Using a dual-pol radar feature to anticipate downburst development

Downbursts—an area of strong winds in a thunderstorm—can damage trees and buildings, disrupt air travel, and cause loss of life. Decades of work by scientists has revealed a lot of information about downbursts including certain features seen on radar, known as precursor signatures, that can help forecasters anticipate when a downburst might develop. However, downbursts are still quite challenging to predict—especially in low-shear, summer-time thunderstorms—perhaps because the downbursts and their precursor signatures can develop quickly and be difficult to observe. 

Therefore, researchers at the University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies with the NOAA National Severe Storms Laboratory decided to look into it further. They studied  a dual-pol radar feature, known as a specific differential phase (KDP) core, because it could provide information about important processes that cause a downdraft to develop and get stronger. Our research shows KDP cores could be a reliable and easily observable downburst precursor signature that can help NOAA National Weather Service forecasters predict where a downburst could develop next. 

Our research began one afternoon in the halls of the National Weather Center in Norman, Oklahoma, when Randy Bowers, a forecaster at the NOAA National Weather Service Norman Forecast Office (OUN) and I (Charles Kuster, a research scientist with the CIMMS working at NSSL), were having an informal discussion about a completely unrelated topic. At the end of our conversation, Randy mentioned seeing a consistent area of high KDP—the KDP core— while issuing severe thunderstorm warnings and wondered if we could do some research on it together. I had been looking for opportunities to work more with forecasters, so this topic sounded like an amazing opportunity. We jumped in and began identifying potential cases, collecting data with a research radar in Norman, and brainstorming how to best study KDP cores.

The KOUN radar with storm clouds behind it.
The NOAA National Severe Storms Laboratory research radar collects data on a downburst-producing thunderstorm in Norman, Oklahoma. (Photo by Charles Kuster, OU CIMMS/NSSL)

Ultimately, we selected 81 downbursts in 10 different states to analyze. I got to work on comparing the size, magnitude, and vertical changes in the KDP cores associated with strong and weak downbursts, while Randy examined atmospheric conditions and possible warning applications. We also began working with Jacob Carlin, a CIMMS research scientist also working at NSSL, who explored model simulations of downdrafts and important microphysical processes—such as melting and evaporation—that can result in a stronger downburst. Another research scientist, Terry Schuur (OU CIMMS/NSSL), also brought microphysics expertise and experience to the team while researchers Jeff Brogden (OU CIMMS/NSSL) and Robert Toomey (OU CIMMS/NSSL) provided extensive support and expertise in radar data analysis software. Andy Dean, a forecaster with the NOAA/NWS Storm Prediction Center, provided data about atmospheric conditions around the downbursts.

Together, we determined KDP cores were a reliable downburst precursor signature in the events we studied. All 81 downbursts were preceded by a KDP core by as much as 30 minutes. The KDP core also reached its maximum intensity, typically about 10–15 minutes before the downburst reached its maximum intensity. In addition, there were very few instances where a KDP core was observed and no downburst occurred (i.e., very few null events).

Anticipating downburst intensity using KDP cores was more challenging because there was overlap between the characteristics of KDP cores associated with strong and weak downbursts, but in general, a stronger KDP core was more likely to be associated with a stronger downburst.

We also found the atmospheric conditions during each event were very important. When atmospheric conditions were less favorable for downburst development, we observed stronger KDP cores, which likely means more melting, precipitation loading, and evaporation are needed for a downburst to develop in such conditions. Ultimately, our work showed KDP cores provide a good signal that a downburst is likely to develop soon, assuming atmospheric conditions are favorable for downburst development, and can help forecasters triage storms to determine which one has the highest chance of producing a downburst.

A screenshot of a graph showing the KDP Core Size Near Melting Layer for all Downbursts.
KDP core size (red line) generally increases in the 30-minute period prior to downburst development. (Photo provided)
A photo montage showing the development of the KDP core.
Example of a KDP core in mid-levels of a thunderstorm (i.e., near the melting layer) and the associated downburst near the ground seen in Doppler velocity data (V). In the velocity images, red colors show air moving away from the radar and green colors show air moving towards the radar. (Photo provided)

We would like to thank everyone who has made this research possible so far and those who continue to help push it forward. Everyone from the CIMMS and NSSL administrative staff, NSSL IT, radar engineers, NOAA National Weather Service Norman Forecast Office, and webinar organizers are very much appreciated. For any questions, please contact nssl.outreach@noaa.gov or see https://doi.org/10.1175/WAF-D-21-0005.1 for more detailed information.

