Tony Segalés Espinosa says his love of small-scale aircraft began as a kid, flying model aircraft with his dad. Today, that love transfers into engineering drones for severe weather research.
Segalés Espinosa combines his robotics background and his electrical engineering knowledge to build severe weather research drones or uncrewed aerial systems. These systems will be utilized in field experiments by the NOAA National Severe Storms Laboratory and the University of Oklahoma.
“I learned there was a gap between state-of-the-art drone technology and its use in weather research,” said Segalés Espinosa, a scientist with the Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO) and the Advanced Radar Research Center (ARRC). Segalés Espinosa is also pursuing a Ph.D. at the University of Oklahoma in electrical and computer engineering.
Some of the problems Segalés Espinosa works to solve include making a sensor system compact, and reliable, for a drone. Drones also need to withstand extreme weather conditions, like changes in temperature. Segalés Espinosa integrated temperature and humidity sensors, while developing new hardware specifically created for sampling the atmosphere.
“You have to connect the dots and find other ways to sample the atmosphere, and drones help us do that,” he said. “That is what motivated me. I am excited to use my knowledge to help people improve their work and research as we capture more data about the atmosphere.”
A native of Paraguay, Segalés Espinosa is building more than drones. He’s building connections.
“My parents told me what I’m doing here is a big collaboration for the world. This research is impacting everyone,” he said. “Myself and others are collaborating with people with different cultural backgrounds while having the same main goal — making the world a better place.”
He said making the world better continues to inspire him to reach his goals within the weather and engineering communities.
Segalés Espinosa admits coming to the United States for his masters, and now Ph.D., was difficult. Now he is developing cutting-edge technology, and that drives him every day.
“I can share my stories and inspire other people, particularly Hispanic people and those from other cultures to do the same thing,” he said. “We should join other communities to make the world a better place and inspire others.”
Segalés Espinosa wants to build more collaborations between his organizations in the U.S. and those in Paraguay.
“Collaborations are what is pushing me forward, creating these bonds between people and nations, and being more inclusive of people and cultures to impact science but also the world,” said Segalés Espinosa.
As a child, Joseph Trujillo Falcón was terrified of thunderstorms. The loud booms and crashes would have him hiding inside, until one day his mother dragged him onto the porch. She told him to look at the beauty within the storm. His perspective changed.
Born in Peru, Trujillo Falcón moved from what can be described as a mild, coastal climate to the storm-riddled Midwest United States.
“At the time, I was translating everything from news reports to weather reports from Spanish to English for my family,” said Trujillo Falcón.
Trujillo Falcón’s fear of thunderstorms and the needs of his community encouraged his path into meteorology, particularly bilingual risk and crisis communication.
“I felt my community wasn’t as prepared as others and communication was part of the issue,” he said.
Trujillo Falcón wanted to be a broadcast meteorologist. However, during an internship, he realized very quickly that, in order to improve weather outreach to Spanish-speaking communities, translations had to improve. Today he researches how Spanish-speaking communities receive, respond, and act to certain messages and climate hazards.
“I realized there were some words that couldn’t be translated equally from English to Spanish,” said Trujillo Falcón. “We only had so many resources. I did a 180 during my undergraduate degree and changed my focus. I realized there is a big community need, but there’s not a big resource for proper translations and research. I said, if I was in broadcast [meteorology] right now, I would be burned out and frustrated.”
Fast forward to today. Trujillo Falcón has recommended a new SPC risk communication scale model based on his research with SPC forecasters and research with language experts.
“Depending on where you’re from, our Spanish can vary slightly and our language is beautiful and diverse. However, when it comes to the severe weather community, we want something all can understand,” he said. “This has blossomed into studies and insights into this community that we’ve never had before. We’re advocating beyond unifying translations and proposing an infrastructure to ensure these efforts strive.”
Understanding the community
Trujillo Falcón says creating words that are easily understandable to all Spanish speakers is the first step. The next step is to better understand factors that affect how Hispanic and Latinx communities perceive, ingest, and respond to weather information and the enterprise.
