(Transcript) Flashback: Tornado Simulations with Dr. Leigh Orf

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[00:00:00] Gabriel: Hey, everyone. You may have noticed that this is a flashback episode. Over the years, the number of people that listen to Chaser Chat has grown significantly, and I wanted to highlight some of the older podcasts so that all of you new folks have a chance to listen. There may be some dated references, advertisements, etc., so just ignore those, and I hope you enjoy the episode.

[00:00:22] Gabriel: Howdy folks, and welcome back to another episode of the Chaser Chat Podcast. I’m your host, Gabriel Harber, and I want to thank all of you for tuning in, whether it is on YouTube, Spotify, Apple Podcasts, Google Podcasts, you can find us in all of those places. Today’s guest interview is really exciting. It’s somebody who you have probably heard about before if you are at all in the weather community on YouTube, on Twitter, reading research articles, or if you listened to my previous podcast with Pecos Hank Shima, because he’s got a video or two with this person on his channel, and we talked about it a little bit on that episode. So without further ado, I am excited to introduce Dr. Leigh Orf. How’s it going today, Dr. Orf?

[00:01:02] Dr. Orf: I’m doing well. It’s a little chilly outside. No storm chasing out here, although I think I saw a snownado earlier

[00:01:09] . Gabriel: It always blows my mind. It always blows my mind when I see the people on Twitter who are in full storm chase mode during the spring and summer months going into full winter chase mode and they’re driving halfway across the country to get snowed on and slide around in ice. I think it’s crazy.

[00:01:24] Dr. Orf: Yeah, you can’t miss when you’re chasing a blizzard. You’re gonna catch it. It’s gonna catch you, though. It’s the same goes with hurricanes. Don’t chase hurricanes, people.

[00:01:34] Gabriel: I see a lot of those pictures on Twitter where people have the rulers just stuck into the snow and then, an hour or two later, they’re in the same location because eventually, once it starts snowing, you can’t really go anywhere or do anything anymore.

[00:01:45] Dr. Orf: Yeah if you live in the South and you’re a chaser, and you’re bored during the off season, you gotta do something, right?

[00:01:50] Gabriel: Yeah, I suppose that’d be the equivalent to the athletes who during the off season travel all over the world to do those exotic training regiments.

[00:01:56] Dr. Orf: Yes, that’s right.

[00:01:58] Gabriel: Maybe that’s the analog there, so.

[00:01:59] Dr. Orf: There you go.

[00:02:00] Gabriel: All right, but we did not set up this conversation to talk about winter storm chasing. We set it up because you have some fascinating research that’s going on right now, and I know that the audio only medium is probably not the best way to convey exactly what it is you’re doing but I’m hoping that this conversation can really pique the curiosity of the people who are listening, give them some new ideas and new ways of thinking about storm development and storm maintenance, and then they’ll be inspired to go check out your YouTube channel. So if you want to just start actually by letting people know where they can find all the different presentations that you’ve done, I think that’d be a good place.

[00:02:36] Dr. Orf: Sure. I want to say first though, I love the audio only mode. I’m a HAM radio operator and I’m not active right now, but I have my license and I used to do radio. So I like theater of the mind kind of stuff. So anyhow, yeah. I have one, a one stop shop that just gets you to all my stuff at the website or stop media. That’s it. ORF.Media, and you’ll get a link to there to my YouTube channel my research profile and things like that. And yeah, so what I’ve done is starting in 2014 when I, my research first broke, I had this idea that I had prepared my talk as a complete video where you just press play and talk, and I had just done all the timings is the first time I had done this. And afterwards I’m like, I should just upload this to YouTube. People would like this. So I did that. And without the audio, cause I hadn’t gotten the, I hadn’t really planned this. This was just all like spur of the moment thing, and I gave the talk, it was well received. I uploaded it. Suddenly there’s 20, 000 views, and I’m like, wait a minute, that’s not supposed to happen for a scientific talk. Then I got the audio from the guy who recorded it, and I had my Final Cut Pro, I stitched it in, I re uploaded it, and then, that’s the one you’ll see now, where it’s all synced up. But, so all I’ve really done with the YouTube, and it’s my only form of social media that I currently employ is I just post my talks with no fancy anything, and I’m probably going to change this and start doing some more production work on this. Nothing like Hank, he’s just so good at this. I just want to kick it up a notch. I’m also working on doing some video work for a museum.

[00:04:01] So there’s a lot of interest in the numerical simulations of the tornadoes. And I guess, and stop me if I start rambling, for me, I’ve been working on work like this for a long time. What got me into weather, and I know it’s a common question to ask, what got you into the weather? And I won’t go back to my childhood just yet because it really, like most meteorologists, it does, but the computer angle is what really brought me into what I’m doing. The kind of work I do, which involves simulating thunderstorms, requires a lot of technical knowledge of how computers work because I’m using the world’s most powerful supercomputers because you need them to resolve flow features in thunderstorms that we’re finding out are probably quite important when it comes to understanding the nature of tornadoes, how they form, how they’re maintained and ultimately how they dissipate. So I basically what I do as my research, my area is numerical simulation of deep moist convection, primarily supercell thunderstorms, but I’ve also done work with air mass thunderstorms and looking at downbursts. My PhD work was on downbursts, which are these intense down drafts that form out of mostly every day sort of regular run of the mill thunderstorms, ordinary thunderstorms where you have rain or maybe even some snow falling into a very deep, neutral dry layer where the thermodynamic processes can kick off a really strong downdraft. So downbursts, of course, were responsible for a lot of air carrier crashes. And the good news on downbursts is that pilots are trained to understand them. Airports have terminal Doppler weather radars. Now there’s just nobody, you don’t really see… planes used to fall out of the sky a lot more than they do in the-

[00:05:33] Gabriel: Yeah. I was reading about that a couple of weeks ago, actually. It’s crazy.

[00:05:36] Dr. Orf: It really is. This was just, obviously nobody probably liked it, but this was just the way it was. There, and I don’t want to go down that road either because the airliner, the airlines have done a lot for safety since then, and some of it had to do with maintenance not really being done right, and other parts were just, pilots weren’t trained enough. To deal with these thunderstorms and the science wasn’t there yet. Ted Fujita did all the credible work on downbursts. But anyway, yeah. Ask me some questions. I could ramble all day. Remember, I’m also a former college professor and we can just ramble on. We don’t even care if anyone’s listening.

[00:06:05] Gabriel: That’s the great thing about the podcast medium is it was invented for people to ramble on and on. So it’s perfect.

[00:06:11] Dr. Orf: There you go. There you go.

[00:06:12] Gabriel: Yeah. Yeah. So there’s a lot of places that that I could start because just there’s so many little details of the presentations of yours that I’ve watched that I want to get into, but I think we should probably start with what you mentioned a few moments ago, which is just give us a little bit of the backstory. Like you said, everyone has a childhood story. They were influenced by the weather some way when they were younger, and it grew up as this passion in them and just, yeah, give us your your quick bio.

