize that again, if we do that difference assessment, thinking about self-driving cars, well, in the first image, we'd wanna tell our self-driving car, travel in the number one or two lane, maybe don't travel in the left lane because it could be slick and maybe that increases the likelihood of a wreck. But 90 minutes later, we can safely say, hey, you know what, things have improved, the snow stopped and the snow is starting to get off the road there. Nebraska DOT's doing a really great job. And so, it all comes down to, why does all of this matter? And it matters first and foremost because of people, because of you, because of me, but also because as we look forward to the future, your vehicle's gonna need this real-time, predictive and hyper-local information to navigate its environment. And so this accurate weather is gonna be the key to safety. And so in this mock-up from another colleague from actually a graduate student, Brittany Welch, that we've had the pleasure to collaborate with here at NCAR, she's developed a blow-over algorithm. And so this is a framework that looks at combining roadside weather information with vehicle information to issue risk alerts for is your pickup truck with an RV or a trailer likely to blow over or maybe you're worried about a blow-over with your large semi-trucks and commercial vehicles. This is just one of the many types of systems that are out there that, whether it's conventional or future automated vehicles are gonna need. And so, maybe I'll leave you with some closing thoughts here and happy to take questions, but transportation agencies and the weather enterprise have some really unique challenges, but those same challenges or opportunities as well for research and development with connected vehicle, automated vehicles. And you'll notice, I've left out electric vehicles largely from this talk because that's a whole other conversation and a whole other area of excitement. But we also know that future weather conditions, climate change are gonna continue to threaten the safe and reliable operation and infrastructure that's out there. And so, we're gonna need broadly trained scientists and engineers to really address what the future holds. And so, again, I'll look forward to any audience questions here, but I do also wanna make sure that I share my contact information so that if anyone is interested out there in following up and do time, feel free to reach out, send an email, bring the phone as well if you prefer. And yeah, no, definitely thanks Dan for the chance to have this conversation with you. I'll turn it back over to you now. Yeah, and thanks so much for being here. I remember when we first started talking about some of your work, I was just like, this is so cool. Like I never even really made the connection between me driving down the road and maybe the weather conditions that I might actually wanna know about before I started driving down the road. So we've got about just under 10 minutes left together and I do see a couple of questions from our audience. So looking at our top rated one from AJ, I know you've talked a little bit about needing to know weather conditions and kind of positionality data as well as reports and traffic jams that you get some of that from, but can you maybe summarize for us like what data do you need to do your research and how do you collect these data? Yeah, so to summarize, the data kind of takes maybe three forms. There's the weather data side. In terms of how that's collected, luckily a lot of it's collected for us by roadside weather information stations or RWIS. It can also be collected from vehicles as well. Some vehicles have mobile sensors that give us temperature, road friction, pavement condition information as well. And so we're quite fortunate that there's a wealth of weather data out there. And then of course there's also the weather forecast itself. Kind of the second area then is gonna be that mobility data, information such as highway speeds or how fast traffic's flowing, travel times, traffic volumes. And again, a lot of that is collected out there, some by transportation agencies. Some of it's done by third party vendors as well that do anonymous cell phone tracking. So every time you click agree when you install a mobile app on your phone, you should read what exactly you're agreeing to because a lot of times it's aggregate data about your location, which can feed in some of these algorithms. And then kind of the third piece of data is that, I'll say the internet of things, it's that other data, it's traffic cameras, it's those crowdsourcing and social media data and other reports about what people are seeing and experiencing on the road and hashtag, blizzard, hashtag road is closed or what have you. And it's that wealth of data and information that can help guide a lot of the research that we're interested in doing. Great, thanks for that. So our next question is from Dylan who's asking, are there any climatological indicators like ENSO or the El Nino Southern Oscillation that can be used to predict what the weather severity indices that you were talking about earlier might be for the coming winter? Yeah, thanks for the question Dylan. And so among colleagues that are out there in the university community and at NCAR, we've definitely had strong interest in, okay, from this weather severity index, how do we now look at those teleconnection patterns you mentioned to try to predict, maybe now there's a lot of interest in the sub-seasonal to seasonal prediction space. And so can we predict a season out or several months out what we might anticipate in terms of winter severity over the course of a season? And so of course, as you mentioned, ENSO, La Nina, El Nino has some broad implications, but those aren't the only teleconnections out there. And some ongoing and active research has demonstrated some capability in getting to that weather severity impact from those teleconnections. Great, and our next question comes from Emery, who's, and I can relate to this. I'm reminded of that time when there was a road closure in Colorado and it like routed everybody into like a dirt field. So with route optimization, how do you mitigate sending all traffic to a different route and causing volume issues? That is the huge crutch of the problem there. So if you have an answer, I'm certainly open for that. I mean, it was actually just earlier today, the Federal Highway Administration's road weather management program had a webinar on this topic. And as you alluded