“Latin American countries don’t often have National Weather Service services like those in the United States,” said Trujillo Falcón. “Many in those areas don’t have access to meteorologists. I feel our next steps are figuring out how one’s heritage, or aspects of culture that are inherited from ancestral origin, impacts how they look at hazards.”
For example, for some in Latin America, the word tornado means a strong gust of wind — not an image of swirling dust, debris, and devastation. Trujillo Falcón explains tornado warnings are not a part of Hispanic and Latinx culture.
“Even if you heard a good translation, you may not know the implications,” he said.
Trujillo Falcón’s research also looks at social inequities — like socioeconomic and immigration status — while analyzing the influence of Hispanic and Latinx heritage. Trujillo Falcón says these factors also influence how people learn about severe weather and what precautions people take into account during storms.
“We have to consider what generation immigrant a person is and if they’ve seen a tornado before — there are a lot of factors,” he said. “They may not know a storm shelter is an important investment. Some outright might not be able to afford it or qualify for post-disaster government programs that aid them in recovery efforts. This research opens a new landscape and allows us to dig deeper into the Hispanic and Latinx communities. We hope to show organizations how to connect with communities to ensure they are safe.”
Trujillo Falcón says he continues to strive to positively impact his community.
As he celebrates Hispanic Heritage Month, he says it goes beyond Spanish speakers.
“It includes indigenous languages, Portuguese, Spanish, and all different parts of Latin America,” he said. “Latin America has its own variety of cultures and languages. The community is so diverse and it has so much beauty. This Hispanic Heritage Month, let’s celebrate all of it and embrace it.”
When Hurricane Ida moved inland along the Gulf Coast of Louisiana in late August 2021, a team of researchers set out to study winds associated with the damaging storm. The group, including scientists from the NOAA National Severe Storms Laboratory, captured unique datasets, marking Hurricane Ida as possibly one of the best-observed hurricanes at landfall.
Here’s a brief overview of the ways scientists were able to gain a better understanding of Hurricane Ida:
Continuous weather balloon launches provide more data
NOAA is leading efforts to launch as many weather balloons with instruments attached as possible into hurricanes and tropical storms. Researchers are particularly interested in launching balloons into the eye and innermost part of a hurricane to measure several atmospheric conditions, like temperature, humidity, and wind.
Instruments launched into the eye of Hurricane Ida identified a recording-breaking amount of moisture in the atmosphere. The data provided key context to the devastating flooding that impacted New York days later.
Multiple data sources help scientists understand the storm’s extreme winds
NOAA NSSL researchers collaborated with the University of Oklahoma and Cooperative Institute for Mesoscale Meteorological Studies to deploy a variety of surface observation units. The goal was to capture a variety of data on extreme winds to improve building codes to mitigate damage to homes and other structures.
Researchers deployed the Portable In Situ Precipitation Station (PIPS), NSSL’s Mobile Mesonet, weather balloons, and OU’s Shared Mobile Atmospheric Research and Teaching Radar (SMART) mobile weather radars. The teams strategized, gathered critical information about Hurricane Ida, and safely deployed their instrumentation. The teams successfully gathered wind data as Hurricane Ida came ashore and moved inland.
Researchers captured the evolution of Hurricane Ida
Researchers safely recorded the complete evolution of Hurricane Ida. Dual-Doppler radar from the SMART radars shows the system making landfall, with maximum wind gusts of 172 mph. Data collected by the teams will allow an opportunity to examine a variety of weather processes essential to understanding the evolution of Ida’s wind field and rainfall distributions. Currently, Hurricane Ida is one of the best well-sampled landfalling hurricanes by NOAA and university researchers. NOAA NSSL researchers will continue to gather hurricane observations in the future in an attempt to gain a better understanding of hazards associated with such storms.
Some of the costliest and deadliest weather events in the United States are flash floods. On average, more fatalities are attributed to flash flooding than other short-fused severe weather hazards, like tornadoes, hurricanes, and lightning.