[00:06:32] Dr. Orf: Yeah, and this is something that’s, I’ve just done some recent research into. So when I was five years old, I lived out in Massachusetts. Suburb of Springfield, it would have been Ludlow, Massachusetts. And on July 6th, 1974, and I found this in the newspaper just the other day, I went to the old newspaper archives online to do this because I wanted to know what day this happened. It was a day, so I was like five years old, and I remember that day in that it was unsettled all day. There was like thunderstorms rolling in the morning, then it broke, and then there was some thunderstorms in the afternoon. And then it was like, “eh” then the weather service is issuing things, and I’m five. I don’t really understand this stuff. But I remember the sense of tension and just the weather is just angry.

[00:07:11] Gabriel: Yeah.

[00:07:11] Dr. Orf: So anyway my sister who was four, she’s like a little over a year younger than I, and we had just taken our baths. We’re going to bed, the storms raging. So it was a thunderstorm outside. This is 1974. Okay. So knowledge of storms and stuff wasn’t in my head yet. So my father and myself were in my room. We lived in old Cape Cod in the upstairs. I’m looking out the window probably towards, I don’t even remember how the house is oriented towards the east let’s say. My sister and my mother are in her room across the house on the same floor watching the storm as well. And then all I remember is my mother screaming at the top of her voice my father’s name. I turned around. I don’t remember a boom or anything, but I saw fire. I saw glowing wires. I saw insulation. I saw paneling. Remember, this is the 70s that was on fire and I just bolted down the stairs. So Dr Orf our house got hit by lightning. My mother couldn’t hear for a couple of days because of the concussion wave just knocked out her hearing. My sister’s canopy was on fire for crying out loud. But there was a small fire. It was put out quickly by my father. That event was a lot of houses got hit by lightning that night. That’s what the newspaper told me. This is a first page of the Springfield union, so that was event number one. Okay. Event number one, Massachusetts house hit by lightning. Oh God. Mother nature is trying to kill me.

[00:08:21] Now at this point in my life, I’m just, I’m scientifically minded. I know I liked science-y stuff. I was just always playing with microscopes and listening to the radio and climbing up trees and putting up antennas, stuff like that. I mean, some of that was a little bit later. So let’s fast forward a few years. This is in Feeding Hills, Massachusetts, also a suburb of Springfield, just north of the Connecticut border. And October 3rd, 1974 was a day when an F4 tornado hit Bradley International Airport. Ted Fujita called this the Windsor Locks tornado, and that sucker went right up north the Connecticut Valley, River Valley, and it did F4 damage, killed a few people, damaged a lot. It was a short lived storm. And it barely grazed our house. I think the, I remember running upstairs. I’d just come home from school, my sister’s a year younger, different school. Turns out that a tree had fallen on the roof or the roof had gotten peeled off and she couldn’t come home cause it was flooding and there was all sorts of stuff going on there. She was fine. There was a little bit of damage in our backyard, but I remember going to school the next day and everyone was talking about this, but that was another scary thing. So here I am, I haven’t even reached puberty basically and Dr Orf Mother Nature’s tried to kill me twice. Now I say this sort of in jest, but I am one of those rare severe weather scientists who really has no urge to chase. And I know this is a chaser blog, and I think what it really is these events weren’t the kind of things that inspired me to want to go out and see this stuff. It scared me. To this day, I don’t really like lightning. I don’t really, I look, man, you can’t outrun that stuff. And lightning is too fast. So when it’s lightning, I’m not as bad as our old dog was. It would crawl into the bathtub and whimper, but I don’t really like being around severe weather. And the good thing is, it turns out I’m pretty good at this computer stuff. I’d be wasting my efforts if I was in the field. I don’t think, I’ve done, I’m a HAM radio guy, built antennas, I built radios. I like doing hands on stuff. I love weather. I love all that stuff, but I don’t want to spend my March, April, May, whatever, driving around the Great Plains. It’s just not something that interests me. And there’s other people who are better at it than I am. And there’s plenty of people who take wonderful video that that I find useful. Folks like Hank and tons of storm chasers. I’m on YouTube all the time looking at tornado videos. Not all the time, but oftentimes I’m looking there for just inspiration or just trying to see things that might show up in my simulations that are also in the field because that’s really important to what I do is being able to match a reality to the models.

[00:10:39] Dr. Orf: But anyway, so that’s my story. I think I decided probably sometime in high school that I wanted to be a college professor, which is really weird. I didn’t think I even knew what a college professor did, but I wanted to do, I wanted to always be doing science. I just, I dabbled in archeology. I won an archeological dig. I won a little scholarship, went down to Campsville, Illinois and dug up some stuff. That was freaking cool. I still love paleoanthropology and archeology. I read all about that stuff. I had a telescope. I used to look at the stars and the planets and watch them zip across the screen cause it was a cheap one and it didn’t turn with the earth. So I was just science minded and I settled on meteorology. I don’t know exactly when, but when I got into college here at UW Madison, where I currently work, my third semester, I think? I took a programming class and boy, that’s where I was like, computers are fun. Programming is cool. And then when I discovered that meteorology has a need for people who can program, then I was set. I knew what I wanted to do. I wanted to use computers to understand the weather and that’s what I’m still trying to do.

[00:11:36] Gabriel: That is certainly a bit of a strange story in terms of the the desire to stay out of the field. You’re right; most of the people I talk to are giddy up, let’s get out there and get in the path of a huge supercell. But, you chose the opposite direction.

[00:11:49] Dr. Orf: I totally get it. I just don’t need to do it to believe it. It’s almost like a faith thing, if someone else is there with a 4K camera going at 120 frames per second, that, I just need to see that. I don’t need to, I don’t need to place myself there in my own mind. Would I be a better meteorologist if I did go to the field? That’s actually a good question. Is there something I’m missing in my own analysis cause I haven’t actually seen storms, seen the full glory- I have, they come to me sometimes, so it’s not probably technically true to say I’ve never chased. I have gotten in my car and looked around my house when there’s something that comes by, but I don’t take a couple of weeks off in the spring, that sort of thing. So yeah that, I’ve thought about this a lot, but I think it really just boils down to the fact that I… it really did… those are those memories that sear into your brain, and it just influenced me in a way that sort of said, I don’t need to do that, and I’m pretty good at the other thing, and it seems like there’s a plenty of people who are willing to go out and chase, so I’ll just work on this stuff.