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Researchers leverage machine learning to improve forecasting tools

Weather models are the basic building blocks of any forecast. NOAA National Weather Service forecasters utilize a variety of models to provide accurate weather information for the public when severe weather threatens. 

NOAA and cooperative institute researchers are leveraging machine learning techniques and high resolution weather models in an effort to improve these tools.

“We hope our research will provide forecasters with more information on when they should, or shouldn’t, rely on certain forecast models,” said Burkely Gallo, a researcher at the University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies, whose work supports the NOAA NWS Storm Prediction Center.

Burkely Gallo presenting a powerpoint of her research in front of people.
Burkely Gallo presenting on her and the team’s machine learning techniques research at the NOAA booth at the American Meteorological Society 100th Annual Meeting in January 2020. Burkely is a University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies research whose work supports the NOAA NWS Storm Prediction Center. (Photo by Emily Summars-Jeffries/OU CIMMS/NOAA NSSL)

Computer weather models continue to improve, providing accurate forecasts as much as a week in advance. Yet, each has strengths and weaknesses and must be interpreted by knowledgeable human forecasters. It could take decades of experience for forecasters to gain expertise on what forecasting models are the most accurate for specific weather events.

A series of preliminary research aims to allow automatic flagging of problematic forecasts, provide quality control for the development of new atmospheric models and allow model developers to learn why a model is or is not valid.

“To achieve the latest and greatest forecasting models, people developing the models need to know how they are performing in certain scenarios,” Gallo said. “We hope this can help them identify priorities for future model development.”

Alex Anderson-Frey is a co-researcher on the project, which began as an internal funding proposal in a competition organized by the NOAA Central Regional Collaboration Team. Anderson-Frey and Gallo won funding for their project and their work was supported by NOAA’s National Severe Storms Laboratory when it began.

Burkely Gallo and Alex Anderson-Frey stand in front of their powerpoint presentation.
The OAR/NWS Shark Tank, Season 2 where Anderson-Frey and Gallo presented their research idea. The OAR/NWS Shark Tank was coordinated by the NOAA Central Region Collaboration Team was held in February 2018 at the National Weather Center in Norman, Oklahoma. (Photo by James Murnan/NOAA)

“Alex and I have wanted to work together since college,” Gallo said. “We decided this internal program could be the spark for collaboration.”

Gallo and Anderson-Frey used the competition funding to hire a graduate student part-time and the result of his efforts allowed them to have a complete dataset to begin their work, which leverages machine learning. Machine learning sorts storms based on different environmental factors surrounding the storms. Environmental factors include fields, like dew point and temperature. Found patterns can then be matched to a current model forecast. This  provides forecasters an idea of how the model they are using performed in similar past scenarios. 

“We want to provide tools that allow forecasters to quickly learn, so they can know if a model has statistically performed very well for tornado detection in this type of model environment,” Gallo said. “Forecasters manage a fire hose of data and we hope to make the fire hose manageable.”

Gallo said she expects the project to continue for several years, with the team’s goal of testing the products in NOAA’s Hazardous Weather Testbed.

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Women of NSSL: Jian Zhang

Jian Zhang, NOAA NSSL research meteorologist.

For the month of October NOAA National Severe Storms Laboratory is publishing a series of stories highlighting some of the women working at the lab. One Q&A segment will be published each Monday in October.

Jian Zhang is a research meteorologist of NOAA NSSL’s Warning Research and Development Division. Zhang completed her Ph.D at The University of Oklahoma in 1999. She worked with the OU Cooperative Institute for Mesosocale Meteorological Studies until 2009 when she became a federal employee.

Q: How did you get into weather?
A: My father was a mechanical engineer and his appreciation for the intricate regularities of math and physics and a passion for solving real-world problems had a big influence on me. As a result, I chose atmospheric physics/meteorology as my major in college and have stayed in the field ever since.

