Search for: All records

Abstract This study describes data-denial experiments conducted to examine the impact of assimilating subsets of data from the Targeted Observation by Radars and Unoccupied Aerial Systems of Supercells (TORUS) project on storm-scale ensemble forecasts of two supercells on 8 June 2019. Assimilated data from TORUS include mobile mesonet, unoccupied aerial system (UAS), and radiosonde observations. The TORUS data are divided into three spatial subsets to evaluate the importance of observing different parts of the atmosphere on forecasts of this case: the surface (SFC) subset consisting of just the near-surface mobile mesonet observations, the PBL subset consisting of UAS observations and radiosonde profiles below 762 m, and the FREE subset consisting of radiosonde profiles above 762 m. Data-denial experiments are then conducted by comparing analyses and free forecasts generated using a cycled EnKF data assimilation system assimilating conventional observations, radar observations, and all of the TORUS observations at once with experiments where one of the three subsets is removed in turn as well as a control experiment assimilating only conventional and radar observations. Our results show that assimilating all of the TORUS observations at once in the ALL experiment improves the storm-scale ensemble forecasts much more often than it degrades them and that no one subset of the TORUS data was consistently most important for improving the analyses or forecasts. 

Abstract The importance of discriminating between environments supportive of supercell thunderstorms and those that are not supportive is widely recognized due to significant hazards associated with supercell storms. Previous research has led to forecast indices such as the energy helicity index and the supercell composite parameter to aid supercell forecasts. In this study three machine learning models are developed to identify environments supportive of supercells: a support vector machine, an artificial neural network, and an ensemble of gradient boosted trees. These models are trained and tested using a sample of over 1000 Rapid Update Cycle version 2 (RUC-2) model soundings from near-storm environments of both supercell and nonsupercell storms. Results show that all three machine learning models outperform classifications using either the energy helicity index or supercell composite parameter by a statistically significant margin. Using several model interpretability methods, it is concluded that generally speaking the relationships learned by the machine learning models are physically reasonable. These findings further illustrate the potential utility of machine learning–based forecast tools for severe storm forecasting. Significance Statement Supercell thunderstorms are a type of thunderstorm that are important to forecast because they produce more tornadoes, hail, and wind gusts compared to other types of thunderstorms. This study uses machine learning to create models that predict if a supercell thunderstorm or nonsupercell thunderstorm is favored for a given environment. These models outperform current methods of assessing if a storm that forms will be a supercell. Using these models as guidance forecasters can better understand and predict if atmospheric conditions are favorable for the development of supercell thunderstorms. Improving forecasts of supercell thunderstorms using machine learning methods like those used in this study has the potential to limit the economic and societal impacts of these storms. 

Traditional configurations for mounting Temperature–Humidity (TH) sensors on multirotor Unmanned Aerial Systems (UASs) often suffer from insufficient radiation shielding, exposure to mixed and turbulent air from propellers, and inconsistent aspiration while situated in the wake of the UAS. Descent profiles using traditional methods are unreliable (when compared to an ascent profile) due to the turbulent mixing of air by the UAS while descending into that flow field. Consequently, atmospheric boundary layer profiles that rely on such configurations are bias-prone and unreliable in certain flight patterns (such as descent). This article describes and evaluates a novel sensor housing designed to shield airborne sensors from artificial heat sources and artificial wet-bulbing while pulling air from outside the rotor wash influence. The housing is mounted above the propellers to exploit the rotor-induced pressure deficits that passively induce a high-speed laminar airflow to aspirate the sensor consistently. Our design is modular, accommodates a variety of other sensors, and would be compatible with a wide range of commercially available multirotors. Extensive flight tests conducted at altitudes up to 500 m Above Ground Level (AGL) show that the housing facilitates reliable measurements of the boundary layer phenomena and is invariant in orientation to the ambient wind, even at high vertical/horizontal speeds (up to 5 m/s) for the UAS. A low standard deviation of errors shows a good agreement between the ascent and descent profiles and proves our unique design is reliable for various UAS missions. 

We use unmanned aerial vehicles to interrogate the surface layer processes during a solar eclipse and gain a comprehensive look at the changes made to the atmospheric surface layer as a result of the rapid change of insolation. Measurements of the atmospheric surface layer structure made by the unmanned systems are connected to surface measurements to provide a holistic view of the impact of the eclipse on the near-surface behaviour, large-scale turbulent structures and small-scale turbulent dynamics. Different regimes of atmospheric surface layer behaviour were identified, with the most significant impact including the formation of a stable layer just after totality and evidence of Kelvin–Helmholtz waves appearing at the interface between this layer and the residual layer forming above it. The decrease in surface heating caused a commensurate decrease in buoyant turbulent production, which resulted in a rapid decay of the turbulence in the atmospheric surface layer both within the stable layer and in the mixed layer forming above it. Significant changes in the wind direction were imposed by the decrease in insolation, with evidence supporting the formation of a nocturnal jet, as well as backing of the wind vector within the stable layer. 

Abstract On 28 May 2019, a tornadic supercell, observed as part of Targeted Observation by UAS and Radars of Supercells (TORUS) produced an EF-2 tornado near Tipton, Kansas. The supercell was observed to interact with multiple preexisting airmass boundaries. These boundaries and attendant air masses were examined using unoccupied aircraft system (UAS), mobile mesonets, radiosondes, and dual-Doppler analyses derived from TORUS mobile radars. The cool-side air mass of one of these boundaries was found to have higher equivalent potential temperature and backed winds relative to the warm-side air mass; features associated with mesoscale air masses with high theta-e (MAHTEs). It is hypothesized that these characteristics may have facilitated tornadogenesis. The two additional boundaries were produced by a nearby supercell and appeared to weaken the tornadic supercell. This work represents the first time that UAS have been used to examine the impact of preexisting airmass boundaries on a supercell, and it provides insights into the influence environmental heterogeneities can have on the evolution of a supercell.