[00:12:38] Gabriel: I think we’re pretty fortunate that you chose to do that because it, you know how there’s that entire idea of chaser convergence during really easily forecastable days and really populated areas? Yeah. It’s almost when it comes to people who are interested in severe weather, it does seem like there’s a bit of a hobby convergence where less people are interested in the side of things that you are, more people are interested in going out and chasing and I could see how that might have the potential to bottleneck some of the research because if you don’t have enough people in the labs doing the research on supercomputers and really pushing the science forward, then you’re not really going to have a lot to do with the data that’s generated and the photos and videos are generated by the storm chasers out in the field.

[00:13:17] Dr. Orf: Yeah it’s interesting perspective. I just, it’s just something that I like. I can focus on it and I’m relatively good at it. If you’re listening to this podcast and you’re wondering what to do with your life, it helps if you do something you’re good at.

[00:13:29] Gabriel: Its true, can confirm.

[00:13:30] Dr. Orf: But just liking it alone sometimes isn’t enough. You have to be good at it. I’m just kidding here but it just comes together. I can sit here where I’m sitting now in my basement during this pandemic, got two 4k monitors going. I’ve I got two operating systems. I got the supercomputer VNC going on here. I’m watching a simulation actually as we talk, making sure everything’s going well. See, I can sit here. It’s like my command control center or whatever, and I’m okay with that. I’m okay with that. What I’ve managed to do, the thing that sort of, I guess you’d say that my research did that really hadn’t been done to that extent, is just an amping up of previous work in the sense that I’m simulating supercells, other people have done that, I’m visualizing the data, other people have done that, but I’m doing it at a resolution, both in time and space. So the temporal resolution thing, I save, I can save data as fast as someone can film it. 30 frames per second, say, I can almost get to 30 frames per second of saving my data, which is ridiculous.

[00:14:22] Gabriel: That’s insane.

[00:14:23] Dr. Orf: Saving data takes a lot of time on a supercomputer and I’ve found ways to make it take less time. So my movies are vibrant and dynamic because you’re not jumping forward in course time intervals of five seconds or 10 seconds. You’re seeing all of the very rapid interaction that goes on in the supercells, including the mergers of tiny vortices that come together to consolidate vorticity and things that lead to the formation of the tornado. And even while the tornado is going, there’s all sorts of interesting things going on, and what I’ve done is I’ve basically taken a model that somebody else wrote, George Brian at NCAR it’s an open source model that is pretty much the main model used for idealized supercell research and thunderstorm research – a very popular model, highly maintained, pretty easy to read pretty easy to modify – I’ve taken that model and I’ve just said, okay I, I changed the IO part of it, the part that does the saves the data, but I’ve left everything else the same. And George has written a very good model that will scale very well right now. I’ve got jobs that that run on a quarter trillion grid points, which is pretty big for an atmospheric model. That’s a lot, that’s a lot of grid points. And so essentially I’ve but the thing is I can create more data than I can analyze. So it’s almost like I’ve got this data glut. I’ve got all this wonderful data on disc and all these simulations. And now it’s enough that to carry my research until I retire, but I’m, but… it’s a funny problem. I mentioned this to NSF and some of these supercomputers create so much data, but yet we only have a few years of an allocation. You have to scale the research expectations somewhat because of the fact that it’s just so much darn data.

[00:15:52] Gabriel: Is that a manpower problem? If you had enough interested students that really wanted to help with projects like this, could that really help you?

[00:15:59] Dr. Orf: That could probably help. But I want to say before I go any further on that route, I’m a research scientist, so I’m not a faculty member. So I’m soft funded, meaning I get all my grant money from myself to support myself. And I’ve learned over the, I don’t want to build an empire. I’m just not interested in that. But I do want to work with students and I have one student I’m working with right now. I’ve graduated a master’s student. So I am working with students, but there’s no expectation that I must. But yes, if I had more people working with me and I have several collaborators, external collaborators that are working with me, then yeah, I could probably I don’t know, get more science published quickly, but again, I’m not really interested in… when I say building an empire, this isn’t my first rodeo. I’ve already been through the whole tenure track process. The research process has been a postdoc, been a researcher, and I’m a scientist. I’m liking where I’m at right now, and I’m liking the the fact that I don’t work 80 hours a week to support, staying awake at night, worrying about feeding grad students. Getting funding’s bad enough as it is. This is, it’s so it’s just yesterday I was rejected another NSF grant. It just happens. It’s very competitive and I don’t, it’s one thing to worry about having to pay myself, but I don’t want to have a bunch of people who are relying on me to to eat, so I’m keeping it small. I do have ideas of just giving the data away to everybody. And that’s something that I’m happy with as well. The data that I’m in, I’ve got the code to read it and can convert it to net CDF, which is a very common data format that people use. Yeah, so there’s a lot of data. I’ve got a lot of, right now what I’m doing with all this data is I’m writing ensembles. So you know what ensemble is? It’s when you run a bunch of simulations of the same event, just tweaking either the environment slightly or parameters slightly to see how stable the solution is. So I can take my storm, the 24 May, 2011 storm and tweak some knobs and rerun the simulation. And maybe the tornado forms, 20 minutes later, or it doesn’t form at all or something. And you, and it probably wouldn’t surprise you to know that sometimes the tornadoes that I show you there are other simulations I’ve done where the tornado doesn’t form, and I’m more interested now in looking at the full spectrum of results and comparing them and trying to figure out what goes on in the storms that produce a big long track EF5 and what goes on in the storms that don’t, so that perhaps someday we can use these identify these features and hopefully be able to identify them on radar so that hopefully, again, I say hopefully at some point in time, maybe the work I’m doing will help issue more accurate warnings, tornado warnings, maybe the area over which the tornado warning is issued is smaller because we’re better at getting that.

[00:18:19] Maybe the work that I’m doing will someday make our forecasts better and maybe not as many false alarms because that’s another big problem we have in forecasting is that, about two out of three or three out of four tornado warnings are false alarms, meaning there’s no tornado. So the public gets the cry wolf syndrome thing where they stop listening to the alerts and then maybe when the big one comes, they’re not as prepared. So that’s another reason why I do this work is because I do really do feel like it has the potential to make a difference. And again, it’s still in the research phase. I’ve got a NOAA proposal that I’m really excited about. I want to simulate a thousand thunderstorms all sorts of cases that that are provided by the center. So there’s, they have like a sounding archive, including, okay, here’s a sounding, here’s a hodograph, here’s what happened. I’m going to take that data, try to run a simulation and see what happens, do an ensemble, see if maybe there’s a way to use these simulations to make better forecasts, so yeah that’s where I’m at. I’m always put, I’m always trying to push the limit of the latest supercomputing hardware. So I spend as much time writing machine allocation proposals as I do NSF proposals. Maybe not as much, but that’s like a huge important part. If I don’t have access to the machine, my research program is shot. So I’ve always got to be chasing the hardware. It’s just the nature of the beast. But anyway, supercomputers are great because because not just atmospheric scientists can use it, everybody can use it. So I’m always competing on the machine for, with astrophysicists and geologists doing earthquake modeling and chemists doing molecule modeling and all this stuff. But it’s great. It’s a great to have that, that really good shared resource.