Q: What is it about your job that interests you?
A: My job is to produce accurate precipitation information for every square kilometer of the U.S. in a timely manner. Such information is critical across several sectors of the U.S. economy and for the protection and well being of the communities. Seeing my job has direct impacts and benefits in the real world interests me.

Q: Tell us about a project or accomplishment you consider to be the most significant in your career?
A: The most significant project of my career is the Multi-Radar Multi-Sensor system for which I am one of the main developers. ​The MRMS​ project provides people with severe weather and flash flood information at an unprecedented resolution down to the street scale.


Q: What is your personal philosophy?

A: Kind. Diligent. Intelligent.

Q: What would you most like to tell your younger self?
A: ​I would like to tell my younger self to be more critically thinking since I grew up in a culture and environment that valued collective interests more than individual interests – especially for women – and valued old wisdom more than adventures.

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Gab at the Lab: Alexander Ryzhkov

Alexander Ryzhkov, Senior Research Scientist (CIMMS/NSSL)

Background:Ph.D. Radio Science, St. Petersburg University (1977)
M.S. Physics, St. Petersburg University (1974)
Experience:Alexander Ryzhkov grew up in Russia, in a small city called Valday, Novgorod Oblast. He attended St. Petersburg University, where he earned degrees in both physics and radio science. After completing his Ph.D. program, Alexander worked at Russia’s Main Geophysical Observatory from 1978 to 1992. During this time, he networked with scientists in Norman, and was eventually invited to come to NSSL as a National Research Council postdoctoral researcher.
What He Does:Alexander was an NRC postdoc at NSSL from 1992 to 1995. He then accepted a research scientist position with OU’s Cooperative Institute for Mesoscale Meteorological Studies, where he has remained for over 20 years. Alexander’s primary research goals are developing operational algorithms for quantitative precipitation estimation, hydrometeor classification, and microphysical retrievals using polarimetric radars, and utilizing polarimetric radars for the improvement of Numerical Weather Prediction model performance. To achieve these objectives, he works to break down walls between radar scientists and cloud modelers and capitalizes on the benefits of international collaboration.
Trivia: Alexander’s favorite pastimes include walking in the woods, strolling the streets of European cities, spending hours in art galleries, and relaxing with some music. He enjoys spending time with his family, which includes his wife, two daughters, and a son.
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New research improves water hazard forecasting

Stream radar is a new tool to monitor stream levels and improve the prediction of flooding. Credit: J.J. Gourley/ NOAA
Stream radar is a new tool to monitor stream levels and improve the prediction of flooding. Credit: J.J. Gourley/ NOAA

A new program supported by NOAA’s National Severe Storms Laboratory is testing the use of stream radar to provide better measurements of stream flow and improve flood forecasting. The project, led by electronics engineer Daniel Wasielewski with the University of Oklahoma’s Cooperative Institute for Mesoscale Meteorological Studies, began in October 2016 and will span two years. NSSL research hydrologist JJ Gourley is collaborating, along with Edward Clark from NOAA’s National Water Center and John Fulton with the United State Geological Survey.

The stream radar project was borne out of a need for improved river monitoring. NOAA’s National Water Model, which became operational in June 2016, forecasts river conditions at substantially more locations than had previously been possible. The United States Geological Survey operates roughly 7,800 stream gauges in the United States, with observations critical to informing forecasters, who rely on the data to verify flood projections. Stream radars are less likely to be lost during a flood, and also have less stringent requirements for annual maintenance, power, and access. Stage and velocity levels calculated by radars will be assimilated into the NWM, and serve as additional verification points for hydrologic forecasts.

Researchers positioning stream radar above Honey Creek in Davis, Oklahoma. Credit: J.J. Gourley/ NOAA
Researchers positioning stream radar above Honey Creek in Davis, Oklahoma. Credit: J.J. Gourley/ NOAA

The NSSL/OU research team plans to install 14 stream radars on cables or bridges across rivers at predetermined, high-priority locations. Installations will take place through 2017, with results expected in early- to mid-2018. To support retrievals performed by stream radars, NSSL will also provide in-house development of a scanning lidar to produce bathymetric measurements.

