New Release: Pod-Sized Science Podcast

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.

Transcript

[INTRO MUSIC]

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.

If you haven’t checked out the Bite-Sized Science video yet, pause this and check that out first.

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.

[OUTRO MUSIC]

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Researchers studying impacts of severe weather threats on community assets, including critical infrastructure

*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 nssl.outreach@noaa.gov*

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.

A graphic of the research framework.
Research framework (Graphic provided)

In recent years, a prototype software system that allows forecasters to generate PHI was developed under the Forecasting A Continuum of Environmental Threats (FACETs) program at NSSL and CIMMS.

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.

A graphic of Probabilistic Hazard Information (PHI) showing the forecasted risk of a tornado hazard.
Probabilistic Hazard Information (PHI) showing the forecasted risk of a tornado hazard. (Graphic provided)

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.

A graphic of machine-learning geodatabase creation.
Machine-learning based geodatabase creation using multiple geodata, like building footprint, and city zoning. (Graphic provided)

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.

Degree of Damage on multiple types of buildings (commercial, industrial and residential) in the Oklahoma City area during a simulated severe weather event. The graphic portrays a system test using a hypothetical, simulated event to demonstrate the results from the proposed model. (Graphic Provided)

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.

A graphic of DI types and areas.
Potential Degree of Damage (DoD) estimates for individual buildings that could be derived from PHI data and building information in geodatabase.(Graphic provided)

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.

Degree of Damage on fire stations in the Oklahoma City area during a simulated severe weather event. Also shown are the probability of DoD. (Graphic Provided)

Potential Impact

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.

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New rating system charts a path to improved tornado forecasts

All tornadoes — whether small or large — originate from thunderstorms, but not all thunderstorms are the same. Different environments and situations create forecasting challenges. For instance, nighttime twisters, summer tornadoes and smaller events can be tougher to forecast.

Researchers wanted to quantify how much tougher, and have published a new method of classifying tornado environments according to their forecast difficulty.

In a new paper published online in the Bulletin of the American Meteorological Society, University of Washington scientist Alexandria Anderson-Frey, and Harold Brooks from the NOAA National Severe Storms Laboratory describe a new way to rate and possibly improve tornado warnings.

“With this research, we’re trying to find ways to truly level the field related to the difficulty of the forecast situation,” said Brooks. “This will help us identify areas for research, as well as better understand the long-term historical statistics.”

 The paper presents a new method to rate the skill of a tornado warning based on the difficulty of the environment. It then evaluates thousands of tornadoes and associated warnings over the continental United States between 2003 and 2017.

The NOAA-funded study finds that nighttime tornadoes have a lower probability of detection and a higher false-alarm rate than the environmental conditions would suggest. Summertime tornadoes, occurring in June, July or August, also are more likely to evade warning.

“The forecasting community is not just looking at the big, photogenic situations that will crop up in the Great Plains,” said Anderson-Frey, the lead author. “We’re looking at tornadoes in regions where vulnerability is high, including in regions that don’t normally get tornadoes, where by definition the vulnerability is high.”

The technique could be applied to forecasts of other types of weather as well.

This research began while Anderson-Frey was a postdoctoral researcher at the Cooperative Institute for Mesoscale Meteorological Studies, a partnership between the University of Oklahoma and NOAA.

This story was adapted from a  University of Washington news release.

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NSSL stages equipment near Hurricane Harvey

NSSL Researcher Sean Waugh with the mobile mesonet. (Photo provided)

NOAA National Severe Storms Laboratory Researcher Sean Waugh will collect weather data in the path of Hurricane Harvey Friday to record how the landfalling hurricane changes as it develops.

The first major hurricane forecast to make landfall in the Gulf Coast in 12 years provides an opportunity to study its development and any potential development of tornadoes.

“While tornadoes are relatively rare in environments associated with landfalling hurricanes, if they occur they can have large impacts,” Waugh said.

Waugh will use a truck with roof mounted instruments called a mobile mesonet to record observations of Hurricane Harvey for an extended period of time. The instruments and weather balloons will record rain, wind and temperature. He will work with scientists from The University of Oklahoma College of Atmospheric and Geographic Sciences. The team is utilizing the university’s Cooperative Institute for Mesoscale Meteorological Studies SMART radar truck.

Researchers will monitor how the hurricane’s structure changes during landfall as well as temperature changes and wind on the surface. Scientists will test a  new instrument developed at NSSL that measures rain size and distribution to help with flood forecasts. Information gathered will be shared with National Weather Service forecasters.

NOAA NSSL and partners are studying the development of tornadoes in the Southeast U.S. in order to improve their prediction through  VORTEX-Southeast.