[00:19:45] Gabriel: Yeah, absolutely. And I think this would be as good a time as any to start digging into some of the questions that I have for you, or rather things that I’d like you to maybe expound upon in relation to the research. Let’s just, let’s dive right into what I feel like is the no pun intended, primary current of a lot of your research, and that is the streamwise vorticity current. Could you just share with folks who maybe are not familiar with exactly what that means, or at least in the context of the way that you place it into the storm?

[00:20:11] Dr. Orf: Yeah, sure. So I was, before I answer that question, I was at a talk it was a severe weather talk a couple of years ago in the before times. And one of the faculty in the OS department who’s not a severe weather guy said, there is no other branch, there’s no other subfield in meteorology that has more jargon than supercells. In other words, you got your RFD, you got your FFD, you got your SVC, you got your LFCB, there’s all of these different things and it’s because of-

[00:20:35] Gabriel: That’s alphabet soup.

[00:20:36] Dr. Orf: It is. And it’s because there’s the conceptual model of what a supercell is, what discriminates it from an ordinary thunderstorm. And we’ve come up with these conceptual models. So the SVC I would say I’m not ready to put it within the conceptual model framework yet, but it’s a feature that shows up at high resolution and not even super high resolution. There’s been work on this, and I want to say I’m not going to bury the lead here. The SVC is real because it’s been detected in the field and that is super, super exciting for me, but let me now explain what it is. So when I was getting ready for the talk that I gave in November, 2014, right here in Madison, which was cool. I was in Michigan at the time. I’m trying to make my movies to show the storm. And when I look at the vorticity field, so the vorticity field is just spin. You can think of it as spinning shear. When I just look at the three dimensional vorticity field along the forward flank of the storm. So if you’re looking at a supercell plan view of a supercell, it’s towards the right and near where the air that is cooled within the cold pool, the supercell sort of meets the environmental air, the ambient air in front of the storm. I was seeing this feature that it was like picture like a a paper towel tube on its side sitting on the counter. So it’s a horizontally oriented feature. Okay. So it starts out on near the ground and the air is rotating as if you were taking that paper towel tube and just rotating it, pushing it forward and rotating it with your hand. Again, theater of the mind here, that tube of air, I call it a tube of air that’s rotating on its side is rotating primarily helically. Meaning what this means in atmospheric science, it means that the velocity vector is pointing in the same direction as the vorticity vector. So if you push your hand away from you while rotating a clockwise, That’s the kind of flow that the SVC shows.

[00:22:12] Gabriel: Now is that almost like a football? Like a football being thrown with a spiral?

[00:22:15] Dr. Orf: Yes, it’s a good example of streamlined vorticity. So if you think of someone throwing a perfect spiral the, this the direction or the velocity of the football and the rotation of the football, the axis of rotation are in the same direction. So you have this thing that’s say, take that football and sort of squish it down and spread it out like taffy that gets ingested by the updraft. And here’s the cool thing. So it takes a really abrupt upward turn. So you have something that’s basically rotating horizontally, and then it’s tilted vertically where it gets amplified by stretching, which is when you have, it’s a term in the vorticity equation, but where it really means is it has to do with the conservation of angular momentum argument of tugging on something and watching it spin faster as it’s drawn inward. So there’s, it’s like that. There’s an amplification mechanism that goes on as this air is accelerated, and that does things to the pressure field. Now tornadoes are regions of very intense, low pressure. The stronger the storm, basically the stronger the tornado, the lower the pressure is in the center of a tornado. This SVC itself is associated with low pressure and it really drops the pressure as it gets tilted upward and sucked into the mesocyclone. And so there’s this, so this feature, this thing I’ve seen it precedes the formation of a tornado. It’s there during the tornado’s life. Sometimes it comes and goes. It’s not something that I necessarily think is required for tornado formation. I’m not going to oversell this thing. All right. It’s something that is interesting. And it’s when I, as I get to higher and higher resolution, it starts to get a little more turbulent and less smooth and laminar. So I’m wondering what does it really look like in the field? Two papers have just come out or one’s probably still on its way out, I’ve reviewed both of them, on the SVC being seen in radar data. And this is for someone like me who’s more on the modeling side, who discovered something in a simulation five years ago to find that it actually exists in reality is probably the most gratifying thing in my career.

[00:24:08] Gabriel: Yeah. That’s so freaking cool.

[00:24:10] Dr. Orf: Yeah. It’s something that was found in a model and then found in the atmosphere. Sometimes it goes both ways. Sometimes you see something in the atmosphere and then the models eventually see it as they figure out how to model it. If it’s missing in one or the other than one of the- the atmosphere is never wrong. Okay. Let’s get that down. The atmosphere is never wrong. All the models are wrong, but they’re wrong at different amounts.

[00:24:29] Gabriel: It reminds me of, I do a lot of trading in markets, and it just reminds me of the age old adage of “the market is never wrong either.” People like to say the price should go up, the price should go down, it’s no, the price is always correct, it’s you that had the misconception.

[00:24:44] Dr. Orf: Here I thought it was just all diamond hands and apes together and all that. Sorry, inside somebody would hear that in 20 years and go, what is he talking about?

[00:24:52] Gabriel: Yeah, some Wall Street bets. Some memeology. I like it.

[00:24:55] Dr. Orf: Yeah. And that’s the thing from the work that I do is really only useful to society if it helps to make better forecasts. And that’s again, I always have that in my mind, but I’m not a forecaster. I didn’t try to be one. I just hope that my work can be useful to the forecasting community. And I think we’re getting there. I hope so. I’m starting to see people like again, finding in the field is the biggest thing. And that’s just been great.

[00:25:18] Gabriel: Yeah. So I think that you’re definitely making strides in the right direction. What more confirmation could you ask for than to have something show up in these high resolution models, then have it start to be actually observed in the radar data afterwards? To me, that’s, that would be probably the strongest confirmation you could get that, even if you don’t have it exactly right at this moment, you’re definitely sniffing up the right tree and you have to continue in that direction.

[00:25:40] Dr. Orf: Exactly. And that’s how I’ve always approached this problem. I’ve always known, before I had the big tornado simulation, the previous work, and people had done previous work, Ming Xue at the University of Oklahoma always should get credit. He commandeered the Pittsburgh supercomputer for a week or something. And he ran the old Dell city sounding and he got a tornado to form, but it was in a weird way. So he never really published it. But he, I remember at the conference, when he first showed this, I’m like, holy crap. That’s amazing. I’ve always known that once the computing technology caught up, it was just a matter of mapping the model to the computer in an efficient way. And I knew from 10 years ago, when I started really working on this problem, when I was on my sabbatical at NCAR, I was like, I always the going to kill us, I always the big bottleneck that needs to be dealt with before you deal with any other bottlenecks in your model. So the model itself can integrate and have all its data, but it’s in the memory core of the machine.