Flash flooding — the rapid rise of water in a normally dry area — is mostly related to excessive rainfall resulting in significant groundwater runoff and quick rises in waterways. NOAA National Weather Service (NWS) forecasters rely on accurate quantitative precipitation estimations (QPEs). QPE are input into diagnostic tools and models to help NWS forecasters predict and warn on the potential for flash flooding, like flash floods that occurred in Tennessee on Aug. 21, 2021.
Areas west of Nashville, particularly in Humphreys County, received over 1 foot of rain in a matter of hours. This included a period where 3-4 inches of rain fell per hour over multiple consecutive hours. Approximately 17.02 inches of rain was recorded in McEwen located in Humphreys County. This preliminary total eclipses the state record for rainfall in a 24-hour period, which was 13.60 inches in 1982. Twenty people perished in this Tennessee flood event.
The MRMS system is a platform that combines various weather observations and model data to create a suite of products, including various QPE fields.
A key to the MRMS system is the quality control of radar data. Quality control algorithms remove radar artifacts from blockages, wind farms, biological scatter (like birds and bugs), and other data contaminations. The MRMS system then applies the latest scientific advancements in precipitation estimation using dual-polarization radar technology to provide accurate precipitation data in real-time every two minutes.
NSSL and CIMMS researchers regularly analyze MRMS QPE performance, including historic events like the Tennessee flash flooding. Product evaluations are conducted through internal web pages that allow for statistical comparisons of MRMS QPEs to independent gauge observations.
Using 24-hour analysis centered around 1200 UTC (7:00 AM local time) to collect both daily CoCoRaHS rain gauges along with hourly automated gauge observations, a few notable trends appear in the data. The overall analysis showed well correlated and clustered comparisons between the MRMS radar-based QPE and the gauge observations with rather small errors. The MRMS dual-polarization radar QPE had some overestimations with totals less than two inches, while some slight underestimation was observed with totals exceeding four inches. Yet, the nearly equivalent values between the gauges and MRMS in the area of greatest rainfall shows how well the system handled the event.
The second application developed by NSSL and CIMMS researchers to help with flash flood prediction is the Flooded Locations and Simulated Hydrographs (FLASH) system. The FLASH system is the first system to generate hydrologic modeling products specific to flash flooding at the flash flood time scale — new model runs are generated every ten minutes — in real-time for the entire country.
The FLASH system also provides products that compare QPE values to flash flood guidance — a measure of how much rainfall is needed to flood small waterways — in addition to the average recurrence intervals — a measure to determine the rarity of the precipitation totals based on how frequently they occur. All products within the FLASH system use the MRMS dual-polarization radar QPE as their input.
At the peak of the rainfall over Humphreys County, Tennessee, the QPE comparison products were at the upper end of the plotted scales. The accumulated rainfall was at least four times that of the NWS flash flood guidance for the area, and the average recurrence interval of the rain was beyond the plotted scale in the system (at least 200 years — approximately 0.5% chance of occurring per year).
The product that best conveys the flash flood potential and its possible severity is the maximum unit streamflow product from the Coupled Routing and Excess Storage (CREST) hydrologic model. The maximum unit streamflow values — the amount of water runoff normalized by its basin area — have been shown to capture the spatial coverage of flash flooding and provide context to its potential severity.
The projected unit streamflow values based on MRMS precipitation rates during the Tennessee flash flood event on Aug. 21, 2021, showed three key features:
How quickly the flash flood threat escalated.
How the extreme values pointed to a potentially catastrophic event.
How the model routed the water to show the impacts on local rivers even after the rainfall ended.
Researchers at NSSL and CIMMS continuously work to enhance the performance of the MRMS and FLASH systems to improve precipitation estimations and flash flood predictions. Efforts with machine learning and artificial intelligence are paving the way for increased performance in areas where radars struggle to accurately capture precipitation. Probabilistic hydrologic modeling with the use of forecast precipitation with the FLASH system looks toward the future of warning for flash floods within the FACETs (Forecasting a Continuum of Environmental Threats) paradigm.
