One year ago, a severe thunderstorm with extreme winds — called a derecho — tore through the Midwest United States. The event brought extensive damage — snapping power poles and damaging an estimated 37.7 million acres of farmland. NOAA estimates indicate this is the costliest thunderstorm event in recorded history for the United States, causing more than $11 billion in damage.
Researchers at the NOAA National Severe Storms Laboratory are studying one of the biggest weather stories of 2020, which occurred at the height of crop growing season. The “Iowa Derecho” had a swath of destructive winds and was not only life-threatening but also obliterated crops in its path.
Predictability varies for thunderstorm events, and many numerical models did not do a particularly good job of helping forecasters anticipate the devastating Iowa event, even the day of the storm. Researchers tested whether the experimental Warn-on-Forecast System (WoFS) could have contributed to an improved forecast of the event.
Researchers expanded the model domain to capture the evolution of the fast-moving and long-lived storm and the results of the forecast runs proved very promising. A forecast based on data that was available 12 hours before the derecho developed correctly predicted a fast-moving, bowing thunderstorm system with significant severe winds (> 75 mph) near the ground. In the future, when a fully developed WoFS becomes available for events such as these, this could lead to earlier anticipation of a high-end event.
Some scientists, like Melissa Wagner, are working in the field to better understand derechos in hopes of providing more accurate warnings in the future. Wagner, a Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) scientist supporting the NSSL, is leading a research effort that’s using drones to gather data on the aftermath of the 2020 derecho.
Wagner’s team used a drone to collect imagery of rural parts of central Iowa hit hard by the derecho in late August and early September. Drones are particularly useful in gathering data on storm damage in rural areas, Wagner said.
Understanding and documenting wind damage helps scientists like Wagner better understand what these storms are capable of and better communicate their risk in the future. It also helps scientists develop more damage indicators for vegetation that are better reflective of storm intensity in rural areas. Wagner’s study is ongoing, and she plans to use UAS to gather this type of data on future derechos and other high-wind events.
The first week of April concluded the end of the 2021 Warn-on-Forecast Testbed Experiment as part of the NOAA Hazardous Weather Testbed. In this experiment, a total of 16 forecasters from nine southern regions National Weather Service Forecast Offices (WFOs), the NOAA NWS Storm Prediction Center (SPC), and the NOAA NWS Weather Prediction Center (WPC) came together over four weeks to explore the use of Warn-on-Forecast System (WoFS) guidance in the watch-to-warning time frame.
Like many other scientific activities, this experiment was delayed and then moved virtually due to the ongoing COVID-19 pandemic. Despite the many challenges this unique situation presented, our research team is pleased to report the experiment was very successful. This success is attributable to the significant efforts of numerous University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) and NOAA National Severe Storms Laboratory scientists, as well as participants and collaborators in the NWS.
Together, Pat Skinner (with OU CIMMS/NSSL), Patrick Burke (with NSSL), Burkely Gallo (with OU CIMMS/SPC), and I (Katie Wilson with OU CIMMS/NSSL) designed and executed this experiment to examine how forecasters envision WoFS guidance fitting into both their existing and visionary forecast processes, and to explore the ways that WoFS guidance can be used most effectively given national center and local office forecasting responsibilities.
A Collaborative Undertaking
During the experiment, Skinner delivered an overview presentation to build familiarity with WoFS guidance prior to participants’ completion of two case studies. These cases formed the first of two major activities during the experiment, which was for participants to immerse themselves in simulated real-time events and use WoFS guidance to make forecast and communication decisions.
To prepare for the case study activity, each week Burke provided participants with a hands-on AWIPS-2 demo. Furthermore, his work to design AWIPS-2 perspectives and procedures, which was conducted jointly with Gallo, enabled a simulation setup that was more familiar to participants, especially to those from national centers who do not use AWIPS-2 in the way local office forecasters do.
The preparation of these case studies was a major task undertaken by Jonny Madden (OU CIMMS/NSSL), Justin Monroe (OU CIMMS/NSSL), Jorge Guerra (OU CIMMS/NSSL), and Dale Morris (OU CIMMS/NWS Warning Decision Training Division).
The case studies presented two notable firsts:
Running AWIPS-2 in-the-cloud such that participants could complete the case studies from their own homes, and;
Presenting WoFS guidance in AWIPS-2, including the development of a tool to visualize paintball plots.
Madden, Monroe, Guerra, and Morris worked together to accomplish numerous tasks, including: aggregating and processing a full suite of observational and model datasets for both cases, setting up the WES- 2 Bridge and AWIPS-2 interfaces, and collaborating with federal partners to get datasets onto the cloud framework. Much of what was accomplished for the case study portion of this experiment has laid the AWIPS-2 in-the-cloud groundwork for future virtual experiments.
The second major activity during the experiment was focus groups. Together, Wilson and Gallo led three semi-structured discussions each week to explore forecasters’ visions for how WoFS will impact the current and future forecast process. Additionally, the presence of both national center and local office forecasters meant that much was learned about each others’ workflows, how one another makes decisions, and where there is an opportunity to strengthen collaboration. In a post-experiment questionnaire, participants rated the focus groups as a highly effective activity for sharing thoughts and ideas, and was the most enjoyed activity of the week.
