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John T. Allen
,
David J. Karoly
, and
Kevin J. Walsh

Abstract

The influence of a warming climate on the occurrence of severe thunderstorm environments in Australia was explored using two global climate models: Commonwealth Scientific and Industrial Research Organisation Mark, version 3.6 (CSIRO Mk3.6), and the Cubic-Conformal Atmospheric Model (CCAM). These models have previously been evaluated and found to be capable of reproducing a useful climatology for the twentieth-century period (1980–2000). Analyzing the changes between the historical period and high warming climate scenarios for the period 2079–99 has allowed estimation of the potential convective future for the continent. Based on these simulations, significant increases to the frequency of severe thunderstorm environments will likely occur for northern and eastern Australia in a warmed climate. This change is a response to increasing convective available potential energy from higher continental moisture, particularly in proximity to warm sea surface temperatures. Despite decreases to the frequency of environments with high vertical wind shear, it appears unlikely that this will offset increases to thermodynamic energy. The change is most pronounced during the peak of the convective season, increasing its length and the frequency of severe thunderstorm environments therein, particularly over the eastern parts of the continent. The implications of this potential increase are significant, with the overall frequency of potential severe thunderstorm days per year likely to rise over the major population centers of the east coast by 14% for Brisbane, 22% for Melbourne, and 30% for Sydney. The limitations of this approach are then discussed in the context of ways to increase the confidence of predictions of future severe convection.

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Natalia Pilguj
,
Mateusz Taszarek
,
John T. Allen
, and
Kimberly A. Hoogewind

Abstract

In this work, long-term trends in convective parameters are compared between ERA5, MERRA-2, and observed rawinsonde profiles over Europe and the United States including surrounding areas. A 39-yr record (1980–2018) with 2.07 million quality-controlled measurements from 84 stations at 0000 and 1200 UTC is used for the comparison, along with collocated reanalysis profiles. Overall, reanalyses provide signals that are similar to observations, but ERA5 features lower biases. Over Europe, agreement in the trend signal between rawinsondes and the reanalyses is better, particularly with respect to instability (lifted index), low-level moisture (mixing ratio), and 0–3-km lapse rates as compared with mixed trends in the United States. However, consistent signals for all three datasets and both domains are found for robust increases in convective inhibition (CIN), downdraft CAPE (DCAPE), and decreases in mean 0–4-km relative humidity. Despite differing trends between continents, the reanalyses capture well changes in 0–6-km wind shear and 1–3-km mean wind with modest increases in the United States and decreases in Europe. However, these changes are mostly insignificant. All datasets indicate consistent warming of almost the entire tropospheric profile, which over Europe is the fastest near ground whereas across the Great Plains it is generally between 2 and 3 km above ground level, thus contributing to increases in CIN. Results of this work show the importance of intercomparing trends between various datasets, as the limitations associated with one reanalysis or observations may lead to uncertainties and lower our confidence in how parameters are changing over time.

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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Roger Edwards
,
John T. Allen
, and
Gregory W. Carbin

Abstract

Convective surface winds in the contiguous United States are classified as severe at 50 kt (58 mi h−1, or 26 m s−1), whether measured or estimated. In 2006, NCDC (now NCEI) Storm Data, from which analyzed data are directly derived, began explicit categorization of such reports as measured gusts (MGs) or estimated gusts (EGs). Because of the documented tendency of human observers to overestimate winds, the quality and reliability of EGs (especially in comparison with MGs) has been challenged, mostly for nonconvective winds and controlled-testing situations, but only speculatively for bulk convective data. For the 10-yr period of 2006–15, 150 423 filtered convective-wind gust magnitudes are compared and analyzed, including 15 183 MGs and 135 240 EGs, both nationally and by state. Nonmeteorological artifacts include marked geographic discontinuities and pronounced “spikes” of an order of magnitude in which EG values (in both miles per hour and knots) end in the digits 0 or 5. Sources such as NWS employees, storm chasers, and the general public overestimate EGs, whereas trained spotters are relatively accurate. Analysis of the ratio of EG to MG and their sources also reveals an apparent warning-verification-influence bias in the climatological distribution of wind gusts imparted by EG reliance in the Southeast. Results from prior wind-tunnel testing of human subjects are applied to 1) illustrate the difference between measured and perceived winds for the database and 2) show the impact on the severe-wind dataset if EGs were bias-corrected for the human overestimation factor.

Full access
Elisa M. Murillo
,
Cameron R. Homeyer
, and
John T. Allen

Abstract

Assessments of spatiotemporal severe hailfall characteristics using hail reports are plagued by serious limitations in report databases, including biases in reported sizes, occurrence time, and location. Multiple studies have used Next Generation Weather Radar (NEXRAD) network observations or environmental hail proxies from reanalyses. Previous work has specifically utilized the single-polarization radar parameter maximum expected size of hail (MESH). In addition to previous work being temporally limited, updates are needed to include recent improvements that have been made to MESH. This study aims to quantify severe hailfall characteristics during a 23-yr period, markedly longer than previous studies, using both radar observations and reanalysis data. First, the improved MESH configuration is applied to the full archive of gridded hourly radar observations known as GridRad (1995–2017). Next, environmental constraints from the Modern-Era Retrospective Analysis for Research and Applications, version 2, are applied to the MESH distributions to produce a corrected hailfall climatology that accounts for the reduced likelihood of hail reaching the ground. Spatial, diurnal, and seasonal patterns show that in contrast to the report climatology indicating one high-frequency hail maximum centered on the Great Plains, the MESH-only method characterizes two regions: the Great Plains and the Gulf Coast. The environmentally filtered MESH climatology reveals improved agreement between report characteristics (frequency, location, and timing) and the recently improved MESH calculation methods, and it reveals an overall increase in diagnosed hail days and westward broadening in the spatial maximum in the Great Plains than that seen in reports.

