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Abstract Harsh winters and hazards such as blizzards are synonymous with the northern Great Plains of the United States. Studying these events is difficult; the juxtaposition of cold temperatures and high winds makes microphysical observations of both blowing and falling snow challenging. Historically, these observations have been provided by costly hydrometeor imagers that have been deployed for field campaigns or at select observation sites. This has slowed the development and validation of microphysics parameterizations and remote-sensing retrievals of various properties. If cheaper, more mobile instrumentation can be developed, this progress can be accelerated. Further, lowering price barriers can make deployment of instrumentation feasible for education and outreach purposes. The Blowing Snow Observations at the University of North Dakota: Education through Research (BLOWN-UNDER) Campaign took place during the winter of 2019-2020 to investigate strategies for obtaining microphysical measurements in the harsh North Dakota winter. Student led, the project blended education, outreach, and scientific objectives. While a variety of in-situ and remote-sensing instruments were deployed for the campaign, the most novel aspect of the project was the development and deployment of OSCRE, the Open Snowflake Camera for Research and Education. Images from this instrument were combined with winter weather educational modules to describe properties of snow to the public, K-12 students, and members of indigenous communities through a tribal outreach program. Along with an educational deployment of a Doppler on Wheels mobile radar, nearly 1000 individuals were reached during the project. 

In this study, we present a climatology of observed cell mergers along the paths of 342 discrete, right-moving supercells and their association with temporal changes in low-level mesocyclone strength (measured using azimuthal shear). Nearly one-half of the examined supercells experience at least one cell merger. The frequency of cell merger occurrence varies somewhat by geographical region and the time of day. No general relationship exists between cell merger occurrence and temporal changes in low-level azimuthal shear; this corroborates prior studies in showing that the outcome of a merger is probably sensitive to storm-scale and environmental details not captured in this study. Interestingly, we find a significant inverse relationship between premerger azimuthal shear and the subsequent temporal evolution of azimuthal shear. In other words, stronger low-level mesocyclones are more likely to weaken after cell mergers and weaker low-level mesocyclones are more likely to strengthen. We also show that shorter-duration cell merger “events” (comprising multiple individual mergers) are more likely to be associated with a steady or weakening low-level mesocyclone whereas longer-duration cell merger events (3–4 individual mergers) are more likely to be associated with a strengthening low-level mesocyclone. These findings suggest what physical processes may influence the outcome of a merger in different scenarios and that the impact of these processes on low-level mesocyclone strength may change depending on storm maturity. We establish a baseline understanding of the supercell–cell merger climatology and highlight areas for future research in how to better anticipate the outcomes of cell mergers.

A common assumption in idealized supercell simulations is that the background environment is homogeneous. Cells merging into a primary supercell represent one of many ways in which the environment might be significantly inhomogeneous. This study analyzes the paths of 342 supercells with a particular focus on how cell merger occurrence influences the strength of the low-level mesocyclone. Almost one-half of all supercells experience at least one cell merger. Supercells are more likely to weaken after a cell merger event if the premerger mesocyclone was strong or if the merger event is relatively short, and vice versa for the likelihood for a supercell to strengthen. These findings are important for those interested in short-term predictions of supercell evolution in response to cell mergers and suggest what dynamic processes may play a role in governing these relationships.

 

The microphysical characteristics of severe storms and deep convection are challenging but critical in situ observations. Adjustments to modeled microphysical parameters, radar‐based hydrometeor classifications, and lightning initiation research all depend on an accurate depiction of real‐world particle size distributions. To obtain these observations, a balloon‐borne particle imaging device known as the Particle Size, Image, and Velocity probe has been developed at the National Severe Storms Laboratory that is capable of measuring particle size distributions of different particle habits on vertical scales as small as 50 m. The Particle Size, Image, and Velocity observations show that there are rapid shifts in particle counts between successive analysis layers, documenting the small‐scale heterogeneity present inside deep convection. Furthermore, when examining functional fits using both a two and three moment scheme on the total distribution as well as individual particle habits, it is clear that a single prescribed parameter space is not adequate to describe the observations collected. When comparing radar reflectivity calculated from the distribution to independent observations from ground based mobile radars, the two sources agree to within 5 dBZ. This provides confidence to the particle density assumptions made here.

 

ABSTRACT 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. 

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. 

A mobile Shared Mobile Atmospheric Research and Teaching (SMART) radar was deployed in Hurricane Harvey and coordinated with the Corpus Christi, TX, WSR‐88D radar to retrieve airflow during landfall. Aerodynamic surface roughness estimates and a logarithmic wind profile assumption were used to project the 500‐m radar‐derived maximum wind field to near the surface. The logarithmic wind assumption was justified using radiosonde soundings taken within the storm, while the radar wind estimates were validated against an array of StickNets. For the data examined here, the radar projections had root‐mean‐squared error of 3.9 m/s and a high bias of 2.3 m/s. Mesovorticies in Harvey's eyewall produced the strongest radar‐observed winds. Given the wind analysis, Harvey was, at most, a Category 3 hurricane (50–58 m/s sustained winds) at landfall. This study demonstrates the utility of integrated remote and in situ observations in deriving spatiotemporal maps of wind maxima during hurricane landfalls.