One year ago, a severe thunderstorm with extreme winds — called a derecho — tore through the Midwest United States. The event brought extensive damage — snapping power poles and damaging an estimated 37.7 million acres of farmland. NOAA estimates indicate this is the costliest thunderstorm event in recorded history for the United States, causing more than $11 billion in damage.
Researchers at the NOAA National Severe Storms Laboratory are studying one of the biggest weather stories of 2020, which occurred at the height of crop growing season. The “Iowa Derecho” had a swath of destructive winds and was not only life-threatening but also obliterated crops in its path.
Predictability varies for thunderstorm events, and many numerical models did not do a particularly good job of helping forecasters anticipate the devastating Iowa event, even the day of the storm. Researchers tested whether the experimental Warn-on-Forecast System (WoFS) could have contributed to an improved forecast of the event.
Researchers expanded the model domain to capture the evolution of the fast-moving and long-lived storm and the results of the forecast runs proved very promising. A forecast based on data that was available 12 hours before the derecho developed correctly predicted a fast-moving, bowing thunderstorm system with significant severe winds (> 75 mph) near the ground. In the future, when a fully developed WoFS becomes available for events such as these, this could lead to earlier anticipation of a high-end event.
Some scientists, like Melissa Wagner, are working in the field to better understand derechos in hopes of providing more accurate warnings in the future. Wagner, a Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) scientist supporting the NSSL, is leading a research effort that’s using drones to gather data on the aftermath of the 2020 derecho.
Wagner’s team used a drone to collect imagery of rural parts of central Iowa hit hard by the derecho in late August and early September. Drones are particularly useful in gathering data on storm damage in rural areas, Wagner said.
Understanding and documenting wind damage helps scientists like Wagner better understand what these storms are capable of and better communicate their risk in the future. It also helps scientists develop more damage indicators for vegetation that are better reflective of storm intensity in rural areas. Wagner’s study is ongoing, and she plans to use UAS to gather this type of data on future derechos and other high-wind events.
Downbursts—an area of strong winds in a thunderstorm—can damage trees and buildings, disrupt air travel, and cause loss of life. Decades of work by scientists has revealed a lot of information about downbursts including certain features seen on radar, known as precursor signatures, that can help forecasters anticipate when a downburst might develop. However, downbursts are still quite challenging to predict—especially in low-shear, summer-time thunderstorms—perhaps because the downbursts and their precursor signatures can develop quickly and be difficult to observe.
Therefore, researchers at the University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies with the NOAA National Severe Storms Laboratory decided to look into it further. They studied a dual-pol radar feature, known as a specific differential phase (KDP) core, because it could provide information about important processes that cause a downdraft to develop and get stronger. Our research shows KDP cores could be a reliable and easily observable downburst precursor signature that can help NOAA National Weather Service forecasters predict where a downburst could develop next.
Our research began one afternoon in the halls of the National Weather Center in Norman, Oklahoma, when Randy Bowers, a forecaster at the NOAA National Weather Service Norman Forecast Office (OUN) and I (Charles Kuster, a research scientist with the CIMMS working at NSSL), were having an informal discussion about a completely unrelated topic. At the end of our conversation, Randy mentioned seeing a consistent area of high KDP—the KDP core— while issuing severe thunderstorm warnings and wondered if we could do some research on it together. I had been looking for opportunities to work more with forecasters, so this topic sounded like an amazing opportunity. We jumped in and began identifying potential cases, collecting data with a research radar in Norman, and brainstorming how to best study KDP cores.
Ultimately, we selected 81 downbursts in 10 different states to analyze. I got to work on comparing the size, magnitude, and vertical changes in the KDP cores associated with strong and weak downbursts, while Randy examined atmospheric conditions and possible warning applications. We also began working with Jacob Carlin, a CIMMS research scientist also working at NSSL, who explored model simulations of downdrafts and important microphysical processes—such as melting and evaporation—that can result in a stronger downburst. Another research scientist, Terry Schuur (OU CIMMS/NSSL), also brought microphysics expertise and experience to the team while researchers Jeff Brogden (OU CIMMS/NSSL) and Robert Toomey (OU CIMMS/NSSL) provided extensive support and expertise in radar data analysis software. Andy Dean, a forecaster with the NOAA/NWS Storm Prediction Center, provided data about atmospheric conditions around the downbursts.
