How NSSL Research Provides Real-Time Precipitation Estimations and Flash Flood Prediction for High-Impact Events

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.

A gif loop of radar reflectivity over middle Tennessee showing the increase flash flood levels.
A Multi-Radar Multi-Sensor reflectivity loop covering the duration of the western Middle Tennessee flash flood event ton Aug. 21. (Gif provided by Randy Bowers.)

NWS forecasters can use a series of products to diagnose an ongoing weather event to determine what might be happening. Researchers at the NOAA National Severe Storms Laboratory (NSSL) and the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) at the University of Oklahoma developed two systems to help with forecaster analysis and warning decision making — the Multi-Radar Multi-Sensor (MRMS) system and the Flooded Locations and Simulated Hydrographs (FLASH) system.

The Systems

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.

A screenshot of MRMS dual-polarization QPE data.
Analysis of MRMS dual-polarization QPE ending 1200 UTC on Aug. 21 (left column) and Aug. 22 (right column) with bubble plots (top row) and scatterplots with statistics (bottom row) using hourly and daily gauge observations. (Screenshot provided.)

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.

Three separate screenshots of the FLASH model products showing QPE and flooding.
Analysis of the following FLASH products at 1300 UTC 21 August 2021: maximum QPE-to-FFG ratio (left), maximum QPE average recurrence interval (center), and CREST maximum unit streamflow (right). (Screenshot provided.)

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.
A graphic of the CREST maximum unit streamflow from the FLASH system. The graphic shows flood waters maxing out over time.
CREST maximum unit streamflow from the FLASH system from 0600–2100 UTC 21 August 2021. (Graphic provided.)

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.

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Researchers study costliest severe thunderstorm event

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.

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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.



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 and follow us on social media.


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New video: Studying tornado damage with Uncrewed Aircraft Systems

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.

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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*

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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WoFS in the virtual NOAA Hazardous Weather Testbed


The first week of April concluded the end of the 2021 Warn-on-Forecast Testbed Experiment as part of the NOAA Hazardous Weather Testbed. In this experiment, a total of 16 forecasters from nine southern regions National Weather Service Forecast Offices (WFOs), the NOAA NWS Storm Prediction Center (SPC), and the NOAA NWS Weather Prediction Center (WPC) came together over four weeks to explore the use of Warn-on-Forecast System (WoFS) guidance in the watch-to-warning time frame.

Like many other scientific activities, this experiment was delayed and then moved virtually due to the ongoing COVID-19 pandemic. Despite the many challenges this unique situation presented, our research team is pleased to report the experiment was very successful. This success is attributable to the significant efforts of numerous University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) and NOAA National Severe Storms Laboratory scientists, as well as participants and collaborators in the NWS

Together, Pat Skinner (with OU CIMMS/NSSL), Patrick Burke (with NSSL), Burkely Gallo (with OU CIMMS/SPC), and I (Katie Wilson with OU CIMMS/NSSL) designed and executed this experiment to examine how forecasters envision WoFS guidance fitting into both their existing and visionary forecast processes, and to explore the ways that WoFS guidance can be used most effectively given national center and local office forecasting responsibilities.

A screenshot showing an experimental National Weather Service severe weather graphic. The black graphic shows storm areas highlighted.
An example of an experimental decision support graphic influenced by WoFS and constructed during one of the 2021 WoFS experiment case studies. (Screenshot)

A Collaborative Undertaking

During the experiment, Skinner delivered an overview presentation to build familiarity with WoFS guidance prior to participants’ completion of two case studies. These cases formed the first of two major activities during the experiment, which was for participants to immerse themselves in simulated real-time events and use WoFS guidance to make forecast and communication decisions. 

To prepare for the case study activity, each week Burke provided participants with a hands-on AWIPS-2 demo. Furthermore, his work to design AWIPS-2 perspectives and procedures, which was conducted jointly with Gallo, enabled a simulation setup that was more familiar to participants, especially to those from national centers who do not use AWIPS-2 in the way local office forecasters do.