In addition to the efforts of scientists at OU CIMMS and NOAA NSSL, we were grateful for input from our collaborators at NWS Southern Region, including Chad Gravelle (SR HQ), Todd Lindley (OUN SOO), Stephen Bieda (AMA SOO), and Randy Bowers (OUN). Gravelle and Lindleyalso joined the experiment for multiple weeks, and Randy created two excellent weather briefing videos to prepare forecasters for the case studies. A big thank you also goes to our pilot participants, Laren Reynolds (El Paso, Texas) and Joseph Merchant (Lubbock, Texas), for volunteering their time to fulfill an important support role throughout the whole experiment. This support role emerged following findings from the pilot week, and made for a much stronger experiment.
We are extremely appreciative to the 16 NWS forecasters who participated in this experiment. We realize the stressful conditions many people continue to live and work with, and have done so over the past year. We also realize the disappointment from not being able to attend this experiment in the NOAA Hazardous Weather Testbed, in Norman, Oklahoma, as originally planned. However, participants showed up to our virtual experiment with enthusiasm and made meaningful contributions to the experiment. We collected an enormous amount of data, and we can’t wait to analyze it and share what was learned.
For questions on this or other WoFS-related research please contact WoFS Program Lead, Patrick Burke, firstname.lastname@example.org.
The Annual Meeting of the American Meteorological Society is the largest recurring conference in our field. In January 2021, the 101st Annual Meeting of AMS took place in a virtual venue, but that didn’t stop the experimental Warn-on-Forecast System (WoFS) from taking center stage in a variety of ways. At least seven posters and a dozen oral presentations covered stories specific to WoFS. Many more covered closely related aspects of mesoscale modeling and forecast and warning operations. WoFS presenters included Norman-community researchers and students, but also National Weather Service forecasters from national centers as well as local offices. One group especially well represented were science operations officers from the group of nine Southern Plains NWS offices that have been evaluating WoFS as part of a two-year project. Many showed real-world examples of the ways in which WoFS is already influencing lead time and specificity of information shared with the public and other users.
There was so much enthusiasm for developing WoFS-style probabilistic and rapidly updating guidance — with novel data assimilation for the watch to warning time scales — an entire conference session was dedicated to WoFS and included a panel discussion titled, “Utilization and Development of Rapidly Updating Mesoscale Models for IDSS (Incident Decision Support Services).”
Perhaps no presentation spoke more to the potential utility of WoFS than Patrick Skinner’s talk, “Predictability of the 10 August 2020 Midwest Derecho.” The “Iowa Derecho” was one of the biggest weather stories of 2020. Occurring at the height of the growing season, the swath of destructive winds was not only life-threatening but also obliterated crops in its path, making this the costliest single thunderstorm event in United States history.
Predictability varies for thunderstorm events, and many numerical models did not do a particularly good job of helping forecasters to anticipate such a devastating event, even the day of the storm. To test whether the experimental WoFS could have contributed to an improved forecast of the event, researchers first had to expand the model domain to capture the evolution of such a fast-moving and long-lived storm. Once this had been accomplished, the results of the forecast runs proved very promising. A forecast based on data that was available 12 hours before the derecho correctly predicted a fast-moving, bowing thunderstorm system with significant severe winds (> 75 mph) near the ground. In the loop below, red shading represents the swath of WoFS-predicted significant severe winds, and the small blue squares and red triangles plot the locations where damaging winds and tornadoes, respectively, were observed on August 10, 2020.
In the future, when a fully developed WoFS becomes available for events such as these, this could lead to earlier anticipation of a high-end event. The initial WoFS forecasts were displaced a little to the north of the worst damage, but as the early stage of the storm development got underway, WoFS forecasts adjusted to the correct latitude/location, still with a few hours of lead time before the worst of the storm would have occurred.
Preliminary results indicate the poor depiction of overnight thunderstorms in Nebraska and South Dakota led to large errors in the operational forecast models. The models generated too many thunderstorms early in the forecast period, thus limiting the energy available for daytime storms in Iowa. Employing rapid and high-resolution assimilation of radar and satellite data, the experimental WoFS forecasts better depicted the overnight storms, and therefore better reflected the large amounts of energy available for the damaging daytime storms in Iowa. The group led by Skinner plans to publish this research in the near future.
For questions on this or other WoFS-related research please contact WoFS Program Lead, Patrick Burke, email@example.com.
The NOAA National Severe Storms Laboratory will host the fifth annual Warn-on-Forecast Workshop April 1-3, 2014 at the National Weather Center in Norman, Okla. NSSL’s Warn-on-Forecast research project aims to increase accuracy and lead times for warnings of storm-specific hazards through high-resolution weather prediction models.
The three-day event gives researchers an opportunity to share progress reports on a variety of operational and experimental models, techniques, and decision-making tools in support of the Warn-on-Forecast project.