Together, we determined KDP cores were a reliable downburst precursor signature in the events we studied. All 81 downbursts were preceded by a KDP core by as much as 30 minutes. The KDP core also reached its maximum intensity, typically about 10–15 minutes before the downburst reached its maximum intensity. In addition, there were very few instances where a KDP core was observed and no downburst occurred (i.e., very few null events).
Anticipating downburst intensity using KDP cores was more challenging because there was overlap between the characteristics of KDP cores associated with strong and weak downbursts, but in general, a stronger KDP core was more likely to be associated with a stronger downburst.
We also found the atmospheric conditions during each event were very important. When atmospheric conditions were less favorable for downburst development, we observed stronger KDP cores, which likely means more melting, precipitation loading, and evaporation are needed for a downburst to develop in such conditions. Ultimately, our work showed KDP cores provide a good signal that a downburst is likely to develop soon, assuming atmospheric conditions are favorable for downburst development, and can help forecasters triage storms to determine which one has the highest chance of producing a downburst.
We would like to thank everyone who has made this research possible so far and those who continue to help push it forward. Everyone from the CIMMS and NSSL administrative staff, NSSL IT, radar engineers, NOAA National Weather Service Norman Forecast Office, and webinar organizers are very much appreciated. For any questions, please contact firstname.lastname@example.org or see https://doi.org/10.1175/WAF-D-21-0005.1 for more detailed information.
The NOAA National Severe Storms Laboratory is excited to share with you its first podcast!
Researchers at the NSSL are using Uncrewed Aircraft Systems (UAS) to study storm damage in rural areas. In March, researchers captured aerial photos and video of storm damage from hard-to-reach locations using UAS, or drones. Learn about the multispectral camera on the UAS, and how the camera provides imagery showing high-resolution damage to vegetation.
EMILY: Hi everybody and welcome to Pod-Sized Science, the podcast about research at the NOAA National Severe Storms Lab. I’m your host Emily Jeffries.
This is our first podcast so we’re excited to share it with you. We have a lot of great interviews at the lab as a result of our video series — Bite-Sized Science. Because of its short format, some interview content gets left on the cutting room floor. So, we thought, hey let’s create a supplemental podcast that allows us to take a deeper dive into these topics — and hear more from the scientists.
Now, without further ado, let’s jump into our first episode.
Researchers are studying tornado damage with Uncrewed Aerial Systems, or UAS for short. We interviewed the project’s principal investigator, Mike Coniglio, and co-investigator, Melissa Wagner.
Before we discuss this current research, let’s hear a little bit about how they both got involved in working with UAS. Here’s Mike, a Research Meteorologist with the NOAA National Severe Storms Lab. He’s been working at the Lab since 1998, when he started as a student employee.
MIKE: So I’ve studied severe weather for a long time here at NOAA NSSL, but it’s mostly been with the atmospheric properties of storms. And I really haven’t focused much on the aftermath of the storms and in the two decades that I’ve been doing this research, one of the Areas that I’ve seen that we lack some knowledge and is understanding how strong storms really are and what exactly happened when a tornado or a high wind event impacts a rural area… So I hope to be able to develop a better database for tornadoes that we can then go back and understand the dynamics of storms better based on better estimates of what actually happened with events over rural areas.
EMILY: And here’s Melissa, she began working as a Postdoctoral Research Associate for the University of Oklahoma’s Cooperative Institute for Mesoscale Meteorological Studies with NOAA NSSL in 2020. Prior to Norman, she was a grad student in Geography at Arizona State University.
MELISSA: So I got involved with UAS Systems particularly when I was working on my Masters thesis. I was doing satellite based damage assessments and I had noticed that there were some limitations and be able to detect the damage and I felt that using UAS would be a great and innovative way to be able to address some of those limitations.
EMILY: So how does UAS fit in with other tools like radar and satellite for observing tornado damage? Mike provides more insight.
