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Abstract
The paths of tornadoes have long been a subject of fascination since the meticulously drawn damage tracks by Dr. Tetsuya Theodore “Ted” Fujita. Though uncommon, some tornadoes have been noted to take sudden left turns from their previous path. This has the potential to present an extreme challenge to warning lead time, and the spread of timely, accurate information to broadcasters and emergency managers. While a few hypotheses exist as to why tornadoes deviate, none have been tested for their potential use in operational forecasting and nowcasting. As a result, such deviations go largely unanticipated by forecasters. A sample of 102 leftward deviant tornadic low-level mesocyclones was tracked via WSR-88D and assessed for their potential predictability. A simple hodograph technique is presented that shows promising skill in predicting the motion of deviant tornadoes, which, upon “occlusion,” detach from the parent storm’s updraft centroid and advect leftward or rearward by the low-level wind. This metric, a vector average of the parent storm motion and the mean wind in the lowest half-kilometer, proves effective at anticipating deviant tornado motion with a median error of less than 6 kt (1 kt ≈ 0.51 m s−1). With over 25% of analyzed low-level mesocyclones deviating completely out of the tornado warning polygon issued by their respective National Weather Service Weather Forecast Office, the adoption of this new technique could improve warning performance. Furthermore, with over 35% of tornadoes becoming “deviant” almost immediately upon formation, the ability to anticipate such events may inspire a new paradigm for tornado warnings that, when covering unpredictable behavior, are proactive instead of reactive.
Abstract
The paths of tornadoes have long been a subject of fascination since the meticulously drawn damage tracks by Dr. Tetsuya Theodore “Ted” Fujita. Though uncommon, some tornadoes have been noted to take sudden left turns from their previous path. This has the potential to present an extreme challenge to warning lead time, and the spread of timely, accurate information to broadcasters and emergency managers. While a few hypotheses exist as to why tornadoes deviate, none have been tested for their potential use in operational forecasting and nowcasting. As a result, such deviations go largely unanticipated by forecasters. A sample of 102 leftward deviant tornadic low-level mesocyclones was tracked via WSR-88D and assessed for their potential predictability. A simple hodograph technique is presented that shows promising skill in predicting the motion of deviant tornadoes, which, upon “occlusion,” detach from the parent storm’s updraft centroid and advect leftward or rearward by the low-level wind. This metric, a vector average of the parent storm motion and the mean wind in the lowest half-kilometer, proves effective at anticipating deviant tornado motion with a median error of less than 6 kt (1 kt ≈ 0.51 m s−1). With over 25% of analyzed low-level mesocyclones deviating completely out of the tornado warning polygon issued by their respective National Weather Service Weather Forecast Office, the adoption of this new technique could improve warning performance. Furthermore, with over 35% of tornadoes becoming “deviant” almost immediately upon formation, the ability to anticipate such events may inspire a new paradigm for tornado warnings that, when covering unpredictable behavior, are proactive instead of reactive.
Abstract
Hodographs are valuable sources of pattern recognition in severe convective storm forecasting. Certain shapes are known to discriminate between single cell, multicell, and supercell storm organization. Various derived quantities such as storm-relative helicity (SRH) have been found to predict tornado potential and intensity. Over the years, collective research has established a conceptual model for tornadic hodographs (large and “looping,” with high SRH). However, considerably less attention has been given to constructing a similar conceptual model for hodographs of severe hail. This study explores how hodograph shape may differentiate between the environments of severe hail and tornadoes. While supercells are routinely assumed to carry the potential to produce all hazards, this is not always the case, and we explore why. The Storm Prediction Center (SPC) storm mode dataset is used to assess the environments of 8958 tornadoes and 7256 severe hail reports, produced by right- and left-moving supercells. Composite hodographs and indices to quantify wind shear are assessed for each hazard, and clear differences are found between the kinematic environments of hail-producing and tornadic supercells. The sensitivity of the hodograph to common thermodynamic variables was also examined, with buoyancy and moisture found to influence the shape associated with the hazards. The results suggest that differentiating between tornadic and hail-producing storms may be possible using properties of the hodograph alone. While anticipating hail size does not appear possible using only the hodograph, anticipating tornado intensity appears readily so. When coupled with buoyancy profiles, the hodograph may assist in differentiating between both hail size and tornado intensity.
