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 test experimental severe weather warning tools

 

When severe weather threatens, NOAA National Weather Service forecasters issue warnings to alert people. Based on the location of the storm, the same warning gives some communities more time than others. Researchers are testing an experimental concept to provide more continuous hazardous weather information for the public.

Throughout February, NOAA National Severe Storms Laboratory, University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies and Earth Systems Research Laboratory Global Systems Division researchers are testing technologies to allow warnings to follow storms continuously, called Threats in Motion, in NOAA’s Hazardous Weather Testbed.

Gif of a leadtime polygonCurrently, when NWS forecasters issue a warning for a specific area, the warning remains over that area and may last up to 45 minutes. If forecasters see the storm is about to exit that current warning area, forecasters will issue an additional warning where the storm is about to move.

Gif of the Threats in Motion conceptThis often results in in-equitable lead times for those who are on the border of a severe thunderstorm or tornado warning. Threats in Motion aims at transforming this traditional warning paradigm.

Threats in Motion, or TiM, warnings move with the storm and thus result in more equitable lead time for all impacted by the storm. Forecasters may tell residents in a more timely manner when they can leave their shelters.

Research and operations collaboration

A paradigm shift cannot be implemented in 122 NWS forecast offices immediately. Each office serves a specific geographic area called a County Warning Area, or CWA, said Alyssa Bates, OU CIMMS researcher supporting NOAA NWS Warning Decision Training Division.

“Our inter-office collaboration experiment is unique because we’re studying the complex interactions between forecasters across the CWA boundary,” Bates said.

The process of transferring ownership of a static warning polygon from one NWS office’s jurisdiction to another may be different office to office as well as situationally based.

“In our research of NWS offices, we ask what drives collaboration and when are warning decisions the most complex,” said Kim Klockow-McClain OU CIMMS research scientist and societal impacts coordinator supporting NOAA NSSL. “We documented those responses and are implementing some of those challenges and decision making into this experiment. Different offices have different warning philosophies.”

Researchers and forecasters testing new forecasting technologies on a computer screen.
NOAA and cooperative institute researchers are working with forecasters to assess new tools in the NOAA Hazardous Weather Testbed during the HS-PHI Interoffice Collaboration experiment. This experiment is part of an effort involving the NWS warning paradigm known as Forecasting a Continuum of Environmental Threats, or FACETs. (Photo by James Murnan/NOAA)

Klockow-McClain said researchers must test not only the technical applications but how forecasters utilize the tools. Otherwise, researchers cannot transfer research to operations if they do not ensure all challenges are tested.

“Warnings are an institutional process and we have to know how that process works to see what the implementation of a moving warning system looks like,” she said. “We want to improve, not harm.”

Simulating an operational work environment with forecasters from across the United States is vital to the success of the experiment before GSD decides the best implementation of TiM into NWS forecaster’s operational software.

“We bring in forecasters to the NOAA HWT to test these concepts not only because we value diverse perspectives, but because we need to test the products in all the different severe weather scenarios that are experienced in the United States,” Bates said.

TiM is part of a broader framework known as Probabilistic-Hazard Information and FACETs, or Forecasting a Continuum of Environmental Threats. FACETs is a paradigm shift to provide more information between the watch and warning system currently in place. NOAA NSSL focuses on severe weather research as it pertains to FACETs. The implementation of TiM is aimed at guiding future products in severe weather of the FACETs paradigm. 

“These concepts are complex, and it requires lots of research to grasp and understand the probabilistic nature of them and how to make them interpretable by NWS forecasters — that’s my job as a trainer,” Bates said.

“These concepts are being thoroughly tested and steps are being taken to ensure the products and concepts are created with forecaster and public well-being and interpretations in mind,” Bates said.

 

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During busiest month for storms, researchers gather in the NOAA Hazardous Weather Testbed

2016_05_06_HWT-1

This week, during the height of severe weather season, a diverse group of scientists is coming together in Norman, Oklahoma, to improve how severe weather information is conveyed to the public. They are gathering in NOAA’s Hazardous Weather Testbed to focus on FACETs-related research and significant collaborative efforts.

The HWT is a joint effort between the National Severe Storms Laboratory, Storm Prediction Center, and the National Weather Service, along with OU’s Cooperative Institute for Mesoscale Meteorological Studies, that aims to develop, test, and evaluate forecast and warning techniques. Each year, the HWT conducts two programs during the spring to review emerging ideas and answer the question, “What do forecasters need?” The Experimental Warning Program and Experimental Forecast Program draw as many as 60 researchers and forecasters together for six to eight weeks from April to June.

While experiments formally began earlier this spring, this week is particularly noteworthy because many aspects of the future warning paradigm have been brought together for the first time. NWS forecasters, emergency managers, broadcast meteorologists, social and behavioral scientists, and research meteorologists are looking at NWS Hazard Simplification concepts, new science (e.g., Warn-on-Forecast model guidance, lightning prediction tools, storm-scale prediction, etc.), and more. With all of these groups involved in the FACETs experimental phase, researchers hope to gain insight on how they might devise a better warning system for the entire nation.