MIKE: Newer radar technology is very good at detecting debris that tornadoes loft into the air But we can’t tell exactly where that tornado occurs or how strong it was from that information and also the radar might not be able to see it if the tornado occurs far from the radar. And satellites also collect imagery of damage, but the images are far coarser than what we can obtain from cameras on our UAS and also we have issues with obtaining the imagery in a timely manner because of clouds and Flexibility issues with the satellites. So UASs give us a very flexible option to get out and obtain imagery in a timely manner and obtain very high resolution imagery of the damage that tornadoes produce.
EMILY: The scientists talk a lot about storm intensity and the importance of assigning accurate ratings.
MIKE: We want to use UAS’s to study storm intensity because it’s very difficult for the Weather Service to assign intensities to tornadoes in rural areas where there may be very few structures that were hit or very few damage indicators to go by with our current rating techniques. So we want to be able to provide some research that can help provide guidance to them to understand how to rate tornadoes in these rural areas where maybe only vegetation was impacted.
EMILY: A key tool is the multispectral camera on the fixed-wing aircraft. Melissa explains why this camera is so important for studying damage to vegetation.
MELISSA: So the multi spectral camera that we use on the UAS provides us very high resolution imagery, so we’re talking about something that’s about 8 centimeters scale. If you’re flying up 400 feet. This camera also has multiple bands, so it collects visible imagery is what we see as a true color, but then it also collects near infrared information and as well as red edge information, so near infrared and red edge are really important because they help us to assess vegetation health. So by looking at the response of vegetation in those two bands we’re able to determine what has been damaged. And what has not been damaged? So that really provides very high detailed information to help us really be able to better understand damage to vegetation.
EMILY: This research is being done in the southeastern United States. The team has traveled to states like Alabama and Louisiana. So why that region?
MELISSA: It’s really important to focus our research in the Southeast US because they tend to have a lot of nocturnal tornadoes, so tornadoes that happen at night. And because because these tornadoes happen while people are sleeping, they can be more deadly, so there’s a greater loss of life with these events as well, as there’s also a more vulnerable housing stock affecting fatalities in this area, so it’s really important that we focus on a better understanding of tornadoes in this area.
EMILY: The researchers plan to study more storm events in the future with UAS’s. They describe the scope of this project and what they’ve learned so far.
MELISSA: So this project is a two year project and what we hope to accomplish is to be able to better characterize damage to vegetation. So have a better understanding of the potential of storm intensity. And really by using additional datasets such as radar or other observational datasets we would like to get a better understanding of what’s going on in terms of storm dynamics, ’cause there’s still a lot of discoveries that are yet to be made in terms of understanding damage, an understanding how landcover can influence damage patterns.
MIKE: We have learned that using a fixed wing UAS is essential for being able to cover a lot of ground very quickly in a timely manner after an event occurs compared to a standard quadcopter, UAS technology because we can get a lot more battery time. A lot more flight time out of it, and which is important because people tend to go out and clean up quickly after an event. So we want to be able to cover as much area as we can for our research.
EMILY: Thank you for tuning into Pod-Sized Science and thank you Melissa and Mike! To learn more about this project and other research at the lab, stop by nssl.noaa.gov and follow us on social media.
Researchers at the NOAA National Severe Storms Lab are using Uncrewed Aircraft Systems (UAS) to study storm damage in rural areas. In March, researchers captured aerial photos and video of storm damage from hard-to-reach locations using UAS, or drones. Learn about the multispectral camera on the UAS, and how the camera provides imagery showing high-resolution damage to vegetation.
Scientists hope images from the research drones will further improve our understanding of tornadoes, provide more information to NOAA National Weather Service forecasters for storm event ratings, and help improve the accuracy of the NOAA Storm Events Database. NWS forecasters reference the database to help predict future outbreaks; researchers use the database to help create new NWS forecast tools.