A screenshot of the experimental WoFS guidance in the AWIPS-2 viewer.
For the first time, experimental WoFS guidance was viewable in the AWIPS-2 interface.

The preparation of these case studies was a major task undertaken by Jonny Madden (OU CIMMS/NSSL), Justin Monroe (OU CIMMS/NSSL), Jorge Guerra (OU CIMMS/NSSL), and Dale Morris (OU CIMMS/NWS Warning Decision Training Division).

The case studies presented two notable firsts:

  • Running AWIPS-2 in-the-cloud such that participants could complete the case studies from their own homes, and;
  • Presenting WoFS guidance in AWIPS-2, including the development of a tool to visualize paintball plots. 

Madden, Monroe, Guerra, and Morris worked together to accomplish numerous tasks, including: aggregating and processing a full suite of observational and model datasets for both cases, setting up the WES- 2 Bridge and AWIPS-2 interfaces, and collaborating with federal partners to get datasets onto the cloud framework. Much of what was accomplished for the case study portion of this experiment has laid the AWIPS-2 in-the-cloud groundwork for future virtual experiments. 

The second major activity during the experiment was focus groups. Together, Wilson and Gallo led three semi-structured discussions each week to explore forecasters’ visions for how WoFS will impact the current and future forecast process. Additionally, the presence of both national center and local office forecasters meant that much was learned about each others’ workflows, how one another makes decisions, and where there is an opportunity to strengthen collaboration. In a post-experiment questionnaire, participants rated the focus groups as a highly effective activity for sharing thoughts and ideas, and was the most enjoyed activity of the week.

A graphic showing how the team used Google Meet Jam Board to spur discussion. The graphic has two circles, with forecast offices in one area and SPC and WPC in another.
The team used the Google Meet jam board to spur discussion in focus groups.

In addition to the efforts of scientists at OU CIMMS and NOAA NSSL, we were grateful for input from our collaborators at NWS Southern Region, including Chad Gravelle (SR HQ), Todd Lindley (OUN SOO), Stephen Bieda (AMA SOO), and Randy Bowers (OUN). Gravelle and Lindleyalso joined the experiment for multiple weeks, and Randy created two excellent weather briefing videos to prepare forecasters for the case studies. A big thank you also goes to our pilot participants, Laren Reynolds (El Paso, Texas) and Joseph Merchant (Lubbock, Texas), for volunteering their time to fulfill an important support role throughout the whole experiment. This support role emerged following findings from the pilot week, and made for a much stronger experiment.

We are extremely appreciative to the 16 NWS forecasters who participated in this experiment. We realize the stressful conditions many people continue to live and work with, and have done so over the past year. We also realize the disappointment from not being able to attend this experiment in the NOAA Hazardous Weather Testbed, in Norman, Oklahoma, as originally planned. However, participants showed up to our virtual experiment with enthusiasm and made meaningful contributions to the experiment. We collected an enormous amount of data, and we can’t wait to analyze it and share what was learned.

For questions on this or other WoFS-related research please contact WoFS Program Lead, Patrick Burke,

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Leader, engineer, and innovator in Doppler radar passes away

Richard “Dick” Doviak

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.”

His career

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.

A grayscale WSR-88 radar display from 1979. (NOAA)

His heart

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.

Staff photo of NSSL employees in 2012
Dick Doviak, top left laying on the concrete barrier, at an NOAA NSSL staff photo in 2012. (Photo by James Murnan/NOAA)

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.”
Dick Doviak receiving an award from former NSSL Director Steve Koch. (Photo by James Murnan/NSSL)


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Researchers developing experimental winter forecasting tools

Last month, millions of people across the United States were impacted by several inches to feet of snow and the coldest temperatures in decades. Thousands lost power and water, and travel was treacherous as multi-vehicle pile-ups forced interstate shutdowns.

To help lessen these impacts, researchers at the Cooperative Institute for Mesoscale Meteorological Studies at the University of Oklahoma and the NOAA National Severe Storms Laboratory in Norman, Oklahoma, are working to improve current winter road tools. They are focused on predicting and monitoring a variety of winter hazards and the potential impacts of such weather.

