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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Collaboration with Taiwanese agency foundational to NSSL’s MRMS system

Recently a delegation from the Taiwan Central Weather Bureau and Soil and Water Conservation Bureau  visited NSSL for a project review. The visitors include the director of the CWB Meteorological Satellite Center, the director of the SWCB Debris Flow Disaster Prevention Center, one research meteorologist from CWB and two engineers from SWCB. NSSL scientists provided briefings on the latest Multi-Radar Multi-Sensor research and development efforts, and the visitors discussed their operational needs and challenges related to hydrological predictions in a tropical region with complex terrain.

Collaboration is a fundamental aspect of research at NOAA National Severe Storms Laboratory and recently a group from Taiwan’s Central Weather Bureau and Soil and Water Conservation Bureau visited the lab in Norman, Oklahoma.

The partnership between NSSL and CWB began 16 years ago when both agencies worked to develop and implement an early Multi-Radar Multi-Sensor System version and integrate it into CWB’s radars.

The collaboration grew with the joint interest in developing advanced quantitative precipitation estimate applications to address heavy precipitation over complex terrain associated with typhoons and hurricanes,” said Kenneth Howard, research scientist with NSSL. “The CWB collaboration was foundational to NSSL’s research and development of MRMS system operationally deployed in the United States.”

Such collaborations are important because the atmosphere does not stop at geographical borders.

“These partnerships and opportunities expand our knowledge of advancements in weather and hydrological research and operations around the world,” said Jian Zhang, research meteorologist. “Further, the collaborations leverage resources and expertise in different agencies for a more effective research and development effort to address challenges for different geographical and climatological regions.”

The recent visit provided an opportunity for each agency to present an update on projects and to review progress. The mid-term review allows the CWB project managers to meet with the various participating NOAA agencies in the U.S. This year SWCB joined the review and visited NSSL and the Global Systems Division of NOAA’s Earth Systems Research Laboratory in Colorado.

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NSSL Technology Helps the FAA

FAA-MRMSNOAA’s National Severe Storms Laboratory has partnered with NOAA’s National Weather Service and the Federal Aviation Administration to develop new tools to improve aviation forecasts.

For the past two years, the FAA has been using NSSL’s Multi-Radar Multi-Sensor system to identify severe and hazardous weather that can affect air travel. The MRMS system merges data from multiple sources, intelligently integrates the information, and provides a detailed picture of the current weather. MRMS detects not only where precipitation is occurring, but also the precipitation type, intensity, and atmospheric characteristics. The system’s products are generated every two minutes, and include three-dimensional radar images that are useful to improving safety and efficiency in the National Airspace System.

MRMS was developed by NSSL researchers, who were awarded the Department of Commerce’s Silver Medal for science/engineering achievement in July 2015. The FAA’s Aviation Weather Research Program provided significant funding and technical support for the project, and NOAA’s National Centers for Environmental Prediction facilitated the system’s implementation into FAA systems.

“They actually put their first test system of MRMS in the [FAA’s William J. Hughes Technical Center],” said Steve Abelman, manager of the NextGen organization’s Aviation Weather Research Team. “We’ve had this relationship to do research on MRMS for the last seven or eight years. Now it’s an actual tool to improve the operational product.”

The FAA has expanded the geographical reach of MRMS, integrating radar networks from Alaska, Canada, the Caribbean, Guam, and Hawaii. System upgrades are scheduled roughly every six months, with future plans for radar in Mexico and the Cayman Islands. MRMS has also been used for icing and turbulence research, with potential benefits throughout the aviation industry.

In 2015, 53 percent of air traffic delays were attributed to weather, costing air carriers $1,400 to $4,500 per hour. Better forecasts allow air traffic planners and controllers to direct aircraft away from aviation weather hazards. The enhanced weather analyses provided by MRMS offer substantial benefits to both NAS efficiency and also passenger safety.