*Authored by Researchers Jooho Kim, Patrick Campbell, and Communications Specialist Emily Jeffries. For more information on this project, or to collaborate with the researchers involved, please email email@example.com*
Severe weather hazards such as hail, high wind speeds, and tornadoes, can impact essential community infrastructure. Researchers are studying the impacts of severe weather threats on a range of community assets, including critical infrastructures like hospitals, fire stations, and schools, to improve the resiliency of communities.
Researchers from the Cooperative Institute for Mesoscale Meteorological Studies at the University of Oklahoma, in collaboration with the NOAA National Severe Storms Laboratory, say these studies could support National Weather Service forecasters, emergency managers, and the public by providing advance notice of the physical risks from severe weather threats.
Researchers recognized a need to effectively help communities predict damage to buildings and other physical impacts of severe weather threats. Prediction of such damage could improve communities’ abilities to manage their preparedness, response, and recovery phases for emergency or disaster management.
Outcomes of this research study may also enhance existing operation systems by providing real-time damage estimates for critical infrastructure and building properties.
The Tools and Results
For an effective community risk assessment from severe weather threats, two components are crucial — accurate weather information and a geodatabase of community assets. This research utilized experimental Probabilistic Hazard Information (PHI) as a weather information source.
PHI for severe weather threats can be represented by continuously updating probabilistic hazard grids, which map the likelihood of an hazard occurring. PHI can be tailored and adapted to meet a variety of needs to effectively predict and communicate the risk of hazardous weather to forecasters, emergency agencies, and communities.
In order to leverage PHI in the assessment of community risk, researchers recognized a need to develop a geodatabase — a database designed to store and query geographic information — for community assets. This information includes multiple building types, like residential, commercial, and industrial, and critical infrastructure.
Using the Enhanced Fujita Scale (EF-Scale), researchers are testing the possibility of estimating the Degree of Damage (DoD) to individual buildings. The EF-Scale is the same standard used by the National Weather Service to rate tornado damage.
Researchers can successfully compute DoD given that a Damage Indicator (DI) and wind speed range are provided for buildings. Currently, the EF-Scale has 28 DIs corresponding to a wide range of building types, such as one- or two-family residences, manufactured homes, and apartments.
While building footprint data, building location, area, perimeter, and sometimes height is often provided by state and city offices, it can take tremendous time to manually categorize millions of different buildings into the 28 types of DI.
In order to identify DI types for large areas, researchers are using multiple cutting-edge machine-learning algorithms, making use of building footprint, city zoning ordinance data, images, and other publicly available data. As a geodatabase of DI information is built, researchers will be able to combine it with PHI data to produce detailed estimates of expected building damage and the likelihood of their occurrence.
If advance warning of damage to structures could be fully developed and incorporated into NWS operations, researchers expect it could become a valuable part of the comprehensive severe weather hazard information that is envisioned by the FACETs program.
Emergency managers could use information about at-risk community assets, including critical infrastructure, to maximize their mitigation and response efforts, and television broadcasters could use estimated damage information to focus their message. This enhanced hazard information can be used by the general public to make better decisions to protect themselves when under threat from severe weather.
Richard “Dick” Doviak, a renowned radar engineer and professor, passed away recently.
Research conducted by Doviak and others at the NOAA National Severe Storms Laboratory helped convince the NOAA National Weather Service of Doppler radar’s crucial use as a forecasting tool. Their work led to the installation of a network of NEXRAD Doppler radars across the United States in the early 1990s and still in use today. This Doppler technology ultimately revolutionized forecasters’ abilities to understand and track severe weather, saving lives and property.
Doviak’s list of accomplishments is long. He managed several research projects, was a Fellow with both the Institute of Electrical and Electronics Engineers and the American Meteorological Society, and authored many articles published in more than 20 journals spanning interests in geosciences, engineering, physics, and meteorology. He also won a gold medal in the Oklahoma Senior Olympics for bicycling.
“Dick [Doviak] was always warm, generous, and friendly, the kind of person that we all enjoy having chance encounters with,” said Jack Kain, NOAA NSSL Director. “That part of his legacy will live on in all of us, and of course his contributions to science, engineering, and mentoring are legendary – at the lab, OU, and elsewhere. His work forms a large part of the foundation of NSSL, and indeed the national infrastructure, with the radar technology that he developed serving to protect lives and property across the nation every day. At NSSL we are all honored to have known Dick and worked with him.”
