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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

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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 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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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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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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Darrel M. Kingfield, Kristin M. Calhoun, Kirsten M. de Beurs, and Geoffrey M. Henebry

Abstract

Five years of 0.01° latitude × 0.01° longitude multiradar multisensor grids of composite reflectivity and vertically integrated signals from the maximum expected size of hail (MESH) and vertically integrated liquid (VIL) were created to examine the role of city size on thunderstorm occurrence and strength around four cities: Dallas–Fort Worth, Texas; Minneapolis–St. Paul, Minnesota; Oklahoma City, Oklahoma; and Omaha, Nebraska. A storm-tracking algorithm identified thunderstorm areas every minute and connected them together to form tracks. These tracks defined the upwind and downwind regions around each city on a storm-by-storm basis and were analyzed in two ways: 1) by sampling the maximum value every 10 min and 2) by accumulating the spatial footprint over its lifetime. Beyond examining all events, a subset of events corresponding to favorable conditions for urban modification was explored. This urban favorable (UF) subset consisted of nonsupercells occurring in the late afternoon/evening in the meteorological summer on weak synoptically forced days. When examining all thunderstorm events, regions at variable ranges upwind of all four cities generally had higher areal mean values of reflectivity, MESH, and VIL relative to downwind areas. In the UF subset, the larger cities (Dallas–Fort Worth and Minneapolis–St. Paul) had a 24%–50% increase in the number of downwind thunderstorms, resulting in a higher areal mean reflectivity, MESH, and VIL in this region. The smaller cities (Oklahoma City and Omaha) did not show such a downwind enhancement in thunderstorm occurrence and strength for the radar variables examined. This pattern suggests that larger cities could increase thunderstorm occurrence and intensity downwind of the prevailing flow under unique environmental conditions.

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Kristin M. Calhoun, Travis M. Smith, Darrel M. Kingfield, Jidong Gao, and David J. Stensrud

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A weather-adaptive three-dimensional variational data assimilation (3DVAR) system was included in the NOAA Hazardous Weather Testbed as a first step toward introducing warn-on-forecast initiatives into operations. NWS forecasters were asked to incorporate the data in conjunction with single-radar and multisensor products in the Advanced Weather Interactive Processing System (AWIPS) as part of their warning-decision process for real-time events across the United States. During the 2011 and 2012 experiments, forecasters examined more than 36 events, including tornadic supercells, severe squall lines, and multicell storms. Products from the 3DVAR analyses were available to forecasters at 1-km horizontal resolution every 5 min, with a 4–6-min latency, incorporating data from the national Weather Surveillance Radar-1988 Doppler (WSR-88D) network and the North American Mesoscale model. Forecasters found the updraft, vertical vorticity, and storm-top divergence products the most useful for storm interrogation and quickly visualizing storm trends, often using these tools to increase the confidence in a warning decision and/or issue the warning slightly earlier. The 3DVAR analyses were most consistent and reliable when the storm of interest was in close proximity to one of the assimilated WSR-88D, or data from multiple radars were incorporated into the analysis. The latter was extremely useful to forecasters in blending data rather than having to analyze multiple radars separately, especially when range folding obscured the data from one or more radars. The largest hurdle for the real-time use of 3DVAR or similar data assimilation products by forecasters is the data latency, as even 4–6 min reduces the utility of the products when new radar scans are available.

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Kristofer S. Tuftedal, Michael M. French, Darrel M. Kingfield, and Jeffrey C. Snyder

Abstract

The time preceding supercell tornadogenesis and tornadogenesis “failure” has been studied extensively to identify differing attributes related to tornado production or lack thereof. Studies from the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX) found that air in the rear-flank downdraft (RFD) regions of non- and weakly tornadic supercells had different near-surface thermodynamic characteristics than that in strongly tornadic supercells. Subsequently, it was proposed that microphysical processes are likely to have an impact on the resulting thermodynamics of the near-surface RFD region. One way to view proxies to microphysical features, namely, drop size distributions (DSDs), is through use of polarimetric radar data. Studies from the second VORTEX used data from dual-polarization radars to provide evidence of different DSDs in the hook echoes of tornadic and nontornadic supercells. However, radar-based studies during these projects were limited to a small number of cases preventing result generalizations. This study compiles 68 tornadic and 62 nontornadic supercells using Weather Surveillance Radar–1988 Doppler (WSR-88D) data to analyze changes in polarimetric radar variables leading up to, and at, tornadogenesis and tornadogenesis failure. Case types generally did not show notable hook echo differences in variables between sets, but did show spatial hook echo quadrant DSD differences. Consistent with past studies, differential radar reflectivity factor (Z DR) generally decreased leading up to tornadogenesis and tornadogenesis failure; in both sets, estimated total number concentration increased during the same times. Relationships between DSDs and the near-storm environment, and implications of results for nowcasting tornadogenesis, also are discussed.

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Katie A. Wilson, Pamela L. Heinselman, Charles M. Kuster, Darrel M. Kingfield, and Ziho Kang

Abstract

Impacts of radar update time on forecasters’ warning decision processes were analyzed in the 2015 Phased Array Radar Innovative Sensing Experiment. Thirty National Weather Service forecasters worked nine archived phased-array radar (PAR) cases in simulated real time. These cases presented nonsevere, severe hail and/or wind, and tornadic events. Forecasters worked each type of event with approximately 5-min (quarter speed), 2-min (half speed), and 1-min (full speed) PAR updates. Warning performance was analyzed with respect to lead time and verification. Combining all cases, forecasters’ median warning lead times when using full-, half-, and quarter-speed PAR updates were 17, 14.5, and 13.6 min, respectively. The use of faster PAR updates also resulted in higher probability of detection and lower false alarm ratio scores. Radar update speed did not impact warning duration or size. Analysis of forecaster performance on a case-by-case basis showed that the impact of PAR update speed varied depending on the situation. This impact was most noticeable during the tornadic cases, where radar update speed positively impacted tornado warning lead time during two supercell events, but not for a short-lived tornado occurring within a bowing line segment. Forecasters’ improved ability to correctly discriminate the severe weather threat during a nontornadic supercell event with faster PAR updates was also demonstrated. Forecasters provided subjective assessments of their cognitive workload in all nine cases. On average, forecasters were not cognitively overloaded, but some participants did experience higher levels of cognitive workload at times. A qualitative explanation of these particular instances is provided.

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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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