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Abstract
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.
Abstract
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.
Abstract
This study focuses on the evolution of the northern end of a bow echo that moved across parts of southwest Mississippi on 8 May 1995. A well-defined, cyclonically rotating “comma head echo” developed north of Natchez, Mississippi, and moved northeast for about 120 km (75 mi) before dissipating. The circulation associated with this comma head echo passed through several diameter changes during which the diameter varied between that of a classic mesocyclone and that more typical of a “bookend vortex.” The circulation and a strong rear inflow jet helped spawn small tornadoes (F0–F2) in Claiborne County, Mississippi, and wind damage in western Hinds County, Mississippi. The observed damage path from the tornadoes was more than 8 km (5 mi) long. For much of the track, the tornadoes paralleled the Natchez Trace, a scenic federal highway that extends from Natchez to Nashville, Tennessee.
Abstract
This study focuses on the evolution of the northern end of a bow echo that moved across parts of southwest Mississippi on 8 May 1995. A well-defined, cyclonically rotating “comma head echo” developed north of Natchez, Mississippi, and moved northeast for about 120 km (75 mi) before dissipating. The circulation associated with this comma head echo passed through several diameter changes during which the diameter varied between that of a classic mesocyclone and that more typical of a “bookend vortex.” The circulation and a strong rear inflow jet helped spawn small tornadoes (F0–F2) in Claiborne County, Mississippi, and wind damage in western Hinds County, Mississippi. The observed damage path from the tornadoes was more than 8 km (5 mi) long. For much of the track, the tornadoes paralleled the Natchez Trace, a scenic federal highway that extends from Natchez to Nashville, Tennessee.
Abstract
Although rare, heavy snowfalls in the southern United States have significant impact and are often associated with distinct surface low pressure systems. However, the central Mississippi record snowfall event of 14 December 1997 displayed mesoscale characteristics and was caused by a rapidly intensifying upper-level system with no surface reflection. Record amounts of unforecast snowfall of up to 8 in. (20.3 cm) occurred. A synoptic and diagnostic analysis of the event determined significant jet streaks, the existence of a middle- and upper-level moisture pool, and a deformation zone with high-level frontogenesis led to the snowfall despite rather unremarkable surface conditions. The system was fully investigated in terms of atmospheric and model diagnostics in an effort to provide clues for an improved forecast. A hindcast simulation of the event using the Pennsylvania State University–National Center for Atmospheric Research fifth-generation Mesoscale Model (MM5) revealed that the use of a mesoscale model in real time may have made a significant difference in forecasts up to 18 h before the event began.
Abstract
Although rare, heavy snowfalls in the southern United States have significant impact and are often associated with distinct surface low pressure systems. However, the central Mississippi record snowfall event of 14 December 1997 displayed mesoscale characteristics and was caused by a rapidly intensifying upper-level system with no surface reflection. Record amounts of unforecast snowfall of up to 8 in. (20.3 cm) occurred. A synoptic and diagnostic analysis of the event determined significant jet streaks, the existence of a middle- and upper-level moisture pool, and a deformation zone with high-level frontogenesis led to the snowfall despite rather unremarkable surface conditions. The system was fully investigated in terms of atmospheric and model diagnostics in an effort to provide clues for an improved forecast. A hindcast simulation of the event using the Pennsylvania State University–National Center for Atmospheric Research fifth-generation Mesoscale Model (MM5) revealed that the use of a mesoscale model in real time may have made a significant difference in forecasts up to 18 h before the event began.
Abstract
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.
Abstract
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.
Abstract
Tornadoes that occur at night pose particularly dangerous societal risks, and these risks are amplified across the southeastern United States. The purpose of this study is to highlight some of the characteristics distinguishing 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 demonstrates 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.
Abstract
Tornadoes that occur at night pose particularly dangerous societal risks, and these risks are amplified across the southeastern United States. The purpose of this study is to highlight some of the characteristics distinguishing 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 demonstrates 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.
Abstract
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.
Significance Statement
Broadcast meteorologists are the primary source of severe weather warning information for the U.S. public. As a result, researchers at NOAA’s National Severe Storms Laboratory and the Cooperative Institute for Mesoscale Meteorological Studies developed a mock television studio to allow broadcast meteorologists to use and communicate experimental products “on air” as part of the research-and-development process. Feedback provided by broadcasters is incorporated into products through an iterative process. Since 2016, 18 broadcasters have tested probabilistic hazard information at the warning time scale (0–1 h) for severe wind and hail, tornadoes, and lightning.
Abstract
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.
Significance Statement
Broadcast meteorologists are the primary source of severe weather warning information for the U.S. public. As a result, researchers at NOAA’s National Severe Storms Laboratory and the Cooperative Institute for Mesoscale Meteorological Studies developed a mock television studio to allow broadcast meteorologists to use and communicate experimental products “on air” as part of the research-and-development process. Feedback provided by broadcasters is incorporated into products through an iterative process. Since 2016, 18 broadcasters have tested probabilistic hazard information at the warning time scale (0–1 h) for severe wind and hail, tornadoes, and lightning.
Abstract
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.
Abstract
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.
Abstract
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.
Abstract
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.
Abstract
During 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. Topics include the following: storm-induced perturbation pressure gradients and their enhancement to supercells, tornadogenesis, tropical cyclone tornadoes, severe wind forecasting, surface and upper-air analyses and their interpretation, and forecast decision-making. This collaborative approach has strengthened bonds between meteorologists in operations, research, and academia, while introducing OU meteorology students to the vast array of severe thunderstorm forecast challenges, state-of-the-art operational and research tools, communication of high-impact weather information, and teamwork skills. The methods of collaborative instruction and experiential education have been found to strengthen both operational–academic relationships and students’ appreciation of the intricacies of severe thunderstorm forecasting, as detailed in this article.
Abstract
During 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. Topics include the following: storm-induced perturbation pressure gradients and their enhancement to supercells, tornadogenesis, tropical cyclone tornadoes, severe wind forecasting, surface and upper-air analyses and their interpretation, and forecast decision-making. This collaborative approach has strengthened bonds between meteorologists in operations, research, and academia, while introducing OU meteorology students to the vast array of severe thunderstorm forecast challenges, state-of-the-art operational and research tools, communication of high-impact weather information, and teamwork skills. The methods of collaborative instruction and experiential education have been found to strengthen both operational–academic relationships and students’ appreciation of the intricacies of severe thunderstorm forecasting, as detailed in this article.
