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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 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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Robert Fritzen, Victoria Lang, and Vittorio A. Gensini

Abstract

Extratropical cyclones are the primary driver of sensible weather conditions across the midlatitudes of North America, often generating various types of precipitation, gusty nonconvective winds, and severe convective storms throughout portions of the annual cycle. Given ongoing modifications of the zonal atmospheric thermal gradient resulting from anthropogenic forcing, analyzing the historical characteristics of these systems presents an important research question. Using the North American Regional Reanalysis, boreal cool-season (October–April) extratropical cyclones for the period 1979–2019 were identified, tracked, and classified on the basis of their genesis location. In addition, bomb cyclones—extratropical cyclones that recorded a latitude-normalized pressure fall of 24 hPa in 24 h—were identified and stratified for additional analysis. Cyclone life span across the domain exhibits a log-linear relationship, with 99% of all cyclones tracked lasting less than 8 days. On average, ≈270 cyclones were tracked across the analysis domain per year, with an average of ≈18 yr−1 being classified as bomb cyclones. The average number of cyclones in the analysis domain has decreased in the last 20 years from 290 per year during 1979–99 to 250 per year during 2000–19. Decreasing trends in the frequency of cyclone track counts were noted across a majority of the analysis domain, with the most significant decreases found in Canada’s Northwest Territories, Colorado, and east of the Graah Mountain Range. No significant interannual or spatial trends were noted in the frequency of bomb cyclones.

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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, Cody Converse, Walker S. Ashley, and Mateusz Taszarek

Abstract

Previous studies have identified environmental characteristics that skillfully discriminate between severe and significant-severe weather events, but they have largely been limited by sample size and/or population of predictor variables. Given the heightened societal impacts of significant-severe weather, this topic was revisited using over 150 000 ERA5 reanalysis-derived vertical profiles extracted at the grid point nearest—and just prior to—tornado and hail reports during the period 1996–2019. Profiles were quality controlled and used to calculate 84 variables. Several machine learning classification algorithms were trained, tested, and cross validated on these data to assess skill in predicting severe or significant-severe reports for tornadoes and hail. Random forest classification outperformed all tested methods as measured by cross-validated critical success index scores and area under the receiver operating characteristic curve values. In addition, random forest classification was found to be more reliable than other methods and exhibited negligible frequency bias. The top three most important random forest classification variables for tornadoes were wind speed at 500 hPa, wind speed at 850 hPa, and 0–500-m storm-relative helicity. For hail, storm-relative helicity in the 3–6 km and −10° to −30°C layers, along with 0–6-km bulk wind shear, were found to be most important. A game theoretic approach was used to help explain the output of the random forest classifiers and establish critical feature thresholds for operational nowcasting and forecasting. A use case of spatial applicability of the random forest model is also presented, demonstrating the potential utility for operational forecasting. Overall, this research supports a growing number of weather and climate studies finding admirable skill in random forest classification applications.

Open access
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, 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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Vittorio A. Gensini, Alex M. Haberlie, and Patrick T. Marsh
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Logan R. Bundy, Vittorio A. Gensini, and Mark S. Russo

Abstract

This study used corn insurance data as a proxy for agricultural loss to better inform producers and decision-makers about resilience and mitigation. Building on previous research examining crop losses based on weather and climate perils, updates to the peril climatology, identification of peril hotspots, and the quantification of annual trends using inflation-adjusted indemnities for corn were performed over the period 1989–2020. Normalization techniques in loss cost and acreage loss at county-level spatial resolution were also calculated. Indemnity data showed drought and excess moisture as the two costliest and most frequent perils for corn in the United States, although changes in the socioeconomic landscape and frequency of extreme weather events in the recent decade have led to significant increases in corn indemnities for drought, heat, excess moisture, flood, hail, excess wind, and cold wet weather. Normalized losses also displayed significant trends but were dependent on the cause of loss and amount of spatial aggregation. Perhaps most notable were the documented robust increases in corn losses associated with excess moisture, especially considering future projections for increased mid and end-of-century extreme precipitation. Subtle decreasing trends in drought, hail, freeze/frost, and flood loss cost over the study period indicates hedging taking place to protect against these perils, especially in corn acreage outside the Corn Belt in high-risk production zones. The use of crop insurance as a proxy for agricultural loss highlights the importance for quantifying spatiotemporal trends by informing targeted adaption to certain hazards and operational management decisions.

Significance Statement

This study quantified the climatology and trends of weather and climate perils affecting corn in the United States. Robust increases in losses were noted with perils causing excess moisture, which is cause for further concern given projected increases in extreme rainfall under a warming climate.

Open access