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.

Small unmanned aircraft systems (sUAS) are rapidly transforming atmospheric research. With the advancement of the development and application of these systems, improving knowledge of best practices for accurate measurement is critical for achieving scientific goals. We present results from an intercomparison of atmospheric measurement data from the Lower Atmospheric Process Studies at Elevation—a Remotely piloted Aircraft Team Experiment (LAPSE-RATE) field campaign. We evaluate a total of 38 individual sUAS with 23 unique sensor and platform configurations using a meteorological tower for reference measurements. We assess precision, bias, and time response of sUAS measurements of temperature, humidity, pressure, wind speed, and wind direction. Most sUAS measurements show broad agreement with the reference, particularly temperature and wind speed, with mean value differences of 1.6 ± 2.6 ∘ C and 0.22 ± 0.59 m/s for all sUAS, respectively. sUAS platform and sensor configurations were found to contribute significantly to measurement accuracy. Sensor configurations, which included proper aspiration and radiation shielding of sensors, were found to provide the most accurate thermodynamic measurements (temperature and relative humidity), whereas sonic anemometers on multirotor platforms provided the most accurate wind measurements (horizontal speed and direction). We contribute both a characterization and assessment of sUAS for measuring atmospheric parameters, and identify important challenges and opportunities for improving scientific measurements with sUAS.

This paper discusses results of the CLOUD-MAP (Collaboration Leading Operational UAS Development for Meteorology and Atmospheric Physics) project dedicated to developing, fielding, and evaluating integrated small unmanned aircraft systems (sUAS) for enhanced atmospheric physics measurements. The project team includes atmospheric scientists, meteorologists, engineers, computer scientists, geographers, and chemists necessary to evaluate the needs and develop the advanced sensing and imaging, robust autonomous navigation, enhanced data communication, and data management capabilities required to use sUAS in atmospheric physics. Annual integrated evaluation of the systems in coordinated field tests are being used to validate sensor performance while integrated into various sUAS platforms. This paper focuses on aspects related to atmospheric sampling of thermodynamic parameters with sUAS, specifically sensor integration and calibration/validation, particularly as it relates to boundary layer profiling. Validation of sensor output is performed by comparing measurements with known values, including instrumented towers, radiosondes, and other validated sUAS platforms. Experiments to determine the impact of sensor location and vehicle operation have been performed, with sensor aspiration a major factor. Measurements are robust provided that instrument packages are properly mounted in locations that provide adequate air flow and proper solar shielding.

No abstract

This paper reports results from field deployments of the Tempest Unmanned Aircraft System, the first of its kind of unmanned aircraft system designed to perform in situ sampling of supercell thunderstorms, including those that produce tornadoes. A description of the critical system components, consisting of the unmanned aircraft, ground support vehicles, communications network, and custom software, is given. The unique concept of operations and regulatory issues for this type of highly nomadic and dynamic system are summarized, including airspace regulatory decisions from the Federal Aviation Administration to accommodate unmanned aircraft system operations for the study of supercell thunderstorms. A review of the system performance and concept of operations effectiveness during flights conducted for the spring 2010 campaign of the VORTEX2 project is provided. These flights resulted in the first-ever sampling of the rear flank gust front and airmass associated with the rear flank downdraft of a supercell thunderstorm by an unmanned aircraft system. A summary of the lessons learned, future work, and next steps is provided. C 2011 Wiley Periodicals, Inc.