Doviak received an invitation to join NOAA NSSL and lead the Doppler Radar Project in December 1971, almost 50 years ago.
“There were two priorities. One was using Doppler radar to study the dynamics of severe thunderstorms,” Doviak said during a “Radar Roundtable.” “The other priority was building a real-time display. I think NSSL had the very first real-time Doppler velocity display in 1972, as a matter of fact.”
Doviak led the radar project until 1987. NSSL spent nearly 30 years researching and developing Doppler radar technology.
However, Doviak considered polarimetric Doppler weather radar the most significant advancement in his field during his time at NOAA. Dual-polarization technology added to NEXRAD about 10 years ago provides National Weather Service forecasters a measure of the size and shape of precipitation and objects, like hail.
These early collaborations and discoveries impacted Doviak’s work and the advice he provided to students throughout his career.
Doviak transitioned as lead on the Doppler Radar Project and became a senior research scientist at NSSL, as well as an affiliate professor with the University of Oklahoma (OU) School of Meteorology and the College of Engineering. One of the main reasons he chose to work at OU was the opportunity to teach and mentor students. Once he arrived, he was instrumental in developing the OU meteorology course on Doppler Radar with fellow NSSL Senior Scientist Dusan Zrnic.
“One thing about Dick is that he was always available to help mentor students,” said Kurt Hondl, NSSL deputy director. “Back when I was a grad student, Dick was always willing to review and discuss my thesis, even though he wasn’t on my Master’s committee.”
Doviak and Zrnic co-authored the book, “Doppler Radar and Weather Observations,” based on their OU course. The book is considered a necessary meteorology text by many in the weather community.
“For a young grad student, it was such a seemingly unreal experience to be discussing my results with Dick and Dusan [Zrnic] who had literally written the book on Doppler Weather Radar Observations. Of all my textbooks over the years, it is the one that I have cracked open time and time again throughout my career,” Hondl said.
Doviak enjoyed sharing his passion for research with those around him. He wanted to see everyone succeed. Researcher Sebastian Torres recalls one of his first projects as a Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) researcher. In the late 1990s, Torres was working with Doviak to measure radiation patterns of the local KOUN Weather Surveillance Radar antenna. This research would serve as a proof-of-concept for the eventual upgrade of the entire NEXRAD network in the early 2010s.
“As a very inexperienced researcher, Dick caringly held my hand through complex data analysis processes and, with his characteristic humbleness, mentored me on the production of figures for formal publications,” Torres said. “Throughout this process, Dick taught me a very valuable lesson that has served me well in my scientific career: pay attention to every detail and leave no stone unturned. You never know where the key that opens the next big discovery will be.”
Sebastian said he will always remember Doviak’s inspiring enthusiasm and contagious joy for inquiry and discovery.
Doviak practiced the art of being good at your work, enjoying life, and being kind to everyone. Mass of Christian Burial will be celebrated and live-streamed from St. Thomas More University Parish, Norman, Oklahoma, at 11:00 am CT on March 23, 2021. A celebration of life is planned once everything is safer. Donations are encouraged to the American Cancer Society.
Richard “Dick” Doviak’s Awards
1980 NOAA Outstanding Scientific Paper
1981 NASA Group Achievement Award for distinguished scientific contributions to the definition, planning, and execution of the Doppler Lidar 1981 Flight Experiment.
1982 NOAA Outstanding Scientific Paper
1988 IEEE Fellow
1988 IEEE Harry Diamon Memorial Award for outstanding technical contributions in the field of government services in any country.
1993 IEEE Geoscience and Remote Sensing Society Outstanding Service Award
1999 AMS Fellow
2014 NOAA Distinguished Career Award “for development of breakthrough radar methods that have greatly enhanced operational severe weather detection and advanced meteorological research.”
2016 Remote Sensing Prize for “fundamental contributions to weather radar science and technology, with applications to observations of severe storms and tropospheric winds.”