Additionally in the HWT, scientists are examining real-time forecasts with an early prototype Warn-on-Forecast system known as the NSSL Experimental WoF System for ensembles (NEWS-e). Early results appear promising, and researchers are enthusiastic about future prospects. NEWS-e’s forecasts for the severe weather events on May 8-9, 2016 were remarkably good.  On both days, the forecasts identified a number of dangerous storms with more than 20 minutes of lead time. The NEWS-e system now combines models from NOAA’s Global Systems Division and NSSL for the first time. This marks a significant milestone in the collaboration on storm-scale numerical weather prediction between the two labs.

For more information about the HWT: http://hwt.nssl.noaa.gov/.

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Forecasters to evaluate sets of advanced weather models that can depict thunderstorms

13985530779_b86a7f9932_kExperiments designed to improve National Weather Service severe weather forecasts will be conducted in the 2015 Spring Forecasting Experiment from May 4 through June 5, part of the NOAA Hazardous Weather Testbed (HWT) Experimental Forecast Program. The effort is a joint project between the Storm Prediction Center (SPC) and NSSL/CIMMS to support NOAA’s goal to evolve the National Weather Service and build a Weather-Ready Nation. The interactions between operational forecasters, model developers, and research scientists are critical for the effective transfer of operationally relevant guidance, tools, and techniques to operational forecasters.

Forecast teams will work with a number of sets of advanced weather computer models, called ensembles, that can depict thunderstorms (4km grid or less) to create experimental severe weather hazard outlooks valid over shorter periods (1-hr and 4-hr periods) than current SPC operational products. The outlooks will define the probability of a hazard occurring, the confidence in its path, and adjust to trends in the threat level based on new observations. These activities are foundational to the emerging FACETs vision and designed to link with initial Warn on Forecast activities conducted by the Experimental Warning Program. In addition, the predictability of severe weather hazards into Days 2 and 3 will be explored using these ultra-high-resolution forecast systems.

The HWT experiments support the NOAA mission of Science, Service, and Stewardship, in addition to providing information that will help communities be more resilient. HWT research will improve the nation’s forecasting and numerical weather prediction capabilities through collaborative efforts between the academic community and NSSL. The effort is highly relevant to NOAA’s goal to evolve the National Weather Service.

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2015 Spring Warning Project will look at new severe weather warning guidance

Researchers and forecasters work side by side in the Hazardous Weather Testbed.

Several experiments to improve National Weather Service severe weather warnings will be conducted this spring in the NOAA Hazardous Weather Testbed (HWT) as part of the annual Experimental Warning Program, a joint project of the National Weather Service and NSSL/CIMMS to support NOAA’s goal to evolve the National Weather Service and build a Weather-Ready Nation. The EWP’s Spring Warning Project will run from May 4 through June 12, and provides a conceptual framework and a physical space to foster collaboration between research and operations to test and evaluate emerging technologies and science.

Forecasters will evaluate an updated Lightning Jump Algorithm (LJA), based on the GOES-R Geostationary Lightning Mapper, that was enhanced based on feedback from forecasters participating in the 2014 program. In severe storms, rapid increases in lightning flash rate, or “lightning jumps,” typically precede severe weather such as tornadoes, hail, and straight line winds at the surface by tens of minutes.  These evaluations will help prepare for possible operational implementation in 2016 following the launch of GOES-R.

Earth Networks’ total lightning and total lightning derived products, including storm-based flash rates tracks, time-series, and three levels of thunderstorm alerts will be evaluated in real time, building upon the initial evaluation in 2014. The 2015 evaluation will test the feasibility of use and performance under the stress of real-time warning operations.

A new set of high-resolution Weather Research and Forecasting (WRF) models will serve as a prototype for developing the “Warn-on-Forecast” warning paradigm. Feedback from this project will go into developing new model tools capable of managing the large amounts of model information associated with future forecast systems.

During three weeks of the experiment, forecasters will assess a new tool using rapidly-updating high-resolution gridded Probabilistic Hazard Information (PHI) as the basis for next-generation severe weather warnings. This experiment is part of a broad effort to revitalize the NWS watch/warning paradigm known as Forecasting a Continuum of Environmental Threats (FACETs). The major emphasis of the HWT PHI experiment will be on initial testing of concepts related to human-computer interaction while generating short-fused high-impact Probabilistic Hazard Information for severe weather. The long-term goal of this effort is to migrate the refined concepts and methodologies that result from this experiment into Hazard Services, the next generation warning tool for the NWS, for further testing and evaluation in the HWT prior to operational deployment.

This year will mark the inaugural HWT Experiment with Emergency Managers. The EMs will be provide feedback on their interpretation of experimental probabilistic forecasts generated in the HWT from the PHI experiment and the Experimental Forecast Program (EFP). This feedback will be used in conjunction with feedback from forecasters to refine how the uncertainty information is generated and disseminated.

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