There are ongoing efforts to move beyond the current paradigm of using deter-ministic products driven by observation-only data to make binary warning decisions. Recent works have focused on severe thunderstorm hazards, such as hail, lightning, and tornadoes. This study discusses one of the first steps toward having probabilistic information combined with convective-scale short-term precipitation forecasts available for the prediction and warning of flash flooding. Participants in the Hydrometeorology Testbed-MRMS Hydrology (HMT-Hydro) experiment evaluated several probabilistic-based hydrologic model output from the probabilistic Flooded Locations and Simulated Hydrographs (PRO-FLASH) system during experimental real-time warning operations. Evaluation of flash flood warning performance combined with product sur-veys highlighted how forecasters perceived biases within the probabilistic information and how the different probabilistic approaches influenced warnings that were verified versus those that were unverified. The incorporation of the Warn-on-Forecast System (WoFS) ensemble precipita-tion forecasts into the PRO-FLASH product generation provided an opportunity to evaluate the first coupling of subhourly convective-scale ensemble precipitation forecasts with probabilistic hydrologic modeling at the flash flood warning time scale through archived case simulations. The addition of WoFS precipitation forecasts resulted in an increase in warning lead time, including four events with >= 29 min of additional lead time but with increased probabilities of false alarms. Additional feedback from participants provided insights into the application of WoFS forecasts into warning decisions, including how flash flood expectations and confidence evolved for verified flash flood events and how forecast probabilistic products can positively influence the communications of the potential for flash flooding.

The occurrence and properties of hail smaller than severe thresholds (diameter < 25 mm) are poorly understood. Prior climatological hail studies have predominantly focused on large or severe hail (diameter at least 25 mm or 1 in.). Through use of data from the Meteorological Phenomena Identification Near the Ground project, Storm Data, and the Community Collaborative Rain, Hail and Snow Network the occurrence and characteristics of both severe and sub-severe hail are explored. Spatial distributions of days with the different classes of hail are developed on an annual and seasonal basis for the period 2013-20. Annually, there are several hail-day maxima that do not follow the maxima of severe hail: the peak is broadly centered over Oklahoma (about 28 days yr(-1)). A secondary maximum exists over the Colorado Front Range (about 26 days yr(-1)), a third extends across northern Indiana from the southern tip of Lake Michigan (about 24 days yr(-1) with hail), and a fourth area is centered over the corners of southwest North Carolina, northwest South Carolina, and the northeast tip of Georgia. Each of these maxima in hail days are driven by sub-severe hail. While similar patterns of severe hail have been previously documented, this is the first clear documentation of sub-severe hail patterns since the early 1990s. Analysis of the hail size distribution suggests that to capture the overall hail risk, each of the datasets provide a complimentary data source.

Broadcast meteorologists play an essential role in communicating severe weather information from the National Weather Service to the public. Because of their importance, researchers incorporated broadcast meteorologists in the development of probabilistic hazard information (PHI) in NOAA's Hazardous Weather Testbed. As part of Forecasting a Continuum of Environmental Threats (FACETs), PHI is meant to bring additional context to severe weather warnings through the inclusion of probability information. Since this information represents a shift in the current paradigm of solely deterministic NWS warnings, understanding end user needs is paramount to create usable and accessible products that result in their intended outcome to serve the public. This paper outlines the establishment of "K-Probabilistic Hazard Information Television" (KPHI-TV), a research infrastructure under the Hazardous Weather Testbed created to study broadcast meteorologists and PHI. A description of the design of KPHI-TV and methods used by researchers are presented, including displaced real-time cases and semistructured interviews. Researchers completed an analysis of the 2018 experiment, using a quantitative analysis of television coverage decisions with PHI, and a thematic analysis of semistructured interviews. Results indicate that no clear probabilistic decision thresholds for PHI emerged among the participants. Other themes arose, including the relationship between PHI and the warning polygon, and communication challenges. Overall, broadcast participants preferred a system that includes PHI over the warning polygon alone, but raised other concerns, suggesting iterative research in the design and implementation of PHI should continue.

