Meet the Experts is a new blog series featuring some of the brilliant minds of CoreLogic. In this edition, I speak with Daniel Betten, a principal research scientist for CoreLogic. Dr. Betten is responsible for meteorological science as it relates to the CoreLogic suite of weather verification products inclusive of proprietary CoreLogic technologies for hail and wind verification.
SS: Thank you for joining me, Daniel. I understand you have a Bachelor’s, Master’s and Doctorate degree in meteorology. Can you talk about your background and how you first became interested in the field?
DB: I was always fascinated by the weather, even from a young age. I grew up in a community north of Dallas where severe storms come through the area every year – hailstorms and tornado warnings are commonplace. Every time there was lightning, I’d be at the window trying to observe what was happening outside.
SS: I have to ask – was this before or after you saw the movie Twister?
DB: Twister was great, but I was a bigger fan of some of the tornado documentaries of the 80’s and 90’s. I would see NOAA scientists and University of Oklahoma (OU) professors driving radar trucks and getting close to tornadoes and I would think, “That is what I want to do!”
SS: How did this interest evolve from watching storm-chaser documentaries to studying real tornadoes?
DB: Fortunately, I was able to gain some experience with research at a very young age. When I was a freshman in high school, I was looking for a project to do for my school’s science fair. This is when I began to seriously investigate the science behind tornadoes and whether studying and specializing in this field could be a real career path for me. My project focused on correlating tornado outbreak patterns to upper-level wind patterns in the jet stream using public data from NOAA and ended up making it all the way to the International Science Fair.
SS: What were your Master’s and Ph.D. theses about?
DB: Most of my Master’s and Ph.D. focused on a single storm in Geary, Oklahoma in May 2004. Using thorough storm datasets my advisor collected, I learned the storm had been producing tornadoes for six hours, and each tornado had characteristics that looked almost exactly the same. My goal was to understand why this storm and storms like it are able to produce the same tornadoes over and over, while other storms will not demonstrate these consistent patterns. I discovered that for the Geary storm, the local environment set up a favorable balance which slowed down processes that naturally prevent or shorten a tornado’s life span.
SS: When was the first time you chased a tornado?
DB: In my first semester at OU, I went tornado chasing with some meteorology classmates. My glimpse of the storm was very brief, but after that, I was hooked. This was the beginning of a lifetime of tornado chasing for me. I have been lucky enough to see at least one tornado every year since 2005.
SS: What exactly do you do in order to collect data when chasing tornadoes for research?
DB: In graduate school, we would have different teams all carrying specific instruments to perform certain jobs. For instance, there were people who would venture inside the storm and near the tornado to measure things like temperature, dew point and wind speed. There were also people who would use radar from outside of the storm to estimate the wind speed of the upper part of the storm.
SS: Over the last few years, you have developed algorithms to simulate these storms. What makes these algorithms and the CoreLogic approach to weather modeling unique?
DB: Before we developed our weather models, the only storm metric available was a warning indication of an areas’s likelihood of being hit by adverse weather events. We developed our algorithm from the ground up to focus on tornadoes, and more specifically, the stronger and longer-lived tornadoes that are responsible for the most significant damage. Even though there are roughly 1,500 tornadoes a year, a few dozen cause the majority of the damage. My team designed an algorithm specifically to detect these more damaging tornadoes.
SS: How are these modern weather simulation technologies helping communities recover from disaster?
DB: The weather service releases damage surveys as early as 24 hours after a tornado comes through a community, and as long as five or six days after. That is a very long time to wait if you are an insurance company that has just lost $100 million after a disaster. What is so special about our algorithm is that it allows insurers and other parties to respond to the storm as fast as possible, within one hour of the storm, rather than waiting several days. This means that disaster-stricken communities can respond and recover faster.
SS: What is your favorite thing about your job?
DB: When you’re in graduate school and your work is largely theoretical, sometimes it is difficult to see how the research you do will directly impact and help other people. I really appreciate that my work here allows me to think like a scientist and try to understand the natural world – while also creating real-world applications of research that help people recover from disaster.
I get to create algorithms for tornadoes, and I have the opportunity to talk to customers and see how they are benefitting and taking advantage of what I designed. Very few scientists get to do really interesting research while also being so close to the insurance industry and creating real-world applications. I also love that I get to tackle a wide variety of weather-related projects, not only studying tornadoes but also hailstorms, hurricanes, flooding, and wildfires. So much of what we design here is possible because of the access to unique data, which allows us to integrate different types of hazard and build a comprehensive understanding of weather risk. And, as a research scientist who has always had a passion for weather and meteorology – that is a dream.
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