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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.

 

Thermolysis is a common technique in materials synthesis; however, oxidation of precursors used in these processes destroys the ability to generate end‐products with the desired composition and structure. Herein, the effects of oxidation on the thermolysis of (NH₄)₂WS₄, a common precursor in the production of WS₂films, are studied utilizing electron microscopy. Specifically,in situbright field, dark field, and secondary electron images were collected during heating of the aforementioned oxidized precursor from RT to 800 °C in 100 °C steps within a low kV scanning transmission electron microscope. These imaging results detail how the precursor transforms from its largely nondescript room temperature morphology to one having well formed 2D, 1D, and 0D nanostructures. Subsequentex situatomic level imaging shows that the 2D and 1D nanomaterials are composed of δ‐WO₃which has grown along itsc‐axis, whereas the 0D nanoparticles are made solely of α‐W. Through the combination of bothinandex situimages, we have gained valuable insights into the structural and chemical changes that occur owing to the additional oxygen in the system, as well as the role that precursor density plays on the formation of these as formed nanomaterials.

 

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.

 

Abstract Sufficient low-level storm-relative flow is a necessary ingredient for sustained supercell thunderstorms and is connected to supercell updraft width. Assuming a supercell exists, the role of low-level storm-relative flow in regulating supercells’ low-level mesocyclone intensity is less clear. One possibility considered in this article is that storm-relative flow controls mesocyclone and tornado width via its modulation of overall updraft extent. This hypothesis relies on a previously postulated positive correspondence between updraft width, mesocyclone width, and tornado width. An alternative hypothesis is that mesocyclone characteristics are primarily regulated by horizontal streamwise vorticity irrespective of storm-relative flow. A matrix of supercell simulations was analyzed to address the aforementioned hypotheses, wherein horizontal streamwise vorticity and storm-relative flow were independently varied. Among these simulations, mesocyclone width and intensity were strongly correlated with horizontal streamwise vorticity, and comparatively weakly correlated with storm-relative flow, supporting the second hypothesis. Accompanying theory and trajectory analysis offers the physical explanation that, when storm-relative flow is large and updrafts are wide, vertically tilted streamwise vorticity is projected over a wider area but with a lesser average magnitude than when these parameters are small. These factors partially offset one another, degrading the correspondence of storm-relative flow with updraft circulation and rotational velocity, which are the mesocyclone attributes most closely tied to tornadoes. These results refute the previously purported connections between updraft width, mesocyclone width, and tornado width, and emphasize horizontal streamwise vorticity as the primary control on low-level mesocyclones in sustained supercells. Significance Statement The intensity of a supercell thunderstorm’s low-level rotation, known as the “mesocyclone,” is thought to influence tornado likelihood. Mesocyclone intensity depends on many environmental attributes that are often correlated with one another and difficult to disentangle. This study used a large body of numerical simulations to investigate the influence of the speed of low-level air entering a supercell (storm-relative flow), the horizontal spin of the ambient air entering the thunderstorm (streamwise vorticity), and the width of the storm’s updraft. Our results suggest that the rotation of the mesocyclone in supercells is primarily influenced by streamwise vorticity, with comparatively weaker connections to storm-relative flow and updraft width. These findings provide important clarification in our scientific understanding of how a storm’s environment influences the rate of rotation of its mesocyclone, and the associated tornado threat. 

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. 

We present and evaluate a deep learning first-guess front-identification system that identifies cold, warm, stationary, and occluded fronts. Frontal boundaries play a key role in the daily weather around the world. Human-drawn fronts provided by the National Weather Service’s Weather Prediction Center, Ocean Prediction Center, Tropical Analysis and Forecast Branch, and Honolulu Forecast Office are treated as ground-truth labels for training the deep learning models. The models are trained using ERA5 data with variables known to be important for distinguishing frontal boundaries, including temperature, equivalent potential temperature, and wind velocity and direction at multiple heights. Using a 250-km neighborhood over the contiguous U.S. domain, our best models achieve critical success index scores of 0.60 for cold fronts, 0.43 for warm fronts, 0.48 for stationary fronts, 0.45 for occluded fronts, and 0.71 using a binary classification system (front/no front), whereas scores over the full unified surface analysis domain were lower. For cold and warm fronts and binary classification, these scores significantly outperform prior baseline methods that utilize 250-km neighborhoods. These first-guess deep learning algorithms can be used by forecasters to locate frontal boundaries more effectively and expedite the frontal analysis process.

Fronts are boundaries that affect the weather that people experience daily. Currently, forecasters must identify these boundaries through manual analysis. We have developed an automated machine learning method for detecting cold, warm, stationary, and occluded fronts. Our automated method provides forecasters with an additional tool to expedite the frontal analysis process.

 

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. 

In this work, long-term trends in convective parameters are compared between ERA5, MERRA2, and observed rawinsonde profiles over Europe and the United States including surrounding areas. A 39-year 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 similar signals 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 to 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, while across the Great Plains generally between 2–3 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. 

Abstract Globally, thunderstorms are responsible for a significant fraction of rainfall, and in the mid-latitudes often produce extreme weather, including large hail, tornadoes and damaging winds. Despite this importance, how the global frequency of thunderstorms and their accompanying hazards has changed over the past 4 decades remains unclear. Large-scale diagnostics applied to global climate models have suggested that the frequency of thunderstorms and their intensity is likely to increase in the future. Here, we show that according to ERA5 convective available potential energy (CAPE) and convective precipitation (CP) have decreased over the tropics and subtropics with simultaneous increases in 0–6 km wind shear (BS06). Conversely, rawinsonde observations paint a different picture across the mid-latitudes with increasing CAPE and significant decreases to BS06. Differing trends and disagreement between ERA5 and rawinsondes observed over some regions suggest that results should be interpreted with caution, especially for CAPE and CP across tropics where uncertainty is the highest and reliable long-term rawinsonde observations are missing. 

Abstract During 2013, multiple tornadoes occurred across Australia, leading to 147 injuries and considerable damage. This prompted speculation as to the frequency of these events in Australia, and whether 2013 constituted a record year. Leveraging media reports, public accounts, and the Bureau of Meteorology observational record, 69 tornadoes were identified for the year in comparison to the official count of 37 events. This identified set and the existing historical record were used to establish that, in terms of spatial distribution, 2013 was not abnormal relative to the existing climatology, but numerically exceeded any year in the bureau’s record. Evaluation of the environments in which these tornadoes formed illustrated that these conditions included tornado environments found elsewhere globally, but generally had a stronger dependence on shear magnitude than direction, and lower lifting condensation levels. Relative to local environment climatology, 2013 was also not anomalous. These results illustrate a range of tornadoes associated with cool season, tropical cyclone, east coast low, supercell tornado, and low shear/storm merger environments. Using this baseline, the spatial climatology from 1980 to 2019 as derived from the nonconditional frequency of favorable significant tornado parameter environments for the year is used to highlight that observations are likely an underestimation. Applying the results, discussion is made of the need to expand observing practices, climatology, forecasting guidelines for operational prediction, and improve the warning system. This highlights a need to ensure that the general public is appropriately informed of the tornado hazard in Australia, and provide them with the understanding to respond accordingly.