to, Dan, yeah, we have to factor things like, we don't want your navigation apps sending you down a dirt road. We have to be cautious here in Colorado, I-70 when Vail Pass closes or the Eisenhower Tunnel, you can't have large commercial trucks going Loveland Pass, the kind of workaround. And so there's so many factors that have to go in in terms of it's not just, is the road open or is it not and how do I get there? But another example I want to share is the Nebraska DOT has actively worked with some navigation partners to discourage people from taking alternatives. So for example, if Interstate 80 closes or there are several other East West roads, Highway 6, Highway 30, Highway 34, but the problem is those are two lane roads and it's like if the interstate's closing because of weather, that two lane road that's a mile off the interstate is also probably closed or not a good idea to travel on. And so we really need to take the narrative too. Instead of trying to find the better way during some events, the answer is simply to adjust the travel schedule, either travel before the storm or delay travel and travel safely after the storm. And yeah, how do we get that messaging? That's the fun of the research. It's part, it's the science, it's the tech side and then it's also the social science side. And again, that human dimensions of how do we communicate and convey risk to people out there and different populations. Trucker has to travel and earn a living and deliver your Amazon Prime package. You wanting to travel to go fishing or something. Yeah, maybe that's a bit more discretionary. And our next question is from Bob who's asking most mesoscale and local driving decisions are road condition dependent rather than weather related. Is there an intent to create a driving condition index? Yeah, definitely. That's certainly a great point. And I'll welcome the opportunity to maybe collaborate on that because right now, as you've seen, right? Much of the weather index that was developed was all atmospheric conditions with a couple of others such as duration and spatial area. But absolutely getting to that. Okay, what about, you know, after a snowstorm we have blowing and drifting that might be 24 to 48 hours after it snows and the roads are still covered in ice. What do we do in that scenario? So absolutely, you know, kind of the next logical pieces. Okay, how do we get then a more integrated index that looks at the totality of the meteorological circumstance and the resultant impacts as well. So I'll say I'm very interested in it. There's been some development in that space but I'll absolutely welcome ideas if you have more. And we got two more questions left, one from the audience and one that I'm interested in asking you. So our last one from the audience is from Lorena, which is a great question when it comes to us thinking about what communities are we actually serving? So Lorena is aware that there are translations for severe weather like fornados or other types of severe warnings. Are the categories in severe the indices that you mentioned being translated into Spanish? I'm so not presently, at least not to my knowledge, but absolutely reaching the diverse audience of communities that are out there and traveling is a huge issue. And something we've actually come to find is that some of the National Weather Service offices are struggling tremendously with non-native English speakers because of the trucker shortage and kind of the personnel shortage that seems to just be existing in every industry. There are a lot of non-native English speakers that are now driving trucks. And while on a clear day, that's great when there's inclement weather, when a road is closed, when it's wet, that's a huge challenge. And so National Weather Service and transportation agencies need to be effective communicators. And so I think absolutely that too is another arena for making sure that the science is accessible and readily available to all, regardless of what your familiarity or language might be. Great. And last question in the last minute that we have together. If I were a student that was interested in this type of work, what advice would you maybe give them? Yeah, hopefully you came to this or you'll see it in the archives, right? But yeah, I mean, in my own academic road, it hasn't been easy because it's not as big of a field as say, hurricanes, tornadoes and kind of the bigger fields that are getting maybe a lot more attention at the moment. However, with that said, it might sound cliche but networking is key and fundamental because the hope is that even if you yourself maybe aren't here at this talk that among connections like your advisors, like faculty within your department, through the grapevine, perhaps someone can get you connected with a resource that has more interest in this space. Maybe eventually we'll get connected as well. And so that's why I hear sharing my email on the last slide. I'll welcome folks to reach out to me and really dig deep. And if you're looking for internships or job programs out there, Google is your best friend. And so I'm certainly aware that some transportation agencies and companies, whether it's airlines, whether it's shipping companies, there's a surprising number of transportation related or transportation meteorology related internships out there that I wasn't aware of as a student. And I'm like, oh, that would have been great, but it set me on my career path a heck of a lot earlier. But definitely Google leveraging your network and if you're curious about it, do your best to try to find out what's out there. And hopefully along the way, you'll make great connections. And again, please feel free to count me as one of them. And with that, Curtis, thank you so much for being here today to chat to us about road weather and just all the really cool things that you're working on right now. Yeah, thanks for having me, Dan. And again, yeah, thanks for coming, everyone. Hope you all have a good night and of course, travel safe. Yeah, definitely. And thanks to Lisa and Holly for your comments. I'm so grateful that you enjoyed our conversation here. And also thanks to the team behind the scenes, Paul and Aliyah for supporting Curtis and I today. And if you're interested in more NCAR Explorer series events, definitely check out our website for upcoming lectures and conversations as well as to view past recordings. And so with that, I hope to see you all next time. Have a great rest of your day. And like Curtis said, travel safe.