Abstract
Hodographs are valuable sources of pattern recognition in severe convective storm forecasting. Certain shapes are known to discriminate between single cell, multicell, and supercell storm organization. Various derived quantities such as storm-relative helicity (SRH) have been found to predict tornado potential and intensity. Over the years, collective research has established a conceptual model for tornadic hodographs (large and “looping,” with high SRH). However, considerably less attention has been given to constructing a similar conceptual model for hodographs of severe hail. This study explores how hodograph shape may differentiate between the environments of severe hail and tornadoes. While supercells are routinely assumed to carry the potential to produce all hazards, this is not always the case, and we explore why. The Storm Prediction Center (SPC) storm mode dataset is used to assess the environments of 8958 tornadoes and 7256 severe hail reports, produced by right- and left-moving supercells. Composite hodographs and indices to quantify wind shear are assessed for each hazard, and clear differences are found between the kinematic environments of hail-producing and tornadic supercells. The sensitivity of the hodograph to common thermodynamic variables was also examined, with buoyancy and moisture found to influence the shape associated with the hazards. The results suggest that differentiating between tornadic and hail-producing storms may be possible using properties of the hodograph alone. While anticipating hail size does not appear possible using only the hodograph, anticipating tornado intensity appears readily so. When coupled with buoyancy profiles, the hodograph may assist in differentiating between both hail size and tornado intensity.
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
Climate Forecast System, version 2, predictions of monthly U.S. severe thunderstorm activity are analyzed for the period 1982–2016. Forecasts are based on a tornado environmental index and a hail environmental index, which are functions of monthly averaged storm relative helicity (SRH), convective precipitation (cPrcp), and convective available potential energy (CAPE). Overall, forecast indices reproduce well the annual cycle of tornado and hail events. Forecast index biases are mostly negative and caused by environment values that are low east of the Rockies, although forecast CAPE is higher than the reanalysis values over the High Plains. Skill is diagnosed spatially for the indices and their constituents separately. SRH is more skillfully forecast than cPrcp and CAPE, especially during December–June. The spatial patterns of forecast skill for CAPE and cPrcp are similar, with higher skill for CAPE and less spatial coherence for cPrcp. The indices are forecast with substantially less skill than the environmental parameters. Numbers of tornado and hail events are forecast with modest but statistically significant skill in some NOAA regions and months of the year. Skill tends to be relatively higher for hail events and in climatologically active seasons and regions. Much of the monthly skill appears to be derived from the first 2 weeks of the forecast. El Niño–Southern Oscillation (ENSO) modulates forecasts and, to a lesser extent, forecast skill, during March–May, with more activity and higher skill during cool ENSO conditions.
Abstract
Climate Forecast System, version 2, predictions of monthly U.S. severe thunderstorm activity are analyzed for the period 1982–2016. Forecasts are based on a tornado environmental index and a hail environmental index, which are functions of monthly averaged storm relative helicity (SRH), convective precipitation (cPrcp), and convective available potential energy (CAPE). Overall, forecast indices reproduce well the annual cycle of tornado and hail events. Forecast index biases are mostly negative and caused by environment values that are low east of the Rockies, although forecast CAPE is higher than the reanalysis values over the High Plains. Skill is diagnosed spatially for the indices and their constituents separately. SRH is more skillfully forecast than cPrcp and CAPE, especially during December–June. The spatial patterns of forecast skill for CAPE and cPrcp are similar, with higher skill for CAPE and less spatial coherence for cPrcp. The indices are forecast with substantially less skill than the environmental parameters. Numbers of tornado and hail events are forecast with modest but statistically significant skill in some NOAA regions and months of the year. Skill tends to be relatively higher for hail events and in climatologically active seasons and regions. Much of the monthly skill appears to be derived from the first 2 weeks of the forecast. El Niño–Southern Oscillation (ENSO) modulates forecasts and, to a lesser extent, forecast skill, during March–May, with more activity and higher skill during cool ENSO conditions.
Abstract
Tornadoes that occur during the cold season, defined here as November–February (NDJF), pose many societal risks, yet less attention has been given to their climatological trends and variability than their warm-season counterparts, and their meteorological environments have been studied relatively recently. This study aims to advance the current state of knowledge of cold-season tornadoes through analysis of these components. A climatology of all (E)F1–(E)F5 NDJF tornadoes from 1953 to 2015 across a domain of 25°–42.5°N, 75°–100°W was developed. An increasing trend in cold-season tornado occurrence was found across much of the southeastern United States, with a bull’s-eye in western Tennessee, while a decreasing trend was found across eastern Oklahoma. Spectral analysis reveals a cyclic pattern of enhanced NDJF counts every 3–7 years, coincident with the period of ENSO. La Niña episodes favor enhanced NDJF counts, but a stronger relationship was found with the Arctic Oscillation (AO). From a meteorological standpoint, the most-tornadic and least-tornadic NDJF seasons were compared using NCEP–NCAR reanalysis data of various severe weather and tornado parameters. The most-tornadic cold seasons are characterized by warm and moist conditions across the Southeast, with an anomalous mean trough across the western United States. In addition, analysis of the convective mode reveals that NDJF tornadoes are common in both discrete and linear storm modes, yet those associated with discrete supercells are more deadly. Taken together, the perspectives presented here provide a deeper understanding of NDJF tornadoes and their societal impacts, an understanding that serves to increase public awareness and reduce human casualty.