This study analyzes the behavior of storm-scale circulations preceding initial tornadogenesis in 179 Kansas and Nebraska storms. Manually determined assessments of radar data for storm-scale circulations preceding the tornadoes are performed as far back in time prior to the tornado as a circulation is apparent, with average rotational velocity (Vrot), circulation diameter, and circulation clarity documented for the 0.5° elevation scan. These data are simultaneously combined with an indication of environmental conditions (as represented by the significant tornado parameter) to determine the tornado probability at each of these times based on a recently developed probabilistic model. By aggregating these parameters in time-range bins, subsequent statistical analyses portray the bulk variability of circulation characteristics and tornado probabilities preceding tornadogenesis. The blended approach for assessing tornado potential yields a stronger relative increase in tornado probabilities leading up to tornadogenesis than the sub-component of average Vrot; this is especially true within 15 min before tornadogenesis. Additionally, significant tornadoes are associated with more substantial increase in tornado probabilities preceding tornadogenesis compared to weak tornadoes, and smaller lead time to tornadogenesis for weak tornadoes. Also, a cycling pattern may appear in velocities prior to significant tornadoes, along with a relative decrease in pretornadic circulation diameter, especially for significant tornadoes. These findings are intended to highlight some of the behaviors of storm-scale circulations and their corresponding environments, which can be used to reinforce meteorologists’ tornado threat assessment. Extending this work to encompass more convective-mode variability, null cases, and geographic expanse will be necessary for more overarching applicability.

NOAA’s Hazardous Weather Testbed (HWT) is a physical space and research framework to foster collaboration and evaluate emerging tools, technology, and products for NWS operations. The HWT’s Experimental Warning Program (EWP) focuses on research, technology, and communication that may improve severe and hazardous weather warnings and societal response. The EWP was established with three fundamental hypotheses: 1) collaboration with operational meteorologists increases the speed of the transition process and rate of adoption of beneficial applications and technology, 2) the transition of knowledge between research and operations benefits both the research and operational communities, and 3) including end users in experiments generates outcomes that are more reliable and useful for society. The EWP is designed to mimic the operations of any NWS Forecast Office, providing the opportunity for experiments to leverage live and archived severe weather activity anywhere in the United States. During the first decade of activity in the EWP, 15 experiments covered topics including new radar and satellite applications, storm-scale numerical models and data assimilation, total lightning use in severe weather forecasting, and multiple social science and end-user topics. The experiments range from exploratory and conceptual research to more controlled experimental design to establish statistical patterns and causal relationships. The EWP brought more than 400 NWS forecasters, 60 emergency managers, and 30 broadcast meteorologists to the HWT to participate in live demonstrations, archive events, and data-denial experiments influencing today’s operational warning environment and shaping the future of warning research, technology, and communication for years to come.

The Multi-Radar Multi-Sensor (MRMS) system is an operational, state-of-the-science hydrometeorological data analysis and nowcasting framework that combines data from multiple radar networks, satellites, surface observational systems, and numerical weather prediction models to produce a suite of real-time, decision-support products every 2 min over the contiguous United States and southern Canada. The Flooded Locations and Simulated Hydrograph (FLASH) component of the MRMS system was designed for the monitoring and prediction of flash floods across small time and spatial scales required for urban areas given their rapid hydrologic response to precipitation. Developed at the National Severe Storms Laboratory in collaboration with the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) and other research entities, the objective for MRMS and FLASH is to be the world's most advanced system for severe weather and storm-scale hydrometeorology, leveraging the latest science and observation systems to produce the most accurate and reliable hydrometeorological and severe weather analyses. NWS forecasters, the public, and the private sector utilize a variety of products from the MRMS and FLASH systems for hydrometeorological situational awareness and to provide warnings to the public and other users about potential impacts from flash flooding. This article will examine the performance of hydrometeorological products from MRMS and FLASH and provide perspectives on how NWS forecasters use these products in the prediction of flash flood events with an emphasis on the urban environment.

Threats-in-Motion (TIM) is a warning generation approach that would enable the NWS to advance severe thunderstorm and tornado warnings from the current static polygon system to continuously updating polygons that move forward with a storm. This concept is proposed as a first stage for implementation of the Forecasting a Continuum of Environmental Threats (FACETs) paradigm, which eventually aims to deliver rapidly updating probabilistic hazard information alongside NWS warnings, watches, and other products. With TIM, a warning polygon is attached to the threat and moves forward along with it. This provides more uniform, or equitable, lead time for all locations downstream of the event. When forecaster workload is high, storms remain continually tracked and warned. TIM mitigates gaps in warning coverage and improves the handling of storm motion changes. In addition, warnings are automatically cleared from locations where the threat has passed. This all results in greater average lead times and lower average departure times than current NWS warnings, with little to no impact to average false alarm time. This is particularly noteworthy for storms expected to live longer than the average warning duration (30 or 45 min) such as long-tracked supercells that are more prevalent during significant tornado outbreaks.

