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Michael M. French and Darrel M. Kingfield

Abstract

Weather Surveillance Radar–1988 Doppler (WSR-88D) data from 36 tornadic supercell cases from 2012 to 2016 are investigated to identify common tornadic vortex signature (TVS) behaviors prior to tornado dissipation. Based on the results of past case studies, four characteristics of TVSs associated with tornado dissipation were identified: weak or decreasing TVS intensity, rearward storm-relative motion of the TVS, large or increasing TVS vertical tilt, and large or increasing TVS horizontal displacement from the main storm updraft. Only cases in which a TVS was within 60 km of a WSR-88D site in at least four consecutive volumes at the end of the tornado life cycle were examined. The space and time restrictions on case selection ensured that the aforementioned quantities could be determined within ~500 m of the surface at several time periods despite the relatively coarse spatiotemporal resolution of WSR-88D systems. It is found that prior to dissipation, TVSs become increasingly less intense, tend to move rearward in a storm-relative framework, and become increasingly more separated from the approximate location of the main storm updraft. There is no clear signal in the relationship between tornado tilt, as measured in inclination angle, and TVS dissipation. The frequency of combinations of TVS dissipation behaviors, the impact of increased low-level WSR-88D scanning on dissipation detection, and prospects for future nowcasting of tornado life cycles also are discussed.

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Darrel M. Kingfield and Michael M. French

Abstract

The Weather Surveillance Radar - 1988 Doppler (WSR-88D) network has undergone several improvements in the last decade with the upgrade to dual-polarization capabilities and the ability for forecasters to re-scan the lowest levels of the atmosphere more frequently through the use of Supplemental Adaptive Intra-volume Scanning (SAILS). SAILS reduces the revisit period for scanning the lowest 1 km of the atmosphere but comes at the cost of a longer delay between scans at higher altitudes. This study quantifies how often radar Volume Coverage Patterns (VCPs) and all available SAILS options are used during the issuance of 148,882 severe thunderstorm and 18,263 tornado warnings, and near 10,474 tornado, 58,934 hail, and 127,575 wind reports in the dual-polarization radar era.

A large majority of warnings and storm reports were measured with a VCP providing denser low-level sampling coverage. More frequent low-level updates were employed near tornado warnings and reports compared to severe thunderstorm warnings and hail or wind hazards. Warnings issued near a radar providing three extra low-level scans (SAILSx3) were more likely to be verified by a hazard with a positive lead time than warnings with fewer low-level scans. However, extra low-level scans were more frequently used in environments supporting organized convection as shown using watches issued by the Storm Prediction Center. Recently, the number of mid-level radar elevation scans is declining per hour, which can adversely affect the tracking of convective polarimetric signatures, like ZDR columns, which were found above the 0.5° elevation angle in over 99% of cases examined.

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Darrel M. Kingfield and Joseph C. Picca

Abstract

Raindrop size sorting is a ubiquitous microphysical occurrence in precipitating systems. Owing to the greater terminal fall speed of larger particles, a raindrop’s fall trajectory can be sensitive to its size, and strong air currents (e.g., a convective updraft) can enhance this sensitivity. Indeed, observational and numerical model simulation studies have confirmed these effects on raindrop size distributions near convective updrafts. One striking example is the lofting of liquid drops and partially frozen hydrometeors above the environmental 0°C level, resulting in a small columnar region of positive differential reflectivity Z DR in polarimetric radar data, known as the Z DR column. This signature can serve as a proxy for updraft location and strength, offering operational forecasters a tool for monitoring convective trends. Beneath the 0°C level, where WSR-88D spatiotemporal resolution is highest, anomalously high Z DR collocated with lower reflectivity factor at horizontal polarization Z H is often observed within and beneath convective updrafts. Here, size sorting creates a deficit in small drops, while relatively large drops and melting hydrometeors are present in low concentrations. As such, this unique raindrop size distribution and its related polarimetric signature can indicate updraft location sooner and more frequently than the detection of a Z DR column. This paper introduces a novel algorithm that capitalizes on the improved radar coverage at lower levels and automates the detection of this size sorting signature. At the algorithm core, unique Z HZ DR relationships are created for each radar elevation scan, and positive Z DR outliers (often indicative of size sorting) are identified. Algorithm design, examples, performance, strengths and limitations, and future development are discussed.