Numerical experiments are conducted using an idealized cloud-resolving model to explore the sensitivity of deep convective initiation (DCI) to the lapse rate of the active cloud-bearing layer [ACBL; the atmospheric layer above the level of free convection (LFC)]. Clouds are initiated using a new technique that involves a preexisting airmass boundary initialized such that the (unrealistic) adjustment of the model state variables to the imposed boundary is disassociated from the simulation of convection. Reference state environments used in the experiment suite have identical mixed layer values of convective inhibition, CAPE, and LFC as well as identical profiles of relative humidity and wind. Of the six simulations conducted for the experiment set, only the three environments with the largest ACBL lapse rates support DCI. The simulated deep convection is initiated from elevated sources (parcels in the convective clouds originate near 1300 m) despite the presence of a surface-based boundary. Thermal instability release is found to be more likely in the experiments with larger ACBL lapse rates because the forced ascent at the preexisting boundary is stronger (despite nearly identical boundary depths) and because the parcels' LFCs are lower, irrespective of parcel dilution. In one experiment without deep convection, DCI failure occurs even though thermal instability is released. Results from this experiment along with the results from a heuristic Lagrangian model reveal the existence of two convective regimes dependent on the environmental lapse rate: a supercritical state capable of supporting DCI and a subcritical state that is unlikely to support DCI. Under supercritical conditions the rate of increase in buoyancy due to parcel ascent exceeds the reduction in buoyancy due to dilution. Under subcritical conditions, the rate of increase in buoyancy due to parcel ascent is outpaced by the rate of reduction in buoyancy from dilution. Overall, results demonstrate that the lapse rate of the ACBL is useful in diagnosing and/or predicting DCI.

The Thunderstorm Observation by Radar (ThOR) algorithm is an objective and tunable Lagrangian approach to cataloging thunderstorms. ThOR uses observations from multiple sensors (principally multisite surveillance radar data and cloud-to-ground lightning) along with established techniques for fusing multisite radar data and identifying spatially coherent regions of radar reflectivity (clusters) that are subsequently tracked using a new tracking scheme. The main innovation of the tracking algorithm is that, by operating offline, the full data record is available, not just previous cluster positions, so all possible combinations of object sequences can be developed using all observed object positions. In contrast to Eulerian methods reliant on thunder reports, ThOR is capable of cataloging nearly every thunderstorm that occurs over regional-scale and continental United States (CONUS)-scale domains, thereby enabling analysis of internal properties and trends of thunderstorms. ThOR is verified against 166 manually analyzed cluster tracks and is also verified using descriptive statistics applied to a large (~35 000 tracks) sample. Verification also relied on a benchmark tracking algorithm that provides context for the verification statistics. ThOR tracks are shown to match the manual tracks slightly better than the benchmark tracks. Moreover, the descriptive statistics of the ThOR tracks are nearly identical to those of the manual tracks, suggesting good agreement. When the descriptive statistics were applied to the ~35 000-track dataset, ThOR tracking produces longer (statistically significant), straighter, and more coherent tracks than those of the benchmark algorithm. Qualitative assessment of ThOR performance is enabled through application to a multiday thunderstorm event and comparison to the behavior of the Storm Cell Identification and Tracking (SCIT) algorithm.

Because unmanned aircraft systems (UAS) offer new perspectives on the atmosphere, their use in atmospheric science is expanding rapidly. In support of this growth, the International Society for Atmospheric Research Using Remotely-Piloted Aircraft (ISARRA) has been developed and has convened annual meetings and “flight weeks.” The 2018 flight week, dubbed the Lower Atmospheric Profiling Studies at Elevation–A Remotely-Piloted Aircraft Team Experiment (LAPSE-RATE), involved a 1-week deployment to Colorado’s San Luis Valley. Between 14 and 20 July 2018 over 100 students, scientists, engineers, pilots, and outreach coordinators conducted an intensive field operation using unmanned aircraft and ground-based assets to develop datasets, community, and capabilities. In addition to a coordinated “Community Day” which offered a chance for groups to share their aircraft and science with the San Luis Valley community, LAPSE-RATE participants conducted nearly 1,300 research flights totaling over 250 flight hours. The measurements collected have been used to advance capabilities (instrumentation, platforms, sampling techniques, and modeling tools), conduct a detailed system intercomparison study, develop new collaborations, and foster community support for the use of UAS in atmospheric science.