Researchers will share results from models that attempt to use satellite, lightning, targeted observations, and radar data, including phased array radar data to predict individual thunderstorms. They will report on how these data impact the model by using case studies of past events, and show comparisons with what actually happened. The group will also address the challenge of how to predict the birth of a storm, and share results using various new techniques.
Warn-on-Forecast collaborators include NOAA National Severe Storms Laboratory and Earth System Research Laboratory’s Global Systems Division, NOAA National Weather Service and Storm Prediction Center, and The University of Oklahoma’s Center for the Analysis and Prediction of Storms.
The NOAA NSSL hosted the Technical Workshop on Numerical Guidance Support Warn-on-Forecast on Tuesday February 5.
The fourth annual Warn on Forecast and High Impact Weather Workshop followed on February 6-7.
Warn-on-Forecast http://www.nssl.noaa.gov/projects/wof/collaborators include NSSL and Earth System Research Laboratory, NOAA National Weather Service and Storm Prediction Center, The University of Oklahoma’s Center for the Analysis and Prediction of Storms, and Social Science Woven Into Meteorology.
These workshops give researchers an opportunity to present progress reports and to discuss plans for further research toward improvements in lead time for severe weather warnings.
The NOAA National Severe Storms Laboratory hosted the third annual Warn-on-Forecast Workshop February 8-9 at the National Weather Center in Norman, Okla. Warn-on-Forecast is a National Oceanic and Atmospheric Administration research program tasked to increase tornado, severe thunderstorm, and flash flood warning lead times.
The Warn-on-Forecast workshop gives researchers an opportunity to present progress reports and to discuss plans for further research toward improvements in lead time for severe weather warnings.
Lead times are the time between a warning and when weather actually strikes. Trends in yearly-averaged tornado warning lead time suggest the present weather warning process, largely based upon a warn-on-detection approach using National Weather Service Doppler radars, is reaching a plateau and further increases in lead time will be difficult to obtain. A new approach is needed. Warn-on-Forecast is a convective-scale probabilistic hazardous weather forecast system. Guidance is provided by an ensemble of forecasts from numerical weather prediction models. Further research is needed to develop this system.
Warn-on-Forecast collaborators include NOAA National Severe Storms Laboratory and Earth System Research Laboratory, NOAA National Weather Service and Storm Prediction Center, The University of Oklahoma’s Center for the Analysis and Prediction of Storms, and Social Science Woven Into Meteorology.
The first annual workshop for the Warn-on-Forecast project was held on 23 February 2011 in Norman, Oklahoma, on the University of Oklahoma campus. Warn-on-Forecast is a NOAA research project to create forecasts of severe weather so specific, forecasters will be able to issue a warning based on that forecast before the weather even forms.
The workshop brought together over 60 participants from across the United States to listen to progress reports from all the groups participating in the project.
Focus topics for discussion included a social science research action plan and the benefits of VORTEX2 research to the Warn-on-Forecast project.
These reports indicated that the project is moving forward with research that will lead to improvements in lead time for severe weather warnings. The project also has the potential to benefit a number of different weather information user communities, including surface transportation, aviation, and renewable energy.
The Bulletin of the American Meteorological Society published two articles by NSSL in the October, 2009 issue.
“Convective-scale Warn-On-Forecast System: A Vision for 2020” calls on the research community to develop warning methods in which numerical model forecasts play a much larger role. Current convective-scale hazard warnings are based on observation. A Warn-on-Forecast system would provide longer lead times through an additional layer of warning information containing probabilistic hazard information. Increasing severe thunderstorm, flash flood, and tornado warning lead times is a key NOAA strategic mission goal designed to reduce the loss of life, injury, and economic costs of high-impact weather by providing more trusted weather and water information in support of organized public mitigation activities. The authors of the article are NSSL’s Dave Stensrud, Lou Wicker, Kevin Kelleher, along with Ming Xue (Center for Analysis and Prediction of Storms), Mike Foster (NOAA NWSFO Norman, Okla.), Joe Schaefer and Russ Schneider (NOAA Storm Prediction Center), Stan Benjamin and Steve Weygandt (NOAA ESRL), John Ferree (NOAA NWS Office of Climate, Water and Weather Services), and Jason Tuell (NOAA NWS Office of Science and Technology Policy).
A largely student run project is described in the article “Severe Hazards Analysis and Verification Experiment “ (SHAVE). A project scientist and operations coordinator guide daily activities, with students making phone calls to the public affected by severe thunderstorms. Their job is to collect information on hail sizes, wind damage and flash flooding. The public reports are then blended with high-resolution radar data and geographic information from Google Earth to create a diverse dataset on all types of storms. This information will be used to improve decision-making tools used by the NWS in the forecast and warning process, and pave the way for improvements to the historical severe storms database. SHAVE is expected to continue beyond 2009, with a possible expansion into winter weather verification. The authors are NSSL/Cooperative Institute for Mesoscale Meteorology Studies Kiel Ortega, Travis Smith, Kevin Manross, and Angelyn Kolodziej, Kevin Scharfenberg (NWS Office of Climate, Water and Weather Services), and Arthur Witt and J.J. Gourley (NOAA NSSL).