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Vittorio A. Gensini and Alan Marinaro

Abstract

Global relative angular momentum and the first time derivative are used to explain nearly an order of magnitude of the variability in 1994–2013 U.S. boreal spring tornado occurrence. When plotted in a phase space, the global wind oscillation (GWO) is obtained. This global index accounts for changes in the global budget of angular momentum through interactions of tropical convection anomalies and extratropical dynamics including the engagement of surface torques. It is shown herein that tornadoes are more likely to occur in low angular momentum base states and less likely to occur in high angular momentum base states. When excluding weak GWO days, a maximum average of 3.9 (E)F1+ tornadoes per day were found during phase 1. This decreases to a minimum of 0.9 (E)F1+ tornadoes per day during phase 5. Composite environmental analysis suggests that increases/decreases in tornado occurrence are closely associated with anomalies in tropospheric ingredients necessary for tornadic storms. In addition, tornado frequency days exceeding the 90th percentile are shown to be favored when the global relative angular momentum budget and first time derivative are negative (GWO phases 1 and 2), as are significant tornado events [(E)F2+]. Implications for using GWO as a predictor for tornado forecasting are also discussed.

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David Changnon and Vittorio A. Gensini

Abstract

This study examined the spatiotemporal variability associated with 5-/10-day heavy precipitation amounts for 48 high-quality and long-duration (1900–2018) stations in Illinois. The top five seasonal and annual heavy precipitation amounts for each duration were determined and examined for each station. Annual and seasonal spatial patterns generally showed a trend of decreasing precipitation amounts as one moved northward through Illinois. Spatial distributions of the top seasonal amounts exhibited the highest values in boreal spring and summer, with the lowest values during winter. Temporal analysis of the top five 5- and 10-day amounts from 1900 to 2018 indicated an increasing trend with a higher frequencies in the 2000–18 period for spring, summer, winter, and annual time periods (statistically significant for spring and annual). No trend was found in autumn heavy precipitation occurrence. In addition, heavy precipitation events were examined in the context of the background atmospheric environment using the Twentieth Century Reanalysis. Event-averaged precipitable water values were shown to scale linearly with total precipitation in the winter season. Low-level circulation fields indicate that the most widespread heavy rain episodes occur when a synoptic anticyclone is positioned off the coast of the eastern United States. Results from this study suggest that design values used for hydrologic structures should be reevaluated given recent observations.

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Vittorio A. Gensini and Lelys Bravo de Guenni

Abstract

The significant tornado parameter is a widely used meteorological composite index that combines several variables known to favor tornadic supercell thunderstorms. This research examines the spatial relationship between U.S. tornado frequency and the significant tornado parameter (the predictor covariate) across four seasons in order to establish a spatial–statistical model that explains significant amounts of variance in tornado occurrence (the predictand). U.S. tornadoes are highly dependent on the significant tornado parameter in a climatological sense. The strength of this dependence is seasonal, with greatest dependence found during December–February and least dependence during June–August. Additionally, the strength of this dependence has not changed significantly through the 39-yr study period (1979–2017). Results herein represent an important step forward for the creation of a predictive spatial–statistical model to aid in tornado prediction at seasonal time scales.

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Vittorio A. Gensini and Thomas L. Mote

Abstract

High-resolution (4 km; hourly) regional climate modeling is utilized to resolve March–May hazardous convective weather east of the U.S. Continental Divide for a historical climate period (1980–90). A hazardous convective weather model proxy is used to depict occurrences of tornadoes, damaging thunderstorm wind gusts, and large hail at hourly intervals during the period of record. Through dynamical downscaling, the regional climate model does an admirable job of replicating the seasonal spatial shifts of hazardous convective weather occurrence during the months examined. Additionally, the interannual variability and diurnal progression of observed severe weather reports closely mimic cycles produced by the regional model. While this methodology has been tested in previous research, this is the first study to use coarse-resolution global climate model data to force a high-resolution regional model with continuous seasonal integration in the United States for purposes of resolving severe convection. Overall, it is recommended that dynamical downscaling play an integral role in measuring climatological distributions of severe weather, both in historical and future climates.