[00:26:30] For it to be useful to you and me, it needs to be saved to disk so we can look at it and make movies and do all the other fun stuff. And if you only save data every 20, 10 minutes or something, and because it takes half, an hour to save one data point in your supercomputer, then you’re never going to really see what’s going on in the simulation. So I came up with a scheme to save data very efficiently that takes that up, basically makes that problem go away, almost go away. It’s still, you still pay a price, but it’s much, I can save every model time step. I’ve done this several times where I save every model time. So you can’t save any more data. There simply isn’t any more data to save. And then I can go and then I can go analyze that data, send trajectories through it, visualize it, analyze, do budget analysis. That’s where I’m going with all this. There’s a well established analysis process in our field for these storms, and I’m going to be doing that. I’m also though, looking at novel ways to analyze storms that are new to the field. And that’s another proposal that’s in review right now, but there’s a researcher at the University of California, San Diego that I’m working with who’s a physicist, and he’s developed a system called Entropy Field Decomposition, which is a uses Bayesian Bayes Theorem, and information-

[00:27:36] Gabriel: I’m a big Bayes fan, yep.

[00:27:38] Dr. Orf: Yeah, oh, alright, you should be if you do gambling, yeah. There’s, it uses Bayes Theorem in a very cool way to strip – basically, it’s hard to really explain, but it’s a way that may help us tease out those patterns that we’re trying to find. It’s agnostic. It doesn’t really understand supercells. It can be used on MRI imagery. It can be used on thunderstorms. It can be used on trying to map the 5G pattern of of signal loss due to reflection. I’ve seen EFT used in these different ways. So again, I’m trying to use computational technology to crack the nut of tornadoes. We’re always trying to find the magical tornado trigger, and I absolutely hate the term tornado trigger, by the way, cause I don’t think it’s really accurate. I don’t one of the things my research has suggested is that some of our preconceived ideas of how tornadoes form are probably not universally right. They’re probably right sometimes, but you don’t need an RFD-

[00:28:25] Gabriel: That’s one of the areas I wanted to get into with you in a few minutes, ’cause it seems like your research has the ability to, we’ll just say disrupt a lot of what’s considered to be the foundational wisdom.

[00:28:36] Dr. Orf: Yes. I would say that’s probably an accurate statement. Yes.

[00:28:40] Gabriel: So so yeah before we get into to that particular aspect though I want to focus in on one of the details that you were talking about a few moments ago that honestly, I found to be one of the most fascinating things in your research. So as I was watching your models pull this streamwise vorticity and ingest it into the mesocyclone, I’m seeing all these small singular vortices start to, I think the word that you use is pile up in the same location and then they begin wrapping around each other, which is just absolutely beautiful to watch from a aesthetic perspective. But also from actually thinking in terms of the way that these tornadoes form, it does look similar to some of these videos you see where you see especially during the formation of a lot of these tornadoes, little spin ups start to hit the ground at different places and they’re not necessarily reflective of where the final tornado finds its positioning.

[00:29:33] Dr. Orf: Yes.

[00:29:33] Gabriel: And your modeling showing these little vortices starting to pile up and one will show up and another, and then eventually they wrap around each other and form this larger tube that gets stretched and becomes more intense. I think that is probably one of the more exciting things that I’ve seen in your work.

[00:29:48] Dr. Orf: Yeah, thanks. And I agree. And when I’m watching tornado videos there’s a couple that you see that same thing where you, of course, when you’re limited with just your eyes in the sense that a lot of the things, the air movement, you can’t necessarily see it. The only way you can see the air is if there’s a condensation funnel or there’s perhaps dust or something that gives you a tracer. There’s so much going on that we can’t see with our eyes. Another reason but on the other hand, I do have a lot of excitement about using photogrammetry, high resolution video cameras up the wazoo, just looking at storms, like what Fujita did, Wakimoto’s done, it’s gone away a little bit and it’s coming back with some of the work that say, I would say even Hank deserves a seat at the table for this. He’s done some scientific work with Anton Simon and some other researchers who go out in the field.

[00:30:37] Gabriel: Yes, Skip Talbot was out there with him this last year.

[00:30:38] Dr. Orf: Skip Talbot. I know all these guys. I don’t know him too personally, but I know I, I’m a big fan of their work and what they’re doing. So yes, what I call the “Parade of Vortices”, I’ve given it different names or my former grad student called it a miso vortex train, along that forward flank, there’s a storm relative motion where these vortices are coming from the forward part of the storm and edging their way towards where the main updraft is. And what happens, these vortices sometimes just pass on by and head towards the rear flank in the 24 May simulation I’m talking about. And then what happens in my estimation is as the SVC is ingested and the mesocyclone gets tight, stronger and stronger, the pressure drop in the mesocyclone is getting more intense, instead of thinking of a surge, pushing those things back, they’re being pulled back by a very strong pressure gradient force. So rather than ascribing the genesis of this, of the tornado to a push, it’s a pull. A push from the rear flank or a pull from the forward flank. You can think of it as two sort of similar things. But it’s not a trigger, there’s, and again, I have, I’m dying to write this paper. I really am. I have the 10 meter simulation that I showed at AMS, not this past AMS, but the one before in person. It’s a 10 meter simulation, I’ve saved the data every one fifth of a second. I’ve written some code that actually goes back to the absolute genesis point of the vortex that becomes a tornado, follows it up into the storm, and I can follow its progression over time. And I’m excited about looking at this whole process from the perspective of the vortex itself. What is happening to the vortex as it becomes a tornado? Because the vortex, there’s a vortex in place before there’s a tornado. At some point it becomes a tornado when the winds are strong enough to dictate that. And then you’ll get the definition in the EMS what a tornado is. But there’s, tornadoes, at least in my experience, I’m not saying I understand all this stuff, but there’s usually something spinning before it’s a tornado, before it reaches that threshold where it becomes a tornado, which is tornado genesis.

[00:32:29] Gabriel: You see that in a lot of the video when like over dust fields are areas where there’s a lot of debris. You see that stuff just start to spin up, but there’s really no clearly defined tornado yet.

[00:32:37] Dr. Orf: And that you’re seeing the process of it forming right there. And then one thing, I want to give away too much, there’s definitely a link between what’s going on in the ground and what’s going on higher in the storm. And that’s what I’m mostly interested in because there’s this question that comes out every so often, Jana Houser at Ohio University is interested in this, do tornadoes form from the bottom up or top down? And I know you talked about that with Hank as well.

[00:32:57] Gabriel: Yeah, I try to talk about it with everyone because it is a source of extreme controversy.

[00:33:00] Dr. Orf: Sure. And I would say that- yeah I would ask you back, what is going up and what is going down? Before I answer that question.

