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Abstract Results from a large eddy simulation of a tornadic supercell developing in a horizontally homogeneous environment are presented which clearly illustrate a connection between low‐level mesoyclone development and the development of a streamwise vorticity current (SVC). Although the environment supports tornadic supercells, a strong low‐level mesocyclone (LLM) does not develop until a well‐defined SVC forms in the storm's forward flank. As the streamwise vorticity in the SVC flows southward and is tilted into the storm updraft creating updraft helicity, the LLM strengthens and lowers toward the surface. The SVC also focuses LLM development in a confined storm‐relative position favorable for converging/stretching preexisting vertical vorticity. Tornadogenesis occurs within ∼5 min of the establishment of a strong LLM. These results illustrate a possible mode of internal storm variability that may be an important factor in explaining why some supercells produce tornadoes while others do not in similar favorable environments. 

Tornados are a major hazard in many regions around the world and as such it is necessary to analyze them. However, such analyses require accurately tracking them first. Currently, there are gaps in the available vortex detection methods when processing a wind-field dataset to locate a series of points that are identifiable as the tornado centreline. This study proposes a novel solution that corrects for deficiencies in previous attempts to identify vortex centres when applied to tornado wind-fields, which would have otherwise led to identifying merely the region of the vortex, several potential centres requiring post-processing, or erroneously approximating the tornado centre. Additionally, this method combines the efficiency required to process large datasets of temporal and spatial wind velocity vector distributions with the accuracy needed to reliably calculate a specific line as a tornado centre. This method is compared to five other approaches commonly used for vortex identification in order to assess: (a) how accurately they identify the centre region, (b) how they handle extraneous vortices that are not of interest, and (c) their computational efficiency in processing a wind-field dataset. With the proposed method, it would be possible to plot a tornado path from formation to dissipation and perform analyses to understand the vortex characteristics with respect to this path without requiring extensive user-intervention. 

Tornadoes remain an active subject of observational and numerical research due to the damage and fatalities they cause worldwide as well as poor understanding of their behavior, such as what processes result in their genesis and what determines their longevity and morphology. A numerical model executed on a supercomputer run at high resolution can serve as a powerful tool for exploring the rapidly evolving tornado-scale features within a simulated storm, but saving large amounts data for meaningful analysis can result in unacceptably slow model performance, an unwieldy amount of saved data, and saved data spread across millions of files. In this paper, a system for efficiently saving and managing hundreds of terabytes of compressed model output is described to support a supercomputer simulation of a tornadic supercell thunderstorm. The challenges of managing a simulation spanning over a quarter-trillion grid volumes across the Blue Waters supercomputer are also described. The simulated supercell produces a long-track EF5 tornado, and the near-tornado environment is described during tornadogenesis, where single upward-growing vortex undergoes several vortex mergers before transitioning into a multiple-vortex tornado. 

Visualization is an essential tool for analysis of data and communication of findings in the sciences, and the Earth System Sciences (ESS) are no exception. However, within ESS, specialized visualization requirements and data models, particularly for those data arising from numerical models, often make general purpose visualization packages difficult, if not impossible, to use effectively. This paper presents VAPOR: a domain-specific visualization package that targets the specialized needs of ESS modelers, particularly those working in research settings where highly-interactive exploratory visualization is beneficial. We specifically describe VAPOR’s ability to handle ESS simulation data from a wide variety of numerical models, as well as a multi-resolution representation that enables interactive visualization on very large data while using only commodity computing resources. We also describe VAPOR’s visualization capabilities, paying particular attention to features for geo-referenced data and advanced rendering algorithms suitable for time-varying, 3D data. Finally, we illustrate VAPOR’s utility in the study of a numerically- simulated tornado. Our results demonstrate both ease-of-use and the rich capabilities of VAPOR in such a use case.