Tornadoes are among nature’s most destructive forces. The most violent, long-lived tornadoes form within supercell thunderstorms. Tornadoes ranked EF4 and EF5 on the Enhanced Fujita scale that exhibit long paths are the least common but most damaging and deadly type of tornado. In this article we describe an ultra-high-resolution (30-m gridpoint spacing) simulation of a supercell that produces a long-track tornado that exhibits instantaneous near-surface storm-relative winds reaching as high as 143 m s−1. The computational framework that enables this work is described, including the Blue Waters supercomputer, the CM1 cloud model, a data management framework built around the HDF5 scientific data format, and the VisIt and Vapor visualization tools. We find that tornadogenesis occurs in concert with processes not clearly seen in previous supercell simulations, including the consolidation of numerous vortices and vorticity patches along the storm’s forward-flank downdraft boundary and the intensification of a feature we call a streamwise vorticity current (SVC), a current of horizontal vorticity that is tilted upward into the storm’s low-level mesocyclone. The SVC is found throughout the genesis and much of the maintenance phase of the tornado, where it appears to help drive the storm’s vigorous low-level updraft. We compare stages of the storm’s maintenance phase to observations. We find that tornado decay occurs rapidly throughout the depth of the tornado and is associated with a weakening of the SVC and the development of a strong rainy downdraft that encircles the tornado, which has moved rearward into the storm’s cold pool.

With exascale computing on the horizon, the performance variability of I/O systems represents a key challenge in sustaining high performance. In many HPC applications, I/O is concurrently performed by all processes, which leads to I/O bursts. This causes resource contention and substantial variability of I/O performance, which significantly impacts the overall application performance and, most importantly, its predictability over time. In this paper, we propose a new approach to I/O, called Damaris, which leverages dedicated I/O cores on each multicore SMP node, along with the use of sharedmemory, to efficiently perform asynchronous data processing and I/O in order to hide this variability. We evaluate our approach on three different platforms including the Kraken Cray XT5 supercomputer (ranked 11th in Top500), with the CM1 atmospheric model, one of the target HPC applications for the Blue Waters postpetascale supercomputer project. By overlapping I/O with computation and by gathering data into large files while avoiding synchronization between cores, our solution brings several benefits: 1) it fully hides jitter as well as all I/O-related costs, which makes simulation performance predictable; 2) it increases the sustained write throughput by a factor of 15 compared to standard approaches; 3) it allows almost perfect scalability of the simulation up to over 9,000 cores, as opposed to state-of-the-art approaches which fail to scale; 4) it enables a 600% compression ratio without any additional overhead, leading to a major reduction of storage requirements.

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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.

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