[00:33:08] Hey everyone, Kay here from Rough Skies Ahead and Chaser Chat. I wanted to give a quick shout out to the new Chaser Chat YouTube page, where you can find all your favorite episodes uploaded in video form with a transcription to follow along with. The link is in the podcast description.

[00:33:24] Gabriel: You’re probably wearing clothes right now, and I know you like listening to podcasts. Why not combine the two and support the show? Head over to chaserchat. com or click the link in the podcast description, and you’ll find all sorts of items like t-shirts, hoodies, beanies, ball caps, coffee mugs, and more. And if none of that sounds good, at least buy a freaking sticker. It’s only three bucks. Visit the merch store today and support the podcast by going to chaserchat. com or clicking the link in the podcast description.

[00:33:55] Gabriel: I like it.

[00:33:55] Dr. Orf: Because are you talking about momentum? Are you talking about a visual condensation funnel? Of course you’re not. Are you talking about vorticity? What is going up and what is going down? What is you know, because I’ll tell you what, from what I see, the analogy is like the dart leader for a lightning strike. This is just an analogy. OK, don’t think I’m trying. This is an analogy. So you think of a dart leader. You see these two little flickery things and then crack. Oh, there is your lightning bolt. The dart leader part is the part I’m focusing on, alright. There’s this stuff that goes on before the tornado really gets going. That’s the Genesis part. And it’s like fascinating, from what I’ve seen, so I’m working on this right now in the 10 meter simulation. I’ve got most of the code figured out. I just need to do the analysis and write it up. But there’s definitely, at least for this, again, I want to be clear here. I’m not trying to say I’m solving all tornadoes because I’m focusing mostly on a specific event that’s a top end event. But you gotta start somewhere, you gotta start somewhere. I am in the process of running dozens to a hundreds of different environments if I can get the funding and the machine time and all that to try to just look at this from almost just a brute force approach. Take all these simulations and run them, use automated processes like EFT to analyze them and to come up with some sort of parameter understanding of what patterns lead to certain things. So I’m taking a brute force approach to the problem while also taking a “let’s just look at one storm and understand it and then learn something from it” but don’t necessarily attribute that to all storms.

[00:35:19] I’m sure there are tornadoes that form when there’s a surge in the rear flank that compresses vorticity, helps to spin it up, and then the tornado forms. I’m absolutely positively sure that happens in many cases, but it’s not necessary. Okay. That’s what I’m saying. It doesn’t have to happen in because it doesn’t happen in my simulations of this event. There’s literally no RFD surge or even an RFD. The meso is so frigging strong, it’s like, where is the RFD? Yeah. And there’s another acronym for you. Anyway, yeah. The small scale stuff is important. It’s important that you capture the full spectrum of turbulence, as full as you can to capture these motions because otherwise they don’t get resolved. And that has a upscale effect. You’re not, you might get what we call a TLV, a tornado-like vortex. And if you look at the literature, most tornado papers are actually TLV papers because they’re run at resolution that really doesn’t resolve a tornado. I would argue that my simulations, at least like the 10 meters for sure, you’re literally resolving the tornado because it breaks down into multiple vortices. It does things that look very similar to what’s in the field. You see it go from a tight needle, a single cell to a two cell, to a multiple vortex that goes undergoes all that morphology. Like we, some storms do the same thing. Some storms come out the gate as a multiple vortex tornado. I have yet to simulate a storm that starts out multiple vortex, but I’ve seen that in the field. What’s the one in Arkansas from a few years ago, the super photogenic… oh, crud. I always forget these names.

[00:36:42] Gabriel: Yeah, I was gonna say Dr. Frame, my co host on the the more educational episodes, he is the best I’ve ever met at this. He can rattle off the date, the time, the place, and everything. I forget the first one that I try to remember.

[00:36:54] Dr. Orf: I should have done my homework before. Anyway there’s one where you’re literally, someone’s right on the storm, and you’re watching these multiple dirt vortices swirl around, and the tornado just forms like that. So it’s, I don’t know, maybe it was a tornado before then, I don’t know. Anyway, coming back to your original point, getting these small scale features right, I think is important to getting the answers right to the scientific problem. So that’s why I’m always trying to push the frontier on resolution. I’m at 10 meters, there’s, they’re already talking about the next supercomputer beyond the one that’s, currently state of the art. And, can we go to higher resolution? Somebody could ask do you really need to go to five meters to four meters or whatnot? And I would say the biggest problem with all the work I do is how we handle the surface boundary condition. It the models are idealized that we use. In other words, there’s no trees, there’s no plants, there’s no topography, the earth is flat, the Coriolis force isn’t turned on because who needs it? It’s a thunderstorm. It’s built into the sounding really, but you don’t have to worry about the air getting deflected in the supercell. It’s such a small factor. So we had, these models are highly idealized.

[00:37:52] We don’t currently the model doesn’t centrifuge debris or say hydrometeors, rain and stuff outside. So our tornadoes are full of rain instead of having a little donut like they should. So I’m very happy to point out the shortcomings of my approach and I’m very aware of them. But on the other hand, there’s no globally recognized answers to some of these problems. How do you handle the ground correctly? And I, this is where video footage helps a lot when I so called “turn friction on”, as we say in the model, it screws it up. It screws up the storm that I’ve got. It no longer produces the long track EF5 tornado. I believe me, I’ve tried many times doing different things with friction. I’ve gently introduced it as if it were like a little ridge. I’ve done all this funny stuff and it just, it really messes things up. And there’s, recent work by Paul Markowski at Penn State who’s looking at this problem. He’s one of the top in the world on this. And, even he acknowledges, maybe there’s no perfect answer to this problem. How do you handle the ground in the most physically correct way that both captures the way that say the cold pool interacts with the ground and your gust fronts are erect because you have some friction, you have that, but doesn’t trash the tornado because the tornado, what’s the boundary layer in a tornado?

[00:39:00] Dr. Orf: How deep is it? Is it millimeters? I don’t know. But if you turn friction on traditionally, it’s just, it might wipe out the forces that make the tornado work. So anyway, but however, there’s certain aspects of tornadoes, the most violent winds in the tornadoes wouldn’t happen if it weren’t for friction on the ground. So there’s all these issues and this is what makes science fun. I’m sitting here, I feel like sometimes I just making this stuff up, but the computer’s doing it. But then it’s trying to make sense of it all. And again, coming back to why I spend so much time looking at real footage is that I’ve I’m still convincing myself that I’m on the right track because even though perhaps the surface boundary isn’t being done correctly, my tornadoes look like real tornadoes. My supercells look like real supercells. They look even similar to the ones that I’m trying to model. So that alone tells me I’m again, I’m not gonna, I’m not going to necessarily hang up my hat because somebody thinks I need to have friction on to be right. It’s, I’ve tried that and I’m still trying to get it to work. But in the meanwhile, let’s see if we can figure some stuff out what’s going on. With the SVC, or tornado genesis, try to figure these things out, and and that’s just what I do. We’re all trying to figure out why tornadoes form sometimes and don’t form other times. I shouldn’t say everyone, but if you’re in, if you’re in severe weather, and you study supercells and tornadoes, that’s one of the biggest, that’s probably the biggest question, right?