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Valliappa Lakshmanan, Benjamin Herzog, and Darrel Kingfield

Abstract

Although existing algorithms for storm tracking have been designed to operate in real time, they are also commonly used to do postevent data analysis and research. Real-time algorithms cannot use information on the subsequent positions of a storm because it is not available at the time that associations between frames are made, but postevent analysis is not similarly constrained. Therefore, it should be possible to obtain better tracks for postevent analysis than those that a real-time algorithm is capable of producing. In this paper, a statistical procedure for determining storm tracks from a set of identified storm cells over time is described. It is found that this procedure results in fewer, longer-lived tracks at the potential cost of a small increase in positional error.

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Darrel M. Kingfield and James G. LaDue

Abstract

The relationship between automated low-level velocity derived from WSR-88D severe storm algorithms and two groups of tornado intensity were evaluated using a 4-yr climatology of 1975 tornado events spawned from 1655 supercells and 320 quasi-linear convective systems (QLCSs). A comparison of peak velocity from groups of detections from the Mesocyclone Detection Algorithm and Tornado Detection Algorithm for each tornado track found overlapping distributions when discriminating between weak [rated as category 0 or 1 on the enhanced Fujita scale (EF0 and EF1)] and strong (EF2–5) events for both rotational and delta velocities. Dataset thresholding by estimated affected population lowered the range of observed velocities, particularly for weak tornadoes while retaining a greater frequency of events for strong tornadoes. Heidke skill scores for strength discrimination were dependent on algorithm, velocity parameter, population threshold, and convective mode, and varied from 0.23 and 0.66. Bootstrapping the skill scores for each algorithm showed a wide range of low-level velocities (at least 7 m s−1 in width) providing an equivalent optimal skill at discriminating between weak and strong tornadoes. This ultimately limits identification of a single threshold for optimal strength discrimination but the results match closely with larger prior manual studies of low-level velocities.

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Darrel M. Kingfield and Kirsten M. de Beurs

Abstract

Multispectral satellite imagery provides a spaceborne perspective on tornado damage identification; however, few studies have explored how tornadoes alter the spectral signature of different land-cover types. In part 1 of this study, Landsat surface reflectance is used to explore how 17 tornadoes modify the spectral signature, NDVI, and “Tassled Cap” parameters inside forest (N = 16), grassland (N = 10), and urban (N = 17) land cover. Land cover influences the magnitude of change observed, particularly in spring/summer imagery, with most tornado-damaged surfaces exhibiting a higher median reflectance in the visible and shortwave infrared, and a lower median reflectance in the near-infrared spectral ranges. These changes result in a higher median Tasseled Cap brightness, lower Tasseled Cap greenness and wetness, and lower NDVI relative to unaffected areas. Other factors affecting the magnitude of change in reflectance include season, vegetation condition, land-cover heterogeneity, and tornado strength. While vegetation indices like NDVI provide a quick way to identify damage, they have limited utility when monitoring recovery because of the cyclical seasonal vegetation cycle. Since tornado damage provides an analogous spectral signal to that of forest clearing, NDVI is compared with a forest disturbance index (DI) across a 5-yr Landsat climatology surrounding the 27 April 2011 tornado outbreak in part 2 of this study. Preoutbreak DI values remain relatively stable across seasons. In the five tornado-damaged areas evaluated, DI values peak within 6 months followed by a decline coincident with ongoing recovery. DI-like metrics provide a seasonally independent mechanism to fill the gap in identifying damage and monitoring recovery.

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Michael M. French and Darrel M. Kingfield

Abstract

A sample of 198 supercells are investigated to determine if a radar proxy for the area of the storm midlevel updraft may be a skillful predictor of imminent tornado formation and/or peak tornado intensity. A novel algorithm, a modified version of the Thunderstorm Risk Estimation from Nowcasting Development via Size Sorting (TRENDSS) algorithm is used to estimate the area of the enhanced differential radar reflectivity factor (Z DR) column in Weather Surveillance Radar–1988 Doppler data; the Z DR column area is used as a proxy for the area of the midlevel updraft. The areas of Z DR columns are compared for 154 tornadic supercells and 44 nontornadic supercells, including 30+ supercells with tornadoes rated EF1, EF2, and EF3; 8 supercells with EF4+ tornadoes also are analyzed. It is found that (i) at the time of their peak 0–1-km azimuthal shear, nontornadic supercells have consistently small (<20 km2) Z DR column areas, while tornadic cases exhibit much greater variability in areas; and (ii) at the time of tornadogenesis, EF3+ tornadic cases have larger Z DR column areas than tornadic cases rated EF1/2. In addition, all eight violent tornadoes sampled have Z DR column areas > 30 km2 at the time of tornadogenesis. However, only weak positive correlation is found between Z DR column area and both radar-estimated peak tornado intensity and maximum tornado path width. Planned future work that focuses on mechanisms linking updraft size and tornado formation and intensity is summarized and the use of the modified TRENDSS algorithm, which is immune to Z DR bias and thus ideal for real-time operational use, is emphasized.

