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.
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.”
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.”
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.
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.
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.”
While scientists have learned a lot about our planet, questions remain about the lowest part of the atmosphere where we live. Researchers at the NOAA National Severe Storms Laboratory are looking for answers. Utilizing a series of instruments located in a mobile research unit, researchers are analyzing data gathered by those tools to improve severe weather forecasts.
The lowest mile or so of the atmosphere, known as the planetary boundary layer, is where several elements mix — from pollution to moisture — and how those elements mix and change during the day impact events in the atmosphere.
“Understanding the boundary layer can improve forecasts of severe weather, pollution, and several other things impacting the surface,” said Elizabeth Smith, NOAA National Severe Storms Laboratory researcher.
In an effort to improve understanding, weather researchers with the Cooperative Institute for Mesoscale Meteorological Studies at the University of Oklahoma and NOAA NSSL deployed two trailers decked out with a collection of weather instruments known as the Collaborative Lower Atmospheric Mobile Profiling System in fall 2020.
The CLAMPS platforms were deployed near a weather radar and a weather station in Oklahoma as well as the National Weather Service Forecast Office in Shreveport, Louisiana. The fast-updating, high-resolution data collected provides a more detailed view of the atmosphere and its processes for researchers to analyze.
In addition, the Shreveport NWS Office utilized CLAMPS to monitor both fog and fire weather forecasts during CLAMPS deployment in the area. That office also noted interesting and surprising boundary layer behavior when smoke from fires raging in the western part of the United States infiltrated into the area.
NWS Shreveport Science and Operations Officer Brad Bryant said output from CLAMPS was particularly useful for refining fog and fire weather forecasts because both sets of parameters are closely tied to specifics of the boundary layer CLAMPS is tuned to monitor.
CIMMS Researcher and Project Lead Jacob Carlin said the CLAMPS platform collects information more frequently than weather balloons launched daily by NWS forecasters across the nation. Although both methods gather similar information about the atmosphere, weather balloons are typically launched twice a day while CLAMPS gather data every couple of minutes.
More data can result in a more accurate representation of atmospheric processes at any moment. Data from the CLAMPS systems is combined with data from the NEXRAD radar, further enhancing researchers’ view of the atmosphere and what is happening.
This project is an extension of a recently published study that compared the twice-a-day balloon launch data with data from a nearby NEXRAD radar. Carlin’s team is going further, comparing CLAMPS minute data with a nearby NEXRAD radar and weather station.
“We want to understand how well this method performs with CLAMPS, because if it is able to reliably observe boundary layer height and development, then this method can improve forecasts and forecasting tools,” Carlin said.
With this new dataset, the researchers hope to learn more about how well NEXRAD radar can detect the boundary layer, expanding the capability of existing infrastructure at no additional cost.
Funding for this study was provided by the Cooperative Institute for Mesoscale Meteorological Studies’ Director’s Discretionary Research Fund, which supports the piloting of small-scale innovative and experimental projects.
From May 15 to June 15, researchers and students on the project deployed a wide-ranging suite of instruments to collect data on supercell thunderstorms across the Great Plains. The project’s main goal is to determine why some supercells create tornadoes and others don’t.
TORUS brought a unique mix of instruments chosen for the science questions being studied.
“I am more confident we will make scientific breakthroughs with this project than any other field project in my 16 years of field work,” said Mike Coniglio, a researcher at NOAA’s National Severe Storms Laboratory and a project lead.
Coniglio called gathering the amount of quality data in such a short time impressive.
“It’s not something I would expect we would be able to do, honestly,” he said. “I expected success but we exceeded our expectations.”
Researcher and project lead Erik Rasmussen echoed Coniglio’s sentiments on the project’s success.
“The atmosphere was cooperative,” said Rasmussen. “We have at least four or five cases that will provide the exact type of data we were looking for. Usually, storms are poorly observed, but in TORUS we have at least six storms we collected the sort of data we believe we need to answer our questions.”
Coniglio said TORUS’ success was not just because several tornadoes impacted on the Great Plains between May and June.
“An active pattern doesn’t guarantee you will get good data,” Coniglio said. “You still have to make good forecasts. We had a better sense of how to forecast these events than we did in the past because convection-allowing model guidance has improved greatly.”
Coniglio said in addition to improved forecasting, the TORUS team’s weather instruments exceeded expectations. UASes launched by the University of Colorado and University of Nebraska-Lincoln performed well. Each UAS had a successful launch, never crashed and received minimal damage from storms.
Rasmussen said the challenge now is combing through the mounds of preliminary data. TORUS acquired more data than expected.