Abstract
Tornadoes that occur during the cold season, defined here as November–February (NDJF), pose many societal risks, yet less attention has been given to their climatological trends and variability than their warm-season counterparts, and their meteorological environments have been studied relatively recently. This study aims to advance the current state of knowledge of cold-season tornadoes through analysis of these components. A climatology of all (E)F1–(E)F5 NDJF tornadoes from 1953 to 2015 across a domain of 25°–42.5°N, 75°–100°W was developed. An increasing trend in cold-season tornado occurrence was found across much of the southeastern United States, with a bull’s-eye in western Tennessee, while a decreasing trend was found across eastern Oklahoma. Spectral analysis reveals a cyclic pattern of enhanced NDJF counts every 3–7 years, coincident with the period of ENSO. La Niña episodes favor enhanced NDJF counts, but a stronger relationship was found with the Arctic Oscillation (AO). From a meteorological standpoint, the most-tornadic and least-tornadic NDJF seasons were compared using NCEP–NCAR reanalysis data of various severe weather and tornado parameters. The most-tornadic cold seasons are characterized by warm and moist conditions across the Southeast, with an anomalous mean trough across the western United States. In addition, analysis of the convective mode reveals that NDJF tornadoes are common in both discrete and linear storm modes, yet those associated with discrete supercells are more deadly. Taken together, the perspectives presented here provide a deeper understanding of NDJF tornadoes and their societal impacts, an understanding that serves to increase public awareness and reduce human casualty.
Abstract
The tornado outbreak of 21–23 January 2017 caused 20 fatalities, more than 200 injuries, and over a billion dollars in damage in the Southeast United States. The event occurred concurrently with a record-breaking warm Gulf of Mexico (GoM) basin. This article explores the influence that warm GoM sea surface temperatures (SSTs) had on the tornado outbreak. Backward trajectory analysis, combined with a Lagrangian-based moisture-attribution algorithm, reveals that the tornado outbreak’s moisture predominantly originated from the southeast GoM and the northwest Caribbean Sea. We used the WRF Model to generate a control simulation of the event and explore the response to perturbed SSTs. With the aid of a tornadic storm proxy derived from updraft helicity, we show that the 21–23 January 2017 tornado outbreak exhibits sensitivity to upstream SSTs during the first day of the event. Warmer SSTs across remote moisture sources and adjacent waters increase tornado frequency, in contrast to cooler SSTs, which reduce tornado activity. Upstream SST sensitivity is reduced once convection is ongoing and modifying local moisture and instability availability. Our results highlight the importance of air–sea interactions before airmass advection toward the continental United States. The complex and nonlinear nature of the relationship between upstream SSTs and local precursor environments is also discussed.
Abstract
The tornado outbreak of 21–23 January 2017 caused 20 fatalities, more than 200 injuries, and over a billion dollars in damage in the Southeast United States. The event occurred concurrently with a record-breaking warm Gulf of Mexico (GoM) basin. This article explores the influence that warm GoM sea surface temperatures (SSTs) had on the tornado outbreak. Backward trajectory analysis, combined with a Lagrangian-based moisture-attribution algorithm, reveals that the tornado outbreak’s moisture predominantly originated from the southeast GoM and the northwest Caribbean Sea. We used the WRF Model to generate a control simulation of the event and explore the response to perturbed SSTs. With the aid of a tornadic storm proxy derived from updraft helicity, we show that the 21–23 January 2017 tornado outbreak exhibits sensitivity to upstream SSTs during the first day of the event. Warmer SSTs across remote moisture sources and adjacent waters increase tornado frequency, in contrast to cooler SSTs, which reduce tornado activity. Upstream SST sensitivity is reduced once convection is ongoing and modifying local moisture and instability availability. Our results highlight the importance of air–sea interactions before airmass advection toward the continental United States. The complex and nonlinear nature of the relationship between upstream SSTs and local precursor environments is also discussed.