Currently, a binary alarm system is used in the United States to issue deterministic warning polygons in case of tornado events. To enhance the effectiveness of the weather information, a likelihood alarm system, which uses a tool called probabilistic hazard information (PHI), is being developed at National Severe Storms Laboratory to issue probabilistic information about the threat. This study aims to investigate the effects of providing the uncertainty information about a tornado occurrence through the PHI's graphical swath on laypeople's concern, fear, and protective action, as compared with providing the warning information with the deterministic polygon. The displays of color-coded swaths and deterministic polygons were shown to subjects. Some displays had a blue background denoting the probability of any tornado formation in the general area. Participants were asked to report their levels of concern, fear, and protective action at randomly chosen locations within each of seven designated levels on each display. Analysis of a three-stage nested design showed that providing the uncertainty information via the PHI would appropriately increase recipients' levels of concern, fear, and protective action in highly dangerous scenarios, with a more than 60% chance of being affected by the threat, as compared with deterministic polygons. The blue background and the color-coding type did not have a significant effect on the people's cognition of the threat and reaction to it. This study shows that using a likelihood alarm system leads to more conscious decision making by the weather information recipients and enhances the system safety.

Tornadoesthatoccur at nightposeparticularlydangeroussocietal risks,and theserisks are amplified across the southeastern United States. The purpose of this study is to highlight some of the characteristics dis-tinguishing the convective environment accompanying these events. This is accomplished by building upon previous research that assesses the predictive power of meteorological parameters. In particular, this study uses the Statistical Severe Convective Risk Assessment Model (SSCRAM) to determine how well convective parameters explain tornado potential across the Southeast during the months of November–May and during the 0300–1200 UTC (nocturnal) time frame. This study compares conditional tornado probabilities across the Southeast during November–May nocturnal hours to those probabilities for all other November–May environments across the contiguous United States. This study shows that effective bulk shear, effective storm-relative helicity, and effective-layer significant tornado parameter yield the strongest predictability for the November–May nocturnal Southeast regime among investigated parameters. This study demon- strates that November–May southeastern U.S. nocturnal predictability is generally similar to that within other regimes across the contiguous United States. However, selected ranges of multiple parameters are associated with slightly better predictability for the nocturnal Southeast regime. Additionally, this study assesses conditional November–May nocturnal tornado probabilities across a coastal domain embedded within the Southeast. Nocturnal coastal tornado predictability is shown to generally be lower than the other regimes. All of the differences highlight several forecast challenges, which this study analyzes in detail.

National Weather Service issues deterministic warnings in a tornado event. An alternative system is being researched at National Severe Storms Laboratory to issue Probabilistic Hazard Information (PHI). This study investigated how providing the uncertainty information about the tornado occurrence through PHI changes people's protective actions. In an experiment, visual displays of the probabilistic information and deterministic warnings were presented to fifty participants to report their expected protective actions in different scenarios. It was found that the percentage of people who expected to immediately take shelter right after receiving the weather information increased exponentially as their proximity to the threat decreased. When there was more chance that the information about occurrence of a particular tornado was false rather than true, in scenarios that the likelihood of the threat occurrence was less than 50%, providing it through PHI lowered the percentage of people who immediately took shelter. The ordinal logistic regression models showed that the probability of taking protective actions significantly changes by providing the uncertainty information when people have less than 20min lead time before getting impacted by the threat. When the lead time is less than 10min, the probability of immediately taking shelter increases to 94 from 71%, and when the lead time is more than 10 but less than 20min, that probability increases from 53 to 70%, if they are provided with the probabilistic information. Presenting the likelihood of any tornado formation in the area did not have significant effect on the people's protective actions.

AbstractDuring the 2014–15 academic year, the National Oceanic and Atmospheric Administration (NOAA) National Weather Service Storm Prediction Center (SPC) and the University of Oklahoma (OU) School of Meteorology jointly created the first SPC-led course at OU focused on connecting traditional theory taught in the academic curriculum with operational meteorology. This class, “Applications of Meteorological Theory to Severe-Thunderstorm Forecasting,” began in 2015. From 2015 through 2017, this spring–semester course has engaged 56 students in theoretical skills and related hands-on weather analysis and forecasting applications, taught by over a dozen meteorologists from the SPC, the NOAA National Severe Storms Laboratory, and the NOAA National Weather Service Forecast Offices. Following introductory material, which addresses many theoretical principles relevant to operational meteorology, numerous presentations and hands-on activities focused on instructors’ areas of expertise are provided to students. Top...