Researchers are currently assembling quality controlled data — basic, quickly compiled data — before in-depth analysis begins over the next four to six years. Rasmussen said preliminary data appears to be intact, with no missing sets, and no instruments appeared to fail in the field.
“When we collect data, we may realize we have something of interest, but we don’t know until
the in-depth analysis,” Coniglio said, who oversaw the operation of a mobile LIght Detection And Ranging, or LiDAR, during the project.
A LiDAR utilizes laser light to detect items like small dust and aerosol particles. Coniglio’s LiDAR team collects observations utilizing the device to track how quickly all the dust, dirt and particles move in the atmosphere.
“The LiDAR saw interesting preliminary differences in airflow among storms and we don’t quite understand that signal yet or what it means, but it is something we will focus on,” he said.
TORUS will collect data again in 2020. Researchers expect to see overarching takeaways based on next year’s data collection.
“This year’s data will help us decide which strategies need to be refined, which tools performed well and if there are any crucial instruments that need to be added,” Rasmussen said.
Researcher Elizabeth Smith sits in the backseat of a white Chevy pick-up truck surrounded by computer equipment on a windy day in Oklahoma.
Smith works as a University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies researcher supporting NOAA’s National Severe Storms Laboratory. On this day she’s testing equipment after a recent deployment on TORUS, or Targeted Observation by Radars and UAS of Supercells. The project aims at understanding the relationships between severe thunderstorms and tornado formation.
In the back of the pick-up truck is a LiDAR system, which stands for LIght Detection And Ranging. Unlike radar systems, LiDAR utilizes laser light.
The LiDAR utilizes a 1.5-micron wavelength, just on the edge of visible light.
“The LiDAR fires out laser light to hit particles that are small enough for that specific wavelength,” Smith said. “Those are things you and I can’t easily see, like really, really small dust and aerosol particles.”
The LiDAR team collects observations utilizing the device to track how quickly all the dust, dirt and particles move in the atmosphere.
“If we know how fast those particles move, we can figure out how fast the wind blows,” Smith said. “Understanding the wind field around severe storms and weather is very important for us to improve our understanding and forecasts.”
The LiDAR is one of many instruments utilized in TORUS — a month long project funded by the National Science Foundation and NOAA. The LiDAR is funded by the NOAA NSSL Director’s Discretionary Research Fund to support TORUS.
TORUS includes many instruments, including those on top of trucks known as mobile mesonets, as well as mobile radar trucks and unmanned aircraft vehicles. Mobile mesonets measure atmospheric factors such as wind speed, temperature and humidity at the surface while radar measures storm factors at much higher levels. As a result there is often a gap in coverage, which may be up to several hundred feet above the surface.
“We use the LiDAR and weather balloons with instruments attached to fill that gap,” Smith said. “The LiDAR system is able to provide us with wind information in minutes. There’s a lot about the environment around storms and near storms we don’t understand yet. Understanding the complex flows in that region can be very important.”
Smith said two of the most vital factors the OU CIMMS and NOAA NSSL team are studying to improve forecasting tools are how supercell thunderstorms move and persist.
The team already captured several data sets to review after successful deployments in mid-May, including in Nebraska and Oklahoma.
“We saw interesting turbulence structure in the wind field,” Smith said. “We don’t know what that means just yet, but this is unprecedented data because this specific LiDAR is faster than those used in past field projects.”
Smith said she doesn’t know what the data means, yet, but she does know it is interesting and poses more questions than answers until she can analyze it.
Researcher Kim Klockow-McClain absorbs the sights and sounds around her at Providence Baptist Church in Lee County, Alabama — almost one month since tornadoes devastated the community.
Klockow-McClain wants to tell people’s stories. They guide her effort to create a more complete picture of storms — not just how they happen meteorologically, but the impression they leave on people’s lives.
As a societal impacts researcher at the University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies, her work supports NOAA’s National Severe Storms Laboratory to improve the tools used by NOAA National Weather Service forecasters.
She wants to learn how emergency management agencies, broadcast meteorologists and NWS forecasters work together in an attempt to impact the public, how they operate individually and how current practices ultimately affect severe weather safety messages the public receives.
“It will kind of be the end of the story for this tornado path that I’ve been following through Alabama and into Georgia.”
So on a dewy Tuesday morning, Klockow-McClain stands among 23 white crosses on the church’s south lawn. The crosses are a symbol of remembrance — of each person who died on March 3 after tornadoes tore through the area. The memorial is disheveled from a storm the night before but some items placed at the base of each cross remain — including a jar of peanut butter.