Earlier this week, NOAA Research announced it would invest $6 million in programs to improve severe weather and water hazards forecasting. The stream radar program is included in this initiative, marking a significant breakthrough in NOAA’s research-to-operations efforts.

To learn more:
http://go.usa.gov/xkf8j

 

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Gab at the Lab: Yunheng Wang

Yunheng Wang, Research Scientist (CIMMS/NSSL)

yunheng-wang

Background:Ph.D. Computer Sciences, University of Oklahoma (2007)
M.S. Meteorology, University of Maryland (2000)
B.S. Meteorology, Nanjing Institute of Meteorology (1993)
Experience:Yunheng grew up in northeastern China and attended Nanjing Institute of Meteorology (now renamed as Nanjing University of Information Science and Technology). He began his career with the China Meteorological Administration before moving to the United States, where he earned his Master’s degree at the University of Maryland. From Maryland, Yunheng made his way to Norman, earning his Ph.D. in computer sciences at the University of Oklahoma. He worked with OU’s Center for Analysis and Prediction of Storms as a research scientist/software manager, then was offered a position with OU CIMMS. He has been a member of the Warn-on-Forecast team since October 2015.
What He Does:Yunheng's work is concentrated on the Warn-on-Forecast project. He develops software running on supercomputers for atmospheric applications. He also uses data assimilation techniques (3D/4D variational method, EnKF, LETKF, etc.) to conduct radar and satellite data analysis. In addition, he is working with numerical weather prediction models, including WRF, NMMB, the Advanced Regional Prediction System, and the Coupled Ocean/Atmosphere Mesoscale Prediction System. Yunheng enjoyed taking part in the 2016 Hazardous Weather Testbed experiments focusing on 3DVAR analysis and the WRF prediction system.
Trivia: Yunheng has many interests, including reading (particularly ancient history), movies, and travel. He is not an avid sports fan, but encourages his two boys to be involved in athletics.
Fun Fact: Wang is the largest surname in China, with over 92 million people sharing the name!

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Gab at the Lab: Heather Reeves

Heather Reeves, Research Associate (CIMMS/NSSL)

reeves

Background:Ph.D. Meteorology, North Carolina State University
M.S. Meteorology, North Carolina State University
B.S. Meteorology, Central Michigan University
Experience:Heather was born in Hemlock, Michigan, where she lived until her family relocated in-state to Mt. Pleasant. She grew up with an interest in music, but quickly discovered the competitive lifestyle did not suit her personality. Instead, she elected to pursue a degree in meteorology at Central Michigan University. Upon graduation, she and her husband decided to continue their education, and moved to Raleigh, North Carolina. There, she earned both her Master’s and Ph.D. at North Carolina State University. After graduating, Heather was offered a NRC Postdoc at NSSL in Norman, Oklahoma. After finishing her postdoc, she joined CIMMS and has contributed to a number of different projects both related to numerical weather prediction and radar meteorology.
What She Does:Heather has been with CIMMS/NSSL since 2009. Initially, she worked jointly for the Forecast and Radar Research Development Divisions, but transitioned to the Warning Research Development Division in 2015 to manage NSSL’s FAA research portfolio. Her specific interests include orographic precipitation and winter weather. She is currently working on projects to support detection and short-range prediction of weather hazards to the transportation sector. In June, Heather was honored with the American Meteorological Society's Service to the Society Award at the 17th Conference on Mountain Meteorology.
Trivia: Heather is married and has four cats. She and her husband enjoy watching low-budget disaster movies. Some of her favorites include "Lightning: Bolts of Destruction," "Tornado Valley," and "Christmas Twister."