“Hazards may include accumulating snow or ice on roadways, slushy roadways, and others,” said Shawn Handler, a researcher at OU CIMMS. His work supports NOAA NSSL. “It’s possible a winter storm may pose a greater threat to one infrastructure more than others, like maybe travel or power outages.”

A mailbox topped with snow.
A snow-covered home and mailbox in Oklahoma. Winter weather ravaged parts of the United States in February, leaving many without power and water. (Photo by James Murnan/ NOAA)

Handler, with a team of other researchers, are developing two experimental products: the Experimental Road Hazards Product and Probability of Subfreezing Road Temperatures (ProbSR) product. These are expected to be integrated into the National Weather Services’ Winter Storm Severity Index (WSSI).

The Experimental Road Hazards Product will provide information on specific hazardous road threats, like ice.
The experimental Probability of Subfreezing Road Temperatures (ProbSR) product uses current and immediately available information to predict if road temperatures are subfreezing.
The Winter Storm Severity Index (WSSI) is an operational product designed to provide impacts-based decision support to NWS forecasters in order to allow them to provide more target messaging to the general public and other government stakeholders. This product is developed and supported by NCEP/Weather Prediction Center.

These tools can be used together to increase the amount of winter-storm information available to National Weather Service forecasters and emergency officials.

Integrating the tools

Aimed to improve winter-weather advisories, the WSSI ingests several different sources of information but none of those sources provide information on the roads. Researchers want to pair WSSI with the ProbSR product, allowing forecasters to have greater confidence about the potential for winter-weather to result in treacherous driving conditions.

“It’s possible a winter storm may pose a greater threat to a certain infrastructure compared to others,” Handler said. “For example, Oklahoma City experienced an ice storm in October and impacts to the power grid outweighed the impacts to road travel, as hundreds of thousands of people lost power for an extended period of time.”

Integrating ProbSR and the road-hazard tool into the WSSI will allow ProbSR to be tested and evaluated as a forecasting tool next winter in a testbed environment.

Hazardous road threats are determined by pairing the road temperature tools of ProbSR with another model providing precipitation classification at the surface, like snow and rain, to create the Experimental Road Hazards Product.

“We are focused on what hazards or threats may be present,” said Handler. “For example, it could be snowing, but if ProbSR has low probabilities, the expected threats to travel may not be as high – such as a wet roadway as the snow is not expected to accumulate. Whereas, if it has been cold enough for a longer stretch of time – a higher ProbSR – and snowing, then accumulating snow would be the resulting hazard.”

Icicles hanging from the edge of a home roof.
Many states experienced the coldest weather in decades. Cold temperatures were accompanied by ice, snow and other winter precipitation. (Photo by James Murnan/NOAA)

Continuing research

Handler said tests with the products are successful, but the team is retraining ProbSR with more recent data from the High-Resolution Rapid Refresh model (HRRR), a high-resolution weather forecasting model used by the NWS. The HRRR updates forecasts hourly over the entire lower 48 United States at a resolution of less than two miles.

The Experimental Road Hazards Product is in the early stages of development. The team continues to investigate ways to improve it, including gathering more inputs, such as precipitation rate and wind speed.

“Precipitation rate will provide information on how fast precip – like snow, rain, ice – is falling, whereas wind speed could be included as a way to assess visibility threats,” he said. “We also want to include more threats utilizing these new inputs, such as reduced visibility from blowing snow.”

The researchers’ next steps regarding the Road Hazards product are to add some of the features described above, and to properly verify the classifications made from the algorithm using traffic camera observations.

Products will be tested by researchers and forecasters in the winter of 2022 in a joint testbed with the NOAA Weather Prediction Center.

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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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New data product offers a more complete picture of storms

Researchers are excited to announce the release of a new, extensive data product that combines a multitude of data sources to help researchers, forecasters, and weather enthusiasts.