Recommendations by the National Research Council (NRC), the National Institute of Standards and Technology (NIST), and Weather-Ready Nation workshop participants have encouraged the National Oceanic and Atmospheric Administration (NOAA) and the broader weather enterprise to explore and expand the use of probabilistic information to convey weather forecast uncertainty. Forecasting a Continuum of Environmental Threats (FACETs) is a concept being explored by NOAA to address those recommendations and also potentially shift the National Weather Service (NWS) from (primarily) teletype-era, deterministic watch-warning products to high-resolution, probabilistic hazard information (PHI) spanning periods from days (and longer) to within minutes of high-impact weather and water events. FACETs simultaneously i) considers a reinvention of the NWS hazard forecasting and communication paradigm so as to deliver multiscale, user-specific probabilistic guidance from numerical weather prediction ensembles and ii) provides a comprehensive framework to organize the physical, social, and behavioral sciences, the technology, and the practices needed to achieve that reinvention. The first applications of FACETs have focused on thunderstorm phenomena, but the FACETs concept is envisioned to extend to the attributes of any environmental hazards that can be described probabilistically (e.g., winter, tropical, and aviation weather). This paper introduces the FACETs vision, the motivation for its creation, the research and development under way to explore that vision, its relevance to operational forecasting and society, and possible strategies for implementation.

Providing advance warning for impending severe convective weather events (i.e., tornadoes, hail, wind) fundamentally requires an ability to predict and/or detect these hazards and subsequently communicate their potential threat in real time. The National Weather Service (NWS) provides advance warning for severe convective weather through the issuance of tornado and severe thunderstorm warnings, a system that has remained relatively unchanged for approximately the past 65 years. Forecasting a Continuum of Environmental Threats (FACETs) proposes a reinvention of this system, transitioning from a deterministic product-centric paradigm to one based on probabilistic hazard information (PHI) for hazardous weather events. Four years of iterative development and rapid prototyping in the National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) with NWS forecasters and partners has yielded insights into this new paradigm by discovering efficient ways to generate, inform, and utilize a continuous flow of information through the development of a human-machine mix. Forecasters conditionally used automated object-based guidance within four levels of automation to issue deterministic products containing PHI. Forecasters accomplished this task in a timely manner while focusing on communication and conveying forecast confidence, elements considered necessary by emergency managers. Observed annual increases in the usage of first-guess probabilistic guidance by forecasters were related to improvements made to the prototyped software, guidance, and techniques. However, increasing usage of automation requires improvements in guidance, data integration, and data visualization to garner trust more effectively. Additional opportunities exist to address limitations in procedures for motion derivation and geospatial mapping of subjective probability.

For Emergency Managers (EMs), preparations for severe weather have always relied on accurate, well-communicated National Weather Service (NWS) forecasts. As part of their constant work to improve these forecasts, the NWS has recently begun to develop impact-based products that share forecast uncertainty information with EMs, including the Probabilistic Hazard Information (PHI) tool. However, there is a lack of research investigating what forecast uncertainty information EMs understand, and what information needs exist in the current communication paradigm. This study used the Critical Incident Technique to identify themes from incidents involving weather forecast information that went well, or not so well, from the perspective of the EMs responding to them. In total, 11 EMs from a variety of locales east of the Rockies were interviewed—six of whom were county-level, two city, two state, and one from a school district. We found that EMs sought increased forecast detail as a potential event approached in time and built relational trust in the NWS through repeated interactions. EMs had difficulty preparing for events when they did not have details of the expected impacts, or the likelihood of those impacts, for their regions. In summary, EMs are already starting to work in an uncertainty-friendly frame and could be responsive to the impact details and increased forecaster relations proposed with the PHI tool.

Effective conveyance of hazard information to society is crucial for enhancing public preparedness for the uncertainty involved in the occurrence of tornadoes. The WarnGen system is currently used for issuing deterministic severe weather warnings. To provide society with probabilistic weather information, researchers at The National Severe Storm Laboratory are developing a tool called Probabilistic Hazard Information (PHI). The current study reports an experiment intended to figure out if including information about the uncertainty of a tornado occurrence provided through PHI would help people understand the weather information better and take more appropriate protective action. Our group also wanted to investigate if including probabilistic information about the surrounding area of the target zone would be effective in people’s decision making regarding their safety. The results show that including information about uncertainty in the weather information makes it more effective than the deterministic hazard information in terms of perception, cognition, and protective actions of recipients. Also, presence of information about the target zone’s surrounding area does not have any significant effect on their decision making.

Shortly after 0000 UTC on 10 December 1999, a relatively low topped supercell moved along the Big Black River in central Mississippi, producing a short track F3 tornado near the small town of Bentonia. This paper will present a multiscale meteorological analysis of the event. To begin, the overall synoptic pattern will be discussed, as well as the near storm environment as indicated by the 0000 UTC 10 December upper air sounding from Jackson, MS (JAN), which was a near proximity sounding for the storm. Storm scale analysis utilizing Weather Surveillance Radar-88 Doppler (WSR-88D) data will then be presented. Additionally, implications that this event has for the warning process will be discussed.