For more information about Hurricane Harvey and the current forecast: http://www.nhc.noaa.gov/#harvey.

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International collaboration benefits US, European forecasters

NOAA National Severe Storms Laboratory Researcher Adam Clark at the European Severe Storms Laboratory Testbed this summer.

Weather doesn’t stop at borders. Nowhere is this more clear than in Europe, where two researchers working at the NOAA National Severe Storms Laboratory went shoulder to shoulder with researchers in the European Severe Storms Laboratory Testbed this summer. The goal was to collaborate on forecast products and learn how NSSL technologies are used abroad.

“As scientists and meteorologists, we need to continue to talk because that’s how true knowledge transfer occurs,” said Darrel Kingfield, University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies researcher working at NSSL. “ESSL researchers came to work with us in the NOAA Hazardous Weather Testbed a couple of years ago and this year we went to them.”

Darrel Kingfield presenting at the European Severe Storms Laboratory Testbed this summer.

During its sixth year, the ESSL Testbed program evaluated forecasts for high-impact weather. Like the HWT, the ESSL testbed serves as a forum to stimulate interaction between product developers and operational forecasters from throughout Europe. Also, lectures from several local and international experts help testbed participants enhance their knowledge and skills.

Different geography, systems

Kingfield and NSSL Research Scientist Adam Clark each spent a full week at ESSL’s testbed. What struck them was the difference in geography between the United States and Europe. Clark said ingredients needed for severe weather come together much differently in Europe than the U.S.

“You have the Mediterranean Sea and the Alps and that affects much of their weather,” Clark said.

Adam Clark working in the European Severe Storms Laboratory Testbed.

Along with geographical differences, Clark and Kingfield learned about the different weather prediction and monitoring systems operated by each European country. A variety of forecasting tools and methods are used throughout Europe, from government operated to privatized systems. This results in data, forecasting and verification inconsistencies.

“For example, after a tornado occurs in the U.S., officials observe and record where it occurred and how severe it was,” Kingfield explained. “Europeans rarely go out and assess tornado damage after a storm. Those surveys are reserved for most damaging events.”

As a result, Europe’s tornado database is not nearly as complete as the United States.

Sharing tools and techniques
While in the testbed, Kingfield and Clark gazed upon a few familiar products.

“The German Weather Service is using a lot of the same techniques developed at NSSL to interpret radar data,” Kingfield said. Some European meteorologists use several products developed in the U.S. by NSSL and OU CIMMS researchers. For instance, one technique allows them to use radar data to visualize the possible track of a tornado based on the storm’s rotation.

Collaboration is an important tool for forecasters and researchers. Participation in ESSL’s testbed allows researchers like Kingfield and Clark to share new technologies, experience new techniques and learn new systems. Opportunities like this allow researchers to collaborate on new products and technology, ultimately leading to better forecasts and warnings for the American public.

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NSSL announces passing of aircraft program pioneer Jean “J.T.” Lee

The National Severe Storms Laboratory is saddened to announce the passing of Jean “J.T.” Lee, a pioneer who managed NSSL’s aircraft program when it began, leading to better weather-related safety.

Lee was a scientists at NSSL for 42 years, discovering and documenting correlations between weather radar and turbulence hazards to aircraft. This work began at the Weather Bureau’s National Severe Storms Project based in Kansas City, Missouri, then was part of the team who moved to Norman to start the National Severe Storms Laboratory in the early 1960s.

During 2004, Lee was interviewed about his job and why he enjoyed working at NSSL.

“I found it fascinating,” he said. “The people we worked with were devoted and many times we weren’t 8 to 5 but 8 until whenever the situation stopped and that would be midnight sometimes,” he said. “There was real camaraderie.”

Lee’s team produced radar criteria for avoiding storms by aircraft. He took part in Project Rough Rider, flying aircraft into thunderstorms to measure turbulence to compare with measurements of rain intensity from the WSR-57 radar. The project led to improved commercial airline safety guidelines.

“The Air Force at that time was beginning to have problems with their jet aircraft,” Lee said during an interview about NSSL’s 40th anniversary. “They were interested in what was the weather above thunderstorms and how high did thunderstorms extend. Our penetration work was around 30,000 feet with the aircraft and we were the first ones to do supersonic penetrations. I feel the greatest accomplishment here was we were able to provide the design of safety procedures for the safety of flight.”

His work contributed to several Federal Aviation Administration guidelines, including a memorandum to the FAA Wind Shear Program Office in 1976 suggesting the usage of anemometers to provide instant reports on winds near airports.