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Vittorio A. Gensini, Alex M. Haberlie, and Patrick T. Marsh

Abstract

This study presents and examines a modern climatology of U.S. severe convective storm frequency using a kernel density estimate to showcase various aspects of climatological risk. Results are presented in the context of specified event probability thresholds that correspond to definitions used at the NOAA/NWS’s Storm Prediction Center following a practically perfect hindcast approach. Spatial climatologies presented herein are closely related to previous research. Spatiotemporal changes were examined by splitting the study period (1979–2018) into two 20-yr epochs and calculating deltas. Portions of the southern Great Plains and High Plains have seen a decrease in counts of tornado event threshold probability, whereas increases have been documented in the middle Mississippi River valley region. Large hail, and especially damaging convective wind gusts, have shown increases between the two periods over a majority of the CONUS. To temporally showcase local climatologies, event threshold days are shown for 12 select U.S. cities. Finally, data created and used in this study are available as an open-source repository for future research applications.

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Maria J. Molina, John T. Allen, and Vittorio A. Gensini

Abstract

El Niño–Southern Oscillation (ENSO) and the Gulf of Mexico (GoM) influence winter tornado variability and significant tornado (EF2+, where EF is the enhanced Fujita scale) environments. Increases occur in the probability of a significant tornado environment from the southern Great Plains to the Midwest during La Niña, and across the southern contiguous United States (CONUS) during El Niño. Winter significant tornado environments are absent across Florida, Georgia, and the coastal Carolinas during moderate-to-strong La Niña events. Jet stream modulation by ENSO contributes to higher tornado totals during El Niño in December and La Niña in January, especially when simultaneous with a warm GoM. ENSO-neutral phases yield fewer and weaker tornadoes, but proximity to warm GoM climate features can enhance the probability of a significant tornado environment. ENSO intensity matters; stronger ENSO phases generate increases in tornado frequency and the probability of a significant tornado environment, but are characterized by large variance, in which very strong El Niño and La Niña events can produce unfavorable tornado climatological states. This study suggests that it is a feasible undertaking to expand spring seasonal and subseasonal tornado prediction efforts to encompass the winter season, which is of importance given the notable threat posed by winter tornadoes. Significant tornadoes account for 95% of tornado fatalities and winter tornadoes are rated significant more frequently than during other seasons.

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Vittorio A. Gensini, Bradford S. Barrett, John T. Allen, David Gold, and Paul Sirvatka

Abstract

Large-scale weather patterns favorable for tornado occurrence have been understood for many decades. Yet prediction of tornadoes, especially at extended lead periods of more than a few days, remains an arduous task, partly due to the space and time scales involved. Recent research has shown that tropical convection, sea surface temperatures, and the Earth-relative atmospheric angular momentum can induce jet stream configurations that may increase or decrease the probability of tornado frequency across the United States. Applying this recent theoretical work in practice, on 1 March 2015, the authors began the Extended-Range Tornado Activity Forecast (ERTAF) project, with the following goals: 1) to have a map room–style discussion of the anticipated atmospheric state in the 2–3-week lead window; 2) to predict categorical level of tornado activity in that lead window; and 3) to learn from the forecasts through experience by identifying strengths and weaknesses in the methods, as well as identifying any potential scientific knowledge gaps. Over the last five years, the authors have shown skill in predicting U.S. tornado activity two to three weeks in advance during boreal spring. Unsurprisingly, skill is shown to be greater for forecasts spanning week 2 versus week 3. This manuscript documents these forecasting efforts, provides verification statistics, and shares the challenges and lessons learned from predicting tornado activity on the subseasonal time scale.

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John A. Knox, Jared A. Rackley, Alan W. Black, Vittorio A. Gensini, Michael Butler, Corey Dunn, Taylor Gallo, Melyssa R. Hunter, Lauren Lindsey, Minh Phan, Robert Scroggs, and Synne Brustad

Using publicly available information gleaned from over 1700 found-and-returned objects on the “Pictures and Documents found after the 27 April 2011 Tornadoes” Facebook page, the authors have created a database of 934 objects lofted by at least 15 different tornadoes during the 27 April 2011 Super Outbreak in the southeast United States. Analysis of the takeoff and landing points of these objects using GIS and high-resolution numerical trajectory modeling techniques extends previous work on this subject that used less specific information for much smaller sets of tracked tornado debris. It was found that objects traveled as far as 353 km, exceeding the previous record for the longest documented tornado debris trajectory. While the majority of debris trajectories were 10° to the left of the average tornado track vector, the longest trajectories exhibited a previously undocumented tendency toward the right of the average tornado track vector. Based on results from a high-resolution trajectory model, a relationship between this tendency and the altitude of lofting of debris is hypothesized, with the debris reaching the highest altitudes taking the rightmost trajectories. The paper concludes with a discussion of the pros and cons of using social media information for meteorological research.

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