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1. An Updated Severe Hail and Tornado Climatology for Eastern Colorado

   https://doi.org/10.1175/JAMC-D-19-0098.1  

   Childs, Samuel J.; Schumacher, Russ S. (October 2019, Journal of Applied Meteorology and Climatology)

   Abstract A localized tornado and severe hail climatology is updated and enhanced for eastern Colorado. This region is one of the most active severe weather areas in the United States because of its location immediately east of the Rocky Mountains, intrusions of Gulf of Mexico moisture into a dry climate, and various small-scale topographically forced features such as the “Denver Cyclone.” Since the 1950s, both annual tornado and severe (≥1.0 in.; 1 in. = 25.4 mm) hail reports and days have been increasing across the area, but several nonmeteorological factors distort the record. Of note is a large population bias in the severe hail data, with reports aligned along major roadways and in cities, and several field projects contributing to an absence of (E)F0 tornado reports [on the (enhanced) Fujita scale] in the 1980s. In the more consistently observed period since 1997, tornado reports and days show a slight decreasing trend while severe hail reports and days show an increasing trend, although large variability exists on the county level. Eastern Colorado tornadoes are predominantly weak, rarely above (E)F1 intensity, and with a maximum just east of the northern urban corridor. Severe hail has a maximum along the foothills and shows a trend toward a larger ratio of significant (≥2.0 in.; ≥50.8 mm) hail to severe hail reports over time. Both tornadoes and severe hail have trended toward shorter seasons since 1997, mostly attributable to an earlier end to the season. By assessing current and historical trends from a more localized perspective, small-scale climatological features and local societal impacts are exposed—features that national climatologies can miss. 

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2. Distinguishing between Hodographs of Severe Hail and Tornadoes

   https://doi.org/10.1175/WAF-D-21-0136.1  

   Nixon, Cameron J.; Allen, John T. (July 2022, Weather and Forecasting)

   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 8,958 tornadoes and 7,256 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. 

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3. A Climatology of Quasi-Linear Convective Systems and Their Hazards in the United States

   https://doi.org/10.1175/WAF-D-19-0014.1  

   Ashley, Walker S.; Haberlie, Alex M.; Strohm, Jacob (October 2019, Weather and Forecasting)

   Abstract This research uses image classification and machine learning methods on radar reflectivity mosaics to segment, classify, and track quasi-linear convective systems (QLCSs) in the United States for a 22-yr period. An algorithm is trained and validated using radar-derived spatial and intensity information from thousands of manually labeled QLCS and non-QLCS event slices. The algorithm is then used to automate the identification and tracking of over 3000 QLCSs with high accuracy, affording the first, systematic, long-term climatology of QLCSs. Convective regions determined by the procedure to be QLCSs are used as foci for spatiotemporal filtering of observed severe thunderstorm reports; this permits an estimation of the number of severe storm hazards due to this morphology. Results reveal that nearly 32% of MCSs are classified as QLCSs. On average, 139 QLCSs occur annually, with most of these events clustered from April through August in the eastern Great Plains and central/lower Mississippi and Ohio River Valleys. QLCSs are responsible for a spatiotemporally variable proportion of severe hazard reports, with a maximum in QLCS-report attribution (30%–42%) in the western Ohio and central Mississippi River Valleys. Over 21% of tornadoes, 28% of severe winds, and 10% of severe hail reports are due to QLCSs across the central and eastern United States. The proportion of QLCS-affiliated tornado and severe wind reports maximize during the overnight and cool season, with more than 50% of tornadoes and wind reports in some locations due to QLCSs. This research illustrates the utility of automated storm-mode classification systems in generating extensive, systematic climatologies of phenomena, reducing the need for time-consuming and spatiotemporal-limiting methods where investigators manually assign morphological classifications. 

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4. Sub-severe and Severe Hail

   Elmore, Kimberly; Allen, John T.; Gerard, Alan (April 2022, Weather and forecasting)

   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 inch). 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 2013-2020. 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 per year). A secondary maxima exists over the Colorado Front Range (about 26 days per year), a third extends across northern Indiana from the southern tip of Lake Michigan (about 24 days per year 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 dataset provides a complimentary data source. 

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5. Discriminant Analysis for Severe Storm Environments in South-Central Brazil

   https://doi.org/10.1175/MWR-D-22-0347.1  

   dos_Santos, Letícia_O; Nascimento, Ernani_L; Allen, John_T (October 2023, Monthly Weather Review)

   Abstract Severe storms produce hazardous weather phenomena, such as large hail, damaging winds, and tornadoes. However, relationships between convective parameters and confirmed severe weather occurrences are poorly quantified in south-central Brazil. This study explores severe weather reports and measurements from newly available datasets. Hail, damaging wind, and tornado reports are sourced from the PREVOTS project from June 2018 to December 2021, while measurements of convectively induced wind gusts from 1996 to 2019 are obtained from METAR reports and from Brazil’s operational network of automated weather stations. Proximal convective parameters were computed from ERA5 reanalysis for these reports and used to perform a discriminant analysis using mixed-layer CAPE and deep-layer shear (DLS). Compared to other regions, thermodynamic parameters associated with severe weather episodes exhibit lower magnitudes in south-central Brazil. DLS displays better performance in distinguishing different types of hazardous weather, but does not discriminate well between distinct severity levels. To address the sensitivity of the discriminant analysis to distinct environmental regimes and hazard types, five different discriminants are assessed. These include discriminants for any severe storm, severe hail only, severe wind gust only, and all environments but broken into “high” and “low” CAPE regimes. The best performance of the discriminant analysis is found for the “high” CAPE regime, followed by the severe wind regime. All discriminants demonstrate that DLS plays a more important role in conditioning Brazilian severe storm environments than other regions, confirming the need to ensure that parameters and discriminants are tuned to local severe weather conditions. 

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