Lee wrote more than 50 research articles in journals on aviation radar interpretation, aircraft turbulence and wind shear, and Doppler radar studies. He received several awards, including the Losey Atmospheric Sciences award in 1981 for his invaluable contributions to flying safety. The award was one of seven presented by the American Institute of Aeronautics and Astronautics. He was also honored in 1982 with the NASA Group Achievement award for MSFC Doppler Lidar 1981 flight experiments.

Lee, 95, passed away June 28, 2017.

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Researchers evaluating lightning data in Hazardous Weather Testbed

For the first time ever, lightning data from a weather satellite is available and being evaluated in the NOAA Hazardous Weather Testbed.

Forecasters, researchers, product developers and broadcast journalists are analyzing recently available experimental data from an instrument on GOES-16, the newly launched NOAA satellite as part of the HWT Experimental Warning Program.

GOES-16, launched by NASA last November, scans the skies five times faster than NOAA’s current geostationary weather satellites and provides images at four times greater resolution.

The higher resolution allows forecasters to see more details in storm systems, particularly during periods of rapid strengthening or weakening. GOES-16 is also the first to carry a lightning detector in geostationary orbit.

The Geostationary Lightning Mapper observes total lightning, meaning in-cloud and cloud-to-ground lightning. GLM can help increase the accuracy of forecasts and warning times when combined with other forecaster tools.

The HWT EWP GOES-16 experiment just wrapped up its second of four weeks. Kristin Calhoun, CIMMS research scientist working at NSSL, said this is the first time forecasters have seen GLM data from GOES-16.

“We are here to test it and to contribute anything from ideas for data integration to training needs,” Calhoun said. “We want people to identify as many training gaps as possible.”

Bill Line, a meteorologist with the NOAA National Weather Service Pueblo forecast office, said if people like him learn to use GLM’s data, it will better his forecasts.

“These are new tools and we want to make sure forecasters are ready to use them,” he said. “There are many combinations of data and probabilities they haven’t looked at before.”

David Stark, a meteorologist with the NOAA National Weather Service New York forecast office, in the Hazardous Weather Testbed working with GLM data. (Photo by James Murnan/NOAA)

That is the purpose of the HWT – the facility allows end users to test new, experimental products before they are released to the NWS or other NOAA entities and partners.

“We’ve held similar experiments in the past but with proxy data,” Calhoun said. “This is the first year we are able to use real data. Ideally we will continue experiments like this, using real GOES-16 data, for years to come.”

David Stark, a meteorologist with the NWS New York forecast office, participated in the first week’s experiment. He described the experience as outstanding.

“Testing out some new products and helping fine tune them so they just aren’t thrown into the NWS is great,” he said. “To be able to see these tools and see the new research, while acting like I’m issuing warnings in an area gives me a good idea and feel of what I could be doing with this in real life and how it would enhance our current products.”

Stark said the product helps better show storm formations, providing the forecaster with a better idea of when and where a storm may form.

“This would add more confidence to my forecasts and allow me to focus more on increasing warning on possible life-threatening storms,” Stark said.

The GOES-16 experiment continues in the NOAA Hazardous Weather Testbed through July 21.

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Weather Reports from Citizens Provide Research Input

Is it raining, snowing or hailing where you are? Tell us about it! Report the weather at your location any time on the mPING app to help NOAA researchers and forecasters, and join citizen scientists all around the world participating in Citizen Science Days through May 20.

Downloadable to your smartphone, mPING (Meteorological Phenomena Identification Near the Ground) is a free application that allows users to submit information about the weather to NOAA’s National Severe Storms Laboratory. Reports are immediately archived into a database at NSSL, and are displayed on a map accessible to anyone.

An mPING report. See more at https://mping.ou.edu/display/.%5B/caption%5D

To use the app, reporters select the type of weather that is occurring, and tap “submit.” The anonymous reports can be submitted as often as every 30 seconds.

The main goal of mPING is to provide more information to researchers and forecasters about the weather affecting the public. As a bonus, that very same public can see these reports! Weather radars cannot “see” at the ground, so mPING reports are used by the NOAA National Weather Service to fine-tune their forecasts.

Reports from mPING are also helping NOAA researchers in a variety of ways, including to develop new radar and forecasting technologies and techniques, said Kim Elmore, research scientist with The University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies working at the NOAA NSSL.

The first goal of mPING was verifying the type of precipitation detected by new radar technology. But, data from mPING proved useful for things not originally envisioned. While checking the accuracy of reports, Elmore and his team learned more about the scale and variability of freezing rain.

One mistake people could reasonably make is calling freezing rain just rain, since freezing rain is rain until it freezes onto something, Elmore explained. But with disagreeing observations, Elmore said they found only about 17 percent of the observations were rain; but about 60 percent of the observations that disagreed with freezing rain were really ice pellets.