The Multi-Year Reanalysis of Remotely Sensed Storms Project, or MYRORSS, combines individual radar data with other sources, like weather models and lightning data, for a more complete picture of storms. MYRORSS data is high-resolution, three-dimensional, and updates more rapidly, unlike some two-dimensional data sets. Created by researchers and students at the Cooperative Institute for Mesoscale Meteorological Studies and NOAA National Severe Storms Laboratory, MYRORSS provides scientists the capability to create new computer programs for storm analysis and climatologies, or storm climatology studies.

“Most storm climatologies are based on reports,” said Kiel Ortega, a researcher at CIMMS supporting NSSL. “Such reports can be biased based on where people live. With MYRORSS data, we can get a better idea, for example, where hail occurs and with what frequency.”

In addition, scientists may use the data in machine learning and artificial intelligence experiments to learn more about specific components and characteristics of severe weather. One example would be how tornado-producing storms may look different from storms that do not produce tornadoes.

A screenshot of a map showing the path of several tornadoes across the southeastern United States.
This an accumulation of azimuthal shear, called rotation tracks, from a tornado outbreak on April 27, 2011. Researchers are using rotation track data in MYRORSS to identify rotating storms and to help with climatologies, like hail, as rotating storms can produce larger hail than non-rotating storms. (Screenshot provided by Kiel Ortega and Skylar Williams, OU CIMMS/NOAA NSSL)

CIMMS Researcher Vanna Chmielewski who is utilizing the product for her lightning research said the data combination in MYRORSS will make a big difference.

“A large time commitment to many studies is quality controlling the data and aligning different data sources,” Chmielewski said. “For a large statistical or machine learning project, it is also important to have uniformity in how that process is done, otherwise there could be some bias which shows up in the statistical model purely due to changes in how the process was done.

“What has been done by the MYRORSS team is huge in building that base dataset over a large area and time period. This database really has infinite potential for future studies,” she said.

Several meteorological studies have sample sizes that are small, such as data from one field experiment or one severe weather season. Larger studies have often been limited in which data could be included due to time restrictions associated with quality control and other initial steps, Chmielewski explained.

“Having a dataset like this can really help improve science by allowing those larger studies to be more easily done,” she said. 

Chmielewski plans to use MYRORSS data to study “bolts from the blue,” cloud-to-ground lightning flashes typically originating from the backside of a thunderstorm cloud. Such flashes can travel a large distance in clear air away from the storm cloud, angling down and striking the ground.  She also intends to research answers to questions like, “how far can a flash of lightning strike from a storm?” with a larger data sample than previously available. 

The MYRORSS database will allow researchers to tackle many atmospheric questions from over many years and across the country. 

For Chmielewski, this presents new opportunities. 

“Are bolt from the blue flashes more common in some parts of the country than others? Has that changed with time? Can we find reasons why some storms do and others don’t? These sorts of questions are really hard to answer without a base dataset like MYRORSS, and the MYRORSS group has done a great job bringing these storm and environmental variables together into a single, quality-controlled dataset,” she said.

MYRORSS began in 2012 and utilizes the Multi-Radar Multi-Sensor framework, along with many other data sources. Students made this project possible as they combed through terabytes of data. Guided by researchers from CIMMS at the University of Oklahoma and NSSL, students processed the data required for MYRORSS while conducting extensive quality control. The NOAA National Centers for Environmental Information also assisted with some of the data processing.

“MYRORSS was my first experience with research as a student and helped me determine my path in the field,” said Skylar Williams, CIMMS researcher supporting NSSL. 

Williams finished her master’s degree and was hired full-time at CIMMS, continuing her work on MYRORSS. She is excited to share the product with others after years of work.

“The processing was time-consuming— even with more than 15 machines —  and because of the extensive quality control, anytime we found bad data we would actually have to go back and reprocess that entire day,” Williams said. “When dealing with 14 years of data, reprocessing that added up. However, reprocessing allowed us to create a great product with good data for anyone to use.”

Learn more about MYRORSS and view the data here.

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