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Gab at the Lab: Jian Zhang

Jian Zhang, Research Scientist


Background:Ph.D. Meteorology, University of Oklahoma (1999)
M.S. Meteorology, Chinese Academy of Meteorological Sciences, Beijing (1987)
B.S. Peking (Beijing) University (1984)
Experience:Jian was born in Loyang, a city in the Henan Province of China. She lived in the cities of Taixing and Lanzhou before moving to Beijing to pursue an education in meteorology. After she earned her Master's degree, Jian became a research associate at the Chinese National Satellite Meteorological Center. She came to Oklahoma when her husband was offered a Ph.D. Graduate Research Assistantship at OU School of Meteorology. She decided to pursue her Ph.D. at OU, and completed the program in 1999. Jian was a research scientist with OU CIMMS until 2009, at which time she was offered a Federal research meteorologist position with NSSL.
What She Does:Jian is the Team Leader of the Warning Research Development Division's Storm-Scale HydroMET Applications Research & Development Group. She is also the Lead Scientist for Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation Research & Development. She considers herself a Subject Matter Expert on the MRMS System. This operational system combines multi-sensor data and produces high-resolution severe weather and precipitation products for the National Weather Service. Jian was an integral part of the MRMS team that was awarded the Department of Commerce Silver Medal in 2015. Her research has been published in numerous peer-reviewed journals.
Trivia: When she is away from the Lab, Jian enjoys spending time with her family, which includes her husband and two children. She likes traveling, cooking, movies, and music. Her favorite TV shows are Seinfeld, The Big Bang Theory, and Shark Tank. Jian also enjoys walking and fits in a 3 mile walk every day.

She also notes that there are 87.5 million people with the surname 'Zhang' in China, making it the 3rd most common surname after Wang (92.8M) and Li (92.1M). It is purely coincidental that her husband, CIMMS/NSSL's Pengfei Zhang, has the same last name!

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Significant Paper: Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities

Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities
Authors: Jian Zhang and Kenneth Howard, Carrie Langston, Brian Kaney, Youcun Qi, Lin Tang, Heather Grams, Yadong Wang, Stephen Cocks, Steven Martinaitis, Ami Arthur, Karen Cooper, and Jeff Brogden, David Kitzmiller
Journal: Bulletin of the American Meteorological Society
Publication Date: In Print 5/2016

This paper provides a comprehensive description of the initial operating capabilities of the MRMS QPE system and would be beneficial to the users of MRMS products.  This is also a follow-up to a previous BAMS paper by Zhang et al. 2011 on the National Mosaic and Multi-sensor QPE system (in the Oct. 2011 issue of BAMS)

Important Conclusions:
Polarimetric upgrade of the NEXRAD network significantly improved the quality of the operational weather and precipitation products.  Further, the real-time dissemination of the radar base level data across the internet facilitated effective integration and assimilation of multi-sensor atmospheric observations. As a result, the resolution and accuracy of precipitation products are improved and new hydrometeorological applications are developed.


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Significant Paper: Using Citizen Science Reports to Evaluate Estimates of Surface Precipitation Type

mPING overlaid on MRMS

Using Citizen Science Reports to Evaluate Estimates of Surface Precipitation Type
Authors: Sheng Chen, Jonathan J. Gourley, Yang Hong, Qing Cao, Nicholas Carr, Pierre-Emmanuel Kirstetter, Jian Zhang, Zac Flamig
Journal: Bulletin of the American Meteorological Society
Publication Date: In Print 2/2016

Important Conclusions: Consistency in results from city to city give an indication that the citizen science reports of rain and snow from the meteorological Phenomena Identification Near the Ground app (mPING) provide useful information about the quality of the MRMS precipitation type algorithm. The MRMS surface precipitation type algorithm has a slight propensity to produce too much rain where there is snow; this suggests some modifications are needed to the temperature thresholds and motivates probabilistic approaches.

Significance: This is the first paper to comprehensively evaluate the MRMS rain-snow product using mPING crowd-sourced observations.

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Significant Paper: Multi-radar Multi-sensor (MRMS) Severe Weather and Aviation Products: Initial Operating Capabilities


Multi-radar Multi-sensor (MRMS) Severe Weather and Aviation Products: Initial Operating Capabilities
Authors: Travis M. Smith, Valliappa Lakshmanan, Gregory J. Stumpf, Kiel L. Ortega, Kurt Hondl, Karen Cooper, Kristin M. Calhoun, Darrel M. Kingfield, Kevin L. Manross, Robert Toomey, Jeff Brogden
Journal: Bulletin of the American Meteorological Society
Publication Date: Online 1/27/16

Important Conclusions:
Several individual, automated algorithms have been developed using the MRMS system to yield a forecasting and analysis system that provide real-time products useful in severe weather and aviation nowcasting. Automated algorithms that operate on data from multiple radars can provide information with greater temporal resolution and better spatial coverage than their single-radar counterparts. MRMS-Severe/Aviation products were developed and tested over a period of more than a decade prior to becoming operational at NCEP. MRMS Severe/Aviation software integrates knowledge from NWS forecasters, as well as scientific research of storms and their environments, to provide a foundation for managing ever-increasing data flows through intelligent integration of remotely sensed information

The paper summarized the initial operating capabilities of the MRMS-Severe Weather applications and 3D radar mosaicking capability built on the WDSS-II infrastructure, which the core of MRMS functionality.  It also covers the history of development and testing of the system. It is a timely submission to BAMS, as the MRMS capabilities are currently rolling out for use in the National Weather Service.