[00:40:14] Why does tornado, Supercell A form a long track EF5 where Supercell B, which is in a very similar environment, very similar SRH, very similar CAPE, wind profile, dew point, all that stuff, and it produces a nice long, the supercell with no tornado. And I think some of the factors involved include inhomogeneities with the surrounding environment, topographical things. I’m thinking that there’s these little pools of vorticity that may be out there that if they’re just ingest, ingested just right, might be like an SVC and waiting, right? It’s just not, and I’m being flipping here, but there’s going to be natural inhomogeneities that the storm is going to gobble up, and some of those might be the factor here, or interaction with different surface roughness because there’s a line of trees over here or a valley over here. Those things may matter on a per storm basis as to why some of these form tornadoes and why don’t. And it may end up being a really difficult problem to solve because of all the subtleties and the nonlinearities and that are inherent to this kind of problem.

[00:41:10] Gabriel: Yeah. And I think that it’s really cool how you’re pressing forward with the research and not getting hung up on letting, what’s the saying? “Letting the perfect be the enemy of the good”. You’re not worried so much about getting everything exactly the way it is with the surface layer because that’ll come. There’ll be enough people that are interested in this and I already see it. There’s enough people, like you said, you uploaded a science video to YouTube. It got 20,000 views. That’s not supposed to happen. There are plenty of people who are going to be thinking about this and maybe non traditional ways and they’re going to be spending their entire days working on this problem. I think it’s going to get there. So I’m glad to see that you’re powering forward with the really cool revelations that you have found.

[00:41:47] Dr. Orf: Yeah. Again I’m honest with this. That’s why I’m very happy to talk about the things that are wrong because it’s not as if I’m unaware of them. It’s just that I’m unaware how to make it quote unquote, or, and I still think that higher resolution is probably going to be the key to getting the surface right. I’ve had discussions with other people who think I’m full of bologne, full of whatever on that issue, but I do think that will help in order to capture the right kind of interaction with the ground. You just need to have a lot more grid points, which is really unfortunate because when you have a higher, the higher the resolution of the model the shorter of a time step, the model can use to maintain stability, computational stability. In other words, when I run I run in 1/20th of, no, actually it’s even worse than that. 1/20th of a second intervals in time in the 10 meter simulation in order to maintain stability. So you have to run a lot of times to get the storm to where you want it to be. So it’s just it’s depressing. There might be enough memory on your machine to fit the problem in the memory, but it’s going to take 74 years of dedicated time to get it to work and no one’s going to give you that. No one’s built me a supercomputer and I don’t want them to, it’s, it would be a terrible use of resources. So yeah we just move forward.

[00:42:55] What makes me feel pretty good is I only have to go think back to my undergrad days in the eighties and early, in the grad days in the 90s to see how far we’ve come to today. And we’ve, we’ve made huge leaps of knowledge. Things like the SVC, I think are potentially helping to nudge things forward, especially when you, if/when these can be detected on radar. It’s going to be difficult, I think, to detect a lot of these things on operational weather radars, like our WSR 88D’s-

[00:43:20] Gabriel: It’s just not close enough to the ground.

[00:43:22] Dr. Orf: Yeah, not close enough to the ground and too low a frequency. The research radars tend to be higher frequency, more attenuation, you get closer to the storm, you get better resolution. In order to capture some of this stuff, you really need to put yourself there and maybe do RHI scans as well as just PPI scans. So don’t just swing that sucker around in a circle, make it go up and down like you’re vertical slice through the cloud as opposed to a horizontal slice. But yeah, it takes a whole lot of, and that’s why I say fieldwork is super duper important, and I’m so glad that people are doing it, and that people are good at it, and people want to do it, and because that data is absolutely precious and it’s what’s need, we need to always be pushing, in my opinion, we always need to be pushing forward on these problems. Both observationally, theoretically, and numerically. That’s the way our field works. You got the three stools of meteorology right there. Three legs on the stool. Sorry, the other three stools, three legs of the stool. They often talk about teaching research and service as being the three legs of the academic stool. It’s It’s theory, modeling, and observations. The three legs of the stool from meteorology. Alright, I spent way too much time talking about that.

[00:44:23] Gabriel: What I wanted to close the podcast out with is part of it from a psychological standpoint that’s really interesting to me. The first thing that came to my mind, I don’t know if it’s the first thing, one of the first things that came to my mind when I was watching your videos is just the fact that it’s going to take some hubris to be the person that is disrupting this conventional knowledge of what causes supercell genesis and maintenance, tornado genesis and maintenance, and I think that’s a good thing. You’ve got to have somebody who says “Yeah, I see what all you guys are saying, and you’re all super smart, and I respect the hell out of all of you, but I think that you might be wrong, or at least you might be a little wrong in this regard”. So how has that actually affected your work? Have you ran into that? And do you think that as your work gains more prominence, and also as more people maybe begin to take it seriously from a research and academic standpoint, you’re gonna encounter that more and more into the future?

[00:45:12] Dr. Orf: Yeah, It’s a small field. Severe storm researchers, everyone knows everyone. It’s just, it’s a small field. I don’t, I just take the, I don’t have a lot of my own identity tied to my research. I am a scientist. I love science. I do it. It makes me happy. I guess I published it and I get grants and yay. I think it’s good for the field when theories are turned on their heads. That’s when the big discoveries are made. That’s what the important history. You see this all the time. And I’m not saying I’ve done that. What I’m saying is there is a tendency for people to build their career around a certain thing, and it can be difficult when maybe the thing that you built your career around, or that you’re mainly many of the papers that you wrote, maybe it wasn’t as important as you thought it was. I’m not saying it’s like I said, I am absolutely sure that some storms have a surge, boom, you get a tornado because it’s just, it makes sense, but is it necessary? Is this the way this is the way the Mandalorian? No, I don’t think it is. I think it’s a lot more complex than that. And I’ve identified a mode, let’s call it a mode, of tornado formation that does not require the ingredients that we thought were inquired for a tornado. So I think the field’s ripe for disruption. And I’m not the only one who’s doing work that’s calling things. That’s science. That’s how progress is made.

[00:46:31] Gabriel: Absolutely.