“What that tells us is people can clearly discriminate between freezing rain and rain and freezing rain and ice pellets, since they all agree on what ice pellets are,” Elmore said. This shows that people know what they are seeing and mPING reports are mostly accurate.

Digging a little deeper, we know that freezing rain can exist in only a very narrow set of environmental conditions. If the air near the ground gets only a little colder, the raindrops will freeze before they reach the surface and be reported as ice pellets. If conditions warm up only a degree or two near the ground, the temperature is no longer below freezing and there can be no freezing rain.

Reports from mPING also helped the team learn about one of the newest numerical weather prediction models called the Rapid Update model, or RAP, which is used for short term weather prediction. Data from mPING showed the old version of RAP did not properly identify ice pellets. Once the model developers learned about this, they immediately made changes to better forecast ice pellets.

For one winter season, NOAA’s Earth System Research Lab and the

 operated the old version (but with the ice pellet fix) and new RAP models at the same time. This was a perfect perfect opportunity to see how much better the new RAP system handled ice pellets. To test this, mPING reports were compared to RAP model forecasts.

“It turns out, ice pellets are reported far more often than the model forecasts them,” he said. “We reported on the fact the new version of RAP helps some — it is a small but statistically significant improvement.”

The mPING app was developed through a partnership between NSSL, the University of Oklahoma and the Cooperative Institute for Mesoscale Meteorological Studies and was included in Scientific American’s list of 8 Apps That Turn Citizens into Scientists. For more information on the application, or to watch a short video about it, visit http://mping.nssl.noaa.gov/.

Scientists will continue to look for new ways to use the mPING data in their research, Elmore said. So keep those reports coming!

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Award winner to present NOAA Hazardous Weather Testbed work on improving severe weather forecasts

NSSL research meteorologist Adam Clark will present his work on improving severe weather forecasts during NOAA Science Days in June.

Clark, a Presidential Early Career Award for Scientists and Engineers 2014 winner, is presenting on the 2016 Spring Forecasting Experiment, which included a new framework for evaluating  convection-allowing models known as the Community Leveraged Unified Ensemble.

Improved technology has allowed faster and more detailed experimental weather forecast models to be used in the NOAA Hazardous Weather Testbed, leading  to model improvements and increased collaboration with other academic and government research agencies. One by product of the increased collaboration was that an increasing number of convection-allowing models, or CAMs, were ingested into the HWT. This was good, but also created a problem — the models designed by different agencies could not be compared because of too many independent variables.

“In 2016, we coordinated across all the different agencies that contributed to this experiment and we decided we’re going to have everyone abide by a set of rules— a bunch of criteria for how everyone will run their modeling systems,” Clark said. The rules included using the same area to run their models, the same resolution, and the same amount of detail to depict storms.

“That way we were able to control as many variables as we could so we could say more about why the different systems worked they way they did,” Clark said. CLUE collaborators then designed experiments testing different aspects of the model configurations.

Collaboration with forecasters is key to the CLUE experiments.

“What drives what we do is being able to work with the forecasters and get their take,” he said. “To design a system that is useful, you have to get feedback from the end user, which is forecasters.”

Last year was the first for CLUE to be used in the Spring Forecasting Experiment. With a goal to make forecasts better, Clark said there would be another this spring, with hopes of building on previous years.

Adam Clark
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Researchers Begin Second Year of Tornado Study in Southeastern United States

The second field observing campaign for the Verification of the Origins of Rotation in Tornadoes EXperiment-Southeast (VORTEX-SE) research program, coordinated by the National Severe Storms Laboratory, began March 8 and continues through May 8. A media day will be held at 10 a.m. CDT March 21 at the Signature Flight Support – Huntsville International Airport. Researchers from NSSL, Air Resources Laboratory, University of Alabama – Huntsville and other participants will discuss their operational plans and show some of the vehicles and instruments they are using, including the NOAA P-3 aircraft, mobile radars and research drones.

VORTEX-SE is a research program designed to understand how environmental factors characteristic of the southeastern United States affect the formation, intensity, structure and path of tornadoes in this region. VORTEX-SE will also determine the best methods for communicating forecast uncertainty to the public and evaluate public response related to these events.

This year’s field project will gather data to address two main research topics:

1. How cold air flowing out of a storm influences the development of tornadoes.

2. The role of terrain in tornado formation and how terrain influences wind, temperature and humidity in storm environments.

The ultimate purpose of this research is better forecasts and warnings for the public.
Erik Rasmussen, VORTEX-SE project manager, speaks during media day in 2016, kicking off the spring 2016 field research campaign. Media day for VORTEX-SE’s 2017 spring field research campaign is Tuesday, March 21.
Credit NOAA/Keli Pirtle.
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