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Authors: Stephen B. Cocks, Steven M. Martinaitis, Brian Kaney, Jian Zhang, Kenneth Howard.

Journal: Journal of Hydrometeorology
Publication Date: Online 12/18/15

Important Conclusions:
This paper documents the performance of the Multi-Radar Multi-Sensor (MRMS) radar-only Quantitative Precipitation Estimate (QPE) product (denoted as Q3RAD), mosaicked single-radar dual polarization QPE (Dual Pol), and the NCEP Stage II QPE against the benchmark forecaster quality-controlled NCEP Stage IV QPE for nine precipitation events during the 2013–2014 cool season over the United States. The study was limited to weather events east of the Rocky Mountains.

Results showed that Q3RAD, Dual Pol, and Stage II all had a tendency to underestimate precipitation, with Stage II having the most distinct underestimation bias. An evaluation of stratified rain gauge amounts when compared to those products also showed an increase in the underestimation bias with higher precipitation amounts. In contrast, the Dual Pol QPE and MRMS Q3RAD exhibited an overestimation bias for 24-hr precipitation totals less than 12.7 mm (0.50 in). There was a marked difference between Stage II and the other QPE products, which reflects the substantial progress in improving precipitation estimates over the last fifteen years.

Future evaluations will analyze warm season precipitation estimates, as well as assess precipitation events in the western United States. Future work with radar-based QPE will assess the integration of dual polarization information into the MRMS Q3RAD precipitation estimates and will examine the feasibility of improving Dual Pol estimates in the melting layer.

These are the first direct comparisons of the real-time MRMS radar-only QPE (Q3RAD) with the mosaicked Dual Pol and NCEP Stage II QPEs against the benchmark NCEP Stage IV QPE, which sees manual quality control and is available 6-12 hour after the analysis time. The research also highlighted the chief error contributions seen in the QPE evaluation. This included the impacts of how Z-R relationships are applied and bright band contamination. And while the Stage IV QPE had the best performance, Q3RAD values were nearly comparable in some events, which is distinct achievement for the fully-automated, real-time MRMS system.



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Significant Paper: Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities


Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities.                                                                                         Authors: Zhang, J., Howard, K., Langston, C., Kaney, B., Qi, Y., Tang, L., Grams, H., Wang, Y., Cocks, S., Martinaitis, S., Arthur, A., Cooper, K., Brogden, J., Kitzmiller, D.

Journal: Bulletin of the AMS.                                                                                          Publication Date: Online 8/4/15

Important Conclusions:

The Multi-Radar Multi-Sensor (MRMS) system, operationally implemented at NOAA National Center for Environmental Prediction, integrates radar, surface observations, satellite data, and numerical analysis and prediction and generates an automated, seamless national products suite of severe weather and quantitative precipitation estimate (QPE) products at very high spatial (1 km) and temporal (2 min update cycle) resolution. The MRMS products are used in operations and research for model assimilation, flash flood monitoring and prediction, and hazardous weather warnings.  The MRMS products are provided to users from government agencies, universities, research institutions, and the private sector and have been utilized in numerous meteorological, aviation, and hydrological applications.


This paper provides a comprehensive description of the operating capabilities and improvements of the MRMS system QPE product suite and would be beneficial to the users of MRMS QPE products. This is also a follow-up to a previous BAMS paper by Zhang et al. 2011 on the National Mosaic and Multi-sensor QPE system (in the Oct. 2011 issues of BAMS).


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Multi-Radar Multi-Sensor Team To Receive NOAA Silver Medal

US_Dept_of_Commerce_Silver_MedalOne of the highest awards presented within NOAA will be awarded to the NSSL team that developed Multi-Radar Multi-Sensor, a system that helps forecasters manage the flood of weather data available to them. Under Secretary of Commerce Kathryn D. Sullivan announced the award of a NOAA silver medal for science/engineering achievement. Their work was a collaborative effort with the University of Oklahoma’s Cooperative Institute for Mesoscale Meteorological Studies.

The MRMS system, which became operational throughout the National Weather Service in October 2014, quickly harnesses the tremendous amount of weather data from multiple sources, intelligently integrates the information, and provides a detailed picture of the current weather. MRMS uses a holistic approach to merging multiple data sources, allowing forecasters to better analyze data and potentially make better predictions.

The new MRMS products, generated every two minutes, combine multiple radars, along with satellites, surface observations, upper air observations, lightning reports, rain gauges, and numerical weather prediction models. With this data, forecasters are able to better visualize high-impact weather threats like heavy rain, snow, hail, and tornadoes. This, in turn, leads to better forecasting techniques and improves lead time.

Congratulations to the team!



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