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Pamela Heinselman, Daphne LaDue, Darrel M. Kingfield, and Robert Hoffman

Abstract

The 2012 Phased Array Radar Innovative Sensing Experiment identified how rapidly scanned full-volumetric data captured known mesoscale processes and impacted tornado-warning lead time. Twelve forecasters from nine National Weather Service forecast offices used this rapid-scan phased-array radar (PAR) data to issue tornado warnings on two low-end tornadic and two nontornadic supercell cases. Verification of the tornadic cases revealed that forecasters’ use of PAR data provided a median tornado-warning lead time (TLT) of 20 min. This 20-min TLT exceeded by 6.5 and 9 min, respectively, participants’ forecast office and regions’ median spring season, low-end TLTs (2008–13). Furthermore, polygon-based probability of detection ranged from 0.75 to 1.0 and probability of false alarm for all four cases ranged from 0.0 to 0.5. Similar performance was observed regardless of prior warning experience. Use of a cognitive task analysis method called the recent case walk-through showed that this performance was due to forecasters’ use of rapid volumetric updates. Warning decisions were based upon the intensity, persistence, and important changes in features aloft that are precursors to tornadogenesis. Precursors that triggered forecasters’ decisions to warn occurred within one or two typical Weather Surveillance Radar-1988 Doppler (WSR-88D) scans, indicating PAR’s temporal sampling better matches the time scale at which these precursors evolve.

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Jacob H. Segall, Michael M. French, Darrel M. Kingfield, Scott D. Loeffler, and Matthew R. Kumjian

Abstract

Polarimetric radar data from the WSR-88D network are used to examine the evolution of various polarimetric precursor signatures to tornado dissipation within a sample of 36 supercell storms. These signatures include an increase in bulk hook echo median raindrop size, a decrease in midlevel differential radar reflectivity factor (Z DR) column area, a decrease in the magnitude of the Z DR arc, an increase in the area of low-level large hail, and a decrease in the orientation angle of the vector separating low-level Z DR and specific differential phase (K DP) maxima. Only supercells that produced “long-duration” tornadoes (with at least four consecutive volumes of WSR-88D data) are investigated, so that signatures can be sufficiently tracked in time, and novel algorithms are used to isolate each storm-scale process. During the time leading up to tornado dissipation, we find that hook echo median drop size (D 0) and median Z DR remain relatively constant, but hook echo median K DP and estimated number concentration (NT) increase. The Z DR arc maximum magnitude and Z DRK DP separation orientation angles are observed to decrease in most dissipation cases. Neither the area of large hail nor the Z DR column area exhibit strong signals leading up to tornado dissipation. Finally, combinations of storm-scale behaviors and TVS behaviors occur most frequently just prior to tornado dissipation, but also are common 15–20 min prior to dissipation. The results from this study provide evidence that nowcasting tornado dissipation using dual-polarization radar may be possible when combined with TVS monitoring, subject to important caveats.

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Katie A. Bowden, Pamela L. Heinselman, Darrel M. Kingfield, and Rick P. Thomas

Abstract

The ongoing Phased Array Radar Innovative Sensing Experiment (PARISE) investigates the impacts of higher-temporal-resolution radar data on the warning decision process of NWS forecasters. Twelve NWS forecasters participated in the 2013 PARISE and were assigned to either a control (5-min updates) or an experimental (1-min updates) group. Participants worked two case studies in simulated real time. The first case presented a marginally severe hail event, and the second case presented a severe hail and wind event. While working each event, participants made decisions regarding the detection, identification, and reidentification of severe weather. These three levels compose what has now been termed the compound warning decision process. Decisions were verified with respect to the three levels of the compound warning decision process and the experimental group obtained a lower mean false alarm ratio than the control group throughout both cases. The experimental group also obtained a higher mean probability of detection than the control group throughout the first case and at the detection level in the second case. Statistical significance (p value = 0.0252) was established for the difference in median lead times obtained by the experimental (21.5 min) and control (17.3 min) groups. A confidence-based assessment was used to categorize decisions into four types: doubtful, uninformed, misinformed, and mastery. Although mastery (i.e., confident and correct) decisions formed the largest category in both groups, the experimental group had a larger proportion of mastery decisions, possibly because of their enhanced ability to observe and track individual storm characteristics through the use of 1-min updates.

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