Abstract
Environments associated with severe hailstorms, compared to those of tornadoes, are often less apparent to forecasters. Understanding has evolved considerably in recent years; namely, that weak low-level shear and sufficient convective available potential energy (CAPE) above the freezing level is most favorable for large hail. However, this understanding comes only from examining the mean characteristics of large hail environments. How much variety exists within the kinematic and thermodynamic environments of large hail? Is there a balance between shear and CAPE analogous to that noted with tornadoes? We address these questions to move toward a more complete conceptual model. In this study, we investigate the environments of 92 323 hail reports (both severe and nonsevere) using ERA5 modeled proximity soundings. By employing a self-organizing map algorithm and subsetting these environments by a multitude of characteristics, we find that the conditions leading to large hail are highly variable, but three primary patterns emerge. First, hail growth depends on a favorable balance of CAPE, wind shear, and relative humidity, such that accounting for entrainment is important in parameter-based hail prediction. Second, hail growth is thwarted by strong low-level storm-relative winds, unless CAPE below the hail growth zone is weak. Finally, the maximum hail size possible in a given environment may be predictable by the depth of buoyancy, rather than CAPE itself.
Abstract
Environments associated with severe hailstorms, compared to those of tornadoes, are often less apparent to forecasters. Understanding has evolved considerably in recent years; namely, that weak low-level shear and sufficient convective available potential energy (CAPE) above the freezing level is most favorable for large hail. However, this understanding comes only from examining the mean characteristics of large hail environments. How much variety exists within the kinematic and thermodynamic environments of large hail? Is there a balance between shear and CAPE analogous to that noted with tornadoes? We address these questions to move toward a more complete conceptual model. In this study, we investigate the environments of 92 323 hail reports (both severe and nonsevere) using ERA5 modeled proximity soundings. By employing a self-organizing map algorithm and subsetting these environments by a multitude of characteristics, we find that the conditions leading to large hail are highly variable, but three primary patterns emerge. First, hail growth depends on a favorable balance of CAPE, wind shear, and relative humidity, such that accounting for entrainment is important in parameter-based hail prediction. Second, hail growth is thwarted by strong low-level storm-relative winds, unless CAPE below the hail growth zone is weak. Finally, the maximum hail size possible in a given environment may be predictable by the depth of buoyancy, rather than CAPE itself.
Abstract
Rapid-scan radar observations of a supercell that produced near-record size hail in Oklahoma are examined. Data from the National Weather Radar Testbed Phased Array Radar (PAR) in Norman, Oklahoma, are used to study the overall character and evolution of the storm. Data from the nearby polarimetric KOUN WSR-88D and rapid-scanning X-band polarimetric (RaXPol) mobile radar are used to study the evolution of low- to midaltitude dual-polarization parameters above two locations where giant hailstones up to 16 cm in diameter were observed. The PAR observation of the supercell’s maximum storm-top divergent outflow is similar to the strongest previously documented value. The storm’s mesocyclone rotational velocity at midaltitudes reached a maximum that is more than double the median value for similar observations from other storms producing giant hail. For the two storm-relative areas where giant hail was observed, noteworthy findings include 1) the giant hail occurred outside the main precipitation core, in areas with low-altitude reflectivities of 40–50 dBZ; 2) the giant hail was associated with dual-polarization signatures consistent with past observations of large hail at 10-cm wavelength, namely, low Z DR, low ρ HV, and low K DP; 3) the giant hail fell along both the northeast and southwest edges of the primary updraft at ranges of 6–10 km from the updraft center; and 4) with the exception of one isolated report, the giant hail fell to the northeast and northwest of the large tornado and the parent mesocyclone.
Abstract
Rapid-scan radar observations of a supercell that produced near-record size hail in Oklahoma are examined. Data from the National Weather Radar Testbed Phased Array Radar (PAR) in Norman, Oklahoma, are used to study the overall character and evolution of the storm. Data from the nearby polarimetric KOUN WSR-88D and rapid-scanning X-band polarimetric (RaXPol) mobile radar are used to study the evolution of low- to midaltitude dual-polarization parameters above two locations where giant hailstones up to 16 cm in diameter were observed. The PAR observation of the supercell’s maximum storm-top divergent outflow is similar to the strongest previously documented value. The storm’s mesocyclone rotational velocity at midaltitudes reached a maximum that is more than double the median value for similar observations from other storms producing giant hail. For the two storm-relative areas where giant hail was observed, noteworthy findings include 1) the giant hail occurred outside the main precipitation core, in areas with low-altitude reflectivities of 40–50 dBZ; 2) the giant hail was associated with dual-polarization signatures consistent with past observations of large hail at 10-cm wavelength, namely, low Z DR, low ρ HV, and low K DP; 3) the giant hail fell along both the northeast and southwest edges of the primary updraft at ranges of 6–10 km from the updraft center; and 4) with the exception of one isolated report, the giant hail fell to the northeast and northwest of the large tornado and the parent mesocyclone.