[00:46:31] Dr. Orf: And someone’s gonna come around and kick my ass at some point, I’m sure. And that’s fine. That’s good. That’s good for science and that’s what we should, how we should be approaching the problem is that it’s good for science. But no, people have been friendly. I have only, no, I, I’ve gotten, I get rejected all the time, but so do other scientists in their grants or their papers. It’s, I’ve never seen anything where somebody is just like, this guy’s full of bologne and it’s because he’s not subscribing to my idea. Criticism is good, but yeah, we’re, it’s a tough crowd. The severe weather community is a tough crowd, believe me.

[00:46:58] Gabriel: I see it anecdotally on Twitter all the time, so I don’t know if it’s a good or a bad thing that you’re not on there, but it definitely would get pretty vicious, I think.

[00:47:05] Dr. Orf: The reason why I, one of the main reasons I spent all this effort on visualizing stuff is I just hold it in front of anyone’s face and say, okay, here it is. Argue with the model. Don’t argue with me. Argue with the fricking model because there it is. I am showing it. The model is showing it. So I just hold it in front of me and say, Hey, sorry, man, this is it. Argue with the model, argue with the environment. I don’t know. But it’s good. It’s good for the field. And I don’t know. I most, I don’t know.

[00:47:33] Gabriel: ‘Cause when I heard you say, if I could interject real quick, when I heard you talking about with, I think it was with Hank, where talking about how the RFD is oftentimes not active or persistent during tornado genesis and a lot of these videos that you see and then your suggestion being that the RFD might be more a result of the actual storm itself causing it rather than, sorry, I’ve got a little, in a truly professional manner, I had an alarm starting to go off on my phone. And yeah, you talking about how the ingestion of the streamlines vorticity and the causing of this low pressure perturbation up into the mesocyclone might actually also be causing the RFD and not the other way around I think has the potential to ruffle a lot of people’s feathers.

[00:48:13] Dr. Orf: Here’s the thing, I’ve watched a lot of chase video and it’s a safer place to be in the RFD area because the storm’s moving away from you, but you’ll see somebody with a camera and then the wind suddenly starts blowing in their face. They’re like ” RFD!”, I’m like, stop. That was a gust, horizontal gust in your face. You are assuming that the, that gust came from a downburst like action in the rear flank that caused air to spread out and blast you in the face. However, it is also possible that a very rapidly tightening pressure gradient has caused the wind to just really get fast, and you happen to be in, it moved over your… I don’t know. There’s a lot of turbulent things. There’s a lot of things going on. And it could be that at the same time these things going on in the rear flank are getting active, there’s also stuff going on in the forward flank and the mesocyclone. So in other words, it might not be one’s causing the other, they’re both caused by something else, or it’s all mixed in together. But I’ve always never liked the argument that there’s this magical downdraft region that just goes poof, and is like trying to set out things to start tornadoes going, which is how I see that theory. The theory used to be required in order to bring vorticity. You can’t grow vorticity out of the ground. So you always want to… people always invoke downdrafts as bringing vorticity to the ground. It’s the only thing you can do is bring vorticity to the ground and some downdrafts. I don’t disagree with that. It’s just that the downdrafts that are bringing vorticity to the ground are like 15 kilometers forward of the storm and there’s already all churning and vorticity. There’s tons of stuff spinning already, so that’s already been taken care of. You don’t need an RFD to add more vorticity. That’s not what’s needed. The horizontal motion of an RFD surge can compress vorticity and shrink it down, and then maybe that’ll intensify a vortex, but it’s got to be somehow connected to the parent cloud, et cetera, et cetera, et cetera.

[00:49:52] Gabriel: All right.

[00:49:53] Dr. Orf: So anyway, I don’t know. Again I make these videos, I make these movies because they tell a better story than I can tell. But obviously you need to break it down into the component bits and that’s the hard part. And yes, that’s where I’m hoping to get another couple of postdocs working with me soon, hoping to get a couple of grants to work out. And to make my team a little bigger. But yeah yeah, this has been fun. It’s always fun to talk about your research and this has been an enjoyable podcast.

[00:50:17] Gabriel: Absolutely. I really appreciate you joining us here to talk about this. I say us, I guess it’s just me. So that would be the-

[00:50:22] Dr. Orf: And all your lovely thousands and thousands of listeners. Millions of listeners!

[00:50:26] Gabriel: That’s true. That’s true. Include all of that. Hopefully at some point, not only will, for my own personal my own personal gain have that, but that’d be pretty cool if there were actually millions of folks who were interested in severe weather and all that stuff. We’d probably be on the fast track to getting research like yours solved.

[00:50:38] Dr. Orf: There you go. No I’m glad you’re doing this. I think it’s really great, and I look forward. I, like I said, I listened to Hank’s interview. I’ll probably listen around a little more and keep an ear on your podcast. So yeah, like I said, I’m not a chaser at heart, but I appreciate all the hard work that people do who go out in the field, whether for fun or for science. And the only thing I ever say is just be careful. I, you’re all adults, but just be careful. It’s, it can be dangerous.

[00:51:02] Gabriel: Yeah, no this is very true. My girlfriend actually, who’s in the other room right now, she won’t she won’t- I won’t say she won’t let me. She does not like it when I go out chasing alone. She prefers to go out with me and be the driver. So I’m with you on that. It’s definitely something that’s more safe, especially if you have another person with you.

[00:51:17] Dr. Orf: Yep. Absolutely. Absolutely. But again, I’m not here to preach to people. People are going to go out. They’re going to do their thing. And that’s just fine. But I’m going to sit here in my basement and try to debug models and figure out what’s going on with tornadoes my own way.

[00:51:29] Gabriel: I’m waiting for the day when Hank actually just decides to say, screw it, gets out of his vehicle and just rides the tornado. Just hops on a piece of debris and that’s how he sails off into the sunset. ‘Cause it seems like that’s what he’s trending towards.

[00:51:41] Dr. Orf: Oh, I’ve talked to him about this, man. At least he showed a little bit of sense and I’ve got a wife now and I got someone else to think about, so maybe I’ll be a little more careful. Dude, be careful. You’re too cool to die right now.

[00:51:52] Gabriel: Yes. I cannot agree more. So on that, we will close out the podcast. If you could real quick, could you please plug where people could find your work?

[00:51:59] Dr. Orf: Sure. So my name’s Leigh Orf, L E I G H. O R F, plop that into Google, it’ll do ya, but O R F dot M E D I A. orf.Media will get ya everywhere, everything you need to know about me.

[00:52:09] Gabriel: Awesome, thanks again for joining us, and folks, I will be back very shortly with another interview coming up in a couple days. Take care, everybody.

[00:52:16] Gabriel: Thanks for listening. If you’re not already subscribed, hit that button right now and then make sure notifications are turned on so you never miss an episode again. There are lots of ways to show your support for Chaser Chat. You can pick up something from the merch store, leave a rating and a review on your favorite podcast app, leave a comment and a like on YouTube, or just share the link to this episode on your preferred social media platform.

[00:52:38] Thanks again for listening, and I’ll catch you on the next episode.