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Gab at the Lab: Matt Mahalik

Matt Mahalik, Research Associate (CIMMS/NSSL)

mahalik

Background:M.S. Atmospheric Science, Texas Tech University (2015)
B.S. Meteorology/Climatology/GIS, The Pennsylvania State University (2012)
Experience:Matt grew up in Pennsylvania and South Carolina, and earned his bachelor’s degree in meteorology from Penn State University. During his undergraduate studies, he was active in the Penn State chapters of the AMS and NWA. He was also a NOAA Hollings Scholar and spent time at the NWS forecast office in Melbourne, Florida, in 2011. He went on to earn his Master’s degree from Texas Tech University in 2015, focusing his studies on supercell modeling and vorticity dynamics, working with mobile radars, and maintaining West Texas Mesonet stations.
What He Does:Matt started with OU CIMMS in July 2015. He is a part of the Severe Weather Warning Applications and Technology Transfer group in the Warning Research and Development Division. He describes himself as a writer, tester, and fixer of algorithms for the Warning Decision Support System -- Integrated Information. Currently, he is working on azimuthal shear applications, including rotation tracks, and developing divergent shear. Matt also contributes to several other projects with the Lab, including Multi-Radar Multi-Sensor severe weather applications and the Multi-Year Reanalysis of Remotely Sensed Storms program. In addition, he is helping develop mesocyclone and tornado detection algorithms with the Radar Operations Center, and assists severe weather researchers at the OU School of Meteorology.
Trivia: Matt was a campus tour guide at Penn State. In his spare time, Matt enjoys road trips, attending college football games, and the occasional storm chase. Some miscellaneous favorites of his include Carolina BBQ, red dirt country music, and a surprising amount of hip hop.

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Gab at the Lab: John Lawson

John Lawson, Postdoctoral Research Associate (CIMMS/NSSL)

john-lawson

Background:Ph.D. Meteorology, Iowa State University
M.S. Meteorology, University of Utah
MMet Meteorology, University of Reading (UK)
Experience:John was born in Stockton-on-Tees in the United Kingdom. He earned his MMet degree at the University of Reading, and was able to come to Oklahoma on a foreign exchange during that program of study. This eventually led to his decision to pursue a Master’s degree at the University of Utah, where he took part in field studies of downslope windstorms. He then went on to earn his Ph.D. at Iowa State University, an excellent location for studying severe weather!
What He Does:John’s passion is in chaos theory and the predictability of weather. At NSSL, he is designing short-range ensemble forecast systems (collections of slightly different weather forecasts) for the Warn-on-Forecast project. The project aims to provide a probabilistic (risk-based) forecast of high-impact weather such as tornadoes and flash flooding to increase warning lead times in these events. His other research areas include supercell and bow-echo predictability, and the development of a Python package that generates and evaluates ensemble forecasts.
Trivia: John runs a UK private forecasting operation called Bolt Forecast. He also enjoys listening to music, and coaching or watching soccer (or football, as it is known in the UK). He also likes spending time with his dog and coloring (see photo).

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Gab at the Lab: Katie Bowden

Katie (Bowden) Wilson, Ph.D. Candidate (University of Oklahoma – CIMMS/NSSL)

KatieBowden

Background:M.S., Meteorology, University of Oklahoma
MMet., Meteorology, University of Reading
Experience:Katie hails from Royal Wootton Bassett, a town in central England about an hour and a half west of London. She attended the University of Reading and came to the University of Oklahoma on an exchange program during her studies. After spending some time in Oklahoma, she decided she wanted more, and came back to earn her Master’s degree from OU in 2012. She is currently a Ph.D. candidate at OU, and has already passed the program’s rigorous General Exam. She expects to complete her Ph.D. by late-2017.
What She Does:Katie works on interdisciplinary experiments to understand the impact of higher-temporal resolution radar data on NWS forecasters’ warning decision processes. She has been a co-lead, along with NSSL’s Pam Heinselman, on the Phased Array Radar Innovative Sensing Experiment. In this displaced real-time simulation, Katie and Pam work with NWS forecasters to test the effects of radar update speed on resultant warning performance and workload. These studies have played a critical role in demonstrating the advantages of rapid-scanning phased array radar over WSR-88D. Katie has also won numerous awards for her studies on Eye-Tracking Technology with CIMMS and NSSL. By studying the movement of forecasters’ eye gaze during the warning decision process, researchers are gaining valuable information that will help develop better tools to guide meteorologists in the future.
Trivia: Katie has two sisters back home in England, including an identical twin! She enjoys staying active and is quite adventurous - she has both skydived and climbed Mount Kilimanjaro! Katie and her husband, Chris, are newlyweds and celebrated their wedding in Chicago this summer.

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