“You can just imagine it was their loved one coming up to their memorial saying, ‘I know you would want your peanut butter,’” she said, tears forming in her eyes.
She takes a moment, lightly hugs the manilla folders filled with her surveys and questions, wipes her eyes and walks toward the church.
A visceral need
Klockow-McClain has a visceral need to visualize things. She says as a geographer she has to see things — maps, pathways, connections. Making those connections helps her build a map.
She visited the memorial first to build that piece of her research map and connections.
“I’m trying to understand the setting, the people and the place — who they are, who they were — the people who are gone,” Klockow-McClain said. “I couldn’t come here and not see the memorial. Ultimately, this is about the families who were left behind and the people who died.”
Her research is part of the Verification of the Origins of Rotation in Tornadoes EXperiment-Southeast, or VORTEX-SE, funded by NOAA.
VORTEX-SE is an effort to understand how environmental factors characteristic of the southeastern U.S. affect the formation, intensity, structure, and path of tornadoes in this region. The experiment will also determine the best methods for communicating the forecast uncertainty related to these events to the public, and evaluate public response.
For three days Klockow-McClain traveled the path of the March 3 tornado through Alabama and Georgia, meeting with those involved in alerting the public and locals who were personally impacted.
“It will kind of be the end of the story for this tornado path that I’ve been following through Alabama and into Georgia,” Klockow-McClain said.
Her research is focused on how messages coming from an Integrated Warning Team — emergency managers, broadcasters and forecasters — serve those living in manufactured and mobile homes, or whether further collective activities may need to be undertaken.
“When talking to all of the vested parties — emergency management agencies, broadcast meteorologists, forecasters and the public — you see places of great opportunity,” Klockow-McClain said. “We as researchers can release the best severe weather technologies for our partners, but if people don’t use, can’t use them and don’t want them for reasons we don’t understand — that helps no one.”
Klockow-McClain’s public response survey work first began in 2011 in Pleasant Grove, Alabama, about two hours from Providence Baptist Church.
Eight years later she revisited where her career began. As she navigates the curves of a rural road, she recounts one of the first times she interviewed someone who had witnessed a deadly storm. The person realized a tornado was near because they felt and heard debris falling on them while they were working on their vehicle.
Klockow-McClain spoke to that individual for nearly an hour in 2011. Now, sitting in the driver’s side of an SUV and staring at that person’s former home, she retells their story.
This person was one of 70 Klockow-McClain interviewed in less than one week. They heard meteorologists talk about an elevated weather risk on that day but didn’t think too much about it. That was until while working on their vehicle outside they described house insulation falling from the sky. Klcokow-McClain said the person ran inside their house, grabbed their significant other and animal and shoved them all in the bathtub. That move saved their lives. Describing the hours to come — losing neighbors, seeing houses gone around them — Klockow-McClain said she will never forget her hour-long conversation with that individual.
“I’m just creating a space for them to talk. I recognize that offers value to people. I use a method that involves care as a core principle. I feel like I’m doing something that matters.”
Inside Providence Baptist Church, Klockow-McClain is in a similar situation. She sits with an interviewee as they recount graphic details, highlighting every megapixel of that photographic day in March. All of the stories she’s heard don’t impact her personally. Klockow-McClain doesn’t let them. Instead, those stories bring her purpose.
“In the role as interviewer, you’re equal parts researcher and counselor. I’m just creating a space for them to talk,” Klockow-McClain said. “I recognize that offers value to people. I use a method that involves care as a core principle. I feel like I’m doing something that matters.”
She specifically chose to visit Alabama and Georgia nearly one month after the event because the crisis stage was ending and people were slowly attempting to recover a sense of normalcy.
Helping people feel heard
Klockow-McClain understands her research with devastating tornadoes can be emotionally taxing, but she never views it that way.
“I’m grateful I get to tell these stories,” she said. “As a meteorologist, you see these things happen and it can feel terrible to feel like you can’t do anything. So for me, to be able to sit there and feel like I’m helping them by helping them feel heard and their story matters by being a part of a bigger picture — that is helpful to me.”
She lets the person being interviewed steer the interview, no matter how graphic the story. Klockow-McClain said she wants to start in their shoes as people share what is most important to them. She listens to them verbalize the items that come to their minds as they help her understand their frame of mind and perspective.
“Research isn’t just about going out and collecting observations of wind, precipitation and atmospheric factors,” she said. “It’s about relating to people deeply enough that you really and truly can understand the context of what they’re telling you and fill this role of having some sympathy that’s meaningful to them for what they’ve experienced because they’ve gone through something very difficult. To come in and just dispassionately have a checklist or survey wouldn’t feel right to me.”