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Letícia O. dos Santos
,
Ernani L. Nascimento
, and
John T. Allen

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.

Restricted access
Xiang Ni
,
Andreas Muehlbauer
,
John T. Allen
,
Qinghong Zhang
, and
Jiwen Fan

Abstract

Hail size records are analyzed at 2254 stations in China and a hail size climatology is developed based on gridded hail observations for the period 1960–2015. It is found that the annual percentiles of hail size records changed sharply and national-wide after 1980, therefore two periods, 1960–79 and 1980–2015, are studied. There are some similarities between the two periods in terms of the characteristics of hail size such as the spatial distribution patterns of mean annual maximum hail size and occurrence week of annual maximum hail size. The 1980–2015 period had higher observation density than the 1960–79 period, but showed smaller mean annual maximum hail size, especially in northern China. In the majority of grid boxes, the annual maximum hail size experienced a decreasing trend during the 1980–2015 period. A Gumbel extreme value model is fitted to each grid box to estimate the return periods of maximum hail size. The scale and location parameter of the fitted Gumbel distributions are higher in eastern China than in western China, thereby reflecting a greater likelihood of large hail in eastern China. In southern China, the maximum hail size exceeds 127 mm for a 10-yr return period, whereas in northern China maximum hail size exceeds this threshold for a 50-yr return period. The Gumbel model is found to potentially underestimate the maximum hail size for certain return periods, but provides a more informed picture of the spatial distribution of extreme hail size and the regional differences.

Free access
Chiara Lepore
,
Michael K. Tippett
, and
John T. Allen

Abstract

Climate Forecast System, version 2, predictions of monthly U.S. severe thunderstorm activity are analyzed for the period 1982–2016. Forecasts are based on a tornado environmental index and a hail environmental index, which are functions of monthly averaged storm relative helicity (SRH), convective precipitation (cPrcp), and convective available potential energy (CAPE). Overall, forecast indices reproduce well the annual cycle of tornado and hail events. Forecast index biases are mostly negative and caused by environment values that are low east of the Rockies, although forecast CAPE is higher than the reanalysis values over the High Plains. Skill is diagnosed spatially for the indices and their constituents separately. SRH is more skillfully forecast than cPrcp and CAPE, especially during December–June. The spatial patterns of forecast skill for CAPE and cPrcp are similar, with higher skill for CAPE and less spatial coherence for cPrcp. The indices are forecast with substantially less skill than the environmental parameters. Numbers of tornado and hail events are forecast with modest but statistically significant skill in some NOAA regions and months of the year. Skill tends to be relatively higher for hail events and in climatologically active seasons and regions. Much of the monthly skill appears to be derived from the first 2 weeks of the forecast. El Niño–Southern Oscillation (ENSO) modulates forecasts and, to a lesser extent, forecast skill, during March–May, with more activity and higher skill during cool ENSO conditions.

Open access
Kimberly L. Elmore
,
John T. Allen
, and
Alan E. Gerard

Abstract

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 in.). 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 2013–20. 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 yr−1). A secondary maximum exists over the Colorado Front Range (about 26 days yr−1), a third extends across northern Indiana from the southern tip of Lake Michigan (about 24 days yr−1 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 of the datasets provide a complimentary data source.

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Cameron J. Nixon
,
John T. Allen
, and
Mateusz Taszarek

Abstract

Environments associated with severe hailstorms, compared to those of tornadoes, are often less apparent to forecasters. Understanding has evolved considerably in recent years; namely, that weak low-level shear and sufficient convective available potential energy (CAPE) above the freezing level is most favorable for large hail. However, this understanding comes only from examining the mean characteristics of large hail environments. How much variety exists within the kinematic and thermodynamic environments of large hail? Is there a balance between shear and CAPE analogous to that noted with tornadoes? We address these questions to move toward a more complete conceptual model. In this study, we investigate the environments of 92 323 hail reports (both severe and nonsevere) using ERA5 modeled proximity soundings. By employing a self-organizing map algorithm and subsetting these environments by a multitude of characteristics, we find that the conditions leading to large hail are highly variable, but three primary patterns emerge. First, hail growth depends on a favorable balance of CAPE, wind shear, and relative humidity, such that accounting for entrainment is important in parameter-based hail prediction. Second, hail growth is thwarted by strong low-level storm-relative winds, unless CAPE below the hail growth zone is weak. Finally, the maximum hail size possible in a given environment may be predictable by the depth of buoyancy, rather than CAPE itself.

Open access