Generating a diagnosis
Klockow-McClain spent several hours speaking with volunteers at the church before following their suggestions to see the tornado damage in person.
She drove for less than 15 minutes before she saw signs of the damage and suddenly she was in the thick of it. Power lines were still down in areas. Piles of debris sat by the road as crews worked to clear side roads. Those living nearby watched for looters, which was a consistent issue after the storms.
Klockow-McClain hopes her research will lead to a better understanding of the needs of specific communities in the southeast to reduce tornado deaths in that area of the United States. Her research is aimed at generating a diagnosis that could ultimately lead to an effective treatment. A part of that is telling people’s stories.
When a tornado threatens a community, NOAA National Weather Service forecasters issue a tornado warning. Local emergency management agencies sound emergency tornado sirens or send out phone alerts. Broadcast meteorologists tell everyone to take shelter. But how does all of this help the public and how does the public respond?
This scenario played out in southeast Alabama about a month ago, when a devastating tornado killed 23 people and injured numerous others, before ending its path in Georgia. The storm system was well forecast — as NOAA’s Storm Prediction Center predicted an elevated risk of severe storms days in advance and local NWS forecasters provided timely warnings.
Kim Klockow-McClain wants to know why that storm system — which included winds that reached 170 mph and rated 4, with 5 being the worst, on the Enhanced Fujita tornado-rating scale— was so deadly. She is a societal impacts researcher at the University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies. Her work supports NOAA’s National Severe Storms Laboratory to improve tools used by NOAA National Weather Service forecasters.
When it comes to Doppler weather radar, scientists with NOAA National Severe Storms Laboratory wrote the book. Literally. Publications authored and co-authored by researchers at NSSL and the Cooperative Institute for Mesoscale Meteorological Studies expanded knowledge on radars and provided strategies used by weather forecasters today.
After more than three decades, those scientists have done it again. A new book by CIMMS Senior Research Scientist Alexander Ryzhkov, and co-authored by NSSL Senior Scientist Dusan Zrnic, highlights the biggest technological upgrade to Doppler radars since they were first installed — dual-polarization technology.
The book, “Radar Polarimetry for Weather Observations,” published by Springer Nature, offers an array of information on weather radar polarimetry. Polarimetric radar — and polarimetry — improves the accuracy of precipitation estimates, detects aviation hazards, can identify precipitation types, and can spot many other items such as bats or even tornado debris.
In addition to connecting processes responsible for the development and evolution of the bulk of clouds’ physical properties, the publication also provides up-to-date polarimetric methodologies.
The publication will appeal to practicing radar meteorologists, hydrologists, microphysicists, and modelers who are interested in the bulk properties of hydrometeors and quantification of these with the goals to improve precipitation measurements, understanding of precipitation processes, or model forecasts.
Radars are a vital tool for weather forecasters because they provide a detailed picture of storms as they’re happening. A new radar technique is improving the picture for forecasters, helping them provide more accurate information about rain and snow storms.
“Through this unique collaboration paradigm, we’ve proven that scientific partnerships can transcend geographical, political and proprietary boundaries,” said Sebastian Torres, CIMMS researcher leading NSSL’s Advanced Radar Techniques Team. “The atmosphere knows no geographical boundaries. Better forecasts in the UK can provide improved information to the United States, and vice versa, as we continue to build partnerships to help save lives, property and minimize the economic impact of severe weather in the U.S.”
Weather radars often pick up noise from various sources like the sun or man-made devices similarly to how a radio or television sometimes retrieve a static signal. The RBRN analyzes radar beam data in real-time and performs several tests to ensure the noise can be detected and measured.
“Accurate measurement of noise on weather radars is critical as it impacts the accuracy of radar data and plays a key role in data quality control,” Torres said.
In addition, a portion of energy from the radar beam may also be absorbed by particles in its path before the radar beam energy is returned.
“This weakens the echoes from locations far from the radar and gives the wrong impression that storms in these locations are weaker than they truly are,” Torres said. “Because the particles in the radar beam path emit noise, the noise measured by RBRN can be used to correct for the weakening or attenuation of echoes as the radar beam intersects storms. The operational RBRN estimator significantly improves the quality of radar data, especially for weak returns associated with snow storms and gust fronts.”
Reducing and accurately measuring contamination from the noise in the radar data equates to better information and more accurate forecasts for the public.
The RBRN was originally developed by CIMMS Researcher Igor Ivic and became operational in the U.S. NEXRAD radar network in 2014.